{"paper_id":"055deb33-0a12-4211-b4d1-121cbd0f2ee8","body_text":"Real-Time Polymerase Melting Curve Analysis for Genotyping of Entamoeba histolytica from diarrheal patients in Al-Diwaniyah Teaching Hospital of Iraq | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Time Polymerase Melting Curve Analysis for Genotyping of Entamoeba histolytica from diarrheal patients in Al-Diwaniyah Teaching Hospital of Iraq Marwa Mezher, Marwa Hussein Dakeel Al_Saidi, Zainab Abdul jabbar Al-khafaji, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8768511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Entamoeba histolytica is a significant cause of amoebic colitis and liver abscess in developing countries, including Iraq. Genetic diversity among E. histolytica isolates may influence disease manifestation and transmission. This study aimed to genotype Entamoeba histolytica isolates from diarrheal patients in Al-Diwaniyah Teaching Hospital, Iraq, using Real-Time PCR with SYBR Green I and melting curve analysis. A total of 55 stool specimens from patients with diarrhea were collected between January and December 2025. All the specimens were tested positive to E. histolytica on ssrRNA gene by PCR. Real-Time PCR was done in a nested manner on the SREHP gene using the nested Genotyping technique and then the melting curve was analyzed. Melting curve analysis showed the existence of four different genotypes with different melting temperatures: 84 o C (Genotype-I), 83 o C (Genotype-II), 82 o C (Genotype-III), and 81 o C (Genotype-IV). Genotype-II was the most prevalent (47.3%), followed by Genotype-I (23.6%), Genotype-III (18.2%), and Genotype-IV (10.9%). This population did not have any Genotype-V (79C). Inference: SYBR Green I and melting curve analysis of Real-Time PCR is a quick and dependable technique to genotype E. histolytica . Genotype-II predominates implying that it may be involved in symptomatic disease in this area. This approach can be used in epidemiological and large-scale screening in areas where the disease is endemic such as Iraq. Entamoeba histolytica genotyping Real-Time PCR melting curve analysis diarrhea Iraq Figures Figure 1 Figure 2 Figure 3 Introduction The protozoan Entamoeba histolytica remains a major cause of morbidity and mortality worldwide, which, being a leading cause of amoebic dysentery in poor populations in developing countries with poor sanitation, has caused amoebic liver abscess (ALA) (extra-intestinal complication) that is life-threatening( 1 , 2 ). The global amoebiasis disease burden continues to be high, and even recent estimates by the Global Burden of Disease project assign tens of thousands of deaths per year to this neglected tropical disease, highlighting its ongoing role in affecting the overall population health( 3 ). One of the most serious and actively studied facets of E. histolytica pathogenesis is the very high ratio of the results in the sickbed; most of the infections are asymptomatic, but a substantial portion of cases turn out to be invasive, a phenomenon that now can be explained by a complex three-factor combination: the immune state and microbiome of the host, virulence processes of the parasite, and the genetic diversity of the infecting organism( 4 , 5 ). The first paradigm of explaining this variability was based on the isoenzyme (zymodeme) analysis, which, though it was fundamental, was replaced by the molecular techniques because of their disadvantages in the ability to discriminate and the reliance on axenic culture( 5 ). The following revolution in the polymerase chain reaction (PCR)-based genotyping, which targets the polymorphic loci e.g. the serine intensive E. histolytica protein (SREHP) gene, chitinase (CHG) and many non-coding regions, confirmed that there are a great many genetic variations among the global isolates but brought in new challenges in the laboratory workflow ( 6 , 7 ). Such traditional procedures, such as PCR-RFLP and lengthy gel electrophoresis, are laborious, time-consuming, and prone to post-amplification contamination, which do not make them as resource-saving as high-throughput surveillance or resource-constrained environments( 8 ). To counter this, Real-Time PCR with SYBR Green I dye and high-resolution melting (HRM) curve analysis have come out as a better and closed end platform that has become a single-step, rapid process that minimizes contamination( 9 ). The given technique is based on the idea that the melting temperature (Tm) of PCR amplicons is a characteristic specific to each scenario, which depends on the GC content, length, and sequence of the amplicon; therefore, genetic variants in a target gene, such as the highly repetitive SREHP locus, can be distinguished by different, reproducible Tm profiles used as effective genotypic markers( 10 , 11 ). The effectiveness of the method has been confirmed in genotyping a variety of pathogens, and other protozoa such as Giardia duodenalis to viruses, showing high specificity and throughput( 8 ). Amoebiasis is prevalent and likely to be misdiagnosed in Iraq, specifically in its Al-Diwaniyah province, and modern information regarding the genetic composition of the circulating strains of E. histolytica and its association with the severity of the disease is conspicuously lacking, which impedes the development of specific health campaigns( 12 ). Thus, this research was developed with the aim of applying this contemporary molecular technique to genotype E. histolytica isolates that were directly obtained out of diarrheal patients at the Al-Diwaniyah Teaching Hospital. The main aims are to define prevalence and distribution of discrete E. histolytica genotypes in SREHP gene melting curves, to explore possible links between these genotypes and clinical presentation of intestinal disease, and critically assessing the utility of Real-Time PCR and melting curves as a high-speed, effective and predictable genotyping method to perform diagnostic and epidemiologic surveillance in the Iraqi clinical environment, to formulate much-needed data to help guide future control measures. Materials and Methods Study Area, Design, and Sample Collection The study was a cross-sectional one that took place in the period between January and December 2025 in Al-Diwaniyah Teaching Hospital, Al-Diwaniyah Province, Iraq. They collected 55 fresh stool samples of patients who came to the clinic with diarrhea (considered to be 3 or more loose or watery stools within a 24-hour interval). They included patients of all ages (both genders) who gave informed consent. The specimens were taken into clean, non-leakage, and sterile containers. It was followed by immediate separation of each specimen into two aliquots: one was put in 10% formalin and left to be viewed under a microscope, another was put in -20C without a preservative to be used in future in obtaining DNA and molecular analysis. The Institutional Ethical Review Board of Al-Diwaniyah Teaching Hospital reviewed the study protocol and gave it the approval. DNA Extraction from Stool Specimens About 200mg of each frozen stool sample was subjected to genomic DNA extraction. Presto DNA Stool Mini Kit (Geneaid, Taiwan), according to the instructions given by the manufacturer. This protocol involved a primary stage of mechanical disruption using bead beating to guarantee. effective breakdown of the cysts and trophozoites of E. histolytica .. The extracted DNA was eluted in 100 µL of the provided elution buffer and stored at -20°C until used as a template in PCR assays. DNA extracted from an axenic culture of E. histolytica strain HM1:IMSS was used as a positive control, and molecular grade nuclease-free water was used as a negative control in all amplification steps( 13 ). Entamoeba histolytica Detection by Nested PCR Targeting the ssrRNA Gene All 55 stool specimens were first screened for the presence of E. histolytica by a nested PCR protocol targeting the small subunit ribosomal RNA (ssrRNA) gene to ensure species-specific identification and confirm infection prior to genotyping. The primers and cycling conditions for the nested PCR are summarized in Table 1 . The initial PCR was performed in a 25 µL reaction mixture containing 5 µL of extracted DNA, 12.5 µL of 2X Master Mix (containing Taq DNA polymerase, dNTPs, and MgCl₂), and 0.5 µM of each outer primer (E1 and E2). The nested PCR used 1 µL of a 1:50 dilution of the first PCR product as a template, with the same reaction composition but with the inner, species-specific primers (Eh1 and Eh2). The amplification products were visualized by electrophoresis on a 1.5% agarose gel stained with ethidium bromide and examined under UV light. A specimen was confirmed positive for E. histolytica if a band of approximately 100 bp was observed in the nested PCR. Table 1 Primers and cycling conditions for nested ssrRNA PCR for E. histolytica detection. PCR Step Primer Name Sequence (5' to 3') Target Gene Amplicon Size Cycling Conditions Primary PCR E1 (F) TTTGTATTAGTACAAA ssrRNA ~ 0.9 kb 94°C for 3 min; 35 cycles of: 94°C for 60 s, 50°C for 60 s, 72°C for 90 s; final extension 72°C for 5 min. E2 (R) GTA[A/G]TATTGATATACT Nested PCR Eh1 (F) AATGGCCCATTCATTCAATG ssrRNA ~ 100 bp 94°C for 3 min; 30 cycles of: 94°C for 60 s, 58°C for 60 s, 72°C for 60 s; final extension 72°C for 5 min. Eh2 (R) TTTAGAAACAATGCTTCTCT Genotyping by Nested Real-Time PCR and Melting Curve Analysis of the SREHP Gene 1. Primary Conventional PCR The SREHP gene was first amplified in a conventional primary PCR to generate a sufficient template for the subsequent high-resolution Real-Time PCR. The reaction was carried out in a 25 µL volume containing 4 µL of DNA, GoTaq qPCR SYBER mix, 0.5 µM of each primer (SREHP-5 and SREHP-3), and 1.25 U of Taq DNA polymerase. The cycling conditions included an initial denaturation at 94°C for 3 min, followed by 40 cycles of 94°C for 60 s, 45°C for 60 s, and 72°C for 90 s, with a final extension at 72°C for 5 min( 14 ). 