Molecular detection of Cyclospora cayetanensis in fresh produce and irrigation water in peri- urban settings: a One Health cross-sectional study

Molecular detection of Cyclospora cayetanensis in fresh produce and irrigation water in peri- urban settings: a One Health cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molecular detection of Cyclospora cayetanensis in fresh produce and irrigation water in peri- urban settings: a One Health cross-sectional study Iffat Naz, Mina A. Almayouf, Abdul Rehman, Abdul Majid, Abdul Basit, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9256916/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Microbiology → Version 1 posted 17 You are reading this latest preprint version Abstract Background Cyclospora cayetanensis is recognized as an important foodborne parasite worldwide, with fresh produce and contaminated irrigation water serving as major transmission vehicles. In South Asia, environmental surveillance data for this pathogen remain limited, hindering the development of evidence-based food safety measures. We investigated the occurrence of C. cayetanensis in fresh produce and irrigation water across peri-urban areas of Khyber Pakhtunkhwa, Pakistan, and assessed environmental and farm-level factors associated with contamination. Methods A cross-sectional study was conducted in Peshawar and Kohat districts from April to September 2025. A total of 420 samples were collected, including 300 fresh produce samples (six commonly consumed vegetables and herbs) and 120 irrigation water samples from canal, tube-well, and mixed sources. Samples were processed using concentration techniques, and detection was performed by nested PCR targeting the 18S rRNA gene. Structured field questionnaires were used to capture farm-level practices, and logistic regression was applied to identify risk factors. Results C. cayetanensis DNA was detected in 6.0% (18/300) of produce samples and 12.5% (15/120) of irrigation water samples (p = 0.028). Contamination was significantly higher in canal water (20.0%) compared to tube-well sources (5.0%) (OR 4.75; 95% CI: 1.01–22.3). Leafy vegetables and herbs showed higher contamination rates than smooth-surfaced produce (p = 0.009). In multivariable analysis, canal irrigation (aOR 3.41; p = 0.031), proximity to drainage channels within 50 meters (aOR 3.98; p = 0.007), and use of untreated surface water for rinsing (aOR 2.91; p = 0.045) were independently associated with contamination. Conclusions This study provides the first molecular evidence of C. cayetanensis contamination at the produce–water interface in peri-urban Khyber Pakhtunkhwa, Pakistan. Surface irrigation systems and inadequate water management practices emerged as critical risk factors. By combining molecular detection with environmental and farm-level assessments under a One Health approach, our findings provide practical guidance for targeted food safety interventions in settings where environmental surveillance has historically been sparse. Cyclospora cayetanensis Foodborne protozoa Irrigation water Fresh produce contamination Nested PCR One Health Introduction Cyclospora cayetanensis is an obligate intracellular coccidian parasite responsible for cyclosporiasis, an intestinal illness typically presenting with prolonged watery diarrhea, abdominal cramping, nausea, and fatigue [ 1 , 2 ]. Infection occurs through ingestion of food or water contaminated with sporulated oocysts. Freshly excreted oocysts are non-infectious and require days to weeks in the environment to sporulate thus, direct person-to-person transmission does not occur, and environmental reservoirs play a central role in the parasite's transmission cycle [ 1 , 3 ]. Over the past several decades, Cyclospora cayetanensis has become a pathogen of growing concern in the context of global food safety. Numerous outbreaks have been linked to the consumption of raw or minimally processed produce, including leafy greens, fresh herbs, and berries, contaminated during cultivation, irrigation, harvesting, or post-harvest handling [ 4 , 5 ]. The globalization of food supply chains, combined with reliance on diverse irrigation water sources, has amplified the risk of widespread distribution of contaminated produce. In particular, the use of untreated or inadequately treated wastewater in agriculture has been identified as a significant pathway for environmental contamination and subsequent human exposure [ 6 , 7 ]. Although the public health importance of C. cayetanensis is increasingly recognized, substantial knowledge gaps persist regarding its environmental epidemiology especially in low- and middle-income countries. Existing literature is disproportionately focused on clinical detection, while environmental surveillance remains limited and geographically uneven [ 3 , 8 ]. Recent advances in molecular epidemiology emphasize the need for region-specific baseline data to better elucidate contamination pathways and inform targeted interventions [ 9 ]. Environmental detection of C. cayetanensis remains technically challenging because oocyst concentrations are often low, distribution in environmental matrices is heterogeneous, and food and water samples frequently contain PCR inhibitors [ 6 , 10 ]. South Asia is a region of particular relevance in this context, yet it remains under-investigated. In countries such as Pakistan, peri-urban agriculture frequently relies on surface water or untreated wastewater, often in settings where sanitation infrastructure is limited and overlaps with agricultural activities. These conditions create a conducive environment for pathogen transmission at the human-environment-food interface [ 7 ]. Despite the high burden of enteric diseases and widespread consumption of raw vegetables and herbs, molecular surveillance of C. cayetanensis in irrigation water and fresh produce in Pakistan remains scarce in peer-reviewed literature. The absence of empirical data limits accurate risk assessment, evidence-based policy formulation, and the design of effective food safety interventions that consider the interconnectedness of human, animal, and environmental health. Furthermore, most environmental studies on Cyclospora have been conducted in high-income settings using advanced real-time PCR platforms. While these methods offer high sensitivity and specificity, they are often not feasible in resource-limited laboratories. Conventional nested PCR targeting ribosomal gene loci provides a cost-effective and sensitive alternative when implemented with rigorous contamination control measures [ 11 , 12 ]. Demonstrating the applicability and epidemiological value of such approaches in underrepresented regions is essential for improving global surveillance equity. In light of these gaps, the present study aims to generate baseline molecular evidence of C. cayetanensis contamination in fresh produce and associated irrigation water in peri-urban areas of Khyber Pakhtunkhwa, Pakistan. Additionally, it seeks to identify environmental and farm-level factors associated with contamination risk. To our knowledge, using molecular detection within a One Health framework, this is first study providing context-specific data from a geographically underrepresented region and contributes to the evidence base needed to inform targeted food safety interventions in South Asia. Methodology Study Design and Study Area We conducted a cross-sectional study from April to September 2025 in two districts of Khyber Pakhtunkhwa, Pakistan: Peshawar and Kohat. These districts were selected purposively because they feature active peri-urban vegetable production, diverse irrigation practices, and high rates of raw vegetable and herb consumption. Peshawar, the provincial capital, is characterized by dense urbanization and intensive peri-urban agriculture supported by canal and tube-well irrigation systems. In contrast, Kohat represents a semi-arid agricultural setting where irrigation depends on both surface water channels and groundwater extraction. Seasonal variability, including monsoon rainfall and temperature fluctuations, may influence environmental contamination dynamics. Sampling sites included peri-urban vegetable farms supplying local markets, wholesale vegetable markets in both districts, and irrigation water sources directly linked to sampled farms. The study design focused on evaluating contamination at the interface between fresh produce and irrigation water, reflecting the interconnected One Health perspective. A structured observational checklist was administered to capture potential contamination determinants. The characteristics of sampling sites, including irrigation sources, drainage proximity, and on-farm practices, are summarized in Supplementary Table S1 . Sample Size Justification We calculated the sample size using the single proportion formula, assuming an expected contamination prevalence of 10% based on preliminary local surveillance and regional studies from comparable settings. With a desired precision of ± 5% and a 95% confidence level, the minimum required sample size was 138 produce samples. To accommodate potential sample loss, PCR inhibition, and stratified sampling by district and produce type, we increased the sample size by 20%, targeting 166 produce samples per district. However, to enable stratified analysis across six produce types and two sampling nodes (farm and market), a total of 300 produce samples (150 per district) was selected, which exceeded the minimum requirement and provided sufficient power for subgroup analyses. For irrigation water, sample size was determined based on the same parameters, with 120 water samples collected to ensure adequate representation of three water source categories (canal, tube-well, and mixed) across both districts. This sample size was sufficient to detect a 15% difference in contamination prevalence between water sources with 80% power at α = 0.05. Sample Size and Sampling Strategy Fresh Produce A total of 300 fresh produce samples were collected (150 per district). Six commonly consumed raw vegetables and herbs were selected based on local dietary practices: coriander ( Coriandrum sativum ), mint ( Mentha spp.), lettuce ( Lactuca sativa ), spinach ( Spinacia oleracea ), cucumber ( Cucumis sativus ), and tomato ( Solanum lycopersicum ). For each commodity, 25 samples per district (n = 50 per commodity) were collected. Sampling was stratified between farm-level samples (n = 150) and wholesale market samples (n = 150). Each sample (50–100 g) was aseptically collected in sterile polyethylene bags and transported under chilled conditions to the laboratory for analysis. Irrigation Water A total of 120 irrigation water samples were collected, including canal/surface water (n = 60), tube-well (groundwater) sources (n = 40), and mixed sources (n = 20). Water samples were obtained directly from irrigation outlets supplying sampled farms. For each sampling event, 5–10 L of water was collected in sterile containers and transported under cooled conditions for laboratory processing. Risk Factor Assessment A structured observational checklist and short questionnaire were administered to farm owners or managers to capture potential contamination determinants. Variables included irrigation source, proximity to sewage or drainage channels (≤ 50 m vs > 50 m), use of untreated water for produce washing, availability of sanitation facilities, presence of livestock near cultivation areas, and produce handling practices. Seasonality (pre-monsoon vs monsoon) and sampling node (farm vs market) were also recorded (Supplementary Questionnaire S1). Sample Processing Produce Samples Each produce sample (~ 50 g) was washed in 200 mL buffered saline containing 0.1% Tween 80 to facilitate detachment of oocysts. Samples were manually agitated for 10 minutes, and the wash solution was filtered through sterile gauze. The filtrate was centrifuged at 3,000 × g for 15 minutes, and the pellet was divided for microscopy and molecular analysis. Similar elution and concentration approaches have been widely applied for parasite recovery from fresh produce [ 13 , 14 ]. Water Concentration Water samples (5–10 L) were concentrated using membrane filtration (1.2 µm pore size) or cartridge filtration techniques. Filters were eluted with sterile buffer and centrifuged at 3,000 × g for 20 minutes. The resulting pellet was resuspended in phosphate-buffered saline and stored at − 20°C until DNA extraction. These concentration methods are standard for protozoan detection in environmental water samples [ 15 ]. Microscopic Examination Pellet aliquots were examined using iodine wet mount followed by modified acid-fast staining. Slides were observed under light microscopy (×400–×1000 magnification) for spherical, variably acid-fast oocysts (8–10 µm). Microscopy was used as a supportive tool but not for definitive identification due to limited sensitivity [ 16 ]. DNA Extraction Genomic DNA was extracted using a commercial stool DNA extraction kit according to the manufacturer’s instructions. To enhance oocyst disruption, samples underwent three freeze–thaw cycles (− 80°C to 56°C), followed by mechanical disruption using bead beating. DNA was eluted in 50 µL nuclease-free water and stored at − 20°C. Combined mechanical and thermal lysis has been shown to improve DNA recovery from protozoan oocysts [ 17 ]. Nested PCR for Cyclospora cayetanensis Detection of C. cayetanensis was performed using nested PCR targeting the 18S rRNA gene [ 18 , 19 ]. The primary PCR employed primers CYCF1E and CYCR2B to amplify an approximately 630 bp fragment. The reaction mixture (25 µL) consisted of 12.5 µL 2× master mix, 0.5 µM primers, 2 µL template DNA, and nuclease-free water. Cycling conditions included initial denaturation at 94°C for 3 minutes, followed by 35 cycles of denaturation (94°C, 30 s), annealing (54°C, 30 s), extension (72°C, 45 s), and a final extension at 72°C for 5 minutes. Nested PCR was performed using primers CC719 and CRP999, generating an approximately 298 bp amplicon. Cycling conditions included 94°C for 3 minutes, 35 cycles (94°C for 30 s, 66°C for 30 s, 72°C for 30 s), and final extension at 72°C for 5 minutes. PCR products were visualized on 1.5–2% agarose gel stained with ethidium bromide or a safer alternative and examined under UV illumination. Nested PCR amplification produced distinct bands at approximately 298 bp, while primary PCR yielded bands around 630 bp, consistent with expected amplicon sizes (Supplementary Figure S1 ). Quality Control Strict contamination control measures were implemented. Separate laboratory areas were maintained for DNA extraction, PCR setup, and post-PCR analysis. Aerosol-resistant tips were used throughout. Each PCR run included a positive control (Cyclospora DNA obtained from Department of Zoology, KUST, Kohat, Pakistan), negative extraction control, and no-template control. A subset of samples was re-tested to confirm reproducibility, and inhibition was assessed using spiked controls. Statistical Analysis Data were analyzed using SPSS version 26. Prevalence was calculated with 95% confidence intervals. Associations were evaluated