The influence of seasonality and antimicrobial resistance genes on biofilm formation in hospital-acquired resistant bacteria

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This work aimed to assess the distribution of HARI-associated bacterial species in north Israel and to investigate associations between biofilm formation and extended beta-lactamase (ESBL) genes, bacterial and patient characteristics, and hospitalization length, season and year. Methods Methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant (MDR) P. aeruginosa and A. baumannii , ESBL-producing Escherichia coli (ESBL- E. coli ), Klebsiela pneumoniae (ESBL- K. pneumoniae ) and Proteus mirabilis (ESBL- P. mirabilis ) were isolated from 569 blood, urine, wound and respiratory samples of patients with HARI hospitalized during 2020–2022 in north Israel. Biofilm-formation capacity was assessed by the crystalline violet method. ESBL genes were detected by real-time PCR. Data regarding season, time to infection, bacterial species, patient demographics, year, and hospital department, were collected from medical records. Results HARI rates were significantly lower in 2022 compared to 2020. ESBL- K. pneumoniae was the most prevalent (31.6%) bacteria. Strong biofilms were produced by 346 (60.8%), and were most common among ESBL- K. pneumoniae samples (46.2%). bla CTX−M was the most commonly detected ESBL gene (87.7%). Most strains (61.3%) carried more than one ESBL gene. Hospitalization season had a notable impact on biofilm production, with a heightened risk of infection by robust biofilm producers during spring, summer and autumn compared to winter. Furthermore, the presence of bla SHV and bla TEM genes were significantly associated with enhanced biofilm production. Bacteria harboring all three ESBL genes exhibited the highest biofilm production capacities, compared to those carrying fewer than three. Conclusions Biofilm-production intensity differs across bacterial species and seasons and is influenced by the presence of ESBL genes. hospital-acquired resistant infections risk factors biofilm antibiotic-resistant bacteria Figures Figure 1 Figure 2 Background The rise in bacterial antimicrobial resistance is one of the most urgent problems worldwide [ 1 ]. The overuse of antibiotics in the hospital environment has led to the emergence of multidrug-resistant (MDR) microorganisms, which are difficult to treat and are associated with fatal infections [ 2 ]. It is estimated that by 2050, 10 million deaths worldwide will be due to MDR-associated infections [ 3 ]. Hospital-acquired resistant infections (HARI) are nosocomial infections, defined as infections acquired 48–72 hours after admission [ 4 ]. Worldwide, HARI have become particularly prominent in intensive care units (ICUs), where the incidence is 2–5 times higher compared to other hospitalized units, due to more prolonged stays, impaired host defences, and more invasive diagnostic and monitoring procedures [ 5 ]. Gram-negative bacteria are responsible for more than 30% of HARI [ 6 ]. The most frequently HARI-associated bacteria are Gram-negative bacilli, particularly Escherichia coli ( E. coli ) and Klebsiela pneumoniae ( K. pneumoniae ), which are responsible for various pathological diseases, such as, urinary tract infections (UTIs), septicaemia, pneumonia, both in community and in healthcare facilities [ 7 ]. Staphylococcus aureus ( S. aureus ) is one of the most common Gram-positive pathogens underlying HARI [ 8 ]. At present, β-lactam drugs constitute a key treatment for bacterial infections worldwide and account for almost 65% of antibiotic usage [ 9 ]. In S. aureus , resistance to the β-lactam drug, methicillin, is driven by the mecA gene, which is carried on variants of the chromosomally inserted gene cassette SCCmec [ 10 ]. The newly discovered mec C gene may also contribute to this resistance. Despite its low prevalence in human compared to veterinary settings, it poses a great threat to community health [ 11 ]. In parallel, intensive use and misuse of β-lactam antibiotics in both human and veterinary medicine have led to the spread of extended spectrum β-lactamase (ESBL)-producing bacteria [ 12 ]. ESBLs are defined as plasmid-mediated enzymes that hydrolyse oximino-cephalosporins and monobactam antibiotics but not cephamycins or carbapenems [ 7 ]. Currently, the predominant ESBL-gene families are bla CTX−M , bla TEM , and bla SHV [ 13 ], with Bla TEM and bla SHV sharing high homology. There are 157 described CTX-M variants, which can be divided into five different groups: CTX-M-1, CTX-M-2, CTX-M-8, CTX-M-9 and CTX-M-25 [ 14 ]. Up to 80% of bacterial infections are linked to biofilm, which can be found on the surface of various medical instruments and materials, in addition to the patient tissues [ 15 ]. Biofilms are comprised of a set of bacterial cells coated by a polysaccharide layer [ 16 ] [ 17 ], which protects the cells from several types of stress, and are tolerant to antibiotics, antiseptics and host immune responses [ 18 ]. Biofilm production is a major contributor to the rise in healthcare-associated infections [ 16 ]. Although the association between antibiotic resistance and biofilm production has been extensively studied, it is still not fully understood. Furthermore, associations between external factors, such as seasonality and hospital unit, and biofilm production have not been investigated in depth. Therefore, this work aimed to characterize HARI distribution with regards to bacterial species and the department in which the patient was hospitalized, and to assess possible associations between biofilm formation and ESBL genes, infection season, bacterial characteristics, hospitalization length, gender and hospitalization year. Methods Study isolates Between 2020 and 2022, 569 resistant isolates were collected at Ziv Medical Center (ZMC, Safed) and at Tzafon Medical Center (TMC, Poriya). The isolates were recovered from respiratory (broncho-alveolar lavage, sputum), urine, wound and blood culture samples collected from adult patients (> 18 years) hospitalized in the ICU, Internal Medicine, Surgical and Orthopaedic departments, as part of the routine medical care at these medical centres. The isolates included methicillin-resistant S. aureus (MRSA), MDR Pseudomonas aeruginosa ( P. aeruginosa ) and Acinetobacter baumannii ( A. baumannii ), ESBL-producing E. coli , K. pneumoniae and Proteus mirabilis ( P. mirabilis ). The study was approved by the ZMC and the TMC Helsinki ethics committees (approval No.0068-19-ZIV, 0002–20 POR, respectively), which waived the need for patient consent. Bacterial isolation and identification Bacterial isolates were identified using routine clinical laboratory methods, including matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) (Bruker Daltonics, Bremen, Germany). Resistant isolates were identified with the Vitek 2 instrument (bioMérieux, Inc., Hazelwood, MO). Acinetobacter and Pseudomonas bacteria were classified as MDR if they exhibited resistance to antibiotics from three different antibiotic families. Determination of ESBL production In combination disc tests performed to confirm ESBL production, E. coli, K. pneumoniae and P. mirabilis isolates were grown on MacConkey agar (BD Diagnostics, Sparks, MD, USA), at 37°C, for 18–24 h. Then, several colonies were suspended in saline to 0.5 McFarland turbidity and then seeded on Muller-Hinton (MH) agar (BD Diagnostics) to which antibiotic discs (BD Diagnostics) of cefotaxime, cefotaxime/clavulanic acid, ceftazidime and ceftazidime/clavulanic acid were added. Agar plates were then incubated at 35°C for 16–20 h and screened for ESBL-positive isolates, which were determined by an increase of ≥ 5 mm between the zone diameter of cefotaxime/clavulanic acid compared to the zone diameter of cefotaxime, or between the zone diameters of ceftazidime/clavulanic acid compared to the zone diameter of ceftazidime [ 19 ]. Detection of blaTEM, blaSHV and blaCTX-M genes Several colonies were collected from overnight cultures of isolates grown on MacConkey agar and suspended in 600 µl nuclease-free water. E. coli BAA-196, BAA-202 and NCTC-13441 served as a positive control for presence of bla TEM , bla SHV and bla CTX−M genes, respectively. DNA was extracted using the NIMBUS automated system (Hamilton, microLab, Nevada, USA). DNA was subjected to real-time PCR in order to detect bla TEM , bla SHV and bla CTX−M , as previously described [ 14 ], with several modifications. Real-time amplifications were performed in 25 µL reactions containing 12.5 µL ABsolute qPCR Mix (Thermo Scientific, St. Leon Roth, Germany), 1 µL of each forward and reverse primer (10 pmol), 0.1 µL TEM TaqMan probe (5 pmol), 0.2 µL of each of the other four TaqMan probes (10 pmol), and 5 µL of DNA-mixture. The reaction was performed with the CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories GmbH, Munich, Germany), under the following conditions: 95 ºC for 15 min, and 30 cycles of 95 ºC for 15 s, 50 ºC for 15 s and 70 ºC for 20 s (Table 1 ). Table 1 Primers and probes used in this study Real-time PCR: bla TEM TEM_fwd. GCATCTTACGGATGGCATGA TEM_rev. GTCCTCCGATCGTTGTCAGAA TEM_probe 6-Fam-CAGTGCTGCCATAACCATGAGTGA-BHQ-1 bla SHV SHV_fwd TCCCATGATGAGCACCTTTAAA SHV_re TCCTGCTGGCGATAGTGGAT SHV_probe Cy5-TGCCGGTGACGAACAGCTGGAG-BBQ-650 bla CTX−M CTX-A_fwd. CGGGCRATGGCGCARAC CTX-A_rev. TGCRCCGGTSGTATTGCC CTX-A_probe Yakima Yellow-CCARCGGGCGCAGYTGGTGAC-BHQ1 CTX-B_fwd ACCGAGCCSACGCTCAA CTX-B_rev. CCGCTGCCGGTTTTATC CTX-B_probe Yakima Yellow- CCCGCGYGATACCACCACGC-BHQ1 Detection of mecA and pvl genes Multiplex PCR was performed for the simultaneous detection of mec A, pvl and 16S rRNA (internal amplification control), as previously described by McClure et al. [ 19 ]. Detection of biofilm formation The crystalline violet staining method was used to assess biofilm formation capacity. Following overnight cultures of isolates grown on MacConkey agar, several colonies were suspended in brain heart infusion (BHI) broth (Hy Laboratories, Rehovot, Israel) to 0.5 McFarland turbidity and triplicate samples were incubated in 96-well culture plates (200 µL/well), at 37°C for 48 h. BHI broth served as a negative control. Following incubation, floating bacteria were removed by washing the wells three times with distilled water. Cells were then incubated for 60 min, at 37°C, after which, crystalline violet dye (1%) was added (200 µL/well) and plates were incubated for 5 min, at room temperature. Thereafter, plates were washed twice with distilled water, and air-dried for 15 min. The crystalline violet was then dissolved with 200 µL 95% ethanol per well for 10 min. Finally, optical density (OD) was read at 595 nm. Biofilm-forming isolates were classified as weak biofilm-forming isolates (OD ≤ 2*ODc (OD of control well)) or strong biofilm-forming isolates (OD > 2* ODc) [ 20 ]. Data sources Patient gender, hospitalization year, duration of hospitalization before infection onset, hospital department, sample source and season of hospitalization were collected from the patient medical records. The time of acquisition was divided into three categories (72 hours-10 days, 11–30 days, and ≥ 31 days