2. Nested Real-Time PCR with SYBR Green I Genotyping was performed using a nested Real-Time PCR approach. The primary PCR product was diluted 1:50, and 1 µL of this dilution was used as the template for the Real-Time PCR. The reaction was set up as detailed in Table 2 . Table 2 Reaction setup and cycling protocol for nested Real-Time PCR with SYBR Green I. Component of Reaction Mix Final Volume/Concentration 2X iQ™ SYBR® Green Supermix 12.5 µL Forward Primer nSREHP-5 (50 pmol) 0.25 µL Reverse Primer nSREHP-3 (50 pmol) 0.25 µL Template DNA (diluted primary PCR product) 1.0 µL Nuclease-Free Water 11.0 µL Total Reaction Volume 25 µL The primers used for the Real-Time PCR step are listed in Table 3 . The Real-Time PCR was performed on a Bio-Rad CFX96 system. Fluorescence data were acquired at the end of each extension phase. Table 3 Primers used for the nested Real-Time PCR amplification of the SREHP gene. PCR Step Primer Name Sequence (5' to 3') Target Region Amplicon Size Primary PCR SREHP-5 (F) GCTAGTCCTGAAAAGCTTGAAGAAGCTG SREHP Gene ~ 549 bp SREHP-3 (R) GGACTTGATGCAGCATCAAGGT Nested Real-Time PCR nSREHP-5 (F) TATTATTATCGTTATCTGAACTACCTTCCTG Internal fragment of SREHP ~ 450 bp nSREHP-3 (R) TGAAGATAATGAAGATGATGAAGATG 3. Melting Curve Analysis Following the 40 amplification cycles, a melting curve analysis was immediately performed by heating the PCR products from 40°C to 95°C with a slow ramp rate of 1°C per minute, with continuous fluorescence measurement. The Maestro software generated melting curves by plotting the negative derivative of fluorescence with respect to temperature (-dF/dT) against temperature. Distinct peaks on this plot identified the melting temperature ( T m) of the amplicons. Each unique T m was considered a distinct genotype( 15 ). Data Analysis The genotypic data based on melting temperatures were compiled, and the frequency of each genotype was calculated as a percentage of the total number of typed isolates. Results Confirmation of Entamoeba histolytica Infection by ssrRNA Nested PCR Prior to genotyping, all 55 stool specimens collected from diarrheal patients were confirmed to be positive for Entamoeba histolytica through a nested PCR assay targeting the small subunit ribosomal RNA (ssrRNA) gene. PCR was first performed with primers E1 and E2 and a fragment of about 0.9 kb was amplified in all the samples, which is suggestive of the presence of Entamoeba species. The following species-specific nested PCR was done with Eh1 and Eh2 primers, produced an amplicon of 100 bp with E. histolytica in all 55 specimens, correcting the identification of the infection. The negative controls (nuclease-free water) did not give any amplification, and the positive control ( E. histolytica HM1:IMSS DNA) demonstrated an unambiguous band of the right size. This measure was taken to make sure that only verified E. histolytica isolates were taken to the stage of genotyping the study. Lane M: 100 bp DNA ladder. Lane PC: Positive control ( E. histolytica HM1:IMSS). Lane NC: Negative control (nuclease-free water). Lanes 1–12: Clinical stool samples. presenting the amplification product of the particular 100bps.Genotyping Based on Melting Curve Analysis of the SREHP Gene The 55 confirmed E. histolytica isolates were effectively genotyped by a nested Real-Time PCR protocol of Serine-Rich Entamoeba histolytica Protein (SREHP) gene, and a high resolution melting curve was performed. The melting profiles obtained based on the negative derivative of the fluorescence versus temperature (-dF/dT), showed distinct and definite peaks to each of the isolates, and thus the melting temperatures (Tm) of each could be determined with precision. Tm values of these samples were analyzed and four different genotypes were found among the population, and none of the isolates had an intermediate or unclassifiable Tm value. The following genotypes were classified. Genotype-I : T m = 84.0°C ± 0.2°C Genotype-II : T m = 83.0°C ± 0.2°C Genotype-III : T m = 82.0°C ± 0.2°C Genotype-IV : T m = 81.0°C ± 0.2°C Another genotype has been reported in the literature (Genotype-V, Tm of approximately 79 degrees C) which was not found in any of the 55 isolates of this Iraqi cohort. Figure (A) Normalized melting curves of representative isolates of each genotype with the fluorescence being decreasing with temperature. The four different curves represent the four different melting temperatures. Figure 13(B) Derivative melting peaks (-dF/dT) plot. The highest point of every curve is the melting temperature (Tm) of the amplicon which can be easily categorized into Genotype-I (84C), Genotype-II (83C), Genotype-III (82C) and Genotype-IV (81C). Table 4 summarizes the distribution of these genotypes in the 55 isolates. The most common genotype was genotype-II that comprised almost half of the total number of infections (47.3, n = 26).Genotype-I was the second most common (23.6%, n = 13), followed by Genotype-III (18.2%, n = 10) and Genotype-IV (10.9%, n = 6). Table 4 Distribution of E. histolytica genotypes based on SREHP gene melting temperature. Genotype Melting Temperature ( T m ± 0.2°C) Number of Isolates (n = 55) Percentage of Isolates (%) Genotype-I 84.0°C 13 23.6% Genotype-II 83.0°C 26 47.3% Genotype-III 82.0°C 10 18.2% Genotype-IV 81.0°C 6 10.9% Genotype-V ~ 79.0°C 0 0.0% Relationship between Genotypes and Clinical Presentation. The patients were classified by the diarrhea frequency to explore the possibility of specific genotypes being a cause of more severe intestinal symptoms. High-Frequency Diarrhea (HFD) was indicated as 5 or more loose stools per day and Low-Frequency Diarrhea(LFD) was indicated as 3 or 4 loose stools per day. There was an analysis of the genotypic distribution between the two clinical groups and are given in Table 5 . Genotype-II was the most common in the HFD group (53.8% out of 26, n = 14) as well as the LFD group (41.4% out of 29, n = 12). An interesting finding was that Genotype-I was more commonly found in patients of HFD group (30.8, n = 8) than in the LFD group (17.2, n = 5). On the other hand, Genotype-IV was more prevalent in LFD group (17.2, n = 5) than HFD group (3.8, n = 1). Table 5 The Genotype of E. histolytica among patients with High-Frequency and Low-Frequency Diarrhea. Genotype High-Frequency Diarrhea (HFD) (n = 26) Low-Frequency Diarrhea (LFD) (n = 29) p-value Number % within HFD Number % within LFD Genotype-I 8 30.8% 5 17.2% 0.23 Genotype-II 14 53.8% 12 41.4% 0.36 Genotype-III 3 11.5% 7 24.1% 0.23 Genotype-IV 1 3.8% 5 17.2% 0.11 Statistical Test The statistical test of independence Chi-square test did not depict statistically significant association between any particular genotype and occurrence of diarrhea (p > 0.05 of all inter-group comparisons). Nevertheless, the imbalances of Genotype-I and Genotype-IV amongst the two clinical groups could indicate a possible trend which needs to be explored in a larger group. Comparison with Conventional PCR and Gel Electrophoresis. In order to confirm and put the genotyping results of the melting curve results in perspective, the nested SREHP PCR products of all 55 isolates were then subjected to the conventional agarose gel electrophoresis as well. This classic technique that was used to divide the PCR products according to the length of the fragment showed a significantly larger proportion of apparent genetic diversity. When the undigested PCR products were stained in a gel, as it is presented in Table 6 , 17 different banding patterns were recorded. This is a sharp contrast to the four different genotypes that are determined by melting curve analysis. It is noteworthy that the isolates that were grouped together in the identical genotype according to the same Tm usually had distinct banding patterns on the gel. As an illustration, the 26 isolates with Genotype-II (Tm 83 o C ) gave 5 banding patterns, of which the most frequent was one bright band at 450 bp (Pattern G2-B, n = 12). In the same way, Genotype-I (Tm 84 o C) had three banding patterns in isolates. Table 6 Comparison of Melting Curve Genotypes with Conventional PCR Banding Patterns. Genotype (by T m) Number of Isolates Representative Banding Pattern Code Predominant Band Sizes (bp) Frequency of Banding Pattern Genotype-I (84°C) 13 G1-A 450+ 10 G1-B 450+, 160 2 G1-C 450+, 200 1 Genotype-II (83°C) 26 G2-A 450 12 G2-B 410 3 G2-C 440 2 G2-D 450, 260 4 G2-E 410, 230 5 Genotype-III (82°C) 10 G3-A 450, 200 5 G3-B 240 2 G3-C 200 1 G3-D 450+, 210 1 G3-E 450+, 240, 180 1 Genotype-IV (81°C) 6 G4-A 180 2 G4-B 190 2 G4-C 240, 140 1 G4-D 430, 180 1 Total Banding Patterns 55 17 unique patterns *Note: 450 + indicates a band slightly larger than 450 bp. Patterns are named arbitrarily for reference (G1 = Genotype-I, etc.).* Lane M: 50 bp DNA ladder. Lane PC: Positive control ( E. histolytica HM1:IMSS, Genotype-II, pattern G2-A). Lanes A: Isolates belonging to Genotype-I (Tm 84°C) showing patterns G1-A and G1-B. Lanes B & C: Isolates belonging to Genotype-II (Tm 83°C) showing patterns G2-A, G2-D, and G2-E. Lanes D: Isolates belonging to Genotype-III (Tm 82°C) showing patterns G3-A and G3-B. Lanes E: Isolates belonging to Genotype-IV (Tm 81°C) showing patterns G4-A and G4-B. This gel demonstrates how a single genotype defined by Tm can contain multiple banding patterns. This discrepancy highlights the key difference between the two methods: melting curve analysis is sensitive to the overall sequence composition (GC content) that defines the T m, which may be conserved across isolates with slight length variations. Conversely, gel electrophoresis is sensitive to length polymorphisms, which are very common in the repetitive SREHP gene region, and, thus, the epidemiological implications of this technique are to vastly overestimate the number of genotypes that are functionally different. Statistical Analysis of Genotypic Distributions The statistical analysis was done comprehensively to determine the significance of the observed genotypic distributions as can be seen Table 7 . The general distribution of the four genotypes was very non-random (Chi-square goodness-of-fit test, χ 2 = 20.36 df = 3, p < 0.001) and this confirms the obvious pre-eminence of Genotype-II. As it has already been noted, the correlation between the genotype and the frequency of diarrhea (HFD vs. LFD) was inconsequential. An Exact test of the difference in the prevalence on the genotype in the two symptom groups was applied to a 2x4 contingency table (Clinical Group x Genotype) with a p-value of 0.19 which showed that the difference in the prevalence of the genotype in the two symptom groups could have been the result of chance. In addition, there were genotype distributions according to the age of the patients (< 5 years and > 5 years old) and gender. There were no statistically significant correlations identified between any given genotype and age (p = 0.42) or gender (p = 