using chi-square or Fisher’s exact test. Variables with p < 0.20 were included in multivariable logistic regression models to identify independent risk factors. Statistical significance was set at p < 0.05. Results Overall detection of Cyclospora cayetanensis A total of 420 environmental samples were analyzed, comprising 300 fresh produce samples and 120 irrigation water samples. Cyclospora cayetanensis DNA was detected in 18/300 produce samples (6.0%; 95% CI: 3.8–9.2%) and 15/120 irrigation water samples (12.5%; 95% CI: 7.8–19.6%). Detection was higher in irrigation water compared with produce (OR 2.24; 95% CI: 1.09–4.61; χ² = 4.81, p = 0.028). The distribution of environmental and farm-level characteristics across sampling sites is summarized in Supplementary Table S1 . Overall, 33/420 samples were positive (7.9%; 95% CI: 5.7–10.8%) including 18 (54.5%) produce and 15 (45.5%) water samples. Among all positive samples (produce and water), 63.6% were collected during the monsoon period.. PCR-positive samples were more frequently associated with irrigation water and monsoon sampling compared with PCR-negative samples (Supplementary Table S4). District-wise distribution Detection was observed in both districts (Table 1 ). In Peshawar, 11/150 produce samples (7.3%; 95% CI: 4.1–12.6%) and 9/60 irrigation water samples (15.0%; 95% CI: 8.1–25.9%) were positive. In Kohat, detection was 7/150 (4.7%; 95% CI: 2.3–9.3%) in produce and 6/60 (10.0%; 95% CI: 4.6–20.2%) in irrigation water. The difference in produce contamination between districts was not statistically significant (χ² = 1.03, p = 0.31). Table 1 Detection of Cyclospora cayetanensis by district District Produce Positive / n (%) [95% CI] Water Positive / n (%) [95% CI] Total Positive / n (%) [95% CI] Peshawar 11/150 (7.3) [4.1–12.6] 9/60 (15.0) [8.1–25.9] 20/210 (9.5) [6.3–14.2] Kohat 7/150 (4.7) [2.3–9.3] 6/60 (10.0) [4.6–20.2] 13/210 (6.2) [3.7–10.3] Total 18/300 (6.0) [3.8–9.2] 15/120 (12.5) [7.8–19.6] 33/420 (7.9) [5.7–10.8] Confidence intervals calculated using the exact (Clopper–Pearson) method. Detection according to produce type As shown in Table 2 , the highest detection rate was observed in coriander (5/50; 10.0%; 95% CI: 4.4–21.4%), followed by mint (4/50; 8.0%; 95% CI: 3.2–18.8%), lettuce (4/50; 8.0%; 95% CI: 3.2–18.8%), and spinach (3/50; 6.0%; 95% CI: 2.1–16.2%). Lower detection rates were observed in cucumber (1/50; 2.0%; 95% CI: 0.3–10.5%) and tomato (1/50; 2.0%; 95% CI: 0.3–10.5%). When grouped, leafy vegetables and herbs (n = 200) showed higher detection (16/200; 8.0%; 95% CI: 5.0–12.5%) than smooth-surfaced vegetables (n = 100; 2.0%; 95% CI: 0.5–7.0%) (χ² = 6.84, p = 0.009). A detailed breakdown by produce type, district, and sampling node is provided in Supplementary Table S6. Table 2 PCR detection by produce type (n = 300) Produce Type Positive (n) Total (n) Detection Rate (%) 95% CI* Coriander 5 50 10.0 4.4–21.4 Mint 4 50 8.0 3.2–18.8 Lettuce 4 50 8.0 3.2–18.8 Spinach 3 50 6.0 2.1–16.2 Cucumber 1 50 2.0 0.3–10.5 Tomato 1 50 2.0 0.3–10.5 Total 18 300 6.0 3.8–9.2 *Exact (Clopper–Pearson) confidence intervals. Stratified analysis by sampling node (farm vs. market) To assess whether contamination occurred primarily during cultivation or during post-harvest handling, detection rates were compared between farm-level samples (collected directly from fields) and market-level samples (collected from wholesale markets). Overall, C. cayetanensis DNA was detected in 9 of 150 (6.0%; 95% CI: 3.2–11.0%) farm-level produce samples and 9 of 150 (6.0%; 95% CI: 3.2–11.0%) market-level produce samples, with no statistically significant difference between the two sampling nodes (Fisher's exact test, p = 1.00). When stratified by produce category, leafy vegetables and herbs showed identical detection rates at farm and market levels (8.0% each), as did smooth-surfaced vegetables (2.0% each) (Table 3 ). Analysis by individual produce type similarly revealed no significant differences between farm and market samples (Table 4 ). Table 3 Detection of Cyclospora cayetanensis in fresh produce by sampling node and produce category Produce Category Farm-Level Positive / n (%) [95% CI] Market-Level Positive / n (%) [95% CI] Total Positive / n (%) [95% CI] Leafy vegetables and herbs* 8/100 (8.0) [4.1–15.0] 8/100 (8.0) [4.1–15.0] 16/200 (8.0) [5.0–12.5] Smooth-surfaced vegetables** 1/50 (2.0) [0.3–10.5] 1/50 (2.0) [0.3–10.5] 2/100 (2.0) [0.5–7.0] Total 9/150 (6.0) [3.2–11.0] 9/150 (6.0) [3.2–11.0] 18/300 (6.0) [3.8–9.2] *Coriander, mint, lettuce, spinach. **Cucumber, tomato. *Note: No significant difference in detection between farm and market samples (Fisher's exact test, p = 1.00 for all comparisons). Detailed detection rates stratified by sampling node and produce category, including exact confidence intervals, are provided in Supplementary Table S2 . Table 4 Detection of Cyclospora cayetanensis in fresh produce by individual produce type and sampling node Produce Type Farm-Level Positive / n (%) Market-Level Positive / n (%) Total Positive / n (%) Fisher's Exact *p*-value Coriander 3/25 (12.0) 2/25 (8.0) 5/50 (10.0) 1.00 Mint 2/25 (8.0) 2/25 (8.0) 4/50 (8.0) 1.00 Lettuce 2/25 (8.0) 2/25 (8.0) 4/50 (8.0) 1.00 Spinach 1/25 (4.0) 2/25 (8.0) 3/50 (6.0) 1.00 Cucumber 0/25 (0.0) 1/25 (4.0) 1/50 (2.0) 1.00 Tomato 1/25 (4.0) 0/25 (0.0) 1/50 (2.0) 1.00 Total 9/150 (6.0) 9/150 (6.0) 18/300 (6.0) 1.00 Irrigation water contamination Detection differed by irrigation water source (Table 5 ). Canal water showed the highest detection (12/60; 20.0%; 95% CI: 11.9–31.4%), followed by tube-well water (2/40; 5.0%; 95% CI: 1.4–16.5%) and mixed sources (1/20; 5.0%; 95% CI: 0.9–23.6%). Compared with tube-well water, canal irrigation was associated with higher odds of detection (OR 4.75; 95% CI: 1.01–22.3; p = 0.048), whereas no difference was observed for mixed sources (OR 1.00; 95% CI: 0.08–12.1; p = 0.99). A district-wise breakdown of irrigation water contamination is presented in Supplementary Table S3 . Table 5 Detection in irrigation water by source (n = 120) Water Source Positive (n) Total (n) Detection Rate (%) 95% CI OR (95% CI) vs. Tube-well p-value Canal 12 60 20.0 11.9–31.4 4.75 (1.01–22.3) 0.048 Tube-well 2 40 5.0 1.4–16.5 Reference — Mixed 1 20 5.0 0.9–23.6 1.00 (0.08–12.1) 0.99 Seasonal variation In produce samples, detection was higher during the monsoon period (12/150; 8.0%; 95% CI: 4.6–13.4%) compared with the pre-monsoon period (6/150; 4.0%; 95% CI: 1.8–8.5%), although this difference was not statistically significant (OR 2.09; 95% CI: 0.77–5.66; p = 0.14) shown in (Table 6 ). Across all sample types, 21/33 (63.6%) of positive samples were detected during the monsoon period. Detailed seasonal variation by water source and district is provided in Supplementary Table S7 Table 6 Seasonal variation in produce contamination Season Positive (n) Total (n) Detection Rate (%) 95% CI OR (95% CI) p-value Pre-monsoon 6 150 4.0 1.8–8.5 Reference — Monsoon 12 150 8.0 4.6–13.4 2.09 (0.77–5.66) 0.14 Seasonal patterns in water contamination varied by source, with canal water detection increasing from 12.0% in pre-monsoon to 25.7% in monsoon, while tube-well water showed detection only during monsoon (10.0%). A detailed seasonal breakdown by water source and district is presented in Supplementary Table S7. Risk factor analysis for produce contamination In univariable analysis, canal irrigation (OR 3.82; 95% CI: 1.08–13.4; p = 0.036), proximity to drainage channels ≤ 50 m (OR 4.39; 95% CI: 1.62–11.9; p = 0.003), and use of untreated surface water for rinsing (OR 3.79; 95% CI: 1.39–10.3; p = 0.009) were associated with detection of C. cayetanensis in produce. Full results for all variables are presented in Supplementary Table S5 . In multivariable analysis, these variables remained independently associated with contamination: canal irrigation (aOR 3.41; 95% CI: 1.12–10.2; p = 0.031), proximity to drainage ≤ 50 m (aOR 3.98; 95% CI: 1.45–10.9; p = 0.007), and use of untreated surface water for rinsing (aOR 2.91; 95% CI: 1.02–8.28; p = 0.045) (Table 7 ). Canal irrigation was associated with higher odds of detection, although the estimate was imprecise (wide confidence interval). Canal irrigation and proximity to drainage channels ≤ 50 m showed the strongest associations with C. cayetanensis detection, while the use of untreated surface water for rinsing demonstrated a comparatively lower effect size. All confidence intervals excluded the null value. Table 7 Univariable and multivariable logistic regression analysis of factors associated with Cyclospora cayetanensis detection in fresh produce (n = 300) Variable Category Univariable OR (95% CI) p-value Multivariable aOR (95% CI) p-value Irrigation source Canal vs. tube-well 3.82 (1.08–13.4) 0.036 3.41 (1.12–10.2) 0.031 Drainage proximity ≤ 50 m vs. >50 m 4.39 (1.62–11.9) 0.003 3.98 (1.45–10.9) 0.007 Surface water rinsing Yes vs. no 3.79 (1.39–10.3) 0.009 2.91 (1.02–8.28) 0.045 Season Monsoon vs. pre-monsoon 2.09 (0.77–5.66) 0.14 — — District Peshawar vs. Kohat 1.60 (0.66–3.89) 0.30 — — Model fit: Hosmer–Lemeshow goodness-of-fit p = 0.64; Nagelkerke R² = 0.18; no evidence of multicollinearity (VIF < 2). Only variables with p < 0.20 in univariable analysis were considered for multivariable model entry. Season and district did not meet inclusion criteria and were excluded from the final model. Discussion To our knowledge, this is the first study to provide molecular evidence of C. cayetanensis contamination at the irrigation water–fresh produce interface in peri-urban Khyber Pakhtunkhwa, Pakistan, a setting where environmental surveillance data have been sparse. We were unable to identify a prior peer-reviewed Pakistan study specifically documenting C. cayetanensis in fresh produce or irrigation water, which underscores the regional evidence gap addressed by the present work. By integrating molecular detection with farm-level risk assessment, this study provides context-specific evidence from a low- and middle-income setting where surveillance for foodborne protozoa is limited [ 20 ]. In this study, C. cayetanensis DNA was detected in 6.0% (18/300) of fresh produce samples and 12.5% (15/120) of irrigation water samples, with a significantly higher detection in water (OR 2.24; p = 0.028). The higher detection frequency in irrigation water (12.5%) compared with fresh produce (6.0%) observed in this study (OR 2.24; p = 0.028) aligns with the established view that agricultural water serves as a major source of contamination. The 12.5% detection rate in water observed here falls within the lower range of reported values from neighboring and international studies, where rates have ranged from approximately 10% to over 30% [ 24 , 25 ]. This discrepancy may reflect differences in wastewater exposure, hydrological characteristics, and climatic conditions across regions, as well as variability in laboratory detection methods. A recent meta-analysis estimated an overall pooled prevalence of approximately 6.9% in water samples globally, while also highlighting substantial heterogeneity across geographic regions and water sources [ 26 ]. Reported detection rates vary considerably across studies, largely due to methodological differences such as PCR sensitivity, sample concentration methods, and sequencing-based confirmation [ 8 ]. The association between canal irrigation and produce contamination highlights the importance of water quality as a critical control point in the production chain. In particular, canal water showed a substantially higher contamination rate (20.0%) compared with tube-well sources (5.0%), corresponding to nearly a fivefold increase in odds (OR 4.75), highlighting the elevated risk associated with surface water irrigation in this setting. From a food safety perspective, agricultural water is widely recognized as a major pathway for contamination of fresh produce, particularly in systems relying on untreated surface water. Recent experimental and field studies indicate that low-cost filtration and water treatment approaches can reduce protozoan contamination, offering practical options in resource-limited settings [ 27 , 28 ]. The contamination rate observed in fresh produce (6.0%) should be interpreted cautiously in light of methodological differences across studies. Within our dataset, contamination was significantly higher in leafy vegetables and herbs (8.0%) compared with smooth-surfaced produce (2.0%; p = 0.009), indicating commodity-specific differences in contamination risk. Compared with earlier microscopy-based produce surveys, which often report higher apparent prevalences, the estimate in the present study is more conservative and likely more specific. This difference likely reflects methodological rather than epidemiological factors, as microscopy is prone to variable staining and misidentification, whereas molecular methods provide greater taxonomic resolution for detecting Cyclospora in fresh produce [ 29 – 32 ]. PCR-based detection in fresh produce can be challenging due to complex sample matrices, inhibitors, and low oocyst loads, which may reduce sensitivity if not properly optimized [ 31 , 32 ]. Together, these findings suggest that the observed prevalence reflects a more specific molecular estimate and is not directly comparable with microscopy-based reports. Higher contamination in leafy vegetables and herbs than in smooth-surfaced produce aligns with evidence from surveillance and outbreak investigations. Cyclosporiasis outbreaks in high-income countries have often been associated with berries, basil, cilantro, and ready-to-eat leafy salads, highlighting the vulnerability of produce consumed raw and subjected to extensive handling [ 33 , 34 ]. The prominence of coriander and mint in the present study may therefore reflect local consumption and handling patterns rather than a fundamentally different contamination process. In this study, coriander (10.0%) and mint (8.0%) exhibited the highest contamination rates among all produce types, reinforcing their role as high-risk commodities in the local context. In addition, leafy greens and herbs differ from other produce types in their physical and biochemical characteristics, which can influence oocyst attachment, recovery efficiency, and removal during washing [ 29 , 35 ]. These regional commodity patterns reinforce the need for context-specific risk assessment, rather than direct extrapolation from outbreak profiles in North America or Europe [ 33 , 34 ]. Multivariable analysis in this study identified canal irrigation (aOR 3.41; p = 0.031), proximity to drainage channels within 50 m (aOR 3.98; p = 0.007), and the use of untreated surface water for rinsing (aOR 2.91; p = 0.045) as independent predictors of contamination. These findings indicate that contamination reflects both pre- and post-harvest processes. Unlike studies focusing primarily on irrigation water, our results point to a broader role for environmental and farm-level factors [ 36 , 37 ]. Proximity to drainage channels may indicate indirect contamination pathways, such as wastewater seepage, runoff, and flood-driven dispersal. Similar findings in other