post-admission). Seasonality was categorized as follow: Winter - December until March, Spring - April through May, Summer - June to September and Autumn - October until November). Statistical analysis A descriptive statistical analysis was performed to determine the distribution of the study variables. Categorical variables are presented as count and percentage. The difference between categories was examined using the Chi-squared test. Generalized linear models with binary family and log link function were used to evaluate the changes in odds ratios (ORs) and 95% CIs of the biofilm production and the independent variables (gender, department, sample source, hospitalization length, season and ESBL genes). All statistical analyses were performed using SPSS software version 25 (IBM) and Office EXCEL 2016 software, with a statistical significance threshold of p < 0.05. Results Bacterial distribution Out of the 569 HARI isolates included in this analysis, the most prevalent bacteria were K. pneumoniae (180 (31.6%)) and E. coli (163 (28.6%)) (Table 2 ). The most common sample source was the respiratory tract (245 (43%)), followed by the urinary tract (125 (21.9%)). Table 2 Distribution of study isolates by sample source and hospital department Distribution (n, %) Bacterial species E. Coli K. pneumoniae P. mirabilis P. aeruginosa A. baumannii MRSA Total Sample source Blood 15 (19.5) 35 (45.5) 2 (2.6) 5 (6.5) 3 (3.9) 17 (22) 77 (13.5) Urinary tract 64 (51.2) 41 (32.8) 9 (7.2) 10 (8) 0 (0) 1 (0.8) 125 (21.9) Respiratory tract 35 (14.3) 82 (33.5) 15 (6.1) 54 (22) 9 (3.7) 50 (20.4) 245 (43) Wound 49 (40.2) 22 (18) 13 (10.7) 11 (9) 8 (6.6) 19 (15.6) 122 (21.4) Department Internal 67 (22.7) 100 (33.9) 21 (7.1) 45 (15.3) 15 (5.1) 47 (15.9) 295 (51.8) ICU 44 (27.3) 54 (33.5%) 7 (4.3) 30 (18.6) 3 (1.9) 23 (14.3) 161 (28.2) Surgical 34 (56.7) 13 (21.7 4 (6.7) 4 (6.7) 0 (0) 5 (8.3) 60 (10.5) Orthopaedics 18 (34) 13 (24.5) 7 (13.2) 1 (1.9) 2 (3.8) 12 (22.6) 53 (9.3) Total 163 (28.6) 180 (31.6) 39 (6.9) 80 (14.1) 20 (3.5) 87 (15.3) 569 ICU: internal care unit Bacterial distribution across the sample sources was significantly different ( p < 0.001). While the most prevalent bacteria in blood cultures and respiratory samples were K. pneumoniae (45.5% and 33.5%, respectively) and MRSA (22% and 20.4%, respectively), the most prevalent bacteria in urine and wound samples were E. coli (51.2% and 40.2%, respectively), followed by K. pneumoniae (32.8%, 18%, respectively). More than half of the isolates (295 (51.8%)) were recovered from patients hospitalized in internal medicine departments, while 28.3% (n = 161) were from patients in the ICU. K. pneumoniae was the dominant bacteria in internal medicine units and ICU, while E. coli was the dominant bacteria in surgical and orthopaedics departments ( p < 0.001) (Table 2 ). HARI prevalence over the study period Overall, the prevalence of HARI was significantly lower in 2022 (n = 161) compared to 2020 (n = 219) and 2021 (n = 189) ( p = 0.01) (Fig. 1 ). While the prevalence of MRSA, P. mirabilis and A. baumannii significantly increased between 2020 (36.8%, 7.7% and 5%, respectively) and 2022 (43.7%, 28.2% and 35%, respectively), the prevalence of K. pneumoniae and E. coli significantly decreased between 2020 (48.3% and 39.9%, respectively) and 2022 (23.9% and 18.4%, respectively) ( p < 0.01). Presence of virulence genes Out of 87 MRSA isolates, 24 (27.5%) were positive for the pvl gene. Among the 382 ESBL isolates, 335 (87.7%) harbored the bla CTX−M gene, including 147 E. coli isolates (43.9%), 149 K. pneumoniae isolates (44.5%), and all 39 P. mirabilis isolates. Additionally, bla TEM was identified in 201 isolates (52.6%): 61 E. coli isolates (30.3%), 118 K. pneumoniae isolates (58.7%), and 22 P. mirabilis isolates (10.9%). Two hundred isolates (52.4%) were positive for bla SHV , including 7 E. coli isolates (3.5%), 178 K. pneumoniae isolates (89%), and 15 P. mirabilis isolates (7.5%). Two ESBL genes were co-detected in 114 isolates (29.8%), including 52 E. coli isolates (45.6%), 37 K. pneumoniae isolates (32.5%), and 25 P. mirabilis isolates (21.9%). Moreover, 120 isolates (31.4%), primarily K. pneumoniae , were positive for all three genes: 114 K. pneumoniae isolates (95%), 6 P. mirabilis isolates (5%), and none of the E. coli isolates were positive for all three genes. Biofilm production by HARI-associated bacteria Out of 569 isolates, 346 (60.8%) were strong biofilm producers (Fig. 2 ). Most of the strong biofilm producers were K. pneumoniae (160/346, 46.2%), followed by P. aeruginosa (61/346, 17.6%) and E. coli (55/346, 15.9%) ( p < 0.001). Examination of biofilm production by species found that all P. mirabilis isolates were strong biofilm producers. With regards to K. pneumoniae , 160 of the 180 isolates (88.9%) were strong producers, while only 11.1% were weak biofilm producers. In contrast, most A. baumannii (85%), MRSA (67.8%) and E. coli (65.9%) isolates were weak biofilm producers (Fig. 2 ). Factors affecting biofilm formation Analyses of the factors that potentially affect biofilm-formation capacity excluded P. mirabilis isolates, as all P. mirabilis isolates were strong biofilm producers. Seasonality was found to significantly influence biofilm-formation capacity. The risk for infection with a strong biofilm producer was significantly higher in spring, summer and autumn compared to winter ( p < 0.01) (Table 3 ). Gender, department, hospitalization length and sample source, showed no correlation with the intensity of biofilm production in the examined isolates (Table 3 ). Table 3 Effect of independent variables on biofilm production Variables OR 95% CI p value Gender Male 1 Female 0.84 0.59–1.22 0.376 Department Orthopaedics 1 ICU 1.073 0.526–2.189 0.847 Surgical 0.935 0.433–2.022 0.865 Internal 1.029 0.523–2.027 0.933 Sample source Blood 1 Respiratory 1.212 0.699–2.102 0.493 Urine 1.034 0.569–1.881 0.912 Wound 0.675 0.363–1.255 0.214 Acquisition time 3–10 days 1 11–30 days 1.164 0.778–1.740 0.461 31 + days 1.525 0.964–2.414 0.071 Season Winter 1 Spring 1.885 1.125–3.160 0.016 Summer 1.986 1.287–3.066 0.002 Autumn 2.106 1.257–3.529 0.005 OR - odds ratio, CI - confidence interval Estimates were derived using a generalized linear model (binomial family and logit link function). Examination of the influence of ESBL genes on biofilm production in ESBL isolates ( K. pneumoniae, E. coli ), showed that biofilm production intensity increased with the number of ESBL genes ( p < 0.001), with an odds ratio of 8.68 in bacteria with three ESBL genes compared to bacteria with only one ESBL gene (Table 4 ). bla SHV and bla TEM had a significantly stronger association with strong biofilm production ( p < 0.001) compared to bla CTX−M , which had no effect on biofilm strength. No significant association was found between pvl in MRSA and the ability to produce biofilm. Table 4: Effect of ESBL gene number on biofilm production Number of ESBL genes OR 95% CI p Value 1 2 3 1 2.030 8.688 1.227-3.359 4.491-16.806 0.06 < 0.001 OR - odds ratio, CI - confidence interval Estimates were derived using a generalized linear model (binomial family and logit link function). Discussion The emergence of resistant bacteria has become a global concern [ 21 ]. The present study examined associations between biofilm formation and clinical and epidemiological factors, as well as antimicrobial resistance genes in hospital-acquired resistant bacteria. HARI distribution and associated bacteria The most prevalent HARI bacterial species [31.6%) and the most common bacteria isolated from blood cultures (45.5%) in the current sample set was K. pneumoniae . Similarly, MDR K. pneumoniae was the most prevalent (23.3%) bacteria in a study characterizing 1895 blood cultures collected in a national referral hospital in Indonesia between the years 2019–2020 [ 22 ]. Another study that examined the distribution of ESBL bacteria among 1486 blood samples collected from hospitalized patients in a 700-bed hospital located in Addis Ababa, Ethiopia between the years 2018 and 2019, showed high prevalence of K. pneumoniae (32.5%) [ 23 ]. As in blood cultures, K. pneumoniae , followed by MRSA, were the most prevalent bacteria in respiratory samples (45.4% and 27.3%, respectively). A recent meta-analysis examining 17,250 samples collected from hospital-acquired pneumonia during the years 2011–2021, found S. aureus (19.3%) to be the most commonly isolated pathogen. Of these samples, 75% were MRSA followed by P. aeruginosa , K. pneumoniae [ 24 ]. In contrast, the current analysis found that the most prevalent bacteria in urine and wound samples were E. coli (56%), which has been reported as the most important causative agent of urinary infections [ 25 ][ 26 ]. In a study examining more than 2,400 urine cultures from patients with community or hospital-acquired urinary infections, E. coli was the most predominant pathogen (63.4%) [ 27 ]. E. coli was also found to be a common pathogen in wounds. Alam et al. showed that out of 738 Gram-negative bacteria isolated from wound samples, P. aeruginosa (27.1%) and E. coli (26.2%) were the most dominant bacteria [ 28 ]. The higher rate of respiratory tract HARI infections in the current sample set (38.9%) aligns with a previous work aimed to evaluate changes in the prevalence of pathogens causing hospital-acquired bacterial pneumonia and their antimicrobial resistance patterns between 2011 and 2021. The analysis found that hospital-acquired bacterial pneumonia is among the most common nosocomial infections [ 24 ]. In the current dataset, a decrease in HARIs was noted between 2020 and 2022. Jabarpour et al. examined the impact of the COVID-19 outbreak on nosocomial infection by comparing nosocomial infection incidence before (2019) and during the COVID-19 outbreak (2022). They reported a decrease in the overall rate of nosocomial infection both in intensive care and in surgical unit in 2022 [ 29 ]. In contrast, several studies reported on increased HARI prevalence during 2020 [ 30 ] [ 31 ]. The reduction in HARI rates in the current analysis may have been the result of increased use of personal protective equipment and stronger adherence to infection control during the first COVID-19 wave. Resistance genes Out of 87 MRSA isolates, 27.5% carried pvl , and none were mecC -positive. These findings align with several studies that found a pvl gene prevalence of 3.4–33% and low prevalence of mecC (0%-12.5%) [ 32 ] [ 33 ]. bla CTX−M was the most prevalent ESBL gene detected (87.7%), followed by bla TEM (52.4%) and bla SHV (52.1%). Previous studies noted that the number of clinical isolates harbouring the bla CTXM gene increased in the last few years [ 34 ]. In a meta-analysis of 27 articles covering 20,000 clinical ESBL isolates, 22 studies (81.5%) reported a predominance of the bla CTX−M gene among isolates [ 35 ]. High prevalence of bla CTX−M among K. pneumoniae may represent selective pressure due to the broad use of cephalosporins, particularly cefotaxime and ceftriaxone, in many geographical regions [ 36 ]. Coexistence of two ESBL genes was detected in 28.7% of the current isolates, in 6line with a report by Ibrahim et al., who found that 22.2% of