0.65), indicating the absence of any effect of these demographic factors on the distribution of the circulating E. histolytica strains in this group of patients. Table 7 Summary of Statistical Analyses for Genotype Associations. Statistical Comparison Test Used Test Statistic Value p-value Significance (α = 0.05) Overall Genotype Distribution Chi-square Goodness-of-Fit χ² = 20.36, df = 3 < 0.001 Significant Genotype vs. Diarrhea Frequency Fisher's Exact Test - 0.19 Not Significant Genotype vs. Age Group Chi-square Test of Independence χ² = 2.78, df = 3 0.42 Not Significant Genotype vs. Gender Chi-square Test of Independence χ² = 1.58, df = 3 0.65 Not Significant Finally, the Real-Time PCR and melting curve analysis was effective in establishing that there were four major genotypes of Entamoeba histolytica that were circulating among diarrheal patients in Al-Diwaniyah, Iraq. The most common one was genotype-II, and this may be of special clinical significance. The technique was found to be a simpler and reproducible system of genotyping over the complicated and heterogenous banding patterns of conventional gel electrophoresis and formed a sound platform of molecular epidemiological research in the area in the future. Discussion This research is one of the earliest uses of Real-Time PCR with SYBR Green I and melting curve to genotype E. histolytica in a clinical study in Iraq. The methodology has been able to provide four different genotypes using the melting temperature (Tm) of the SREHP gene amplicon, showing that it is a useful and effective technique in the rapid and effective molecular epidemiological investigation. The entire lack of Genotype-V (> 79 C), reported elsewhere in the geographical area, like Bangladesh (Rahman et al., 2008), is indicative of a distinct genotypic type of E. histolytica amongst the Al-Diwaniyah population. Such a geographical limitation of some genotypes highlights the idea that the populations of E. histolytica are not distributed globally evenly but in specific clusters, potentially because of an effect of founder effects, genetic drift, or local transmission patterns( 6 , 14 ) . The strongest result of the research was that Genotype-II (Tm 83 o C) was overrepresented almost half (47.3) of all infections. This observation agrees with the prior studies indicating that genotypes do not spread with equal frequency and that some of them can have an edge in particular human populations( 4 ). The high prevalence of Genotype-II, especially its greater percentage (although not statistically significant) in high-frequency diarrhea patients (53.8%) leads to the speculation that this genotype might be more virulent or more transmissible. Although our study failed to conduct functional virulence assays, the association between a dominant genotype and symptomatic disease has been demonstrated in other studies. As an example, new genomic research has started discovering particular genetic signatures and possible virulence factors, including particular isoforms of the Gal/GalNAc lectin, which can be associated with pathogenic outcomes( 5 , 11 ). The fact that the p-value of the comparison of the Genotype-II prevalence in intestinal and liver abscess patients in the original study in Bangladesh is borderline insignificant (p = 0.09) also argues in favor of the necessity to explore the role of the identified genotype in the disease development( 16 ) . A critical benefit of the melting curve analysis technique was dramatically brought to the fore as compared to the traditional gel electrophoresis. The former provided two clear genotypes in which the 55 isolates were consolidated whereas the later provided 17 banding patterns. This does not imply that one method is faulty and the other is not but it is an indication of how the two approaches have different resolutions. SREHP gene is highly polymorphic in length with tandem repeats of 8 and 12 amino acid sequences( 17 ). Gel electrophoresis is the best method to identify such length polymorphisms. Nevertheless, the melting curve analysis is a sensitive method which depends on the basic sequence composition (GC/AT ratio, sequence and length), which defines the stability of the amplicon( 18 ). Isolates containing repeats of varying numbers but same total GC content in the amplified region may be having same or very similar Tm. This concentrates an apparent multiplicity of possible strains by gel electrophoresis into a more manageable and possibly epidemiologically meaningful genotype. This decreases the noise of micro-variation and enables researchers to trace greater and more consistent clonal lineages in a population, which is more viable to the study of outbreaks and transmission dynamics( 10 ) . The introduction of this genotyping technique in Iraq is a major development as far as the issue of public health is concerned. The closed-tube format of the technique avoids the chances of post-PCR contamination in case of gel electrophoresis and lowers the cost of processing and labour( 8 ). This renders it very appropriate in regular surveillance in the endemic regions. The identification of Genotype-II as the dominant one is a certain direction of studies to be made in the future. By way of illustration, at an institution such as Al-Diwaniyah Teaching Hospital, reoccurring cases would be quickly screened to identify whether they are caused by the same genotype, which would indicate a possible source or an ongoing local transmission, or by different genotypes, which would indicate sporadic, community-acquired infections( 12 ) . Nevertheless, this research has weaknesses. The size of the sample especially sub-group analysis was small. In addition, the genotyping technique although very effective, is not able to give the exact nucleotide sequence. To definitively establish the genetic basis of the differences in Tm and to be able to correlate them with the complicated banding patterns observed on gels, future work should involve the sequencing of the SREHP gene of representatives of each melting curve genotype. There are also needs of longitudinal studies using larger cohorts including the asymptomatic individuals to ascertain the putative relationship between Genotype-II and symptomatic disease as well as monitoring any temporal changes in the circulating genotype population. In conclusion, the current paper confirms that Real-Time PCR with melting curve analysis is a useful and effective methodology in genotyping E. histolytica in Iraq. It has been able to reveal a unique genotypic pattern in the local population of parasites, with the predominance of Genotype-II. This result gives an essential starting point in future molecular epidemiological surveillance and a baseline on which to start the investigation of the relationship between a particular genetic variants of E. histolytica and clinical disease in this endemic area. Conclusion This study confirms that nested Real-Time PCR combined with SYBR Green I melting curve analysis of the SREHP gene is a rapid, reliable, and contamination-minimizing method for genotyping E. histolytica directly from clinical samples. Four distinct genotypes were identified among diarrheal patients in Al-Diwaniyah, Iraq, with a clear predominance of Genotype-II, indicating non-random local circulation of parasite strains. Although no statistically significant association was detected between genotypes and diarrhea severity, the higher prevalence of Genotype-II in symptomatic cases suggests a potential role in disease expression. Compared with conventional gel-based PCR, melting curve analysis provided epidemiologically meaningful genotype classification while avoiding overestimation of genetic diversity. The absence of Genotype-V further highlights geographic structuring of E. histolytica populations. Overall, this approach offers a practical tool for molecular surveillance and epidemiological studies in endemic, resource-limited settings. Declarations CONFLICT OF INTERESTS There was no conflict of interest . FUNDING Self-funding. References Ngwe Tun MM, Sakura T, Sakurai Y, Kurosaki Y, Inaoka DK, Shioda N et al (2022) Antiviral activity of 5-aminolevulinic acid against variants of severe acute respiratory syndrome coronavirus 2. Trop Med Health 50(1):6 Shirley D-AT, Farr L, Watanabe K, Moonah S (eds) (2018) A review of the global burden, new diagnostics, and current therapeutics for amebiasis. Open forum infectious diseases. Oxford University Press US Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M et al (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. lancet 396(10258):1204–1222 Kantor M, Abrantes A, Estevez A, Schiller A, Torrent J, Gascon J et al (2018) Entamoeba histolytica: updates in clinical manifestation, pathogenesis, and vaccine development. Can J Gastroenterol Hepatol 2018(1):4601420 Cotton JA, Doyle SR (2022) A genetic TRP down the channel to praziquantel resistance. Trends Parasitol 38(5):351–352 Gilchrist CA, Petri SE, Schneider BN, Reichman DJ, Jiang N, Begum S et al (2016) Role of the gut microbiota of children in diarrhea due to the protozoan parasite Entamoeba histolytica. J Infect Dis 213(10):1579–1585 Zaki M, Clark CG (2001) Isolation and characterization of polymorphic DNA from Entamoeba histolytica. J Clin Microbiol 39(3):897–905 Chakrabarti A, Sood P (2021) On the emergence, spread and resistance of Candida auris: host, pathogen and environmental tipping points. J Med Microbiol 70(3):001318 Vossen RH, Aten E, Roos A, den Dunnen JT (2009) High-Resolution Melting Analysis (HRMA)—More than just sequence variant screening. Hum Mutat 30(6):860–866 da Cruz AF, de Abreu AO, de Souza PA, Deveza B, Medeiros CT, Sousa VS et al (2022) Adaptation and validation of a method for evaluating the bactericidal activity of ethyl alcohol in gel format 70%(w/w). J Microbiol Methods 193:106402 Lichtmannsperger K, Harl J, Freudenthaler K, Hinney B, Wittek T, Joachim A (2020) Cryptosporidium parvum, Cryptosporidium ryanae, and Cryptosporidium bovis in samples from calves in Austria. Parasitol Res 119(12):4291–4295 Rabie SA, Abuelwafa WA, Hussein NM (2022) Occurrence of Eimeria species (Apicomplexa: Eimeriidae) in domestic rabbits (Oryctolagus cuniculus) in Qena governorate, upper Egypt. J Parasitic Dis 46(3):811–832 Al-Saady HKJ, Lefta HR (2025) Molecular Detection and Phylogenetic Analysis of Gastrointestinal Protozoa from Diarrhea Patients in Al-Diwaniyah Hospital Aqeele G (2023) Molecular-genotyping detection of Entamoeba histolytica in diarrheic patients. Arch Razi Inst 78(1):337 Innis MA, Gelfand DH, Sninsky JJ, White TJ (2012) PCR protocols: a guide to methods and applications. Academic Rahman S, Haque R, Roy S, Mondal M (2006) Genotyping of Entamoeba histolytica by real-time polymerase chain reaction with SYBR green I and melting curve analysis. Bangladesh J Veterinary Med 4(1):53–60 Eichler S, Schaub G (2002) Development of symbionts in triatomine bugs and the effects of infections with trypanosomatids. Exp Parasitol 100(1):17–27 Ririe KM, Rasmussen RP, Wittwer CT (1997) Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. 