agricultural systems highlight the importance of landscape-level contamination dynamics [ 38 , 39 ]. Post-harvest practices are also important, as the use of untreated water for rinsing may reintroduce contamination after harvest. Evidence suggests that handling steps such as washing, cooling, and transport can act as key points of cross-contamination when water quality is not controlled [ 40 , 41 ]. This is particularly relevant in peri-urban agricultural systems, where produce may undergo multiple handling steps and water reuse is common. These findings support the need for integrated control strategies addressing both environmental and post-harvest factors. The similar contamination rates at farm and market levels (6.0% each; p = 1.00) suggest that contamination likely occurs during the pre-harvest stage. In this study, detection in produce increased from 4.0% in the pre-monsoon period to 8.0% during the monsoon (p = 0.14), with 63.6% of all positive samples occurring during the monsoon season. Unlike studies from endemic regions showing clear seasonal peaks during warm and rainy months, the weaker signal observed here may reflect limited statistical power or shorter sampling duration [ 42 , 43 ]. In tropical and subtropical regions, higher prevalence during rainy seasons is associated with increased oocyst transport and favorable sporulation conditions. The observed trend remains epidemiologically plausible, as monsoon conditions can increase runoff, flooding, and the resuspension of fecal contaminants, promoting the spread of protozoan pathogens into irrigation systems [ 44 , 45 ]. In addition, increased turbidity and organic load during rainfall events may reduce the effectiveness of natural filtration processes in surface water systems, further elevating contamination risk [ 36 ]. The higher proportion of positive samples during the monsoon period highlights the need for seasonally targeted surveillance and mitigation strategies, especially in regions where agricultural activity coincides with limited sanitation infrastructure. Detection rates in this study fall within the range reported elsewhere, although comparisons should be interpreted cautiously due to methodological heterogeneity. Differences in sampling, concentration methods, and molecular targets contribute to variability across studies [ 46 , 47 ]. Nested PCR improves specificity over microscopy for detecting C. cayetanensis , but may still detect DNA from non-viable or closely related organisms, meaning results do not necessarily reflect infectious risk [ 48 ]. Recent sequencing-based investigations indicate that some 18S rRNA detections in environmental samples correspond to non-target coccidia or closely related organisms, emphasizing the importance of confirmatory methods, including sequencing or mitochondrial gene targets, to enhance specificity [ 49 ]. Nested PCR proved to be a feasible approach for environmental surveillance in this resource-limited setting. The successful detection of C. cayetanensis DNA in both irrigation water and fresh produce samples in this study further supports the applicability of nested PCR for environmental surveillance in resource-limited field settings. While real-time PCR provides greater analytical sensitivity and enables quantification, nested PCR remains a more accessible and cost-effective alternative in resource-limited laboratory settings [ 46 , 50 ]. DNA-based detection does not indicate organism viability or infectivity, so positive findings should be interpreted with caution [ 48 , 51 ]. Incorporating viability assays, PMA-based methods, or sequencing could improve the epidemiological relevance of future studies. More broadly, these findings illustrate the convergence of environmental, agricultural, and infrastructural factors in shaping food safety outcomes. DNA-based detection does not indicate organism viability or infectivity, so positive findings should be interpreted with caution [ 48 , 51 ]. Incorporating viability assays, PMA-based methods, or sequencing could improve the epidemiological relevance of future studies. Peri-urban farming systems in Pakistan differ from highly regulated systems in their reliance on diverse water sources, limited wastewater control, and variable post-harvest practices. These contextual factors likely shape both the magnitude and pathways of contamination. Effective interventions should go beyond end-product testing to address upstream factors such as water quality, sanitation, and handling practices [ 52 , 53 ]. From a One Health perspective, these findings highlight the close link between environmental contamination and human exposure. Improving water management, reducing drainage-related contamination, and strengthening post-harvest hygiene represent practical intervention points in this setting. Continued environmental surveillance using more specific molecular approaches, including sequencing-based confirmation and mitochondrial markers, will be important for refining risk assessment and informing evidence-based food safety policies in South Asia. Limitations There are several limitations to this study. First, the cross-sectional design captures contamination at a single time point and may not reflect seasonal variability beyond the study period. Second, detection of C. cayetanensis DNA by nested PCR does not confirm oocyst viability or infectivity, limiting direct inference of public health risk. Third, the use of 18S rRNA targets, while sensitive, may detect DNA from closely related coccidian species, as shown in recent sequencing-based studies reporting non-target detections in environmental samples. Incorporating confirmatory sequencing or more specific mitochondrial targets (e.g., cox3 , cob ) in future studies would improve specificity. Fourth, environmental contamination is inherently heterogeneous, and single-point sampling may underestimate localized or intermittent contamination events. Conclusions This study provides baseline molecular evidence of C. cayetanensis contamination at the produce–irrigation water interface in peri-urban Khyber Pakhtunkhwa, Pakistan. Surface irrigation, proximity to drainage channels, and the use of untreated water for rinsing were identified as key determinants of contamination risk. By combining molecular detection with environmental and farm-level assessment within a One Health framework, these findings offer practical insight for food safety interventions in a setting where such data have been limited. Targeted strategies including improved irrigation water management, better sanitation to reduce drainage-related contamination, and safer post-harvest handling may help reduce contamination risk. Continued environmental surveillance using more specific molecular approaches, including sequencing confirmation and mitochondrial markers, will be important for informing evidence-based policy and protecting public health in South Asia. Declarations Ethics approval and consent to participate : This study involved environmental sampling of fresh produce and irrigation water and did not include human participants, human data, or human biological materials. Therefore, ethical approval was not required in accordance with institutional and national guidelines. All procedures were conducted in compliance with institutional biosafety regulations. Permission to access sampling sites was obtained from farm owners and market authorities prior to sample collection. The principles of the Declaration of Helsinki are not applicable in the present study. Consent to participate : Not applicable. This study did not involve human participants. Consent for publication: Not applicable. This manuscript contains no individual person's data, images, or videos. Competing interests: The authors declare that they have no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Mina A. Almayouf Validation, Investigation, Formal analysis, Resources, Writing – Review & Editing, Funding acquisition. Abdul Rehman Conceptualization, Methodology, Investigation, Writing – Original Draft, Writing – Review & Editing. Abdul Majid Methodology, Investigation, Formal analysis, Writing – Review & Editing. Abdul Basit Methodology, Investigation, Data curation, Writing – Review & Editing. Mubbashir Hussain Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft, Writing – Review & Editing, Visualization. Funding: This research was supported by the Deanship of Graduate Studies and Scientific Research, Qassim University, Buraydah, Kingdom of Saudi Arabia (QU-APC-2025). The funders had no role in study design, data collection, analysis, interpretation, or manuscript preparation. Author Contribution Iffat Naz: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing – Original Draft, Writing – Review & Editing, Visualization, Project administration, Funding acquisition.Mina A. Almayouf: Validation, Investigation, Formal analysis, Resources, Writing – Review & Editing, Funding acquisition.Abdul Rehman: Conceptualization, Methodology, Investigation, Writing – Original Draft, Writing – Review & Editing.Abdul Majid: Methodology, Investigation, Formal analysis, Writing – Review & Editing.Abdul Basit: Methodology, Investigation, Data curation, Writing – Review & Editing.Mubbashir Hussain: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft, Writing – Review & Editing, Visualization.All authors read and approved the final manuscript. Acknowledgement The authors gratefully acknowledge the Deanship of Graduate Studies and Scientific Research, Qassim University (QU-APC-2025), for providing financial and logistical support in collaboration of Department of Microbiology, Kohat University of Science and Technology,(KUST), Kohat, Pakistan. We extend our sincere thanks to the farm owners and market authorities in Peshawar and Kohat districts for their cooperation and permission to collect samples. We also acknowledge the technical staff at the Department of Microbiology, Kohat University of Science and Technology, for their assistance with laboratory analyses. Special thanks to Dr. Saqib (Assistant Professor, Institute of Numerical Sciences, KUST, Kohat, Pakistan) for his valuable input on statistical analysis and to Mr. Muhammad Ali, Mr. Banaras Khan, Mr. Abdullah Riaz, Mr. Farman Ullah Khan (Students of Department of Microbiology, KUST) for their support during field sampling. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. This study did not generate new DNA or RNA sequencing data requiring deposition in public repositories. Representative gel images and associated raw data have been provided as supplementary materials in accordance with journal requirements References Centers for Disease Control and Prevention (CDC). Cyclospora infection (Cyclosporiasis). Atlanta: CDC. 2023. Available from: https://www.cdc.gov/parasites/cyclosporiasis/ Ortega YR, Sanchez R. Update on Cyclospora cayetanensis , a food-borne and waterborne parasite. Clin Microbiol Rev. 2010;23(1):218–34. Chacín-Bonilla L. Cyclospora cayetanensis. In: Xiao L, Ryan U, Feng Y, editors. Biology of Foodborne Parasites. Boca Raton: CRC; 2015. pp. 271–303. Hall RL, Jones JL, Hurd S, Smith G, Mahon BE, Herwaldt BL. Population-based active surveillance for Cyclospora infection-United States, FoodNet Sites, 1997–2009. Clin Infect Dis. 2012;54(Suppl 5):S411–7. Herwaldt BL, Beach MJ. The return of Cyclospora in 1997: another outbreak of cyclosporiasis in North America. Emerg Infect Dis. 1999;5(1):33–6. Murphy HR, Almeria S, da Silva AJ. Molecular detection of Cyclospora cayetanensis in fresh produce and soil samples. Food Microbiol. 2017;68:155–62. World Health Organization (WHO). Guidelines for the safe use of wastewater, excreta and greywater. Volume II: Wastewater use in agriculture. Geneva: WHO; 2006. Dixon BR, Bussey JM, Parrington LJ, Parenteau M. Detection of Cyclospora cayetanensis oocysts in environmental samples using molecular methods. J Microbiol Methods. 2013;94(3):298–302. Li J, Wang Z, Karim MR, Zhang L. Cyclospora cayetanensis in fresh produce and water: molecular detection and public health implications. Food Waterborne Parasitol. 2022;27:e00163. Qvarnstrom Y, Wei-Pridgeon Y, Li W, Nascimento FS, Bishop HS, Herwaldt BL, et al. Real-time PCR detection of Cyclospora cayetanensis in produce. Appl Environ Microbiol. 2015;81(2):554–63. Orlandi PA, Chu DM, Bier JW, Jackson GJ. Parasite detection in food and water by nucleic acid amplification methods. J Food Prot. 2003;66(12):2295–307. Ali MA, Naji GM. Multiplex PCR for detection of intestinal parasites in patients at Wasit province. Int J Health Sci. 2022;6(S8):6002–8. https://doi.org/10.53730/ijhs.v6nS8.13677 . Utaaker KS, Skjerve E, Robertson LJ. Keeping it cool: survival of parasites on fresh produce. Int J Food Microbiol. 2017;255:14–21. Cook N, Paton CA, Wilkinson N. Evaluation of methods for detection of protozoan parasites on fresh produce. Int J Food Microbiol. 2006;109(1–2):7–14. Hill VR, Kahler AM, Jothikumar N, Johnson TB, Hahn D, Cromeans TL. Multistate evaluation of an ultrafiltration-based procedure for simultaneous recovery of enteric microbes in 100-L tap water samples. Appl Environ Microbiol. 2005;71(11):6878–84. Garcia LS. Diagnostic Medical Parasitology. 6th ed. Washington DC: ASM; 2016. Bartosova B, Koudela B, Slana I. Detection of Cyclospora cayetanensis , Echinococcus multilocularis , Toxocara spp. and microsporidia in fresh produce using molecular methods: - A review. Food Waterborne Parasitol. 2021;23:e00124. 10.1016/j.fawpar.2021.e00124 . PMID: 34169159; PMCID: PMC8209397. Relman DA, Schmidt TM, Gajadhar A, Sogin M, Cross J, Yoder K, Sethabutr O, Echeverria P. Molecular phylogenetic analysis of Cyclospora, the human intestinal pathogen, suggests that it is closely related to Eimeria species. J Infect Dis. 1996;173:440–5. 10.1093/infdis/173.2.440 . Orlandi PA, Carter L, Brinker AM, da Silva AJ, Chu D-M, Lampel KA, Monday SR. Targeting single-nucleotide polymorphisms in the 18S rRNA gene to differentiate Cyclospora species from Eimeria species by multiplex PCR. Appl Environ Microbiol. 2003;69:4806–13. 10.1128/AEM.69.8.4806-4813.2003 . Ahmed H, Al-Sharif MS, Al-Malki ES, Al-Ghamdi AA. Environmental surveillance of foodborne parasites in low- and middle-income countries: challenges and opportunities. Food Waterborne Parasitol. 2024;35:e00215. McCaughan KJ, and K. E.Kniel. Current Knowledge and Future Directions for Cyclospora cayetanensis Research and Its Surrogates. Compr Reviews Food Sci Food Safety25 no. 2026;1e70327. https://doi.org/10.1111/1541-4337.70327 . Hadjilouka A, Tsaltas D. Cyclospora Cayetanensis—Major Outbreaks from Ready to Eat Fresh Fruits and Vegetables. Foods 2020, 9, 1703. https://doi.org/10.3390/foods9111703 Naganathan A, Sivakumar M, Paramasivam S, et al. The prevalence of Cyclospora cayetanensis in water: a systematic review and meta-analysis. Epidemiol Infect. 2022;150:e15. Hofstetter J, Kahler AM, Arrowood M, et al. Evaluation of coccidia DNA in irrigation pond water and wastewater associated with Cyclospora cayetanensis detections. Microbiol Spectr. 2024;12:e0090624. Naganathan T, O'Connor A, Sargeant J et al. The prevalence of Cyclospora cayetanensis in water: a systematic review and meta-analysis Epidemiology and Infection. 