Traditional Microscopy Methods Research Articles

Human-Validated Neural Networks for Precise Amastigote Categorization and Quantification to Accelerate Drug Discovery in Leishmaniasis.

Leishmaniases present a significant global health challenge with limited and often inadequate treatment options available. Traditional microscopic methods for detecting Leishmania amastigotes are time-consuming and error-prone, highlighting the need for automated approaches. This study aimed to implement and validate the YOLOv8 deep learning model for real-time detection, quantification, and categorization of Leishmania amastigotes to enhance drug screening assays. YOLOv8 was trained on 470 images from two microscopes, classifying them into categories such as "infected cells," "intracellular amastigotes," "uninfected cells," and "edge cells." The model's performance was compared to human operators using Pearson and Spearman correlation analyses. YOLOv8 achieved strong performance in detecting "infected cells" (AUC = 0.934) and "intracellular amastigotes" (AUC = 0.846). However, challenges remained in differentiating extracellular amastigotes from background noise (AUC = 0.672). Despite these challenges, the YOLOv8 model effectively minimized human variability in drug screening, providing a reliable and efficient tool for the quantification and categorization of Leishmania amastigotes in drug discovery efforts. While further refinements are required to resolve misclassification issues, the model demonstrates significant potential in enhancing both accuracy and throughput in preclinical assays.

Integration of Morphology‐Based and Molecular Techniques to Study the Most Significant Diatom Order (Thalassiosirales) of Phytoplankton in the River Danube

ABSTRACT Metabarcoding is a rapidly developing field for studying aquatic life and provides a promising alternative to microscopy. However, accurate assessments require that database errors and species boundaries be addressed, yet the use of only a short gene sequences in metabarcoding, may be insufficient for accurate species identification. This study examines the potential of metabarcoding in replacing traditional microscopic methods in planktic diatom identification. Phytoplankton samples were collected monthly from May 2021 to April 2022 at 13 sites on the Hungarian section of the River Danube. Environmental variables were measured, and electron microscopy and metabarcoding analyses were conducted. Both morphological and DNA analysis methods were used to study the Thalassiosirales order. Although there was some overlap in the taxa identified by both methods, there were also discrepancies, with certain taxa detected exclusively by one method. P‐distance analysis and BLAST search were used to correct misidentifications, revealing mismatches between database sequences and observed species. Phytoplankton community exhibited varying temperature and nutrients optima and tolerance ranges, which influenced their distribution patterns. During spring, diatoms—particularly Thalassiosirales—dominated the phytoplankton, with proportions decreasing in summer. Algal biomass in the Danube was highest in March, decreasing sharply by the end of summer and remaining low until the end of the growing season, the decrease relating to changes in the TN:TP ratio, which was very low in the warm water period (mostly below 10), leading to nitrogen limitation. Discrepancies between bioinformatic analysis and SEM observations revealed errors in the reference database. Our results clarify the functional group (FG) classification, which is important for both ecological status assessment and understanding of ecosystem functioning. Temperature is one of the most fundamental drivers of microbial nitrogen dynamics in rivers. Global warming is driving up the average temperature of the Danube, creating more favourable conditions for denitrification since the speed of microbial processes is higher in warm water. The increasingly common nitrogen limitation could potentially limit algal growth. Environmental factors, especially temperature and nutrient concentrations, significantly influenced the Danube's phytoplankton communities, with implications for ecosystem health and water quality assessment. Integrated approaches combining molecular techniques with traditional morphological analysis are needed for comprehensive ecological assessments, but accurate species identification and ecological status assessment require completeness in reference databases and the correction of database errors.

A dataset on environmental DNA, bacterio-, phyto- and zooplankton from an emerging periglacial lagoon in Svalbard, Arctic.

Over the last few decades, climate change in Svalbard (European Arctic) has led to the emergence and growth of periglacial coastal lagoons in the place of retreating glaciers. In these emerging water bodies, new ecosystems are formed, consisting of elements presumably entering the lagoon from the melting glacier, the surrounding tundra water bodies and the coastal ocean. The data presented here were collected from an emerging lagoon in the western region of Spitsbergen, Svalbard, situated between the retreating Eidembreen Glacier and Eidembukta Bay in 2022-2023. The current size of the lagoon area is approximately 6 square kilometers. The sampling was carried out at 26 sites across various sections of the lagoon, spanning from close proximity to the glacier to the furthest point away. The dataset contains the results of bacterioplankton (total cell concentration and carbon biomass), phytoplankton (taxonomic composition, cell size for selected taxa, abundance, biomass and carbon biomass), zooplankton (taxonomic composition, abundance), and environmental DNA (eDNA) metabarcoding. The dataset will be utilized to provide a comprehensive description of the structure of the lagoon ecosystem. It will also facilitate a comparison of its various parts, which vary in terms of their age of origin, i.e., release from the glacier. Additionally, the dataset will aid in the understanding of the intricate interactions between the freshwater and marine elements of the ecosystem. It can be used for comparative analysis of biodiversity assessment using eDNA and traditional microscopy methods in the identification of phyto- and zooplankton. Furthermore, these data can be utilized for environmental monitoring, tracing the temporal shifts and conducting comparative analysis of periglacial lagoons that are emerging in various regions of Svalbard as a result of climate change.

A RAPID DIAGNOSTIC PCR ASSAY FOR THE DETECTION OF SCHISTOSOMA MANSONI IN THEIR SNAIL VECTORS.