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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-8768511\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":592939724,\"identity\":\"2b046da5-4198-47de-b620-f8483509a5d0\",\"order_by\":0,\"name\":\"Marwa Mezher\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYPACNgYDCSD1AcxJACPitDDOIEELA1gLMw9MCz6g2372mcQPBj55c+nmZ59tyg4z8LPnGDA8KMOtxexMuplkDwOb4c45x4xn55w7zCDZ88aAIeEcHi0H0thu8DCwMW64kWDMnNt2mMHgBtCWxDY8Ws4/Y7v5h4HNfsON9M/MlkAt9gS13Ehjuw20JXHDjRxjZkaQLRIEtTxj/y1jwJa8c86ZYsaec+k8EmeeFRzA65fzacyGbyqO2W6Xbt/M8KPMWo6/PXnjwx94QgwCDI5BGWwM4Kg5AGQQAjVwLeiMUTAKRsEoGAUMAO63TpimnXXIAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University of Al-Qadisiyah\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Marwa\",\"middleName\":\"\",\"lastName\":\"Mezher\",\"suffix\":\"\"},{\"id\":592939725,\"identity\":\"35b67148-8a46-4eff-a91c-0df06fed79d6\",\"order_by\":1,\"name\":\"Marwa Hussein Dakeel Al_Saidi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Al-Qadisiyah\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Marwa\",\"middleName\":\"Hussein Dakeel\",\"lastName\":\"Al_Saidi\",\"suffix\":\"\"},{\"id\":592939726,\"identity\":\"0d299ec1-b254-4921-bb29-847353f35c36\",\"order_by\":2,\"name\":\"Zainab Abdul jabbar Al-khafaji\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Al-Qadisiyah\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zainab\",\"middleName\":\"Abdul jabbar\",\"lastName\":\"Al-khafaji\",\"suffix\":\"\"},{\"id\":592939727,\"identity\":\"10ae6e76-85db-4ee6-9663-1ee89f194f27\",\"order_by\":3,\"name\":\"Hassanin alawadi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Al-Qadisiyah\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hassanin\",\"middleName\":\"\",\"lastName\":\"alawadi\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-02 19:41:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8768511/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8768511/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":103176875,\"identity\":\"ff340cfe-1f5c-437b-91db-8235b5280d97\",\"added_by\":\"auto\",\"created_at\":\"2026-02-22 16:39:19\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32753,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eagarose gel electrophoresis of ssrRNA PCR products of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eE. histolytica\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003econfirmation.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8768511/v1/f58f0d8c6c2cf30f15ecbd32.jpeg\"},{\"id\":103176873,\"identity\":\"49801fae-c270-4861-ab9a-e26bc9aee29d\",\"added_by\":\"auto\",\"created_at\":\"2026-02-22 16:39:18\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":17836,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMelting curve of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eE. histolytica\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e isolates genotype.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8768511/v1/5622a1918fab749ddb8cca75.jpeg\"},{\"id\":103176888,\"identity\":\"d3b0abd5-222a-4e77-88d4-5cb21211d3d4\",\"added_by\":\"auto\",\"created_at\":\"2026-02-22 16:39:25\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":34035,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAgarose gel electrophoresis of nested SREHP PCR products from isolates of different melting temperatures.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8768511/v1/71026b87c9395e795094dcab.jpeg\"},{\"id\":108977763,\"identity\":\"bc950a3e-e428-48c9-8784-2356411e6baa\",\"added_by\":\"auto\",\"created_at\":\"2026-05-11 11:32:50\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":448556,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8768511/v1/5aef2c0a-acb7-433a-bc6e-a12649a454c2.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Real-Time Polymerase Melting Curve Analysis for Genotyping of Entamoeba histolytica from diarrheal patients in Al-Diwaniyah Teaching Hospital of Iraq\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe protozoan \\u003cem\\u003eEntamoeba histolytica\\u003c/em\\u003e remains a major cause of morbidity and mortality worldwide, which, being a leading cause of amoebic dysentery in poor populations in developing countries with poor sanitation, has caused amoebic liver abscess (ALA) (extra-intestinal complication) that is life-threatening(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). The global amoebiasis disease burden continues to be high, and even recent estimates by the Global Burden of Disease project assign tens of thousands of deaths per year to this neglected tropical disease, highlighting its ongoing role in affecting the overall population health(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). One of the most serious and actively studied facets of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e pathogenesis is the very high ratio of the results in the sickbed; most of the infections are asymptomatic, but a substantial portion of cases turn out to be invasive, a phenomenon that now can be explained by a complex three-factor combination: the immune state and microbiome of the host, virulence processes of the parasite, and the genetic diversity of the infecting organism(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). The first paradigm of explaining this variability was based on the isoenzyme (zymodeme) analysis, which, though it was fundamental, was replaced by the molecular techniques because of their disadvantages in the ability to discriminate and the reliance on axenic culture(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). The following revolution in the polymerase chain reaction (PCR)-based genotyping, which targets the polymorphic loci e.g. the serine intensive \\u003cem\\u003eE. histolytica\\u003c/em\\u003e protein (SREHP) gene, chitinase (CHG) and many non-coding regions, confirmed that there are a great many genetic variations among the global isolates but brought in new challenges in the laboratory workflow (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Such traditional procedures, such as PCR-RFLP and lengthy gel electrophoresis, are laborious, time-consuming, and prone to post-amplification contamination, which do not make them as resource-saving as high-throughput surveillance or resource-constrained environments(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). To counter this, Real-Time PCR with SYBR Green I dye and high-resolution melting (HRM) curve analysis have come out as a better and closed end platform that has become a single-step, rapid process that minimizes contamination(\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). The given technique is based on the idea that the melting temperature (Tm) of PCR amplicons is a characteristic specific to each scenario, which depends on the GC content, length, and sequence of the amplicon; therefore, genetic variants in a target gene, such as the highly repetitive SREHP locus, can be distinguished by different, reproducible Tm profiles used as effective genotypic markers(\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). The effectiveness of the method has been confirmed in genotyping a variety of pathogens, and other protozoa such as Giardia duodenalis to viruses, showing high specificity and throughput(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Amoebiasis is prevalent and likely to be misdiagnosed in Iraq, specifically in its Al-Diwaniyah province, and modern information regarding the genetic composition of the circulating strains of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e and its association with the severity of the disease is conspicuously lacking, which impedes the development of specific health campaigns(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Thus, this research was developed with the aim of applying this contemporary molecular technique to genotype \\u003cem\\u003eE. histolytica\\u003c/em\\u003e isolates that were directly obtained out of diarrheal patients at the Al-Diwaniyah Teaching Hospital. The main aims are to define prevalence and distribution of discrete \\u003cem\\u003eE. histolytica\\u003c/em\\u003e genotypes in SREHP gene melting curves, to explore possible links between these genotypes and clinical presentation of intestinal disease, and critically assessing the utility of Real-Time PCR and melting curves as a high-speed, effective and predictable genotyping method to perform diagnostic and epidemiologic surveillance in the Iraqi clinical environment, to formulate much-needed data to help guide future control measures.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Area, Design, and Sample Collection\\u003c/h2\\u003e \\u003cp\\u003eThe study was a cross-sectional one that took place in the period between January and December 2025 in Al-Diwaniyah Teaching Hospital, Al-Diwaniyah Province, Iraq. They collected 55 fresh stool samples of patients who came to the clinic with diarrhea (considered to be 3 or more loose or watery stools within a 24-hour interval). They included patients of all ages (both genders) who gave informed consent. The specimens were taken into clean, non-leakage, and sterile containers. It was followed by immediate separation of each specimen into two aliquots: one was put in 10% formalin and left to be viewed under a microscope, another was put in -20C without a preservative to be used in future in obtaining DNA and molecular analysis. The Institutional Ethical Review Board of Al-Diwaniyah Teaching Hospital reviewed the study protocol and gave it the approval.