2021;150. PMCID: PMC8796144. Yeager C, Gutierrez A, Tucker MS, et al. Filters comprised of sand and zero-valent iron hold promise as tools to mitigate risk posed by Cyclospora cayetanensis . Food Waterborne Parasitol. 2024;37:e00243. Gutierrez A, Tucker MS, Yeager C, et al. Zero-valent iron and sand filtration reduces levels of Cyclospora cayetanensis surrogates. Microorganisms. 2024;12(11):2344. Al-Braiken FA, Amin A, Beeching NJ, Hommel M, Hart CA. Detection of Cryptoporidium amongst diarrhoeic and asymptomatic children in Jeddah, Saudi Arabia. Ann Trop Med Parasitol. 2003;97:505–10. Barratt JLN, Shen J, Houghton K, Richins T, Sapp SGH, Cama V, Arrowood MJ, Straily A, Qvarnstrom Y. Cyclospora cayetanensis comprises at least 3 species that cause human cyclosporiasis. Parasitology. 2022;150:269–85. Centers for Disease Control and Prevention. DPDx: diagnostic procedures—stool specimens. Atlanta: CDC. Modified acid-fast staining for Cyclospora may show variable staining, leading to possible misidentification. Almeria S, Grocholl J, Mullins J, Durigan M, Ewing-Peeples L, Rogers EL, et al. Multi-laboratory validation of a modified real-time PCR assay (Mit1C) for the detection of Cyclospora cayetanensis in fresh produce. Food Microbiol. 2025;128:104727. Durigan M, Ewing-Peeples L, Almeria S, Balan KV, Grocholl J, Irizawa S, et al. Detection of Cyclospora cayetanensis in food and water samples: optimized protocols for specific and sensitive molecular methods from a regulatory agency perspective. J Food Prot. 2024;87:100291. Hadjilouka A, Tsaltas D. Cyclospora cayetanensis —major outbreaks from ready-to-eat fresh fruits and vegetables. Foods. 2020;9(11):1703. Almeria S, Chacin-Bonilla L, Maloney JG, Santin M. Cyclospora cayetanensis : a perspective (2020–2023) with emphasis on epidemiology and detection methods. Microorganisms. 2023;11(9):2171. Lalonde LF, Gajadhar AA, Dixon BR, Jian Y, Hohmann S, Ndao M. Optimization and validation of methods for isolation and real-time PCR identification of protozoan oocysts on leafy green vegetables and berry fruits. Food Control. 2016;60:617–23. Steele M, Odumeru J. Irrigation water as source of foodborne pathogens on fresh produce. J Food Prot. 2004;67(12):2839–49. Gil MI, Selma MV, López-Gálvez F, Allende A. Fresh-cut product sanitation and wash water disinfection: problems and solutions. Int J Food Microbiol. 2009;134(1–2):37–45. Amoah P, Drechsel P, Abaidoo RC, Ntow WJ. Pesticide and pathogen contamination of vegetables in wastewater-irrigated urban agriculture. J Food Prot. 2006;69(8):1869–75. Keraita B, Drechsel P, Konradsen F. Using on-farm sedimentation ponds to improve microbial quality of irrigation water. Agric Water Manag. 2008;95(6):599–608. U.S. Food and Drug Administration (FDA). Guide to minimize microbial food safety hazards for fresh fruits and vegetables. Washington, DC: FDA; 2023. FAO/WHO. Microbiological hazards in fresh fruits and vegetables: Meeting report. Rome: FAO; 2008. Chacín-Bonilla L. Epidemiology of Cyclospora cayetanensis : a review focusing in endemic areas. Acta Trop. 2010;115(3):181–93. Dixon BR. Parasitic illnesses associated with the consumption of fresh produce—an emerging issue in developed countries. Curr Opin Food Sci. 2016;8:104–9. Curriero FC, Patz JA, Rose JB, Lele S. The association between extreme precipitation and waterborne disease outbreaks. Am J Public Health. 2001;91(8):1194–9. Kistemann T, Classen T, Koch C, Dangendorf F, Fischeder R, Gebel J, et al. Microbial load of drinking water reservoir tributaries during extreme rainfall and runoff. Appl Environ Microbiol. 2002;68(5):2188–97. Li D, Zhang L, Karim MR, et al. Molecular detection of foodborne parasites in environmental samples: challenges and advances. Food Waterborne Parasitol. 2021;24:e00136. Hofstetter J, Arfken A, Kahler A, Qvarnstrom Y, Rodrigues C, Mattioli M. Evaluation of coccidia DNA in irrigation pond water and wastewater sludge associated with Cyclospora cayetanensis 18S rRNA gene qPCR detections. Microbiol Spectr. 2024;12(8):e0090624. 10.1128/spectrum.00906-24 . Epub 2024 Jun 25. Rochelle PA, De Leon R. Viability of protozoan pathogens in water: detection limitations and public health implications. Microbiol Spectr. 2017;5(3):1–12. Kahler AM, et al. Sources and Prevalence of Cyclospora cayetanensis in Southeastern U.S. Growing Environments. J Food Prot. 2024;87(7):100309. U.S. Food and Drug Administration. BAM Chap. 19b: Molecular Detection of Cyclospora cayetanensis in Fresh Produce Using Real-Time PCR. December 2024 Edition. Keegan AR, Fanok S, Monis PT, Saint CP. Cell culture–PCR for assessing protozoan viability: limitations and applications. Appl Environ Microbiol. 2013;79(19):5883–90. World Health Organization (WHO). Guidelines on sanitation and health. Geneva: WHO; 2018. FAO. Food safety risk management in fresh produce supply chains. Rome: FAO; 2022. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfilesS1S7cyclospora.pdf SupplementaryS1fileQuestionnaireCycolspora.pdf SupplementaryfigureS1v3cyclospora.pdf Cite Share Download PDF Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Editor invited by journal 03 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9256916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619605025,"identity":"8435af6e-79d8-4c03-ad36-312e2aea60b9","order_by":0,"name":"Iffat Naz","email":"","orcid":"","institution":"Qassim University","correspondingAuthor":false,"prefix":"","firstName":"Iffat","middleName":"","lastName":"Naz","suffix":""},{"id":619605027,"identity":"b4aeff9b-fe3c-49b8-8d12-632f81bc7c14","order_by":1,"name":"Mina A. Almayouf","email":"","orcid":"","institution":"Qassim University","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"A.","lastName":"Almayouf","suffix":""},{"id":619605029,"identity":"b22fb77a-2bd4-4d5b-9bf5-79caf3c78dc3","order_by":2,"name":"Abdul Rehman","email":"","orcid":"","institution":"Kohat University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Rehman","suffix":""},{"id":619605030,"identity":"e44722dd-1683-4735-9ed1-74bc65080add","order_by":3,"name":"Abdul Majid","email":"","orcid":"","institution":"A'Sharqiyah University","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Majid","suffix":""},{"id":619605031,"identity":"f2153ddf-9e05-4dcf-92ad-3e217e5383f4","order_by":4,"name":"Abdul Basit","email":"","orcid":"","institution":"Hayatabad Medical Complex","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Basit","suffix":""},{"id":619605032,"identity":"5d542a69-a99c-4947-a34f-0d2a1dc5c79b","order_by":5,"name":"Mubbashir Hussain","email":"data:image/png;base64,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","orcid":"","institution":"Kohat University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Mubbashir","middleName":"","lastName":"Hussain","suffix":""}],"badges":[],"createdAt":"2026-03-29 06:53:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9256916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9256916/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-026-05107-3","type":"published","date":"2026-05-01T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437547,"identity":"01776738-3c22-4eb9-989d-6573a0a64a8b","added_by":"auto","created_at":"2026-05-04 15:59:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":426478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256916/v1/f28bf5f2-de28-4260-8a01-8879907ee142.pdf"},{"id":106604914,"identity":"373e2c97-3411-45cf-b035-9f0c327d3402","added_by":"auto","created_at":"2026-04-10 11:01:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":275089,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfilesS1S7cyclospora.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256916/v1/36a3556043b028a1747ecaf1.pdf"},{"id":106604915,"identity":"f971357d-e2b3-43d6-8ce3-d729ef6fa640","added_by":"auto","created_at":"2026-04-10 11:01:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":685807,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS1fileQuestionnaireCycolspora.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256916/v1/9512a088834bcb01573c83ef.pdf"},{"id":106604916,"identity":"7e075b0c-e5ef-49f0-a9dc-b12e5f5b4b2d","added_by":"auto","created_at":"2026-04-10 11:01:10","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":348624,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfigureS1v3cyclospora.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256916/v1/2d02d8327577592ef164c8cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular detection of Cyclospora cayetanensis in fresh produce and irrigation water in peri- urban settings: a One Health cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e is an obligate intracellular coccidian parasite responsible for cyclosporiasis, an intestinal illness typically presenting with prolonged watery diarrhea, abdominal cramping, nausea, and fatigue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Infection occurs through ingestion of food or water contaminated with sporulated oocysts. Freshly excreted oocysts are non-infectious and require days to weeks in the environment to sporulate thus, direct person-to-person transmission does not occur, and environmental reservoirs play a central role in the parasite's transmission cycle [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past several decades, \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e has become a pathogen of growing concern in the context of global food safety. Numerous outbreaks have been linked to the consumption of raw or minimally processed produce, including leafy greens, fresh herbs, and berries, contaminated during cultivation, irrigation, harvesting, or post-harvest handling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The globalization of food supply chains, combined with reliance on diverse irrigation water sources, has amplified the risk of widespread distribution of contaminated produce. In particular, the use of untreated or inadequately treated wastewater in agriculture has been identified as a significant pathway for environmental contamination and subsequent human exposure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the public health importance of \u003cem\u003eC. cayetanensis\u003c/em\u003e is increasingly recognized, substantial knowledge gaps persist regarding its environmental epidemiology especially in low- and middle-income countries. Existing literature is disproportionately focused on clinical detection, while environmental surveillance remains limited and geographically uneven [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent advances in molecular epidemiology emphasize the need for region-specific baseline data to better elucidate contamination pathways and inform targeted interventions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Environmental detection of \u003cem\u003eC. cayetanensis\u003c/em\u003e remains technically challenging because oocyst concentrations are often low, distribution in environmental matrices is heterogeneous, and food and water samples frequently contain PCR inhibitors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSouth Asia is a region of particular relevance in this context, yet it remains under-investigated. In countries such as Pakistan, peri-urban agriculture frequently relies on surface water or untreated wastewater, often in settings where sanitation infrastructure is limited and overlaps with agricultural activities. These conditions create a conducive environment for pathogen transmission at the human-environment-food interface [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite the high burden of enteric diseases and widespread consumption of raw vegetables and herbs, molecular surveillance of \u003cem\u003eC. cayetanensis\u003c/em\u003e in irrigation water and fresh produce in Pakistan remains scarce in peer-reviewed literature. The absence of empirical data limits accurate risk assessment, evidence-based policy formulation, and the design of effective food safety interventions that consider the interconnectedness of human, animal, and environmental health.\u003c/p\u003e \u003cp\u003eFurthermore, most environmental studies on Cyclospora have been conducted in high-income settings using advanced real-time PCR platforms. While these methods offer high sensitivity and specificity, they are often not feasible in resource-limited laboratories. Conventional nested PCR targeting ribosomal gene loci provides a cost-effective and sensitive alternative when implemented with rigorous contamination control measures [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Demonstrating the applicability and epidemiological value of such approaches in underrepresented regions is essential for improving global surveillance equity.\u003c/p\u003e \u003cp\u003eIn light of these gaps, the present study aims to generate baseline molecular evidence of \u003cem\u003eC. cayetanensis\u003c/em\u003e contamination in fresh produce and associated irrigation water in peri-urban areas of Khyber Pakhtunkhwa, Pakistan. Additionally, it seeks to identify environmental and farm-level factors associated with contamination risk. To our knowledge, using molecular detection within a One Health framework, this is first study providing context-specific data from a geographically underrepresented region and contributes to the evidence base needed to inform targeted food safety interventions in South Asia.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Study Area\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional study from April to September 2025 in two districts of Khyber Pakhtunkhwa, Pakistan: Peshawar and Kohat. These districts were selected purposively because they feature active peri-urban vegetable production, diverse irrigation practices, and high rates of raw vegetable and herb consumption.\u003c/p\u003e \u003cp\u003ePeshawar, the provincial capital, is characterized by dense urbanization and intensive peri-urban agriculture supported by canal and tube-well irrigation systems. In contrast, Kohat represents a semi-arid agricultural setting where irrigation depends on both surface water channels and groundwater extraction. Seasonal variability, including monsoon rainfall and temperature fluctuations, may influence environmental contamination dynamics.\u003c/p\u003e \u003cp\u003eSampling sites included peri-urban vegetable farms supplying local markets, wholesale vegetable markets in both districts, and irrigation water sources directly linked to sampled farms. The study design focused on evaluating contamination at the interface between fresh produce and irrigation water, reflecting the interconnected One Health perspective.