MDR Enterobacteriaceae isolated mainly from urine, sputum, wound swab and blood, harboured two ESBL genes [ 37 ]. Silago et el. found that 62.9% of ESBL-producing K. pneumoniae and E. coli were positive for all three ESBL genes [ 38 ], a higher percentage than in the current sample set (33.6%). There is a paucity of information regarding the prevalence of resistance genes in MRSA and of coexistence of different ESBL genes in clinical samples of hospital-acquired infection in Israel. Thus, the current analysis was new and important. Biofilm production Overall, 60.9% of the HARI isolates were strong biofilm producers. All P. mirabilis isolates and most K. pneumoniae isolates were strong producers. Studies have shown that ESBL K. pneumoniae strains have high ability to form biofilms [ 39 ]. In their analysis of 74 K. pneumoniae isolates from a bank of 325 respiratory and urinary tract specimens, Said et al. identified 28 as ESBL-producing bacteria. When they compared biofilm production between ESBL and non-ESBL K. pneumoniae , they found that 93% of the ESBL isolates were strong biofilm producers compared to only 6.5% strong biofilm producers among the non-ESBL isolates [ 40 ]. In contrast to P. mirabilis and K. pneumoniae , most MRSA and E. coli isolates in the current research were weak biofilm producers. This finding correlates with the conclusions of a meta-analysis performed by Garousi et al., who examined 37 studies conducted between 2000–2021 on biofilm production and antibiotic resistance in uropathogenic E. coli . In their analysis, 38.6% of the E. coli isolates were weak producers [ 41 ]. Biofilm-formation capacity was influenced by several factors. Firstly, in the winter season, the risk for infection with strong biofilm producers was significantly lower as compared to other seasons. In their examination of biofilm formation by S. aureus on food contact surfaces at different temperatures (12°C and 37°C), Ciccio et al. found that while 38/67 (56.7%) of the isolates produced biofilm at 37°C, only one strain produced biofilm at 12°C [ 42 ]. According to the Israel Meteorological Service, the average winter temperature in northern Israel is 12°C, which can explain the low rate of infection with biofilm-forming bacteria in the winter in the two participating medical centers, located in northern Israel. Another study which examined biofilm formation by Vibrio parahaemolyticus on food contact surfaces, found decreased biofilm formation at low (4–10°C) as compared to higher (15–37°C) temperatures [ 43 ]. Temperature is a recognized central factor affecting bacterial growth; most clinically important pathogens are mesophilic and grow well at optimum temperatures between 25°C and 40°C [ 44 ]. Temperature also enhances bacterial metabolism and thereby may indirectly influence biofilm parameters [ 45 ]. Meaning, the reduction observed in biofilm formation in the winter might be due to slower growth and metabolism of bacteria [ 46 ]. Additionally, changes in seasonality have been linked to variations in patient microbiomes, irrespective of temperature fluctuations. Davenport et al. observed a notably greater diversity in the gut microbiome of the same individuals during winter as compared to summer [ 47 ]. It is important to consider that bacterial genes are regulated by cell density through a process known as quorum sensing. This mechanism governs various physiological functions, including biofilm formation [ 48 ]. Since quorum sensing is affected by bacterial density, which tends to increase in winter [ 47 ], it is plausible that the elevated bacterial density may influence biofilm production through quorum sensing. Further research is required to examine whether decreased biofilm production during the winter is microbiome- and/or quorum-sensing-related. Another factor that influenced biofilm formation was the coexistence of ESBL genes, with a positive correlation found between the number of ESBL genes and intensity of biofilm production. These findings align with those of Zubair et al., who examined biofilm formation among 167 ESBL bacteria isolated between the years 2020 and 2022. The group found that out of 101 biofilm-producing ESBL cefotaxime-resistant isolates, 86.1% were positive for bla CTX−M , bla TEM or bla SHV genes [ 49 ] and 26.4% had all three genes. Dan et al. also found a correlation between ESBL gene presence, specifically bla SHV , and biofilm formation [ 50 ]. ESBL enzymes are encoded by genes harboured in plasmids, which play an important role in bacterial evolution by transferring beneficial traits within and between bacterial species, as they carry virulence and antibiotic resistance genes. It is possible that genes encoding for proteins central to biofilm production are harboured in the same plasmids with antibiotic resistance genes and when such a plasmid is transferred to another bacteria, the recipient acquires both traits. Thus, the co-existence of virulence and antibiotic resistance genes may contribute to the association between ESBL genes and biofilm production [ 51 ]. Gender, hospitalization year, hospital department, sample source and hospitalization length were not associated with the intensity of biofilm production in the examined bacteria. To the best of our knowledge, this was the first study to investigate the possibility of an association between these factors and biofilm production intensity by HARI. Future research should prioritize examining how biofilm production changes with temperature among various bacterial species. Additionally, more genomic studies will be essential to investigate the prevalence of other resistance genes and their relationships with factors such as seasonality, timing of acquisition, virulence factors, and mortality rates. It is also crucial to assess the impact of resistance genes on the microbiome. Conclusions Seasonality and the coexistence of all tested ESBL genes in HARI-associated bacterial species are independent risk factors for biofilm production. Patients with these risk factors should be carefully monitored. Further research is needed to evaluate additional risk factors for biofilm production in HARI bacteria. Abbreviations BHI brain heart infusion ESBL extended beta-lactamase HARI hospital-acquired resistant infections ICU intensive care units MALDI-TOF matrix-assisted laser desorption ionization-time of flight MDR multidrug-resistant MRSA Methicillin-resistant Staphylococcus aureus OD optical density TMC Tzafon Medical Center UTI urinary tract infections ZMC Ziv Medical Center Declarations Ethics approval and consent to participate The study was approved by the ZMC and the TMC Helsinki ethics committees (approval No.0068-19-ZIV, 0002-20 POR), which waived the need for patient consent. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests None declared Funding None Authors' contributions Conceptualization, H.BA., and K.A.S., and A.P.; Data curation, H.BA.; Formal analysis, H.BA.; M.A., S.E., K.A.S., and A.P.; Investigation, H.BA., and K.A.S.; Methodology, H.BA., D.B.G., S.E., and M.A.; Project administration, K.A.S., and A.P.; Supervision, K.A.S., and A.P.; Validation, H.BA., D.B.G., K.A.S., and A.P.; Visualization, H.BA., K.A.S., and A.P.; Writing—original draft, H.BA., M.A., and K.A.S., and A.P.; Writing—review & editing, H.BA., D. B.G., M.A., S.E., K.A.S., and A.P. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable References Cepas V, López Y, Muñoz E, Rolo D, Ardanuy C, Martí S, et al. Relationship between biofilm formation and antimicrobial resistance in gram-negative bacteria. Microb Drug Resist. 2019;25(1):72–9. Morens DM, Fauci AS. Emerging infectious diseases: threats to human health and global stability. PLoS Pathog. 2013;9(7):e1003467. 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Infect Genet Evol. 2012;12(5):883–93. Roschanski N, Fischer J, Guerra B, Roesler U. Development of a multiplex real-time PCR for the rapid detection of the predominant beta-lactamase genes CTX-M, SHV, TEM and CIT-type AmpCs in Enterobacteriaceae. PLoS ONE. 2014;9(7):e100956. Abebe GM. The role of bacterial biofilm in antibiotic resistance and food contamination. Int J Microbiol. 2020;2020. Heidari R, Farajzadeh Sheikh A, Hashemzadeh M, Farshadzadeh Z, Salmanzadeh S, Saki M. Antibiotic resistance, biofilm production ability and genetic diversity of carbapenem-resistant Pseudomonas aeruginosa strains isolated from nosocomial infections in southwestern Iran. Mol Biol Rep. 2022;49(5):3811–22. Khan MS, Rehman S, Ali MA, Sultan B, Sultan S. Infection in orthopedic implant surgery, its risk factors and outcome. J Ayub Med Coll Abbottabad. 2008;20(1):23–5. Banerjee A, Batabyal K, Singh AD, Joardar SN, Dey S, Isore DP, et al. Multi-drug resistant, biofilm‐producing high‐risk clonal lineage of Klebsiella in companion and household animals. Lett Appl Microbiol. 2020;71(6):580–7. McClure JA, Conly JM, Lau V, Elsayed S, Louie T, Hutchins W, et al. Novel multiplex PCR assay for detection of the staphylococcal virulence marker Panton-Valentine leukocidin genes and simultaneous discrimination of methicillin-susceptible from-resistant staphylococci. J Clin Microbiol. 2006;44(3):1141–4. Zheng Jxin, Bai B, Lin Z, wei, Pu Z ya, Yao W ming, Chen Z et al. Characterization of biofilm formation by Enterococcus faecalis isolates derived from urinary tract infections in China. J Med Microbiol. 2018;67(1):60–7. Alyamani EJ, Khiyami MA, Booq RY, Alnafjan BM, Altammami MA, Bahwerth FS. Molecular characterization of extended-spectrum beta-lactamases (ESBLs) produced by clinical isolates of Acinetobacter baumannii in Saudi Arabia. Ann Clin Microbiol Antimicrob. 2015;14(1):1–9. Sinto R, Lie KC, Setiati S, Suwarto S, Nelwan EJ, Djumaryo DH, et al. Blood culture utilization and epidemiology of antimicrobial-resistant bloodstream infections before and during the COVID-19 pandemic in the Indonesian national referral hospital. Antimicrob Resist Infect Control. 2022;11(1):1–12. Beshah D, Desta AF, Woldemichael GB, Belachew EB, Derese SG, Zelelie TZ, et al. High burden of ESBL and carbapenemase-producing gram-negative bacteria in bloodstream infection patients at a tertiary care hospital in Addis Ababa, Ethiopia. PLoS ONE. 2023;18(6):e0287453. Choi MH, Kim D, Lee KH, Cho JH, Jeong SH. Changes in the prevalence of pathogens causing hospital-acquired bacterial pneumonia and the impact of their antimicrobial resistance patterns on clinical outcomes: A propensity-score-matched study. Int J Antimicrob Agents. 2023;62(3):106886. Radera S, Srivastava S, Agarwal J. Virulence genotyping and multidrug resistance pattern of Escherichia coli isolated from community-acquired and hospital-acquired urinary tract infections. Cureus. 2022;14(9). Foxman B. The epidemiology of urinary tract infection. Nat Rev Urol. 2010;7(12):653–60. Mohamed AH, Mohamud MFY, Mohamud HA. Epidemiology and antimicrobial susceptibility pattern of uropathogens in patients with the community-and hospital-acquired urinary tract infections at a tertiary hospital in Somalia. Jundishapur J Microbiol. 