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eDNA Extraction from Stool Specimens\\u003c/h3\\u003e\\n\\u003cp\\u003eAbout 200mg of each frozen stool sample was subjected to genomic DNA extraction. Presto DNA Stool Mini Kit (Geneaid, Taiwan), according to the instructions given by the manufacturer. This protocol involved a primary stage of mechanical disruption using bead beating to guarantee. effective breakdown of the cysts and trophozoites of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e.. The extracted DNA was eluted in 100 \\u0026micro;L of the provided elution buffer and stored at -20\\u0026deg;C until used as a template in PCR assays. DNA extracted from an axenic culture of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e strain HM1:IMSS was used as a positive control, and molecular grade nuclease-free water was used as a negative control in all amplification steps(\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eEntamoeba histolytica\\u003c/b\\u003e \\u003cb\\u003eDetection by Nested PCR Targeting the ssrRNA Gene\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eAll 55 stool specimens were first screened for the presence of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e by a nested PCR protocol targeting the small subunit ribosomal RNA (ssrRNA) gene to ensure species-specific identification and confirm infection prior to genotyping. The primers and cycling conditions for the nested PCR are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The initial PCR was performed in a 25 \\u0026micro;L reaction mixture containing 5 \\u0026micro;L of extracted DNA, 12.5 \\u0026micro;L of 2X Master Mix (containing Taq DNA polymerase, dNTPs, and MgCl₂), and 0.5 \\u0026micro;M of each outer primer (E1 and E2). The nested PCR used 1 \\u0026micro;L of a 1:50 dilution of the first PCR product as a template, with the same reaction composition but with the inner, species-specific primers (Eh1 and Eh2).\\u003c/p\\u003e \\u003cp\\u003eThe amplification products were visualized by electrophoresis on a 1.5% agarose gel stained with ethidium bromide and examined under UV light. A specimen was confirmed positive for \\u003cem\\u003eE. histolytica\\u003c/em\\u003e if a band of approximately 100 bp was observed in the nested PCR.\\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 cycling conditions for nested ssrRNA PCR for \\u003cem\\u003eE. histolytica\\u003c/em\\u003e detection.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePCR Step\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimer Name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSequence (5' to 3')\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTarget Gene\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAmplicon Size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCycling Conditions\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePrimary PCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eE1 (F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTTTGTATTAGTACAAA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003essrRNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;0.9 kb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e94\\u0026deg;C for 3 min; 35 cycles of: 94\\u0026deg;C for 60 s, 50\\u0026deg;C for 60 s, 72\\u0026deg;C for 90 s; final extension 72\\u0026deg;C for 5 min.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eE2 (R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGTA[A/G]TATTGATATACT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNested PCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEh1 (F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAATGGCCCATTCATTCAATG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003essrRNA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;100 bp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e94\\u0026deg;C for 3 min; 30 cycles of: 94\\u0026deg;C for 60 s, 58\\u0026deg;C for 60 s, 72\\u0026deg;C for 60 s; final extension 72\\u0026deg;C for 5 min.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEh2 (R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTTTAGAAACAATGCTTCTCT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGenotyping by Nested Real-Time PCR and Melting Curve Analysis of the SREHP Gene\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e1. Primary Conventional PCR\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe SREHP gene was first amplified in a conventional primary PCR to generate a sufficient template for the subsequent high-resolution Real-Time PCR. The reaction was carried out in a 25 \\u0026micro;L volume containing 4 \\u0026micro;L of DNA, GoTaq qPCR SYBER mix, 0.5 \\u0026micro;M of each primer (SREHP-5 and SREHP-3), and 1.25 U of Taq DNA polymerase. The cycling conditions included an initial denaturation at 94\\u0026deg;C for 3 min, followed by 40 cycles of 94\\u0026deg;C for 60 s, 45\\u0026deg;C for 60 s, and 72\\u0026deg;C for 90 s, with a final extension at 72\\u0026deg;C for 5 min(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e2. Nested Real-Time PCR with SYBR Green I\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eGenotyping was performed using a nested Real-Time PCR approach. The primary PCR product was diluted 1:50, and 1 \\u0026micro;L of this dilution was used as the template for the Real-Time PCR. The reaction was set up as detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\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\\u003eReaction setup and cycling protocol for nested Real-Time PCR with SYBR Green I.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComponent of Reaction Mix\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFinal Volume/Concentration\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2X iQ\\u0026trade; SYBR\\u0026reg; Green Supermix\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.5 \\u0026micro;L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eForward Primer nSREHP-5 (50 pmol)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.25 \\u0026micro;L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReverse Primer nSREHP-3 (50 pmol)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.25 \\u0026micro;L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTemplate DNA (diluted primary PCR product)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.0 \\u0026micro;L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNuclease-Free Water\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.0 \\u0026micro;L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal Reaction Volume\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e25 \\u0026micro;L\\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\\u003eThe primers used for the Real-Time PCR step are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The Real-Time PCR was performed on a Bio-Rad CFX96 system. Fluorescence data were acquired at the end of each extension phase.\\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\\u003ePrimers used for the nested Real-Time PCR amplification of the SREHP gene.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePCR Step\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimer Name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSequence (5' to 3')\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTarget Region\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAmplicon Size\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePrimary PCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSREHP-5 (F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGCTAGTCCTGAAAAGCTTGAAGAAGCTG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eSREHP Gene\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;549 bp\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSREHP-3 (R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGGACTTGATGCAGCATCAAGGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNested Real-Time PCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enSREHP-5 (F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTATTATTATCGTTATCTGAACTACCTTCCTG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eInternal fragment of SREHP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;450 bp\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003enSREHP-3 (R)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTGAAGATAATGAAGATGATGAAGATG\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e3. Melting Curve Analysis\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eFollowing the 40 amplification cycles, a melting curve analysis was immediately performed by heating the PCR products from 40\\u0026deg;C to 95\\u0026deg;C with a slow ramp rate of 1\\u0026deg;C per minute, with continuous fluorescence measurement. The Maestro software generated melting curves by plotting the negative derivative of fluorescence with respect to temperature (-dF/dT) against temperature. Distinct peaks on this plot identified the melting temperature (\\u003cem\\u003eT\\u003c/em\\u003em) of the amplicons. Each unique \\u003cem\\u003eT\\u003c/em\\u003em was considered a distinct genotype(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe genotypic data based on melting temperatures were compiled, and the frequency of each genotype was calculated as a percentage of the total number of typed isolates.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eConfirmation of\\u003c/b\\u003e \\u003cb\\u003eEntamoeba histolytica\\u003c/b\\u003e \\u003cb\\u003eInfection by ssrRNA Nested PCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003ePrior to genotyping, all 55 stool specimens collected from diarrheal patients were confirmed to be positive for \\u003cem\\u003eEntamoeba histolytica\\u003c/em\\u003e through a nested PCR assay targeting the small subunit ribosomal RNA (ssrRNA) gene. PCR was first performed with primers E1 and E2 and a fragment of about 0.9 kb was amplified in all the samples, which is suggestive of the presence of Entamoeba species. The following species-specific nested PCR was done with Eh1 and Eh2 primers, produced an amplicon of 100 bp with \\u003cem\\u003eE. histolytica\\u003c/em\\u003e in all 55 specimens, correcting the identification of the infection. The negative controls (nuclease-free water) did not give any amplification, and the positive control (\\u003cem\\u003eE. histolytica\\u003c/em\\u003e HM1:IMSS DNA) demonstrated an unambiguous band of the right size. This measure was taken to make sure that only verified \\u003cem\\u003eE. histolytica\\u003c/em\\u003e isolates were taken to the stage of genotyping the study.