\u003c/p\u003e \u003cp\u003eA structured observational checklist was administered to capture potential contamination determinants. The characteristics of sampling sites, including irrigation sources, drainage proximity, and on-farm practices, are summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Size Justification\u003c/h3\u003e\n\u003cp\u003eWe calculated the sample size using the single proportion formula, assuming an expected contamination prevalence of 10% based on preliminary local surveillance and regional studies from comparable settings. With a desired precision of \u0026plusmn;\u0026thinsp;5% and a 95% confidence level, the minimum required sample size was 138 produce samples. To accommodate potential sample loss, PCR inhibition, and stratified sampling by district and produce type, we increased the sample size by 20%, targeting 166 produce samples per district. However, to enable stratified analysis across six produce types and two sampling nodes (farm and market), a total of 300 produce samples (150 per district) was selected, which exceeded the minimum requirement and provided sufficient power for subgroup analyses. For irrigation water, sample size was determined based on the same parameters, with 120 water samples collected to ensure adequate representation of three water source categories (canal, tube-well, and mixed) across both districts. This sample size was sufficient to detect a 15% difference in contamination prevalence between water sources with 80% power at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eSample Size and Sampling Strategy\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFresh Produce\u003c/h2\u003e \u003cp\u003eA total of 300 fresh produce samples were collected (150 per district). Six commonly consumed raw vegetables and herbs were selected based on local dietary practices: coriander (\u003cem\u003eCoriandrum sativum\u003c/em\u003e), mint (\u003cem\u003eMentha\u003c/em\u003e spp.), lettuce (\u003cem\u003eLactuca sativa\u003c/em\u003e), spinach (\u003cem\u003eSpinacia oleracea\u003c/em\u003e), cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e), and tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eFor each commodity, 25 samples per district (n\u0026thinsp;=\u0026thinsp;50 per commodity) were collected. Sampling was stratified between farm-level samples (n\u0026thinsp;=\u0026thinsp;150) and wholesale market samples (n\u0026thinsp;=\u0026thinsp;150). Each sample (50\u0026ndash;100 g) was aseptically collected in sterile polyethylene bags and transported under chilled conditions to the laboratory for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIrrigation Water\u003c/h3\u003e\n\u003cp\u003eA total of 120 irrigation water samples were collected, including canal/surface water (n\u0026thinsp;=\u0026thinsp;60), tube-well (groundwater) sources (n\u0026thinsp;=\u0026thinsp;40), and mixed sources (n\u0026thinsp;=\u0026thinsp;20). Water samples were obtained directly from irrigation outlets supplying sampled farms.\u003c/p\u003e \u003cp\u003eFor each sampling event, 5\u0026ndash;10 L of water was collected in sterile containers and transported under cooled conditions for laboratory processing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factor Assessment\u003c/h2\u003e \u003cp\u003eA structured observational checklist and short questionnaire were administered to farm owners or managers to capture potential contamination determinants. Variables included irrigation source, proximity to sewage or drainage channels (\u0026le;\u0026thinsp;50 m vs\u0026thinsp;\u0026gt;\u0026thinsp;50 m), use of untreated water for produce washing, availability of sanitation facilities, presence of livestock near cultivation areas, and produce handling practices. Seasonality (pre-monsoon vs monsoon) and sampling node (farm vs market) were also recorded (Supplementary Questionnaire S1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Processing\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eProduce Samples\u003c/h2\u003e \u003cp\u003eEach produce sample (~\u0026thinsp;50 g) was washed in 200 mL buffered saline containing 0.1% Tween 80 to facilitate detachment of oocysts. Samples were manually agitated for 10 minutes, and the wash solution was filtered through sterile gauze.\u003c/p\u003e \u003cp\u003eThe filtrate was centrifuged at 3,000 \u0026times; g for 15 minutes, and the pellet was divided for microscopy and molecular analysis. Similar elution and concentration approaches have been widely applied for parasite recovery from fresh produce [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWater Concentration\u003c/h2\u003e \u003cp\u003eWater samples (5\u0026ndash;10 L) were concentrated using membrane filtration (1.2 \u0026micro;m pore size) or cartridge filtration techniques. Filters were eluted with sterile buffer and centrifuged at 3,000 \u0026times; g for 20 minutes.\u003c/p\u003e \u003cp\u003eThe resulting pellet was resuspended in phosphate-buffered saline and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until DNA extraction. These concentration methods are standard for protozoan detection in environmental water samples [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicroscopic Examination\u003c/h2\u003e \u003cp\u003ePellet aliquots were examined using iodine wet mount followed by modified acid-fast staining. Slides were observed under light microscopy (\u0026times;400\u0026ndash;\u0026times;1000 magnification) for spherical, variably acid-fast oocysts (8\u0026ndash;10 \u0026micro;m). Microscopy was used as a supportive tool but not for definitive identification due to limited sensitivity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDNA Extraction\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted using a commercial stool DNA extraction kit according to the manufacturer\u0026rsquo;s instructions. To enhance oocyst disruption, samples underwent three freeze\u0026ndash;thaw cycles (\u0026minus;\u0026thinsp;80\u0026deg;C to 56\u0026deg;C), followed by mechanical disruption using bead beating.\u003c/p\u003e \u003cp\u003eDNA was eluted in 50 \u0026micro;L nuclease-free water and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. Combined mechanical and thermal lysis has been shown to improve DNA recovery from protozoan oocysts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eNested PCR for\u003c/b\u003e \u003cb\u003eCyclospora cayetanensis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDetection of \u003cem\u003eC. cayetanensis\u003c/em\u003e was performed using nested PCR targeting the 18S rRNA gene [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The primary PCR employed primers CYCF1E and CYCR2B to amplify an approximately 630 bp fragment. The reaction mixture (25 \u0026micro;L) consisted of 12.5 \u0026micro;L 2\u0026times; master mix, 0.5 \u0026micro;M primers, 2 \u0026micro;L template DNA, and nuclease-free water. Cycling conditions included initial denaturation at 94\u0026deg;C for 3 minutes, followed by 35 cycles of denaturation (94\u0026deg;C, 30 s), annealing (54\u0026deg;C, 30 s), extension (72\u0026deg;C, 45 s), and a final extension at 72\u0026deg;C for 5 minutes.\u003c/p\u003e \u003cp\u003eNested PCR was performed using primers CC719 and CRP999, generating an approximately 298 bp amplicon. Cycling conditions included 94\u0026deg;C for 3 minutes, 35 cycles (94\u0026deg;C for 30 s, 66\u0026deg;C for 30 s, 72\u0026deg;C for 30 s), and final extension at 72\u0026deg;C for 5 minutes.\u003c/p\u003e \u003cp\u003ePCR products were visualized on 1.5\u0026ndash;2% agarose gel stained with ethidium bromide or a safer alternative and examined under UV illumination. Nested PCR amplification produced distinct bands at approximately 298 bp, while primary PCR yielded bands around 630 bp, consistent with expected amplicon sizes (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control\u003c/h2\u003e \u003cp\u003eStrict contamination control measures were implemented. Separate laboratory areas were maintained for DNA extraction, PCR setup, and post-PCR analysis. Aerosol-resistant tips were used throughout. Each PCR run included a positive control (Cyclospora DNA obtained from Department of Zoology, KUST, Kohat, Pakistan), negative extraction control, and no-template control. A subset of samples was re-tested to confirm reproducibility, and inhibition was assessed using spiked controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 26. Prevalence was calculated with 95% confidence intervals. Associations were evaluated using chi-square or Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eVariables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were included in multivariable logistic regression models to identify independent risk factors. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eOverall detection of\u003c/b\u003e \u003cb\u003eCyclospora cayetanensis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 420 environmental samples were analyzed, comprising 300 fresh produce samples and 120 irrigation water samples. \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e DNA was detected in 18/300 produce samples (6.0%; 95% CI: 3.8\u0026ndash;9.2%) and 15/120 irrigation water samples (12.5%; 95% CI: 7.8\u0026ndash;19.6%). Detection was higher in irrigation water compared with produce (OR 2.24; 95% CI: 1.09\u0026ndash;4.61; χ\u0026sup2; = 4.81, p\u0026thinsp;=\u0026thinsp;0.028). The distribution of environmental and farm-level characteristics across sampling sites is summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOverall, 33/420 samples were positive (7.9%; 95% CI: 5.7\u0026ndash;10.8%) including 18 (54.5%) produce and 15 (45.5%) water samples. Among all positive samples (produce and water), 63.6% were collected during the monsoon period.. PCR-positive samples were more frequently associated with irrigation water and monsoon sampling compared with PCR-negative samples (Supplementary Table S4).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDistrict-wise distribution\u003c/h2\u003e \u003cp\u003eDetection was observed in both districts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Peshawar, 11/150 produce samples (7.3%; 95% CI: 4.1\u0026ndash;12.6%) and 9/60 irrigation water samples (15.0%; 95% CI: 8.1\u0026ndash;25.9%) were positive. In Kohat, detection was 7/150 (4.7%; 95% CI: 2.3\u0026ndash;9.3%) in produce and 6/60 (10.0%; 95% CI: 4.6\u0026ndash;20.2%) in irrigation water. The difference in produce contamination between districts was not statistically significant (χ\u0026sup2; = 1.03, p\u0026thinsp;=\u0026thinsp;0.31).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e by district\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduce Positive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Positive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Positive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeshawar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11/150 (7.3) [4.1\u0026ndash;12.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9/60 (15.0) [8.1\u0026ndash;25.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20/210 (9.5) [6.3\u0026ndash;14.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKohat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7/150 (4.7) [2.3\u0026ndash;9.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6/60 (10.0) [4.6\u0026ndash;20.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13/210 (6.2) [3.7\u0026ndash;10.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18/300 (6.0) [3.8\u0026ndash;9.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15/120 (12.5) [7.8\u0026ndash;19.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33/420 (7.9) [5.7\u0026ndash;10.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConfidence intervals calculated using the exact (Clopper\u0026ndash;Pearson) method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDetection according to produce type\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the highest detection rate was observed in coriander (5/50; 10.0%; 95% CI: 4.4\u0026ndash;21.4%), followed by mint (4/50; 8.0%; 95% CI: 3.2\u0026ndash;18.8%), lettuce (4/50; 8.0%; 95% CI: 3.2\u0026ndash;18.8%), and spinach (3/50; 6.0%; 95% CI: 2.1\u0026ndash;16.2%). Lower detection rates were observed in cucumber (1/50; 2.0%; 95% CI: 0.3\u0026ndash;10.5%) and tomato (1/50; 2.0%; 95% CI: 0.3\u0026ndash;10.5%).\u003c/p\u003e \u003cp\u003eWhen grouped, leafy vegetables and herbs (n\u0026thinsp;=\u0026thinsp;200) showed higher detection (16/200; 8.0%; 95% CI: 5.0\u0026ndash;12.5%) than smooth-surfaced vegetables (n\u0026thinsp;=\u0026thinsp;100; 2.0%; 95% CI: 0.5\u0026ndash;7.0%) (χ\u0026sup2; = 6.84, p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eA detailed breakdown by produce type, district, and sampling node is provided in Supplementary Table S6.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCR detection by produce type (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduce Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoriander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u0026ndash;21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u0026ndash;18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLettuce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u0026ndash;18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026ndash;16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026ndash;10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u0026ndash;10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Exact (Clopper\u0026ndash;Pearson) confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStratified analysis by sampling node (farm vs. market)\u003c/h2\u003e \u003cp\u003eTo assess whether contamination occurred primarily during cultivation or during post-harvest handling, detection rates were compared between farm-level samples (collected directly from fields) and market-level samples (collected from wholesale markets). Overall, \u003cem\u003eC. cayetanensis\u003c/em\u003e DNA was detected in 9 of 150 (6.0%; 95% CI: 3.2\u0026ndash;11.0%) farm-level produce samples and 9 of 150 (6.0%; 95% CI: 3.2\u0026ndash;11.0%) market-level produce samples, with no statistically significant difference between the two sampling nodes (Fisher's exact test, p\u0026thinsp;=\u0026thinsp;1.00).\u003c/p\u003e \u003cp\u003eWhen stratified by produce category, leafy vegetables and herbs showed identical detection rates at farm and market levels (8.0% each), as did smooth-surfaced vegetables (2.0% each) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Analysis by individual produce type similarly revealed no significant differences between farm and market samples (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in fresh produce by sampling node and produce category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduce Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarm-Level\u003c/p\u003e \u003cp\u003ePositive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarket-Level Positive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Positive / n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeafy vegetables and herbs*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8/100 (8.0) [4.1\u0026ndash;15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8/100 (8.0) [4.1\u0026ndash;15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16/200 (8.0) [5.0\u0026ndash;12.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmooth-surfaced vegetables**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1/50 (2.0) [0.3\u0026ndash;10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/50 (2.0) [0.3\u0026ndash;10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2/100 (2.0) [0.5\u0026ndash;7.