2020;13(9). Alam MM, Islam MN, Hawlader MDH, Ahmed S, Wahab A, Islam M, et al. Prevalence of multidrug resistance bacterial isolates from infected wound patients in Dhaka, Bangladesh: a cross-sectional study. Int J Surg Open. 2021;28:56–62. Jabarpour M, Dehghan M, Afsharipour G, Hajipour Abaee E, Mangolian Shahrbabaki P, Ahmadinejad M et al. The impact of COVID-19 outbreak on nosocomial infection rate: a case of Iran. Can J Infect Dis Med Microbiol. 2021;2021. Garcia-Vidal C, Sanjuan G, Moreno-García E, Puerta-Alcalde P, Garcia-Pouton N, Chumbita M, et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study. Clin Microbiol Infect. 2021;27(1):83–8. Dudoignon E, Caméléna F, Deniau B, Habay A, Coutrot M, Ressaire Q, et al. Bacterial pneumonia in COVID-19 critically ill patients: a case series. Clin Infect Dis. 2021;72(5):905–6. Shrestha B, Singh W, Raj VS, Pokhrel BM, Mohapatra TM. High prevalence of Panton-Valentine leukocidin (PVL) genes in nosocomial-acquired Staphylococcus aureus isolated from tertiary care hospitals in Nepal. Biomed Res Int. 2014;2014. Sağlam M, Kılıç İH, Yasemin ZER. Investigation of SCCmec Types using the Real Time PCR Method in Cefoxitin-Resistant Staphylococcus aureus Isolates. 2022. Manyahi J, Moyo SJ, Tellevik MG, Ndugulile F, Urassa W, Blomberg B, et al. Detection of CTX-M-15 beta-lactamases in Enterobacteriaceae causing hospital-and community-acquired urinary tract infections as early as 2004, in Dar es Salaam, Tanzania. BMC Infect Dis. 2017;17:1–7. Onduru OG, Mkakosya RS, Aboud S, Rumisha SF. Genetic determinants of resistance among ESBL-producing enterobacteriaceae in community and hospital settings in east, central, and Southern Africa: A systematic review and meta-analysis of prevalence. Can J Infect Dis Med Microbiol. 2021;2021:1–9. Ahmed OI, El-Hady SA, Ahmed TM, Ahmed IZ. Detection of bla SHV and bla CTX-M genes in ESBL producing Klebsiella pneumoniae isolated from Egyptian patients with suspected nosocomial infections. Egypt J Med Hum Genet [Internet]. 2013;14(3):277–83. Available from: http://dx.doi.org/10.1016/j.ejmhg.2013.05.002 Ibrahim M, Algak T, Abbas M, Elamin B. Emergence of bla TEM, bla CTX–M, bla SHV and bla OXA genes in multidrug–resistant Enterobacteriaceae and Acinetobacter baumannii in Saudi Arabia. Exp Ther Med. 2021;22(6):1–11. Silago V, Kovacs D, Samson H, Seni J, Matthews L, Oravcová K, et al. Existence of multiple ESBL genes among phenotypically confirmed ESBL producing Klebsiella pneumoniae and Escherichia coli concurrently isolated from clinical, colonization and contamination samples from neonatal units at Bugando Medical Center, Mwanza, Ta. Antibiotics. 2021;10(5):476. Ashwath P, Deekshit VK, Rohit A, Dhinakaran I, Karunasagar I, Karunasagar I, et al. Biofilm formation and associated gene expression in multidrug-resistant Klebsiella pneumoniae isolated from clinical specimens. Curr Microbiol. 2022;79(3):73. Sa’id AS, Mukhkar MD, Bukar A, Yusha’u M. Extended Spectrum Beta-Lactamase production, Biofilm Formation and Antibiotic Resistance in Clinical Isolates of Klebsiella pneumoniae. Niger J Microbiol. 2020. Garousi M, Tabar SM, Mirazi H, Asgari P, Sabeghi P, Salehi A, et al. A global systematic review and meta-analysis on correlation between biofilm producers and non-biofilm producers with antibiotic resistance in Uropathogenic Escherichia coli. Microb Pathog. 2022;164:105412. Di Ciccio P, Vergara A, Festino AR, Paludi D, Zanardi E, Ghidini S, et al. Biofilm formation by Staphylococcus aureus on food contact surfaces: Relationship with temperature and cell surface hydrophobicity. Food Control. 2015;50:930–6. Han N, Mizan MFR, Jahid IK, Ha SD. Biofilm formation by Vibrio parahaemolyticus on food and food contact surfaces increases with rise in temperature. Food Control. 2016;70:161–6. Mizan MFR, Jahid IK, Park SY, Silva JL, Kim TJ, Myoung J, et al. Effects of temperature on biofilm formation and quorum sensing of Aeromonas hydrophila. Ital J Food Sci. 2018;30(3):456–66. Rao TS. Comparative effect of temperature on biofilm formation in natural and modified marine environment. Aquat Ecol. 2010;44(2):463–78. Alotaibi GF, Bukhari MA. Characterization and evaluation of biofilm formation by. Kleb pneumonia. 2021;14–24. Davenport ER, Mizrahi-Man O, Michelini K, Barreiro LB, Ober C, Gilad Y. Seasonal variation in human gut microbiome composition. PLoS ONE. 2014;9(3):e90731. Hammer BK, Bassler BL. Quorum sensing controls biofilm formation in Vibrio cholerae. Mol Microbiol. 2003;50(1):101–4. Zubair M, Mohammad I. Interrelationship of Extended Spectrum Beta-Lactamase Producers and Biofilm Formation among the Gram-Negative Bacteria from Tabuk, KSA. Open Access Maced J Med Sci. 2023;11(A):15–22. Dan B, Dai H, Zhou D, Tong H, Zhu M. Relationship Between Drug Resistance Characteristics and Biofilm Formation in Klebsiella Pneumoniae Strains. Infect Drug Resist. 2023;16:985–98. Mohsen SMY, Hamzah HA, Al-Deen MMI, Baharudin R. Antimicrobial susceptibility of Klebsiella pneumoniae and Escherichia coli with extended-spectrum β-lactamase associated genes in Hospital Tengku Ampuan Afzan, Kuantan, Pahang. Malaysian J Med Sci MJMS. 2016;23(2):14. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 08 Jul, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 19 Jun, 2025 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. 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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-6927998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482622740,"identity":"cfafcdd5-4269-4542-a878-66ec3cac88f1","order_by":0,"name":"Hila Ben-Amram","email":"","orcid":"","institution":"Bar Ilan University","correspondingAuthor":false,"prefix":"","firstName":"Hila","middleName":"","lastName":"Ben-Amram","suffix":""},{"id":482622744,"identity":"28b7a91a-9a8a-4c29-b286-7e8790a559c3","order_by":1,"name":"Doron Ben-Gad","email":"","orcid":"","institution":"Ziv Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Doron","middleName":"","lastName":"Ben-Gad","suffix":""},{"id":482622745,"identity":"1af34904-b140-4430-babd-1173309e09e2","order_by":2,"name":"Maya Azrad","email":"","orcid":"","institution":"Poriya Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Maya","middleName":"","lastName":"Azrad","suffix":""},{"id":482622747,"identity":"09ef03e6-51d1-4d71-83a3-2566f7a98ea5","order_by":3,"name":"Shimon Edelshtein","email":"","orcid":"","institution":"Ziv Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Shimon","middleName":"","lastName":"Edelshtein","suffix":""},{"id":482622749,"identity":"f7522110-b8a9-4ddc-bc1a-aa5c9a1663fd","order_by":4,"name":"Keren Agay-Shay","email":"","orcid":"","institution":"Bar Ilan University","correspondingAuthor":false,"prefix":"","firstName":"Keren","middleName":"","lastName":"Agay-Shay","suffix":""},{"id":482622750,"identity":"9352c0cc-0407-44ed-a3c2-9bd1fcc317a7","order_by":5,"name":"Avi Peretz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBADOQSTmbmBCA0JDMY8CC2MxGlJ7EHwCGjhn9187MPHH3Xp+/lPJ35g+GWT2MBOQIvEnWPJM2cksOX2MJzdLMHYl5bYQNBhN3KMmXkSeHJ7GHu3MTD2HDYm6Bf5G/mfmf8kSKTzMPMSqcXgRg4zM0OCQQIPG1ALw4/DcgS1GN45ZszYk5Zg2HOGd7NEYkOaHBshLXK3mx8z/LCpk2fvP7vxw4c/Njz8/IcP4NXCIIHMSWxjYGDDrx5dC8MfgupHwSgYBaNgBAIA/4Q/HN8L/1sAAAAASUVORK5CYII=","orcid":"","institution":"Poriya Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Avi","middleName":"","lastName":"Peretz","suffix":""}],"badges":[],"createdAt":"2025-06-19 06:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6927998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6927998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86655509,"identity":"4f8eee33-92ac-448a-97e8-d14d2a633b7d","added_by":"auto","created_at":"2025-07-14 10:14:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial distribution of hospital-acquired resistant infections (HARI)- between the years 2020 and 2022.\u003c/strong\u003e Bacteria were isolated from clinical samples of patients with HARI, admitted to Ziv Medical Center or Tzafon Medical Center between 2020 and 2022. Bacterial isolates were identified using routine clinical laboratory methods, including matrix-assisted laser desorption ionization-time of flight (MALDI-TOF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eStatistically significant differences are represented by a, b and c.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-6927998/v1/40a84d61d363896da01f58e0.png"},{"id":86655510,"identity":"52360535-706c-44aa-b9b4-199a440fab80","added_by":"auto","created_at":"2025-07-14 10:14:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiofilm production by different HARI-associated bacteria isolated during the years 2020-2022\u003c/strong\u003e. The crystalline violet staining method was used to assess biofilm formation capacity, among the study's isolates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eStatistically significant differences are represented by a and b.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-6927998/v1/d5db78ac5fcd73141621bbca.png"},{"id":86659175,"identity":"962b8c13-f799-4966-8c06-04599982b7c0","added_by":"auto","created_at":"2025-07-14 10:30:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1245796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6927998/v1/fc908a64-48c1-47f5-b3ec-1420bcaaec83.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of seasonality and antimicrobial resistance genes on biofilm formation in hospital-acquired resistant bacteria","fulltext":[{"header":"Background","content":"\u003cp\u003eThe rise in bacterial antimicrobial resistance is one of the most urgent problems worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The overuse of antibiotics in the hospital environment has led to the emergence of multidrug-resistant (MDR) microorganisms, which are difficult to treat and are associated with fatal infections [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is estimated that by 2050, 10\u0026nbsp;million deaths worldwide will be due to MDR-associated infections [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hospital-acquired resistant infections (HARI) are nosocomial infections, defined as infections acquired 48\u0026ndash;72 hours after admission [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Worldwide, HARI have become particularly prominent in intensive care units (ICUs), where the incidence is 2\u0026ndash;5 times higher compared to other hospitalized units, due to more prolonged stays, impaired host defences, and more invasive diagnostic and monitoring procedures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGram-negative bacteria are responsible for more than 30% of HARI [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The most frequently HARI-associated bacteria are Gram-negative bacilli, particularly \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) and \u003cem\u003eKlebsiela pneumoniae\u003c/em\u003e (\u003cem\u003eK. pneumoniae\u003c/em\u003e), which are responsible for various pathological diseases, such as, urinary tract infections (UTIs), septicaemia, pneumonia, both in community and in healthcare facilities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (\u003cem\u003eS. aureus\u003c/em\u003e) is one of the most common Gram-positive pathogens underlying HARI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt present, β-lactam drugs constitute a key treatment for bacterial