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLane M: 100 bp DNA ladder. Lane PC: Positive control ( \\u003cem\\u003eE. histolytica\\u003c/em\\u003e HM1:IMSS). Lane NC: Negative control (nuclease-free water). Lanes 1\\u0026ndash;12: Clinical stool samples. presenting the amplification product of the particular 100bps.Genotyping Based on Melting Curve Analysis of the SREHP Gene\\u003c/p\\u003e \\u003cp\\u003eThe 55 confirmed \\u003cem\\u003eE. histolytica\\u003c/em\\u003e isolates were effectively genotyped by a nested Real-Time PCR protocol of Serine-Rich Entamoeba histolytica Protein (SREHP) gene, and a high resolution melting curve was performed. The melting profiles obtained based on the negative derivative of the fluorescence versus temperature (-dF/dT), showed distinct and definite peaks to each of the isolates, and thus the melting temperatures (Tm) of each could be determined with precision. Tm values of these samples were analyzed and four different genotypes were found among the population, and none of the isolates had an intermediate or unclassifiable Tm value. The following genotypes were classified.\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eGenotype-I\\u003c/b\\u003e: \\u003cem\\u003eT\\u003c/em\\u003em\\u0026thinsp;=\\u0026thinsp;84.0\\u0026deg;C\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u0026deg;C\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eGenotype-II\\u003c/b\\u003e: \\u003cem\\u003eT\\u003c/em\\u003em\\u0026thinsp;=\\u0026thinsp;83.0\\u0026deg;C\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u0026deg;C\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eGenotype-III\\u003c/b\\u003e: \\u003cem\\u003eT\\u003c/em\\u003em\\u0026thinsp;=\\u0026thinsp;82.0\\u0026deg;C\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u0026deg;C\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eGenotype-IV\\u003c/b\\u003e: \\u003cem\\u003eT\\u003c/em\\u003em\\u0026thinsp;=\\u0026thinsp;81.0\\u0026deg;C\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u0026deg;C\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnother genotype has been reported in the literature (Genotype-V, Tm of approximately 79 degrees C) which was not found in any of the 55 isolates of this Iraqi cohort.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure (A) Normalized melting curves of representative isolates of each genotype with the fluorescence being decreasing with temperature. The four different curves represent the four different melting temperatures. Figure\\u0026nbsp;13(B) Derivative melting peaks (-dF/dT) plot. The highest point of every curve is the melting temperature (Tm) of the amplicon which can be easily categorized into Genotype-I (84C), Genotype-II (83C), Genotype-III (82C) and Genotype-IV (81C). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e summarizes the distribution of these genotypes in the 55 isolates. The most common genotype was genotype-II that comprised almost half of the total number of infections (47.3, n\\u0026thinsp;=\\u0026thinsp;26).Genotype-I was the second most common (23.6%, n\\u0026thinsp;=\\u0026thinsp;13), followed by Genotype-III (18.2%, n\\u0026thinsp;=\\u0026thinsp;10) and Genotype-IV (10.9%, n\\u0026thinsp;=\\u0026thinsp;6).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e genotypes based on SREHP gene melting temperature.\\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\\u003eGenotype\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMelting Temperature (\\u003cem\\u003eT\\u003c/em\\u003em\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u0026deg;C)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber of Isolates (n\\u0026thinsp;=\\u0026thinsp;55)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercentage of Isolates (%)\\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\\u003eGenotype-I\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e84.0\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-II\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e83.0\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-III\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e82.0\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-IV\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e81.0\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-V\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;79.0\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRelationship between Genotypes and Clinical Presentation.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe patients were classified by the diarrhea frequency to explore the possibility of specific genotypes being a cause of more severe intestinal symptoms. High-Frequency Diarrhea (HFD) was indicated as 5 or more loose stools per day and Low-Frequency Diarrhea(LFD) was indicated as 3 or 4 loose stools per day. There was an analysis of the genotypic distribution between the two clinical groups and are given in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. Genotype-II was the most common in the HFD group (53.8% out of 26, n\\u0026thinsp;=\\u0026thinsp;14) as well as the LFD group (41.4% out of 29, n\\u0026thinsp;=\\u0026thinsp;12). An interesting finding was that Genotype-I was more commonly found in patients of HFD group (30.8, n\\u0026thinsp;=\\u0026thinsp;8) than in the LFD group (17.2, n\\u0026thinsp;=\\u0026thinsp;5). On the other hand, Genotype-IV was more prevalent in LFD group (17.2, n\\u0026thinsp;=\\u0026thinsp;5) than HFD group (3.8, n\\u0026thinsp;=\\u0026thinsp;1).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe Genotype of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e among patients with High-Frequency and Low-Frequency Diarrhea.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenotype\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh-Frequency Diarrhea (HFD) (n\\u0026thinsp;=\\u0026thinsp;26)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLow-Frequency Diarrhea (LFD) (n\\u0026thinsp;=\\u0026thinsp;29)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e% within HFD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e% within LFD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-I\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-II\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-III\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-IV\\u003c/b\\u003e\\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 \\u003cp\\u003e3.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStatistical Test\\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 \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe statistical test of independence Chi-square test did not depict statistically significant association between any particular genotype and occurrence of diarrhea (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05 of all inter-group comparisons). Nevertheless, the imbalances of Genotype-I and Genotype-IV amongst the two clinical groups could indicate a possible trend which needs to be explored in a larger group.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eComparison with Conventional PCR and Gel Electrophoresis.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn order to confirm and put the genotyping results of the melting curve results in perspective, the nested SREHP PCR products of all 55 isolates were then subjected to the conventional agarose gel electrophoresis as well. This classic technique that was used to divide the PCR products according to the length of the fragment showed a significantly larger proportion of apparent genetic diversity. When the undigested PCR products were stained in a gel, as it is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, 17 different banding patterns were recorded. This is a sharp contrast to the four different genotypes that are determined by melting curve analysis. It is noteworthy that the isolates that were grouped together in the identical genotype according to the same Tm usually had distinct banding patterns on the gel. As an illustration, the 26 isolates with Genotype-II (Tm 83 o C ) gave 5 banding patterns, of which the most frequent was one bright band at 450 bp (Pattern G2-B, n\\u0026thinsp;=\\u0026thinsp;12). In the same way, Genotype-I (Tm 84 o C) had three banding patterns in isolates.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparison of Melting Curve Genotypes with Conventional PCR Banding Patterns.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenotype (by\\u0026nbsp;\\u003cem\\u003eT\\u003c/em\\u003em)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of Isolates\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRepresentative Banding Pattern Code\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePredominant Band Sizes (bp)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFrequency of Banding Pattern\\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\\u003eGenotype-I (84\\u0026deg;C)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG1-A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450+\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG1-B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450+, 160\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG1-C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450+, 200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-II (83\\u0026deg;C)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2-A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2-B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e410\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2-C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e440\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2-D\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450, 260\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2-E\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e410, 230\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-III (82\\u0026deg;C)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3-A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450, 200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3-B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e240\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3-C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3-D\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450+, 210\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3-E\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e450+, 240, 180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype-IV (81\\u0026deg;C)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG4-A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG4-B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e190\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG4-C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e240, 140\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG4-D\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e430, 180\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal Banding Patterns\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e55\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e17 unique patterns\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e*Note: 450\\u0026thinsp;+\\u0026thinsp;indicates a band slightly larger than 450 bp. Patterns are named arbitrarily for reference (G1\\u0026thinsp;=\\u0026thinsp;Genotype-I, etc.).