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9/150 (6.0) [3.2\u0026ndash;11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9/150 (6.0) [3.2\u0026ndash;11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18/300 (6.0) [3.8\u0026ndash;9.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Coriander, mint, lettuce, spinach. **Cucumber, tomato. *Note: No significant difference in detection between farm and market samples (Fisher's exact test, p\u0026thinsp;=\u0026thinsp;1.00 for all comparisons).\u003c/p\u003e \u003cp\u003eDetailed detection rates stratified by sampling node and produce category, including exact confidence intervals, are provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in fresh produce by individual produce type and sampling node\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduce Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarm-Level Positive / n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarket-Level Positive / n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Positive / n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFisher's Exact\u0026nbsp;*p*-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoriander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3/25 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5/50 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4/50 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLettuce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4/50 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpinach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1/25 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2/25 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3/50 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0/25 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1/25 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1/50 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1/25 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0/25 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1/50 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9/150 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9/150 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18/300 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIrrigation water contamination\u003c/h2\u003e \u003cp\u003eDetection differed by irrigation water source (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Canal water showed the highest detection (12/60; 20.0%; 95% CI: 11.9\u0026ndash;31.4%), followed by tube-well water (2/40; 5.0%; 95% CI: 1.4\u0026ndash;16.5%) and mixed sources (1/20; 5.0%; 95% CI: 0.9\u0026ndash;23.6%).\u003c/p\u003e \u003cp\u003eCompared with tube-well water, canal irrigation was associated with higher odds of detection (OR 4.75; 95% CI: 1.01\u0026ndash;22.3; p\u0026thinsp;=\u0026thinsp;0.048), whereas no difference was observed for mixed sources (OR 1.00; 95% CI: 0.08\u0026ndash;12.1; p\u0026thinsp;=\u0026thinsp;0.99). A district-wise breakdown of irrigation water contamination is presented in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection in irrigation water by source (n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI) vs. Tube-well\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.9\u0026ndash;31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.75 (1.01\u0026ndash;22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTube-well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026ndash;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u0026ndash;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (0.08\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation\u003c/h2\u003e \u003cp\u003eIn produce samples, detection was higher during the monsoon period (12/150; 8.0%; 95% CI: 4.6\u0026ndash;13.4%) compared with the pre-monsoon period (6/150; 4.0%; 95% CI: 1.8\u0026ndash;8.5%), although this difference was not statistically significant (OR 2.09; 95% CI: 0.77\u0026ndash;5.66; p\u0026thinsp;=\u0026thinsp;0.14) shown in (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross all sample types, 21/33 (63.6%) of positive samples were detected during the monsoon period. Detailed seasonal variation by water source and district is provided in Supplementary Table S7\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeasonal variation in produce contamination\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDetection Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026ndash;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.09 (0.77\u0026ndash;5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeasonal patterns in water contamination varied by source, with canal water detection increasing from 12.0% in pre-monsoon to 25.7% in monsoon, while tube-well water showed detection only during monsoon (10.0%). A detailed seasonal breakdown by water source and district is presented in Supplementary Table S7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRisk factor analysis for produce contamination\u003c/h2\u003e \u003cp\u003eIn univariable analysis, canal irrigation (OR 3.82; 95% CI: 1.08\u0026ndash;13.4; p\u0026thinsp;=\u0026thinsp;0.036), proximity to drainage channels\u0026thinsp;\u0026le;\u0026thinsp;50 m (OR 4.39; 95% CI: 1.62\u0026ndash;11.9; p\u0026thinsp;=\u0026thinsp;0.003), and use of untreated surface water for rinsing (OR 3.79; 95% CI: 1.39\u0026ndash;10.3; p\u0026thinsp;=\u0026thinsp;0.009) were associated with detection of \u003cem\u003eC. cayetanensis\u003c/em\u003e in produce. Full results for all variables are presented in Supplementary Table S5 .\u003c/p\u003e \u003cp\u003eIn multivariable analysis, these variables remained independently associated with contamination: canal irrigation (aOR 3.41; 95% CI: 1.12\u0026ndash;10.2; p\u0026thinsp;=\u0026thinsp;0.031), proximity to drainage\u0026thinsp;\u0026le;\u0026thinsp;50 m (aOR 3.98; 95% CI: 1.45\u0026ndash;10.9; p\u0026thinsp;=\u0026thinsp;0.007), and use of untreated surface water for rinsing (aOR 2.91; 95% CI: 1.02\u0026ndash;8.28; p\u0026thinsp;=\u0026thinsp;0.045) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Canal irrigation was associated with higher odds of detection, although the estimate was imprecise (wide confidence interval). Canal irrigation and proximity to drainage channels\u0026thinsp;\u0026le;\u0026thinsp;50 m showed the strongest associations with \u003cem\u003eC. cayetanensis\u003c/em\u003e detection, while the use of untreated surface water for rinsing demonstrated a comparatively lower effect size. All confidence intervals excluded the null value.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable logistic regression analysis of factors associated with \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e detection in fresh produce (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnivariable OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariable aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanal vs. tube-well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.82 (1.08\u0026ndash;13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41 (1.12\u0026ndash;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrainage proximity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50 m vs. \u0026gt;50 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.39 (1.62\u0026ndash;11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.98 (1.45\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface water rinsing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79 (1.39\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91 (1.02\u0026ndash;8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon vs. pre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09 (0.77\u0026ndash;5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeshawar vs. Kohat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60 (0.66\u0026ndash;3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel fit: Hosmer\u0026ndash;Lemeshow goodness-of-fit p\u0026thinsp;=\u0026thinsp;0.64; Nagelkerke R\u0026sup2; = 0.18; no evidence of multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2). Only variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariable analysis were considered for multivariable model entry. Season and district did not meet inclusion criteria and were excluded from the final model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to provide molecular evidence of \u003cem\u003eC. cayetanensis\u003c/em\u003e contamination at the irrigation water\u0026ndash;fresh produce interface in peri-urban Khyber Pakhtunkhwa, Pakistan, a setting where environmental surveillance data have been sparse. We were unable to identify a prior peer-reviewed Pakistan study specifically documenting \u003cem\u003eC. cayetanensis\u003c/em\u003e in fresh produce or irrigation water, which underscores the regional evidence gap addressed by the present work. By integrating molecular detection with farm-level risk assessment, this study provides context-specific evidence from a low- and middle-income setting where surveillance for foodborne protozoa is limited [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, \u003cem\u003eC. cayetanensis\u003c/em\u003e DNA was detected in 6.0% (18/300) of fresh produce samples and 12.5% (15/120) of irrigation water samples, with a significantly higher detection in water (OR 2.24; p\u0026thinsp;=\u0026thinsp;0.028). The higher detection frequency in irrigation water (12.5%) compared with fresh produce (6.0%) observed in this study (OR 2.24; p\u0026thinsp;=\u0026thinsp;0.028) aligns with the established view that agricultural water serves as a major source of contamination. The 12.5% detection rate in water observed here falls within the lower range of reported values from neighboring and international studies, where rates have ranged from approximately 10% to over 30% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This discrepancy may reflect differences in wastewater exposure, hydrological characteristics, and climatic conditions across regions, as well as variability in laboratory detection methods. A recent meta-analysis estimated an overall pooled prevalence of approximately 6.9% in water samples globally, while also highlighting substantial heterogeneity across geographic regions and water sources [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Reported detection rates vary considerably across studies, largely due to methodological differences such as PCR sensitivity, sample concentration methods, and sequencing-based confirmation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between canal irrigation and produce contamination highlights the importance of water quality as a critical control point in the production chain. In particular, canal water showed a substantially higher contamination rate (20.0%) compared with tube-well sources (5.0%), corresponding to nearly a fivefold increase in odds (OR 4.75), highlighting the elevated risk associated with surface water irrigation in this setting. From a food safety perspective, agricultural water is widely recognized as a major pathway for contamination of fresh produce, particularly in systems relying on untreated surface water. Recent experimental and field studies indicate that low-cost filtration and water treatment approaches can reduce protozoan contamination, offering practical options in resource-limited settings [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe contamination rate observed in fresh produce (6.0%) should be interpreted cautiously in light of methodological differences across studies. Within our dataset, contamination was significantly higher in leafy vegetables and herbs (8.0%) compared with smooth-surfaced produce (2.0%; p\u0026thinsp;=\u0026thinsp;0.009), indicating commodity-specific differences in contamination risk. Compared with earlier microscopy-based produce surveys, which often report higher apparent prevalences, the estimate in the present study is more conservative and likely more specific. This difference likely reflects methodological rather than epidemiological factors, as microscopy is prone to variable staining and misidentification, whereas molecular methods provide greater taxonomic resolution for detecting \u003cem\u003eCyclospora\u003c/em\u003e in fresh produce [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. PCR-based detection in fresh produce can be challenging due to complex sample matrices, inhibitors, and low oocyst loads, which may reduce sensitivity if not properly optimized [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Together, these findings suggest that the observed prevalence reflects a more specific molecular estimate and is not directly comparable with microscopy-based reports.\u003c/p\u003e \u003cp\u003eHigher contamination in leafy vegetables and herbs than in smooth-surfaced produce aligns with evidence from surveillance and outbreak investigations. Cyclosporiasis outbreaks in high-income countries have often been associated with berries, basil, cilantro, and ready-to-eat leafy salads, highlighting the vulnerability of produce consumed raw and subjected to extensive handling [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The prominence of coriander and mint in the present study may therefore reflect local consumption and handling patterns rather than a fundamentally different contamination process. In this study, coriander (10.0%) and mint (8.0%) exhibited the highest contamination rates among all produce types, reinforcing their role as high-risk commodities in the local context. In addition, leafy greens and herbs differ from other produce types in their physical and biochemical characteristics, which can influence oocyst attachment, recovery efficiency, and removal during washing [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These regional commodity patterns reinforce the need for context-specific risk assessment, rather than direct extrapolation from outbreak profiles in North America or Europe [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultivariable analysis in this study identified canal irrigation (aOR 3.41; p\u0026thinsp;=\u0026thinsp;0.031), proximity to drainage channels within 50 m (aOR 3.98; p\u0026thinsp;=\u0026thinsp;0.007), and the use of untreated surface water for rinsing (aOR 2.91; p\u0026thinsp;=\u0026thinsp;0.045) as independent predictors of contamination. These findings indicate that contamination reflects both pre- and post-harvest processes. Unlike studies focusing primarily on irrigation water, our results point to a broader role for environmental and farm-level factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Proximity to drainage channels may indicate indirect contamination pathways, such as wastewater seepage, runoff, and flood-driven dispersal. Similar findings in other agricultural systems highlight the importance of landscape-level contamination dynamics [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePost-harvest practices are also important, as the use of untreated water for rinsing may reintroduce contamination after harvest. Evidence suggests that handling steps such as washing, cooling, and transport can act as key points of cross-contamination when water quality is not controlled [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This is particularly relevant in peri-urban agricultural systems, where produce may undergo multiple handling steps and water reuse is common. These findings support the need for integrated control strategies addressing both environmental and post-harvest factors. The similar contamination rates at farm and market levels (6.0% each; p\u0026thinsp;=\u0026thinsp;1.00) suggest that contamination likely occurs during the pre-harvest stage.