infections worldwide and account for almost 65% of antibiotic usage [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In \u003cem\u003eS. aureus\u003c/em\u003e, resistance to the β-lactam drug, methicillin, is driven by the \u003cem\u003emecA\u003c/em\u003e gene, which is carried on variants of the chromosomally inserted gene cassette SCCmec [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The newly discovered \u003cem\u003emec\u003c/em\u003eC gene may also contribute to this resistance. Despite its low prevalence in human compared to veterinary settings, it poses a great threat to community health [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In parallel, intensive use and misuse of β-lactam antibiotics in both human and veterinary medicine have led to the spread of extended spectrum β-lactamase (ESBL)-producing bacteria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. ESBLs are defined as plasmid-mediated enzymes that hydrolyse oximino-cephalosporins and monobactam antibiotics but not cephamycins or carbapenems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, the predominant ESBL-gene families are \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e, and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with \u003cem\u003eBla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e sharing high homology. There are 157 described CTX-M variants, which can be divided into five different groups: CTX-M-1, CTX-M-2, CTX-M-8, CTX-M-9 and CTX-M-25 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUp to 80% of bacterial infections are linked to biofilm, which can be found on the surface of various medical instruments and materials, in addition to the patient tissues [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Biofilms are comprised of a set of bacterial cells coated by a polysaccharide layer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which protects the cells from several types of stress, and are tolerant to antibiotics, antiseptics and host immune responses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Biofilm production is a major contributor to the rise in healthcare-associated infections [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although the association between antibiotic resistance and biofilm production has been extensively studied, it is still not fully understood. Furthermore, associations between external factors, such as seasonality and hospital unit, and biofilm production have not been investigated in depth. Therefore, this work aimed to characterize HARI distribution with regards to bacterial species and the department in which the patient was hospitalized, and to assess possible associations between biofilm formation and ESBL genes, infection season, bacterial characteristics, hospitalization length, gender and hospitalization year.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy isolates\u003c/h2\u003e\u003cp\u003eBetween 2020 and 2022, 569 resistant isolates were collected at Ziv Medical Center (ZMC, Safed) and at Tzafon Medical Center (TMC, Poriya). The isolates were recovered from respiratory (broncho-alveolar lavage, sputum), urine, wound and blood culture samples collected from adult patients (\u0026gt;\u0026thinsp;18 years) hospitalized in the ICU, Internal Medicine, Surgical and Orthopaedic departments, as part of the routine medical care at these medical centres. The isolates included methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA), MDR \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (\u003cem\u003eP. aeruginosa\u003c/em\u003e) and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (\u003cem\u003eA. baumannii\u003c/em\u003e), ESBL-producing \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eProteus mirabilis\u003c/em\u003e (\u003cem\u003eP. mirabilis\u003c/em\u003e). The study was approved by the ZMC and the TMC Helsinki ethics committees (approval No.0068-19-ZIV, 0002\u0026ndash;20 POR, respectively), which waived the need for patient consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBacterial isolation and identification\u003c/h3\u003e\n\u003cp\u003eBacterial isolates were identified using routine clinical laboratory methods, including matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) (Bruker Daltonics, Bremen, Germany). Resistant isolates were identified with the Vitek 2 instrument (bioM\u0026eacute;rieux, Inc., Hazelwood, MO). \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e bacteria were classified as MDR if they exhibited resistance to antibiotics from three different antibiotic families.\u003c/p\u003e\n\u003ch3\u003eDetermination of ESBL production\u003c/h3\u003e\n\u003cp\u003eIn combination disc tests performed to confirm ESBL production, \u003cem\u003eE. coli, K. pneumoniae\u003c/em\u003e and \u003cem\u003eP. mirabilis\u003c/em\u003e isolates were grown on MacConkey agar (BD Diagnostics, Sparks, MD, USA), at 37\u0026deg;C, for 18\u0026ndash;24 h. Then, several colonies were suspended in saline to 0.5 McFarland turbidity and then seeded on Muller-Hinton (MH) agar (BD Diagnostics) to which antibiotic discs (BD Diagnostics) of cefotaxime, cefotaxime/clavulanic acid, ceftazidime and ceftazidime/clavulanic acid were added. Agar plates were then incubated at 35\u0026deg;C for 16\u0026ndash;20 h and screened for ESBL-positive isolates, which were determined by an increase of \u0026ge;\u0026thinsp;5 mm between the zone diameter of cefotaxime/clavulanic acid compared to the zone diameter of cefotaxime, or between the zone diameters of ceftazidime/clavulanic acid compared to the zone diameter of ceftazidime [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDetection of blaTEM, blaSHV and blaCTX-M genes\u003c/h3\u003e\n\u003cp\u003eSeveral colonies were collected from overnight cultures of isolates grown on MacConkey agar and suspended in 600 \u0026micro;l nuclease-free water. \u003cem\u003eE. coli\u003c/em\u003e BAA-196, BAA-202 and NCTC-13441 served as a positive control for presence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e genes, respectively. DNA was extracted using the NIMBUS automated system (Hamilton, microLab, Nevada, USA). DNA was subjected to real-time PCR in order to detect \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e, as previously described [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with several modifications. Real-time amplifications were performed in 25 \u0026micro;L reactions containing 12.5 \u0026micro;L ABsolute qPCR Mix (Thermo Scientific, St. Leon Roth, Germany), 1 \u0026micro;L of each forward and reverse primer (10 pmol), 0.1 \u0026micro;L TEM TaqMan probe (5 pmol), 0.2 \u0026micro;L of each of the other four TaqMan probes (10 pmol), and 5 \u0026micro;L of DNA-mixture. The reaction was performed with the CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories GmbH, Munich, Germany), under the following conditions: 95 \u0026ordm;C for 15 min, and 30 cycles of 95 \u0026ordm;C for 15 s, 50 \u0026ordm;C for 15 s and 70 \u0026ordm;C for 20 s (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003ePrimers and probes used in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal-time PCR:\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTEM_fwd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCATCTTACGGATGGCATGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTEM_rev.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGTCCTCCGATCGTTGTCAGAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTEM_probe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6-Fam-CAGTGCTGCCATAACCATGAGTGA-BHQ-1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSHV_fwd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCCCATGATGAGCACCTTTAAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSHV_re\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCCTGCTGGCGATAGTGGAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSHV_probe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCy5-TGCCGGTGACGAACAGCTGGAG-BBQ-650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-A_fwd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCGGGCRATGGCGCARAC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-A_rev.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGCRCCGGTSGTATTGCC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-A_probe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYakima Yellow-CCARCGGGCGCAGYTGGTGAC-BHQ1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-B_fwd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACCGAGCCSACGCTCAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-B_rev.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCGCTGCCGGTTTTATC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTX-B_probe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYakima Yellow- CCCGCGYGATACCACCACGC-BHQ1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eDetection of mecA and pvl genes\u003c/h3\u003e\n\u003cp\u003eMultiplex PCR was performed for the simultaneous detection of \u003cem\u003emec\u003c/em\u003eA, \u003cem\u003epvl\u003c/em\u003e and \u003cem\u003e16S\u003c/em\u003e rRNA (internal amplification control), as previously described by McClure et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDetection of biofilm formation\u003c/h2\u003e\u003cp\u003eThe crystalline violet staining method was used to assess biofilm formation capacity. Following overnight cultures of isolates grown on MacConkey agar, several colonies were suspended in brain heart infusion (BHI) broth (Hy Laboratories, Rehovot, Israel) to 0.5 McFarland turbidity and triplicate samples were incubated in 96-well culture plates (200 \u0026micro;L/well), at 37\u0026deg;C for 48 h. BHI broth served as a negative control. Following incubation, floating bacteria were removed by washing the wells three times with distilled water. Cells were then incubated for 60 min, at 37\u0026deg;C, after which, crystalline violet dye (1%) was added (200 \u0026micro;L/well) and plates were incubated for 5 min, at room temperature. Thereafter, plates were washed twice with distilled water, and air-dried for 15 min. The crystalline violet was then dissolved with 200 \u0026micro;L 95% ethanol per well for 10 min. Finally, optical density (OD) was read at 595 nm. Biofilm-forming isolates were classified as weak biofilm-forming isolates (OD\u0026thinsp;\u0026le;\u0026thinsp;2*ODc (OD of control well)) or strong biofilm-forming isolates (OD\u0026thinsp;\u0026gt;\u0026thinsp;2* ODc) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003ePatient gender, hospitalization year, duration of hospitalization before infection onset, hospital department, sample source and season of hospitalization were collected from the patient medical records. The time of acquisition was divided into three categories (72 hours-10 days, 11\u0026ndash;30 days, and \u0026ge;\u0026thinsp;31 days post-admission). Seasonality was categorized as follow: Winter - December until March, Spring - April through May, Summer - June to September and Autumn - October until November).