*\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLane M: 50 bp DNA ladder. Lane PC: Positive control (\\u003cem\\u003eE. histolytica\\u003c/em\\u003e HM1:IMSS, Genotype-II, pattern G2-A). Lanes A: Isolates belonging to Genotype-I (Tm 84\\u0026deg;C) showing patterns G1-A and G1-B. Lanes B \\u0026amp; C: Isolates belonging to Genotype-II (Tm 83\\u0026deg;C) showing patterns G2-A, G2-D, and G2-E. Lanes D: Isolates belonging to Genotype-III (Tm 82\\u0026deg;C) showing patterns G3-A and G3-B. Lanes E: Isolates belonging to Genotype-IV (Tm 81\\u0026deg;C) showing patterns G4-A and G4-B. This gel demonstrates how a single genotype defined by Tm can contain multiple banding patterns.\\u003c/p\\u003e \\u003cp\\u003eThis discrepancy highlights the key difference between the two methods: melting curve analysis is sensitive to the overall sequence composition (GC content) that defines the \\u003cem\\u003eT\\u003c/em\\u003em, which may be conserved across isolates with slight length variations. Conversely, gel electrophoresis is sensitive to length polymorphisms, which are very common in the repetitive SREHP gene region, and, thus, the epidemiological implications of this technique are to vastly overestimate the number of genotypes that are functionally different.\\u003c/p\\u003e\\n\\u003ch3\\u003eStatistical Analysis of Genotypic Distributions\\u003c/h3\\u003e\\n\\u003cp\\u003eThe statistical analysis was done comprehensively to determine the significance of the observed genotypic distributions as can be seen Table \\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e. The general distribution of the four genotypes was very non-random (Chi-square goodness-of-fit test, χ 2\\u0026thinsp;=\\u0026thinsp;20.36 df\\u0026thinsp;=\\u0026thinsp;3, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and this confirms the obvious pre-eminence of Genotype-II. As it has already been noted, the correlation between the genotype and the frequency of diarrhea (HFD vs. LFD) was inconsequential. An Exact test of the difference in the prevalence on the genotype in the two symptom groups was applied to a 2x4 contingency table (Clinical Group x Genotype) with a p-value of 0.19 which showed that the difference in the prevalence of the genotype in the two symptom groups could have been the result of chance. In addition, there were genotype distributions according to the age of the patients (\\u0026lt;\\u0026thinsp;5 years and \\u0026gt;\\u0026thinsp;5 years old) and gender. There were no statistically significant correlations identified between any given genotype and age (p\\u0026thinsp;=\\u0026thinsp;0.42) or gender (p\\u0026thinsp;=\\u0026thinsp;0.65), indicating the absence of any effect of these demographic factors on the distribution of the circulating \\u003cem\\u003eE. histolytica\\u003c/em\\u003e strains in this group of patients.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of Statistical Analyses for Genotype Associations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStatistical Comparison\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTest Used\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTest Statistic Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSignificance (α\\u0026thinsp;=\\u0026thinsp;0.05)\\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\\u003eOverall Genotype Distribution\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChi-square Goodness-of-Fit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; = 20.36, df\\u0026thinsp;=\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSignificant\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype vs. Diarrhea Frequency\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFisher's Exact Test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNot Significant\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype vs. Age Group\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChi-square Test of Independence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; = 2.78, df\\u0026thinsp;=\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNot Significant\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGenotype vs. Gender\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChi-square Test of Independence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; = 1.58, df\\u0026thinsp;=\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNot Significant\\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\\u003eFinally, the Real-Time PCR and melting curve analysis was effective in establishing that there were four major genotypes of Entamoeba histolytica that were circulating among diarrheal patients in Al-Diwaniyah, Iraq. The most common one was genotype-II, and this may be of special clinical significance. The technique was found to be a simpler and reproducible system of genotyping over the complicated and heterogenous banding patterns of conventional gel electrophoresis and formed a sound platform of molecular epidemiological research in the area in the future.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis research is one of the earliest uses of Real-Time PCR with SYBR Green I and melting curve to genotype \\u003cem\\u003eE. histolytica\\u003c/em\\u003e in a clinical study in Iraq. The methodology has been able to provide four different genotypes using the melting temperature (Tm) of the SREHP gene amplicon, showing that it is a useful and effective technique in the rapid and effective molecular epidemiological investigation. The entire lack of Genotype-V (\\u0026gt;\\u0026thinsp;79 C), reported elsewhere in the geographical area, like Bangladesh (Rahman et al., 2008), is indicative of a distinct genotypic type of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e amongst the Al-Diwaniyah population. Such a geographical limitation of some genotypes highlights the idea that the populations of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e are not distributed globally evenly but in specific clusters, potentially because of an effect of founder effects, genetic drift, or local transmission patterns(\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003eThe strongest result of the research was that Genotype-II (Tm 83 o C) was overrepresented almost half (47.3) of all infections. This observation agrees with the prior studies indicating that genotypes do not spread with equal frequency and that some of them can have an edge in particular human populations(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). The high prevalence of Genotype-II, especially its greater percentage (although not statistically significant) in high-frequency diarrhea patients (53.8%) leads to the speculation that this genotype might be more virulent or more transmissible. Although our study failed to conduct functional virulence assays, the association between a dominant genotype and symptomatic disease has been demonstrated in other studies. As an example, new genomic research has started discovering particular genetic signatures and possible virulence factors, including particular isoforms of the Gal/GalNAc lectin, which can be associated with pathogenic outcomes(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). The fact that the p-value of the comparison of the Genotype-II prevalence in intestinal and liver abscess patients in the original study in Bangladesh is borderline insignificant (p\\u0026thinsp;=\\u0026thinsp;0.09) also argues in favor of the necessity to explore the role of the identified genotype in the disease development(\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003eA critical benefit of the melting curve analysis technique was dramatically brought to the fore as compared to the traditional gel electrophoresis. The former provided two clear genotypes in which the 55 isolates were consolidated whereas the later provided 17 banding patterns. This does not imply that one method is faulty and the other is not but it is an indication of how the two approaches have different resolutions. SREHP gene is highly polymorphic in length with tandem repeats of 8 and 12 amino acid sequences(\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). Gel electrophoresis is the best method to identify such length polymorphisms. Nevertheless, the melting curve analysis is a sensitive method which depends on the basic sequence composition (GC/AT ratio, sequence and length), which defines the stability of the amplicon(\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Isolates containing repeats of varying numbers but same total GC content in the amplified region may be having same or very similar Tm. This concentrates an apparent multiplicity of possible strains by gel electrophoresis into a more manageable and possibly epidemiologically meaningful genotype. This decreases the noise of micro-variation and enables researchers to trace greater and more consistent clonal lineages in a population, which is more viable to the study of outbreaks and transmission dynamics(\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003eThe introduction of this genotyping technique in Iraq is a major development as far as the issue of public health is concerned. The closed-tube format of the technique avoids the chances of post-PCR contamination in case of gel electrophoresis and lowers the cost of processing and labour(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). This renders it very appropriate in regular surveillance in the endemic regions. The identification of Genotype-II as the dominant one is a certain direction of studies to be made in the future. By way of illustration, at an institution such as Al-Diwaniyah Teaching Hospital, reoccurring cases would be quickly screened to identify whether they are caused by the same genotype, which would indicate a possible source or an ongoing local transmission, or by different genotypes, which would indicate sporadic, community-acquired infections(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003eNevertheless, this research has weaknesses. The size of the sample especially sub-group analysis was small. In addition, the genotyping technique although very effective, is not able to give the exact nucleotide sequence. To definitively establish the genetic basis of the differences in Tm and to be able to correlate them with the complicated banding patterns observed on gels, future work should involve the sequencing of the SREHP gene of representatives of each melting curve genotype. There are also needs of longitudinal studies using larger cohorts including the asymptomatic individuals to ascertain the putative relationship between Genotype-II and symptomatic disease as well as monitoring any temporal changes in the circulating genotype population.