\u003c/p\u003e \u003cp\u003eIn this study, detection in produce increased from 4.0% in the pre-monsoon period to 8.0% during the monsoon (p\u0026thinsp;=\u0026thinsp;0.14), with 63.6% of all positive samples occurring during the monsoon season. Unlike studies from endemic regions showing clear seasonal peaks during warm and rainy months, the weaker signal observed here may reflect limited statistical power or shorter sampling duration [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In tropical and subtropical regions, higher prevalence during rainy seasons is associated with increased oocyst transport and favorable sporulation conditions.\u003c/p\u003e \u003cp\u003eThe observed trend remains epidemiologically plausible, as monsoon conditions can increase runoff, flooding, and the resuspension of fecal contaminants, promoting the spread of protozoan pathogens into irrigation systems [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, increased turbidity and organic load during rainfall events may reduce the effectiveness of natural filtration processes in surface water systems, further elevating contamination risk [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The higher proportion of positive samples during the monsoon period highlights the need for seasonally targeted surveillance and mitigation strategies, especially in regions where agricultural activity coincides with limited sanitation infrastructure. Detection rates in this study fall within the range reported elsewhere, although comparisons should be interpreted cautiously due to methodological heterogeneity. Differences in sampling, concentration methods, and molecular targets contribute to variability across studies [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Nested PCR improves specificity over microscopy for detecting \u003cem\u003eC. cayetanensis\u003c/em\u003e, but may still detect DNA from non-viable or closely related organisms, meaning results do not necessarily reflect infectious risk [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Recent sequencing-based investigations indicate that some 18S rRNA detections in environmental samples correspond to non-target coccidia or closely related organisms, emphasizing the importance of confirmatory methods, including sequencing or mitochondrial gene targets, to enhance specificity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNested PCR proved to be a feasible approach for environmental surveillance in this resource-limited setting. The successful detection of \u003cem\u003eC. cayetanensis\u003c/em\u003e DNA in both irrigation water and fresh produce samples in this study further supports the applicability of nested PCR for environmental surveillance in resource-limited field settings. While real-time PCR provides greater analytical sensitivity and enables quantification, nested PCR remains a more accessible and cost-effective alternative in resource-limited laboratory settings [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. DNA-based detection does not indicate organism viability or infectivity, so positive findings should be interpreted with caution [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Incorporating viability assays, PMA-based methods, or sequencing could improve the epidemiological relevance of future studies.\u003c/p\u003e \u003cp\u003eMore broadly, these findings illustrate the convergence of environmental, agricultural, and infrastructural factors in shaping food safety outcomes. DNA-based detection does not indicate organism viability or infectivity, so positive findings should be interpreted with caution [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Incorporating viability assays, PMA-based methods, or sequencing could improve the epidemiological relevance of future studies.\u003c/p\u003e \u003cp\u003ePeri-urban farming systems in Pakistan differ from highly regulated systems in their reliance on diverse water sources, limited wastewater control, and variable post-harvest practices. These contextual factors likely shape both the magnitude and pathways of contamination. Effective interventions should go beyond end-product testing to address upstream factors such as water quality, sanitation, and handling practices [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a One Health perspective, these findings highlight the close link between environmental contamination and human exposure. Improving water management, reducing drainage-related contamination, and strengthening post-harvest hygiene represent practical intervention points in this setting. Continued environmental surveillance using more specific molecular approaches, including sequencing-based confirmation and mitochondrial markers, will be important for refining risk assessment and informing evidence-based food safety policies in South Asia.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere are several limitations to this study. First, the cross-sectional design captures contamination at a single time point and may not reflect seasonal variability beyond the study period. Second, detection of \u003cem\u003eC. cayetanensis\u003c/em\u003e DNA by nested PCR does not confirm oocyst viability or infectivity, limiting direct inference of public health risk. Third, the use of 18S rRNA targets, while sensitive, may detect DNA from closely related coccidian species, as shown in recent sequencing-based studies reporting non-target detections in environmental samples. Incorporating confirmatory sequencing or more specific mitochondrial targets (e.g., \u003cem\u003ecox3\u003c/em\u003e, \u003cem\u003ecob\u003c/em\u003e) in future studies would improve specificity. Fourth, environmental contamination is inherently heterogeneous, and single-point sampling may underestimate localized or intermittent contamination events.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides baseline molecular evidence of \u003cem\u003eC. cayetanensis\u003c/em\u003e contamination at the produce\u0026ndash;irrigation water interface in peri-urban Khyber Pakhtunkhwa, Pakistan. Surface irrigation, proximity to drainage channels, and the use of untreated water for rinsing were identified as key determinants of contamination risk. By combining molecular detection with environmental and farm-level assessment within a One Health framework, these findings offer practical insight for food safety interventions in a setting where such data have been limited. Targeted strategies including improved irrigation water management, better sanitation to reduce drainage-related contamination, and safer post-harvest handling may help reduce contamination risk. Continued environmental surveillance using more specific molecular approaches, including sequencing confirmation and mitochondrial markers, will be important for informing evidence-based policy and protecting public health in South Asia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eEthics approval and consent to participate\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThis study involved environmental sampling of fresh produce and irrigation water and did not include human participants, human data, or human biological materials. Therefore, ethical approval was not required in accordance with institutional and national guidelines. All procedures were conducted in compliance with institutional biosafety regulations. Permission to access sampling sites was obtained from farm owners and market authorities prior to sample collection. The principles of the Declaration of Helsinki are not applicable in the present study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent to participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable. This study did not involve human participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable. This manuscript contains no individual person's data, images, or videos.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eMina A. Almayouf\u003c/h2\u003e \u003cp\u003eValidation, Investigation, Formal analysis, Resources, Writing \u0026ndash; Review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbdul Rehman\u003c/strong\u003e \u003cp\u003eConceptualization, Methodology, Investigation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbdul Majid\u003c/strong\u003e \u003cp\u003eMethodology, Investigation, Formal analysis, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbdul Basit\u003c/strong\u003e \u003cp\u003eMethodology, Investigation, Data curation, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMubbashir Hussain\u003c/strong\u003e \u003cp\u003eConceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was supported by the Deanship of Graduate Studies and Scientific Research, Qassim University, Buraydah, Kingdom of Saudi Arabia (QU-APC-2025). The funders had no role in study design, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIffat Naz: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, Project administration, Funding acquisition.Mina A. Almayouf: Validation, Investigation, Formal analysis, Resources, Writing \u0026ndash; Review \u0026amp; Editing, Funding acquisition.Abdul Rehman: Conceptualization, Methodology, Investigation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing.Abdul Majid: Methodology, Investigation, Formal analysis, Writing \u0026ndash; Review \u0026amp; Editing.Abdul Basit: Methodology, Investigation, Data curation, Writing \u0026ndash; Review \u0026amp; Editing.Mubbashir Hussain: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the Deanship of Graduate Studies and Scientific Research, Qassim University (QU-APC-2025), for providing financial and logistical support in collaboration of Department of Microbiology, Kohat University of Science and Technology,(KUST), Kohat, Pakistan. We extend our sincere thanks to the farm owners and market authorities in Peshawar and Kohat districts for their cooperation and permission to collect samples. We also acknowledge the technical staff at the Department of Microbiology, Kohat University of Science and Technology, for their assistance with laboratory analyses. Special thanks to Dr. Saqib (Assistant Professor, Institute of Numerical Sciences, KUST, Kohat, Pakistan) for his valuable input on statistical analysis and to Mr. Muhammad Ali, Mr. Banaras Khan, Mr. Abdullah Riaz, Mr. Farman Ullah Khan (Students of Department of Microbiology, KUST) for their support during field sampling.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. This study did not generate new DNA or RNA sequencing data requiring deposition in public repositories. Representative gel images and associated raw data have been provided as supplementary materials in accordance with journal requirements\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention (CDC). Cyclospora infection (Cyclosporiasis). Atlanta: CDC. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/parasites/cyclosporiasis/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/parasites/cyclosporiasis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega YR, Sanchez R. Update on \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e, a food-borne and waterborne parasite. Clin Microbiol Rev. 2010;23(1):218\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChac\u0026iacute;n-Bonilla L. Cyclospora cayetanensis. In: Xiao L, Ryan U, Feng Y, editors. Biology of Foodborne Parasites. Boca Raton: CRC; 2015. pp. 271\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall RL, Jones JL, Hurd S, Smith G, Mahon BE, Herwaldt BL. Population-based active surveillance for Cyclospora infection-United States, FoodNet Sites, 1997\u0026ndash;2009. Clin Infect Dis. 2012;54(Suppl 5):S411\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerwaldt BL, Beach MJ. The return of Cyclospora in 1997: another outbreak of cyclosporiasis in North America. Emerg Infect Dis. 1999;5(1):33\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy HR, Almeria S, da Silva AJ. Molecular detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in fresh produce and soil samples. Food Microbiol. 2017;68:155\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Guidelines for the safe use of wastewater, excreta and greywater. Volume II: Wastewater use in agriculture. Geneva: WHO; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon BR, Bussey JM, Parrington LJ, Parenteau M. Detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e oocysts in environmental samples using molecular methods. J Microbiol Methods. 2013;94(3):298\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Wang Z, Karim MR, Zhang L. \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in fresh produce and water: molecular detection and public health implications. Food Waterborne Parasitol. 2022;27:e00163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQvarnstrom Y, Wei-Pridgeon Y, Li W, Nascimento FS, Bishop HS, Herwaldt BL, et al. Real-time PCR detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in produce. Appl Environ Microbiol. 2015;81(2):554\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrlandi PA, Chu DM, Bier JW, Jackson GJ. Parasite detection in food and water by nucleic acid amplification methods. J Food Prot. 2003;66(12):2295\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli MA, Naji GM. Multiplex PCR for detection of intestinal parasites in patients at Wasit province. Int J Health Sci. 2022;6(S8):6002\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.53730/ijhs.v6nS8.13677\u003c/span\u003e\u003cspan address=\"10.53730/ijhs.v6nS8.13677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUtaaker KS, Skjerve E, Robertson LJ. Keeping it cool: survival of parasites on fresh produce. Int J Food Microbiol. 2017;255:14\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook N, Paton CA, Wilkinson N. Evaluation of methods for detection of protozoan parasites on fresh produce. Int J Food Microbiol. 2006;109(1\u0026ndash;2):7\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill VR, Kahler AM, Jothikumar N, Johnson TB, Hahn D, Cromeans TL. Multistate evaluation of an ultrafiltration-based procedure for simultaneous recovery of enteric microbes in 100-L tap water samples. Appl Environ Microbiol. 2005;71(11):6878\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia LS. Diagnostic Medical Parasitology. 