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eA descriptive statistical analysis was performed to determine the distribution of the study variables. Categorical variables are presented as count and percentage. The difference between categories was examined using the Chi-squared test. Generalized linear models with binary family and log link function were used to evaluate the changes in odds ratios (ORs) and 95% CIs of the biofilm production and the independent variables (gender, department, sample source, hospitalization length, season and ESBL genes). All statistical analyses were performed using SPSS software version 25 (IBM) and Office EXCEL 2016 software, with a statistical significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBacterial distribution\u003c/h2\u003e\u003cp\u003eOut of the 569 HARI isolates included in this analysis, the most prevalent bacteria were \u003cem\u003eK. pneumoniae\u003c/em\u003e (180 (31.6%)) and \u003cem\u003eE. coli\u003c/em\u003e (163 (28.6%)) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most common sample source was the respiratory tract (245 (43%)), followed by the urinary tract (125 (21.9%)).\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\u003eDistribution of study isolates by sample source and hospital department\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDistribution\u003c/p\u003e\u003cp\u003e(n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eBacterial species\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eColi\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eK. pneumoniae\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emirabilis\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP. aeruginosa\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eA. baumannii\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eMRSA\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample source\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77 (13.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrinary tract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e125 (21.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory tract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e245 (43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e122 (21.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDepartment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e295 (51.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (33.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e161 (28.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrthopaedics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53 (9.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e569\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\u003eICU: internal care unit\u003c/p\u003e\u003cp\u003eBacterial distribution across the sample sources was significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While the most prevalent bacteria in blood cultures and respiratory samples were \u003cem\u003eK. pneumoniae\u003c/em\u003e (45.5% and 33.5%, respectively) and MRSA (22% and 20.4%, respectively), the most prevalent bacteria in urine and wound samples were \u003cem\u003eE. coli\u003c/em\u003e (51.2% and 40.2%, respectively), followed by \u003cem\u003eK. pneumoniae\u003c/em\u003e (32.8%, 18%, respectively). More than half of the isolates (295 (51.8%)) were recovered from patients hospitalized in internal medicine departments, while 28.3% (n\u0026thinsp;=\u0026thinsp;161) were from patients in the ICU. \u003cem\u003eK. pneumoniae\u003c/em\u003e was the dominant bacteria in internal medicine units and ICU, while \u003cem\u003eE. coli\u003c/em\u003e was the dominant bacteria in surgical and orthopaedics departments (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHARI prevalence over the study period\u003c/h2\u003e\u003cp\u003eOverall, the prevalence of HARI was significantly lower in 2022 (n\u0026thinsp;=\u0026thinsp;161) compared to 2020 (n\u0026thinsp;=\u0026thinsp;219) and 2021 (n\u0026thinsp;=\u0026thinsp;189) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While the prevalence of MRSA, \u003cem\u003eP. mirabilis\u003c/em\u003e and \u003cem\u003eA. baumannii\u003c/em\u003e significantly increased between 2020 (36.8%, 7.7% and 5%, respectively) and 2022 (43.7%, 28.2% and 35%, respectively), the prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e significantly decreased between 2020 (48.3% and 39.9%, respectively) and 2022 (23.9% and 18.4%, respectively) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePresence of virulence genes\u003c/h2\u003e\u003cp\u003eOut of 87 MRSA isolates, 24 (27.5%) were positive for the \u003cem\u003epvl\u003c/em\u003e gene. Among the 382 ESBL isolates, 335 (87.7%) harbored the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e gene, including 147 \u003cem\u003eE. coli\u003c/em\u003e isolates (43.9%), 149 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates (44.5%), and all 39 \u003cem\u003eP. mirabilis\u003c/em\u003e isolates. Additionally, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e was identified in 201 isolates (52.6%): 61 \u003cem\u003eE. coli\u003c/em\u003e isolates (30.3%), 118 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates (58.7%), and 22 \u003cem\u003eP. mirabilis\u003c/em\u003e isolates (10.9%). Two hundred isolates (52.4%) were positive for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e, including 7 \u003cem\u003eE. coli\u003c/em\u003e isolates (3.5%), 178 K. \u003cem\u003epneumoniae\u003c/em\u003e isolates (89%), and 15 \u003cem\u003eP. mirabilis\u003c/em\u003e isolates (7.5%).\u003c/p\u003e\u003cp\u003eTwo ESBL genes were co-detected in 114 isolates (29.8%), including 52 \u003cem\u003eE. coli\u003c/em\u003e isolates (45.6%), 37 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates (32.5%), and 25 \u003cem\u003eP. mirabilis\u003c/em\u003e isolates (21.9%). Moreover, 120 isolates (31.4%), primarily \u003cem\u003eK. pneumoniae\u003c/em\u003e, were positive for all three genes: 114 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates (95%), 6 \u003cem\u003eP. mirabilis\u003c/em\u003e isolates (5%), and none of the \u003cem\u003eE. coli\u003c/em\u003e isolates were positive for all three genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eBiofilm production by HARI-associated bacteria\u003c/h2\u003e\u003cp\u003eOut of 569 isolates, 346 (60.8%) were strong biofilm producers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most of the strong biofilm producers were \u003cem\u003eK. pneumoniae\u003c/em\u003e (160/346, 46.2%), followed by \u003cem\u003eP. aeruginosa\u003c/em\u003e (61/346, 17.6%) and \u003cem\u003eE. coli\u003c/em\u003e (55/346, 15.9%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Examination of biofilm production by species found that all \u003cem\u003eP. mirabilis\u003c/em\u003e isolates were strong biofilm producers. With regards to \u003cem\u003eK. pneumoniae\u003c/em\u003e, 160 of the 180 isolates (88.9%) were strong producers, while only 11.1% were weak biofilm producers. In contrast, most \u003cem\u003eA. baumannii\u003c/em\u003e (85%), MRSA (67.8%) and \u003cem\u003eE. coli\u003c/em\u003e (65.9%) isolates were weak biofilm producers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFactors affecting biofilm formation\u003c/h2\u003e\u003cp\u003eAnalyses of the factors that potentially affect biofilm-formation capacity excluded \u003cem\u003eP. mirabilis\u003c/em\u003e isolates, as all \u003cem\u003eP. mirabilis\u003c/em\u003e isolates were strong biofilm producers. Seasonality was found to significantly influence biofilm-formation capacity. The risk for infection with a strong biofilm producer was significantly higher in spring, summer and autumn compared to winter (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Gender, department, hospitalization length and sample source, showed no correlation with the intensity of biofilm production in the examined isolates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eEffect of independent variables on biofilm production\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\u0026ndash;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrthopaedics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.526\u0026ndash;2.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.433\u0026ndash;2.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.523\u0026ndash;2.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample source\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.699\u0026ndash;2.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.569\u0026ndash;1.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.363\u0026ndash;1.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcquisition time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;10 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778\u0026ndash;1.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u0026thinsp;+\u0026thinsp;days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.964\u0026ndash;2.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.125\u0026ndash;3.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.287\u0026ndash;3.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.257\u0026ndash;3.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\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\u003eOR - odds ratio, CI - confidence interval\u003c/p\u003e\u003cp\u003eEstimates were derived using a generalized linear model (binomial family and logit link function).\u003c/p\u003e\u003cp\u003eExamination of the influence of ESBL genes on biofilm production in ESBL isolates (\u003cem\u003eK. pneumoniae, E. coli\u003c/em\u003e), showed that biofilm production intensity increased with the number of ESBL genes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an odds ratio of 8.68 in bacteria with three ESBL genes compared to bacteria with only one ESBL gene (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e had a significantly stronger association with strong biofilm production (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) compared to \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e, which had no effect on biofilm strength. No significant association was found between \u003cem\u003epvl\u003c/em\u003e in MRSA and the ability to produce biofilm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Effect of ESBL gene number on biofilm production\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7732%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eESBL genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4622%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.406%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3585%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7732%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4622%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.030\u003c/p\u003e\n \u003cp\u003e8.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.406%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.227-3.359\u003c/p\u003e\n \u003cp\u003e4.491-16.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3585%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR - odds ratio, CI - confidence interval\u003c/p\u003e\n\u003cp\u003eEstimates were derived using a generalized linear model (binomial family and logit link function).