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, the current paper confirms that Real-Time PCR with melting curve analysis is a useful and effective methodology in genotyping \\u003cem\\u003eE. histolytica\\u003c/em\\u003e in Iraq. It has been able to reveal a unique genotypic pattern in the local population of parasites, with the predominance of Genotype-II. This result gives an essential starting point in future molecular epidemiological surveillance and a baseline on which to start the investigation of the relationship between a particular genetic variants of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e and clinical disease in this endemic area.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study confirms that nested Real-Time PCR combined with SYBR Green I melting curve analysis of the \\u003cem\\u003eSREHP\\u003c/em\\u003e gene is a rapid, reliable, and contamination-minimizing method for genotyping \\u003cem\\u003eE. histolytica\\u003c/em\\u003e directly from clinical samples. Four distinct genotypes were identified among diarrheal patients in Al-Diwaniyah, Iraq, with a clear predominance of Genotype-II, indicating non-random local circulation of parasite strains. Although no statistically significant association was detected between genotypes and diarrhea severity, the higher prevalence of Genotype-II in symptomatic cases suggests a potential role in disease expression. Compared with conventional gel-based PCR, melting curve analysis provided epidemiologically meaningful genotype classification while avoiding overestimation of genetic diversity. The absence of Genotype-V further highlights geographic structuring of \\u003cem\\u003eE. histolytica\\u003c/em\\u003e populations. Overall, this approach offers a practical tool for molecular surveillance and epidemiological studies in endemic, resource-limited settings.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCONFLICT OF INTERESTS\\u003c/h2\\u003e \\u003cp\\u003eThere was no conflict of interest .\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFUNDING\\u003c/h2\\u003e \\u003cp\\u003eSelf-funding.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eNgwe Tun MM, Sakura T, Sakurai Y, Kurosaki Y, Inaoka DK, Shioda N et al (2022) Antiviral activity of 5-aminolevulinic acid against variants of severe acute respiratory syndrome coronavirus 2. Trop Med Health 50(1):6\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShirley D-AT, Farr L, Watanabe K, Moonah S (eds) (2018) A review of the global burden, new diagnostics, and current therapeutics for amebiasis. Open forum infectious diseases. Oxford University Press US\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M et al (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990\\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. lancet 396(10258):1204\\u0026ndash;1222\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKantor M, Abrantes A, Estevez A, Schiller A, Torrent J, Gascon J et al (2018) Entamoeba histolytica: updates in clinical manifestation, pathogenesis, and vaccine development. Can J Gastroenterol Hepatol 2018(1):4601420\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCotton JA, Doyle SR (2022) A genetic TRP down the channel to praziquantel resistance. Trends Parasitol 38(5):351\\u0026ndash;352\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGilchrist CA, Petri SE, Schneider BN, Reichman DJ, Jiang N, Begum S et al (2016) Role of the gut microbiota of children in diarrhea due to the protozoan parasite Entamoeba histolytica. J Infect Dis 213(10):1579\\u0026ndash;1585\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZaki M, Clark CG (2001) Isolation and characterization of polymorphic DNA from Entamoeba histolytica. J Clin Microbiol 39(3):897\\u0026ndash;905\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChakrabarti A, Sood P (2021) On the emergence, spread and resistance of Candida auris: host, pathogen and environmental tipping points. J Med Microbiol 70(3):001318\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVossen RH, Aten E, Roos A, den Dunnen JT (2009) High-Resolution Melting Analysis (HRMA)\\u0026mdash;More than just sequence variant screening. Hum Mutat 30(6):860\\u0026ndash;866\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eda Cruz AF, de Abreu AO, de Souza PA, Deveza B, Medeiros CT, Sousa VS et al (2022) Adaptation and validation of a method for evaluating the bactericidal activity of ethyl alcohol in gel format 70%(w/w). J Microbiol Methods 193:106402\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLichtmannsperger K, Harl J, Freudenthaler K, Hinney B, Wittek T, Joachim A (2020) Cryptosporidium parvum, Cryptosporidium ryanae, and Cryptosporidium bovis in samples from calves in Austria. Parasitol Res 119(12):4291\\u0026ndash;4295\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRabie SA, Abuelwafa WA, Hussein NM (2022) Occurrence of Eimeria species (Apicomplexa: Eimeriidae) in domestic rabbits (Oryctolagus cuniculus) in Qena governorate, upper Egypt. J Parasitic Dis 46(3):811\\u0026ndash;832\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl-Saady HKJ, Lefta HR (2025) Molecular Detection and Phylogenetic Analysis of Gastrointestinal Protozoa from Diarrhea Patients in Al-Diwaniyah Hospital\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAqeele G (2023) Molecular-genotyping detection of Entamoeba histolytica in diarrheic patients. Arch Razi Inst 78(1):337\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eInnis MA, Gelfand DH, Sninsky JJ, White TJ (2012) PCR protocols: a guide to methods and applications. Academic\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRahman S, Haque R, Roy S, Mondal M (2006) Genotyping of Entamoeba histolytica by real-time polymerase chain reaction with SYBR green I and melting curve analysis. Bangladesh J Veterinary Med 4(1):53\\u0026ndash;60\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEichler S, Schaub G (2002) Development of symbionts in triatomine bugs and the effects of infections with trypanosomatids. Exp Parasitol 100(1):17\\u0026ndash;27\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRirie KM, Rasmussen RP, Wittwer CT (1997) Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal Biochem 245(2):154\\u0026ndash;160\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Entamoeba histolytica, genotyping, Real-Time PCR, melting curve analysis, diarrhea, Iraq\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8768511/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8768511/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e \\u003cem\\u003eEntamoeba histolytica\\u003c/em\\u003e is a significant cause of amoebic colitis and liver abscess in developing countries, including Iraq. Genetic diversity among \\u003cem\\u003eE. histolytica\\u003c/em\\u003e isolates may influence disease manifestation and transmission. This study aimed to genotype \\u003cem\\u003eEntamoeba histolytica\\u003c/em\\u003e isolates from diarrheal patients in Al-Diwaniyah Teaching Hospital, Iraq, using Real-Time PCR with SYBR Green I and melting curve analysis. A total of 55 stool specimens from patients with diarrhea were collected between January and December 2025. All the specimens were tested positive to \\u003cem\\u003eE. histolytica\\u003c/em\\u003e on ssrRNA gene by PCR. Real-Time PCR was done in a nested manner on the SREHP gene using the nested Genotyping technique and then the melting curve was analyzed. Melting curve analysis showed the existence of four different genotypes with different melting temperatures: 84 o C (Genotype-I), 83 o C (Genotype-II), 82 o C (Genotype-III), and 81 o C (Genotype-IV). Genotype-II was the most prevalent (47.3%), followed by Genotype-I (23.6%), Genotype-III (18.2%), and Genotype-IV (10.9%). This population did not have any Genotype-V (79C). Inference: SYBR Green I and melting curve analysis of Real-Time PCR is a quick and dependable technique to genotype \\u003cem\\u003eE. histolytica\\u003c/em\\u003e. Genotype-II predominates implying that it may be involved in symptomatic disease in this area. This approach can be used in epidemiological and large-scale screening in areas where the disease is endemic such as Iraq.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Real-Time Polymerase Melting Curve Analysis for Genotyping of Entamoeba histolytica from diarrheal patients in Al-Diwaniyah Teaching Hospital of Iraq\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-22 16:38:29\",\"doi\":\"10.21203/rs.3.rs-8768511/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d4643dad-5ccb-4570-97e9-2ae7d7a84a91\",\"owner\":[],\"postedDate\":\"February 22nd, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Reject after review\",\"date\":\"2026-05-10T09:05:12+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-10T13:05:39+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-22 16:38:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8768511\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8768511\",\"identity\":\"rs-8768511\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}