6th ed. Washington DC: ASM; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartosova B, Koudela B, Slana I. Detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e, \u003cem\u003eEchinococcus multilocularis\u003c/em\u003e, \u003cem\u003eToxocara\u003c/em\u003e spp. and microsporidia in fresh produce using molecular methods: - A review. Food Waterborne Parasitol. 2021;23:e00124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.fawpar.2021.e00124\u003c/span\u003e\u003cspan address=\"10.1016/j.fawpar.2021.e00124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 34169159; PMCID: PMC8209397.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRelman DA, Schmidt TM, Gajadhar A, Sogin M, Cross J, Yoder K, Sethabutr O, Echeverria P. Molecular phylogenetic analysis of Cyclospora, the human intestinal pathogen, suggests that it is closely related to Eimeria species. J Infect Dis. 1996;173:440\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/infdis/173.2.440\u003c/span\u003e\u003cspan address=\"10.1093/infdis/173.2.440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrlandi PA, Carter L, Brinker AM, da Silva AJ, Chu D-M, Lampel KA, Monday SR. Targeting single-nucleotide polymorphisms in the 18S rRNA gene to differentiate Cyclospora species from Eimeria species by multiplex PCR. Appl Environ Microbiol. 2003;69:4806\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/AEM.69.8.4806-4813.2003\u003c/span\u003e\u003cspan address=\"10.1128/AEM.69.8.4806-4813.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed H, Al-Sharif MS, Al-Malki ES, Al-Ghamdi AA. Environmental surveillance of foodborne parasites in low- and middle-income countries: challenges and opportunities. Food Waterborne Parasitol. 2024;35:e00215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCaughan KJ, and K. E.Kniel. Current Knowledge and Future Directions for Cyclospora cayetanensis Research and Its Surrogates. Compr Reviews Food Sci Food Safety25 no. 2026;1e70327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1541-4337.70327\u003c/span\u003e\u003cspan address=\"10.1111/1541-4337.70327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadjilouka A, Tsaltas D. Cyclospora Cayetanensis\u0026mdash;Major Outbreaks from Ready to Eat Fresh Fruits and Vegetables. Foods 2020, 9, 1703. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods9111703\u003c/span\u003e\u003cspan address=\"10.3390/foods9111703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaganathan A, Sivakumar M, Paramasivam S, et al. The prevalence of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in water: a systematic review and meta-analysis. Epidemiol Infect. 2022;150:e15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstetter J, Kahler AM, Arrowood M, et al. Evaluation of coccidia DNA in irrigation pond water and wastewater associated with \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e detections. Microbiol Spectr. 2024;12:e0090624.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaganathan T, O'Connor A, Sargeant J et al. The prevalence of Cyclospora cayetanensis in water: a systematic review and meta-analysis Epidemiology and Infection. 2021;150. PMCID: PMC8796144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeager C, Gutierrez A, Tucker MS, et al. Filters comprised of sand and zero-valent iron hold promise as tools to mitigate risk posed by \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e. Food Waterborne Parasitol. 2024;37:e00243.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez A, Tucker MS, Yeager C, et al. Zero-valent iron and sand filtration reduces levels of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e surrogates. Microorganisms. 2024;12(11):2344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Braiken FA, Amin A, Beeching NJ, Hommel M, Hart CA. Detection of \u003cem\u003eCryptoporidium\u003c/em\u003e amongst diarrhoeic and asymptomatic children in Jeddah, Saudi Arabia. Ann Trop Med Parasitol. 2003;97:505\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarratt JLN, Shen J, Houghton K, Richins T, Sapp SGH, Cama V, Arrowood MJ, Straily A, Qvarnstrom Y. \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e comprises at least 3 species that cause human cyclosporiasis. Parasitology. 2022;150:269\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention. DPDx: diagnostic procedures\u0026mdash;stool specimens. Atlanta: CDC. Modified acid-fast staining for \u003cem\u003eCyclospora\u003c/em\u003e may show variable staining, leading to possible misidentification.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeria S, Grocholl J, Mullins J, Durigan M, Ewing-Peeples L, Rogers EL, et al. Multi-laboratory validation of a modified real-time PCR assay (Mit1C) for the detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in fresh produce. Food Microbiol. 2025;128:104727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurigan M, Ewing-Peeples L, Almeria S, Balan KV, Grocholl J, Irizawa S, et al. Detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in food and water samples: optimized protocols for specific and sensitive molecular methods from a regulatory agency perspective. J Food Prot. 2024;87:100291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadjilouka A, Tsaltas D. \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e\u0026mdash;major outbreaks from ready-to-eat fresh fruits and vegetables. Foods. 2020;9(11):1703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeria S, Chacin-Bonilla L, Maloney JG, Santin M. \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e: a perspective (2020\u0026ndash;2023) with emphasis on epidemiology and detection methods. Microorganisms. 2023;11(9):2171.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalonde LF, Gajadhar AA, Dixon BR, Jian Y, Hohmann S, Ndao M. Optimization and validation of methods for isolation and real-time PCR identification of protozoan oocysts on leafy green vegetables and berry fruits. Food Control. 2016;60:617\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteele M, Odumeru J. Irrigation water as source of foodborne pathogens on fresh produce. J Food Prot. 2004;67(12):2839\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil MI, Selma MV, L\u0026oacute;pez-G\u0026aacute;lvez F, Allende A. Fresh-cut product sanitation and wash water disinfection: problems and solutions. Int J Food Microbiol. 2009;134(1\u0026ndash;2):37\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmoah P, Drechsel P, Abaidoo RC, Ntow WJ. Pesticide and pathogen contamination of vegetables in wastewater-irrigated urban agriculture. J Food Prot. 2006;69(8):1869\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeraita B, Drechsel P, Konradsen F. Using on-farm sedimentation ponds to improve microbial quality of irrigation water. Agric Water Manag. 2008;95(6):599\u0026ndash;608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Food and Drug Administration (FDA). Guide to minimize microbial food safety hazards for fresh fruits and vegetables. Washington, DC: FDA; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO/WHO. Microbiological hazards in fresh fruits and vegetables: Meeting report. Rome: FAO; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChac\u0026iacute;n-Bonilla L. Epidemiology of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e: a review focusing in endemic areas. Acta Trop. 2010;115(3):181\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon BR. Parasitic illnesses associated with the consumption of fresh produce\u0026mdash;an emerging issue in developed countries. Curr Opin Food Sci. 2016;8:104\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurriero FC, Patz JA, Rose JB, Lele S. The association between extreme precipitation and waterborne disease outbreaks. Am J Public Health. 2001;91(8):1194\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKistemann T, Classen T, Koch C, Dangendorf F, Fischeder R, Gebel J, et al. Microbial load of drinking water reservoir tributaries during extreme rainfall and runoff. Appl Environ Microbiol. 2002;68(5):2188\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi D, Zhang L, Karim MR, et al. Molecular detection of foodborne parasites in environmental samples: challenges and advances. Food Waterborne Parasitol. 2021;24:e00136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstetter J, Arfken A, Kahler A, Qvarnstrom Y, Rodrigues C, Mattioli M. Evaluation of coccidia DNA in irrigation pond water and wastewater sludge associated with \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e 18S rRNA gene qPCR detections. Microbiol Spectr. 2024;12(8):e0090624. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/spectrum.00906-24\u003c/span\u003e\u003cspan address=\"10.1128/spectrum.00906-24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Jun 25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRochelle PA, De Leon R. Viability of protozoan pathogens in water: detection limitations and public health implications. Microbiol Spectr. 2017;5(3):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahler AM, et al. Sources and Prevalence of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in Southeastern U.S. Growing Environments. J Food Prot. 2024;87(7):100309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Food and Drug Administration. BAM Chap. 19b: Molecular Detection of \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e in Fresh Produce Using Real-Time PCR. December 2024 Edition.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeegan AR, Fanok S, Monis PT, Saint CP. Cell culture\u0026ndash;PCR for assessing protozoan viability: limitations and applications. Appl Environ Microbiol. 2013;79(19):5883\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Guidelines on sanitation and health. Geneva: WHO; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO. Food safety risk management in fresh produce supply chains. Rome: FAO; 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cyclospora cayetanensis, Foodborne protozoa, Irrigation water, Fresh produce contamination, Nested PCR, One Health","lastPublishedDoi":"10.21203/rs.3.rs-9256916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9256916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e is recognized as an important foodborne parasite worldwide, with fresh produce and contaminated irrigation water serving as major transmission vehicles. In South Asia, environmental surveillance data for this pathogen remain limited, hindering the development of evidence-based food safety measures. We investigated the occurrence of \u003cem\u003eC. cayetanensis\u003c/em\u003e in fresh produce and irrigation water across peri-urban areas of Khyber Pakhtunkhwa, Pakistan, and assessed environmental and farm-level factors associated with contamination.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in Peshawar and Kohat districts from April to September 2025. A total of 420 samples were collected, including 300 fresh produce samples (six commonly consumed vegetables and herbs) and 120 irrigation water samples from canal, tube-well, and mixed sources. Samples were processed using concentration techniques, and detection was performed by nested PCR targeting the 18S rRNA gene. Structured field questionnaires were used to capture farm-level practices, and logistic regression was applied to identify risk factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eC. cayetanensis\u003c/em\u003e DNA was detected in 6.0% (18/300) of produce samples and 12.5% (15/120) of irrigation water samples (p\u0026thinsp;=\u0026thinsp;0.028). Contamination was significantly higher in canal water (20.0%) compared to tube-well sources (5.0%) (OR 4.75; 95% CI: 1.01\u0026ndash;22.3). Leafy vegetables and herbs showed higher contamination rates than smooth-surfaced produce (p\u0026thinsp;=\u0026thinsp;0.009). In multivariable analysis, canal irrigation (aOR 3.41; p\u0026thinsp;=\u0026thinsp;0.031), proximity to drainage channels within 50 meters (aOR 3.98; p\u0026thinsp;=\u0026thinsp;0.007), and use of untreated surface water for rinsing (aOR 2.91; p\u0026thinsp;=\u0026thinsp;0.045) were independently associated with contamination.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides the first molecular evidence of \u003cem\u003eC. cayetanensis\u003c/em\u003e contamination at the produce\u0026ndash;water interface in peri-urban Khyber Pakhtunkhwa, Pakistan. Surface irrigation systems and inadequate water management practices emerged as critical risk factors. By combining molecular detection with environmental and farm-level assessments under a One Health approach, our findings provide practical guidance for targeted food safety interventions in settings where environmental surveillance has historically been sparse.\u003c/p\u003e","manuscriptTitle":"Molecular detection of Cyclospora cayetanensis in fresh produce and irrigation water in peri- urban settings: a One Health cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 11:01:05","doi":"10.21203/rs.3.rs-9256916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T11:17:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T02:39:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296204474984953102936046517692178797517","date":"2026-04-06T00:55:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T05:19:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124162592382370352831064223915311361196","date":"2026-04-04T15:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135327124535454968933755670180668371816","date":"2026-04-04T12:31:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208826115572341499174228071336340663420","date":"2026-04-04T09:44:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103710673789194693741685843174539145326","date":"2026-04-04T04:48:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115412797093596047949013531143452149084","date":"2026-04-04T04:39:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219844419516594853106210649213215297629","date":"2026-04-03T15:01:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22687701941225962698287077949634476950","date":"2026-04-03T14:13:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235490395586193254471973875071305433029","date":"2026-04-03T14:11:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T13:58:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T13:55:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T12:08:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T15:47:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-04-02T14:33:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bd8b8cd-f531-458d-85bf-12d1e940a52d","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:58:50+00:00","versionOfRecord":{"articleIdentity":"rs-9256916","link":"https://doi.org/10.1186/s12866-026-05107-3","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2026-05-01 15:57:01","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2026-04-10 11:01:05","video":"","vorDoi":"10.1186/s12866-026-05107-3","vorDoiUrl":"https://doi.org/10.1186/s12866-026-05107-3","workflowStages":[]},"version":"v1","identity":"rs-9256916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9256916","identity":"rs-9256916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}