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe emergence of resistant bacteria has become a global concern [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The present study examined associations between biofilm formation and clinical and epidemiological factors, as well as antimicrobial resistance genes in hospital-acquired resistant bacteria.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eHARI distribution and associated bacteria\u003c/h2\u003e\u003cp\u003eThe most prevalent HARI bacterial species [31.6%) and the most common bacteria isolated from blood cultures (45.5%) in the current sample set was \u003cem\u003eK. pneumoniae\u003c/em\u003e. Similarly, MDR \u003cem\u003eK. pneumoniae\u003c/em\u003e was the most prevalent (23.3%) bacteria in a study characterizing 1895 blood cultures collected in a national referral hospital in Indonesia between the years 2019\u0026ndash;2020 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Another study that examined the distribution of ESBL bacteria among 1486 blood samples collected from hospitalized patients in a 700-bed hospital located in Addis Ababa, Ethiopia between the years 2018 and 2019, showed high prevalence of \u003cem\u003eK. pneumoniae\u003c/em\u003e (32.5%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs in blood cultures, \u003cem\u003eK. pneumoniae\u003c/em\u003e, followed by MRSA, were the most prevalent bacteria in respiratory samples (45.4% and 27.3%, respectively). A recent meta-analysis examining 17,250 samples collected from hospital-acquired pneumonia during the years 2011\u0026ndash;2021, found \u003cem\u003eS. aureus\u003c/em\u003e (19.3%) to be the most commonly isolated pathogen. Of these samples, 75% were MRSA followed by \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, the current analysis found that the most prevalent bacteria in urine and wound samples were \u003cem\u003eE. coli\u003c/em\u003e (56%), which has been reported as the most important causative agent of urinary infections [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a study examining more than 2,400 urine cultures from patients with community or hospital-acquired urinary infections, \u003cem\u003eE. coli\u003c/em\u003e was the most predominant pathogen (63.4%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eE. coli\u003c/em\u003e was also found to be a common pathogen in wounds. Alam et al. showed that out of 738 Gram-negative bacteria isolated from wound samples, \u003cem\u003eP. aeruginosa\u003c/em\u003e (27.1%) and \u003cem\u003eE. coli\u003c/em\u003e (26.2%) were the most dominant bacteria [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The higher rate of respiratory tract HARI infections in the current sample set (38.9%) aligns with a previous work aimed to evaluate changes in the prevalence of pathogens causing hospital-acquired bacterial pneumonia and their antimicrobial resistance patterns between 2011 and 2021. The analysis found that hospital-acquired bacterial pneumonia is among the most common nosocomial infections [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the current dataset, a decrease in HARIs was noted between 2020 and 2022. Jabarpour et al. examined the impact of the COVID-19 outbreak on nosocomial infection by comparing nosocomial infection incidence before (2019) and during the COVID-19 outbreak (2022). They reported a decrease in the overall rate of nosocomial infection both in intensive care and in surgical unit in 2022 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In contrast, several studies reported on increased HARI prevalence during 2020 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The reduction in HARI rates in the current analysis may have been the result of increased use of personal protective equipment and stronger adherence to infection control during the first COVID-19 wave.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eResistance genes\u003c/h2\u003e\u003cp\u003eOut of 87 MRSA isolates, 27.5% carried \u003cem\u003epvl\u003c/em\u003e, and none were \u003cem\u003emecC\u003c/em\u003e-positive. These findings align with several studies that found a \u003cem\u003epvl\u003c/em\u003e gene prevalence of 3.4\u0026ndash;33% and low prevalence of \u003cem\u003emecC\u003c/em\u003e (0%-12.5%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e was the most prevalent ESBL gene detected (87.7%), followed by \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e (52.4%) and \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e (52.1%). Previous studies noted that the number of clinical isolates harbouring the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTXM\u003c/sub\u003e gene increased in the last few years [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In a meta-analysis of 27 articles covering 20,000 clinical ESBL isolates, 22 studies (81.5%) reported a predominance of the \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e gene among isolates [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. High prevalence of \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e among \u003cem\u003eK. pneumoniae\u003c/em\u003e may represent selective pressure due to the broad use of cephalosporins, particularly cefotaxime and ceftriaxone, in many geographical regions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Coexistence of two ESBL genes was detected in 28.7% of the current isolates, in 6line with a report by Ibrahim et al., who found that 22.2% of MDR \u003cem\u003eEnterobacteriaceae\u003c/em\u003e isolated mainly from urine, sputum, wound swab and blood, harboured two ESBL genes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Silago et el. found that 62.9% of ESBL-producing \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e were positive for all three ESBL genes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], a higher percentage than in the current sample set (33.6%).\u003c/p\u003e\u003cp\u003eThere is a paucity of information regarding the prevalence of resistance genes in MRSA and of coexistence of different ESBL genes in clinical samples of hospital-acquired infection in Israel. Thus, the current analysis was new and important.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eBiofilm production\u003c/h2\u003e\u003cp\u003eOverall, 60.9% of the HARI isolates were strong biofilm producers. All \u003cem\u003eP. mirabilis\u003c/em\u003e isolates and most \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates were strong producers. Studies have shown that ESBL \u003cem\u003eK. pneumoniae\u003c/em\u003e strains have high ability to form biofilms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In their analysis of 74 \u003cem\u003eK. pneumoniae\u003c/em\u003e isolates from a bank of 325 respiratory and urinary tract specimens, Said et al. identified 28 as ESBL-producing bacteria. When they compared biofilm production between ESBL and non-ESBL \u003cem\u003eK. pneumoniae\u003c/em\u003e, they found that 93% of the ESBL isolates were strong biofilm producers compared to only 6.5% strong biofilm producers among the non-ESBL isolates [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast to \u003cem\u003eP. mirabilis\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e, most MRSA and \u003cem\u003eE. coli\u003c/em\u003e isolates in the current research were weak biofilm producers. This finding correlates with the conclusions of a meta-analysis performed by Garousi et al., who examined 37 studies conducted between 2000\u0026ndash;2021 on biofilm production and antibiotic resistance in uropathogenic \u003cem\u003eE. coli\u003c/em\u003e. In their analysis, 38.6% of the \u003cem\u003eE. coli\u003c/em\u003e isolates were weak producers [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBiofilm-formation capacity was influenced by several factors. Firstly, in the winter season, the risk for infection with strong biofilm producers was significantly lower as compared to other seasons. In their examination of biofilm formation by \u003cem\u003eS. aureus\u003c/em\u003e on food contact surfaces at different temperatures (12\u0026deg;C and 37\u0026deg;C), Ciccio et al. found that while 38/67 (56.7%) of the isolates produced biofilm at 37\u0026deg;C, only one strain produced biofilm at 12\u0026deg;C [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. According to the Israel Meteorological Service, the average winter temperature in northern Israel is 12\u0026deg;C, which can explain the low rate of infection with biofilm-forming bacteria in the winter in the two participating medical centers, located in northern Israel. Another study which examined biofilm formation by \u003cem\u003eVibrio parahaemolyticus\u003c/em\u003e on food contact surfaces, found decreased biofilm formation at low (4\u0026ndash;10\u0026deg;C) as compared to higher (15\u0026ndash;37\u0026deg;C) temperatures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Temperature is a recognized central factor affecting bacterial growth; most clinically important pathogens are mesophilic and grow well at optimum temperatures between 25\u0026deg;C and 40\u0026deg;C [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Temperature also enhances bacterial metabolism and thereby may indirectly influence biofilm parameters [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Meaning, the reduction observed in biofilm formation in the winter might be due to slower growth and metabolism of bacteria [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, changes in seasonality have been linked to variations in patient microbiomes, irrespective of temperature fluctuations. Davenport et al. observed a notably greater diversity in the gut microbiome of the same individuals during winter as compared to summer [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. It is important to consider that bacterial genes are regulated by cell density through a process known as quorum sensing. This mechanism governs various physiological functions, including biofilm formation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Since quorum sensing is affected by bacterial density, which tends to increase in winter [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], it is plausible that the elevated bacterial density may influence biofilm production through quorum sensing.\u003c/p\u003e\u003cp\u003eFurther research is required to examine whether decreased biofilm production during the winter is microbiome- and/or quorum-sensing-related.\u003c/p\u003e\u003cp\u003eAnother factor that influenced biofilm formation was the coexistence of ESBL genes, with a positive correlation found between the number of ESBL genes and intensity of biofilm production. These findings align with those of Zubair et al., who examined biofilm formation among 167 ESBL bacteria isolated between the years 2020 and 2022. The group found that out of 101 biofilm-producing ESBL cefotaxime-resistant isolates, 86.1% were positive for \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e, \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eTEM\u003c/sub\u003e or \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e genes [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and 26.4% had all three genes. Dan et al. also found a correlation between ESBL gene presence, specifically \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eSHV\u003c/sub\u003e, and biofilm formation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. ESBL enzymes are encoded by genes harboured in plasmids, which play an important role in bacterial evolution by transferring beneficial traits within and between bacterial species, as they carry virulence and antibiotic resistance genes. It is possible that genes encoding for proteins central to biofilm production are harboured in the same plasmids with antibiotic resistance genes and when such a plasmid is transferred to another bacteria, the recipient acquires both traits. Thus, the co-existence of virulence and antibiotic resistance genes may contribute to the association between ESBL genes and biofilm production [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender, hospitalization year, hospital department, sample source and hospitalization length were not associated with the intensity of biofilm production in the examined bacteria. To the best of our knowledge, this was the first study to investigate the possibility of an association between these factors and biofilm production intensity by HARI. Future research should prioritize examining how biofilm production changes with temperature among various bacterial species. Additionally, more genomic studies will be essential to investigate the prevalence of other resistance genes and their relationships with factors such as seasonality, timing of acquisition, virulence factors, and mortality rates. It is also crucial to assess the impact of resistance genes on the microbiome.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSeasonality and the coexistence of all tested ESBL genes in HARI-associated bacterial species are independent risk factors for biofilm production. Patients with these risk factors should be carefully monitored. Further research is needed to evaluate additional risk factors for biofilm production in HARI bacteria.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBHI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebrain heart infusion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eESBL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eextended beta-lactamase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHARI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehospital-acquired resistant infections\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintensive care units\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMALDI-TOF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ematrix-assisted laser desorption ionization-time of flight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emultidrug-resistant\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMethicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoptical density\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTzafon Medical Center\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUTI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eurinary tract infections\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eZMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eZiv Medical Center\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e The study was approved by the ZMC and the TMC Helsinki ethics committees (approval No.0068-19-ZIV, 0002-20 POR), which waived the need for patient consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, H.BA., and K.A.S., and A.P.; Data curation, H.BA.; Formal analysis, H.BA.; M.A., S.E., K.A.S., and A.P.; Investigation, H.BA., and K.A.S.; Methodology, H.BA., D.B.G., S.E., and M.A.; Project administration, K.A.S., and A.P.; Supervision, K.A.S., and A.P.; Validation, H.BA., D.B.G., K.A.S., and A.P.; Visualization, H.BA., K.A.S., and A.P.; Writing\u0026mdash;original draft, H.BA., M.A., and K.A.S., and A.P.; Writing\u0026mdash;review \u0026amp; editing, H.BA., D.\u003cbr\u003e\u0026nbsp;B.G., M.A., S.E., K.A.S., and A.P. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCepas V, L\u0026oacute;pez Y, Mu\u0026ntilde;oz E, Rolo D, Ardanuy C, Mart\u0026iacute; S, et al. Relationship between biofilm formation and antimicrobial resistance in gram-negative bacteria. Microb Drug Resist. 2019;25(1):72\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorens DM, Fauci AS. Emerging infectious diseases: threats to human health and global stability. PLoS Pathog. 2013;9(7):e1003467.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuval RE, Grare M, Demor\u0026eacute; B. Fight against antimicrobial resistance: we always need new antibacterials but for right bacteria. 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High prevalence of Panton-Valentine leukocidin (PVL) genes in nosocomial-acquired Staphylococcus aureus isolated from tertiary care hospitals in Nepal. Biomed Res Int. 2014;2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSağlam M, Kılı\u0026ccedil; İH, Yasemin ZER. Investigation of SCCmec Types using the Real Time PCR Method in Cefoxitin-Resistant Staphylococcus aureus Isolates. 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManyahi J, Moyo SJ, Tellevik MG, Ndugulile F, Urassa W, Blomberg B, et al. Detection of CTX-M-15 beta-lactamases in Enterobacteriaceae causing hospital-and community-acquired urinary tract infections as early as 2004, in Dar es Salaam, Tanzania. BMC Infect Dis. 2017;17:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOnduru OG, Mkakosya RS, Aboud S, Rumisha SF. Genetic determinants of resistance among ESBL-producing enterobacteriaceae in community and hospital settings in east, central, and Southern Africa: A systematic review and meta-analysis of prevalence. Can J Infect Dis Med Microbiol. 2021;2021:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed OI, El-Hady SA, Ahmed TM, Ahmed IZ. Detection of bla SHV and bla CTX-M genes in ESBL producing Klebsiella pneumoniae isolated from Egyptian patients with suspected nosocomial infections. Egypt J Med Hum Genet [Internet]. 2013;14(3):277\u0026ndash;83. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ejmhg.2013.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejmhg.2013.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIbrahim M, Algak T, Abbas M, Elamin B. 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Infect Drug Resist. 2023;16:985\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohsen SMY, Hamzah HA, Al-Deen MMI, Baharudin R. Antimicrobial susceptibility of Klebsiella pneumoniae and Escherichia coli with extended-spectrum β-lactamase associated genes in Hospital Tengku Ampuan Afzan, Kuantan, Pahang. Malaysian J Med Sci MJMS. 2016;23(2):14.\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":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hospital-acquired resistant infections, risk factors, biofilm, antibiotic-resistant bacteria","lastPublishedDoi":"10.21203/rs.3.rs-6927998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6927998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHospital-acquired resistant infections (HARI) are difficult to manage due to limited treatment options and to their ability to further resistance stress conditions by producing biofilm. This work aimed to assess the distribution of HARI-associated bacterial species in north Israel and to investigate associations between biofilm formation and extended beta-lactamase (ESBL) genes, bacterial and patient characteristics, and hospitalization length, season and year.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMethicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA), multidrug-resistant (MDR) \u003cem\u003eP. aeruginosa\u003c/em\u003e and \u003cem\u003eA. baumannii\u003c/em\u003e, ESBL-producing \u003cem\u003eEscherichia coli\u003c/em\u003e (ESBL-\u003cem\u003eE. coli\u003c/em\u003e), \u003cem\u003eKlebsiela pneumoniae\u003c/em\u003e (ESBL-\u003cem\u003eK. pneumoniae\u003c/em\u003e) and \u003cem\u003eProteus mirabilis\u003c/em\u003e (ESBL- \u003cem\u003eP. mirabilis\u003c/em\u003e) were isolated from 569 blood, urine, wound and respiratory samples of patients with HARI hospitalized during 2020\u0026ndash;2022 in north Israel. Biofilm-formation capacity was assessed by the crystalline violet method. ESBL genes were detected by real-time PCR. Data regarding season, time to infection, bacterial species, patient demographics, year, and hospital department, were collected from medical records.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHARI rates were significantly lower in 2022 compared to 2020. ESBL-\u003cem\u003eK. pneumoniae\u003c/em\u003e was the most prevalent (31.6%) bacteria. Strong biofilms were produced by 346 (60.8%), and were most common among ESBL-\u003cem\u003eK. pneumoniae\u003c/em\u003e samples (46.2%). \u003cem\u003ebla\u003c/em\u003e\u003csub\u003eCTX\u0026minus;M\u003c/sub\u003e was the most commonly detected ESBL gene (87.7%). Most strains (61.3%) carried more than one ESBL gene. Hospitalization season had a notable impact on biofilm production, with a heightened risk of infection by robust biofilm producers during spring, summer and autumn compared to winter. Furthermore, the presence of bla\u003csub\u003eSHV\u003c/sub\u003e and bla\u003csub\u003eTEM\u003c/sub\u003e genes were significantly associated with enhanced biofilm production. Bacteria harboring all three ESBL genes exhibited the highest biofilm production capacities, compared to those carrying fewer than three.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eBiofilm-production intensity differs across bacterial species and seasons and is influenced by the presence of ESBL genes.\u003c/p\u003e","manuscriptTitle":"The influence of seasonality and antimicrobial resistance genes on biofilm formation in hospital-acquired resistant bacteria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:14:09","doi":"10.21203/rs.3.rs-6927998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-14T00:30:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T14:44:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312089712336135189492118720476454541383","date":"2025-11-05T14:15:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T01:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264382800860292868005059304145869483759","date":"2025-08-01T08:03:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146044276740998815564746585271968658702","date":"2025-07-26T13:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T12:21:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T08:40:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T00:24:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Epidemiology and Global Health","date":"2025-06-19T06:19:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed93a5ff-9c36-4a25-899e-c153b714bb45","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T21:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:14:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6927998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6927998","identity":"rs-6927998","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}