Accurate detection of schistosome infections in snails is vital for epidemiologic and laboratory studies. Traditional microscopy methods to detect schistosomes in snails are hindered by long prepatent periods and snail survivorship, leading to inaccurate assessment of infections. A rapid, multiplexed PCR assay targeting Biomphalaria sudanica or Biomphalaria glabrata (internal control) and Schistosoma mansoni DNA is described. The method takes less than 90 min starting from extracted snail DNA and is successful at amplifying schistosome DNA in snail tissue as soon as 30 min following exposure. Accurate measures of schistosome infection success in snails (compatibility) are possible by 4-7 days postexposure.

Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization.

Holotomograhic microscopy (HTM) has emerged as a non-invasive imaging technique that offers high-resolution, quantitative 3D imaging of biological samples. This study explores the application of HTM in examining endothelial cells (ECs). HTM overcomes the limitations of traditional microscopy methods in capturing the real-time dynamics of ECs by leveraging the refractive index (RI) to map 3D distributions label-free. This work demonstrates the utility of HTM in visualizing key cellular processes during endothelialization, wherein ECs anchor, adhere, migrate, and proliferate. Leveraging the high resolution and quantitative power of HTM, we show that lipid droplets and mitochondria are readily visualized, enabling more comprehensive studies on their respective roles during endothelialization. The study highlights how HTM can uncover novel insights into EC behavior, offering potential applications in medical diagnostics and research, particularly in developing treatments for cardiovascular diseases. This advanced imaging technique not only enhances our understanding of EC biology but also presents a significant step forward in the study of cardiovascular diseases, providing a robust platform for future research and therapeutic development.

Enhancing mosquito classification through self-supervised learning

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model’s overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies.

Optimized expansion microscopy reveals species-specific spindle microtubule organization in Xenopus egg extracts.

The spindle is a key structure in cell division as it orchestrates the accurate segregation of genetic material. While its assembly and function are well-studied, the mechanisms regulating spindle architecture remain elusive. In this study, we investigate the differences in spindle organization between Xenopus laevis and Xenopus tropicalis, leveraging expansion microscopy (ExM) to overcome the limitations of conventional imaging techniques. We optimized an ExM protocol tailored for Xenopus egg extract spindles, improving upon fixation, denaturation and gelation methods to achieve higher resolution imaging of spindles. Our protocol preserves spindle integrity and allows effective pre-expansion immunofluorescence. This method enabled detailed analysis of the differences in microtubule organization between the two species. X. laevis spindles overall exhibited a broader range of bundle sizes, while X. tropicalis spindles contained mostly smaller bundles. Moreover, while both species exhibited larger bundle sizes near and at the spindle center, X. tropicalis spindles otherwise consisted of very small bundles, and X. laevis spindles medium-sized bundles. By enhancing resolution and minimizing distortions and fixation artifacts, our optimized ExM approach offers new insights into spindle morphology and provides a robust tool for studying the structural intricacies of these large cellular assemblies. This work advances our understanding of spindle architecture and opens up new avenues for exploring underlying mechanisms.

DETECTION OF CRYPTOSPORIDIUM SPECIES IN CATTLE AND HANDLERS BY MOLECULAR TECHNIQUE IN WASIT PROVINCE

I collected 300 cow feces samples (130 males - 170 females) and used molecular analysis to detect the parasite. The results showed significant differences (p ≤ 0.05) between the infection rate using particle technology of 38% (38/100) and 41% (41/100) in cows and breeders, respectively, while traditional microscopic methods showed 21% (21/100) and 17% (17/100) in cows and breeders, respectively. DNA sequence analyzes of 10 isolates from infected cows identified four species of the Cryptosporidium parasite genus which included: C. parvum (6/10), C. andersoni (2/10), C. bovis (1/10) and C. ryanae (1/10. The species C. andersoni, C. parvum, C. bovis and C. ryanae have been genetically confirmed through DNA sequence analysis, and it is considered the first recording of these species in local cattle in Iraq. In breeders, three types of the Cryptosporidium parasite were recorded through DNA sequence analysis in 10 isolates from infected individuals, which included: C. parvum (2/10), C. andersoni (5/10) and C. hominis (3/10). It is considered the first recording of this species among breeders in Iraq.

High-throughput detection of parasites and ova in stool using the fully automatic digital feces analyzer, orienter model fa280

BackgroundIntestinal parasitic infections can harm health by causing malnutrition, anemia, impaired growth and cognitive development, and alterations in microbiota composition and immune responses. Therefore, it is crucial to examine stool samples to diagnose parasitic infections. However, the traditional microscopic detection method is time-consuming, labor-intensive, and dependent on the expertise and training of microscopists. Hence, there is a need for a low-complexity, high-throughput, and cost-effective alternative to labor-intensive microscopic examinations.MethodsThis study aimed to compare the performance of a fully automatic digital feces analyzer, Orienter Model FA280 (People’s Republic of China) with that of the formalin-ethyl acetate concentration technique (FECT). We assessed and compared the agreement between the FA280 and the FECT for parasite detection and species identification in stool samples. The first part of the study analyzed 200 fresh stool samples for parasite detection using the FECT and FA280. With the FA280, the automatic feces analyzer performed the testing, and the digital microscope images were uploaded and automatically evaluated using an artificial intelligence (AI) program. Additionally, a skilled medical technologist conducted a user audit of the FA280 findings. The second set of samples comprised 800 preserved stool samples (preserved in 10% formalin). These samples were examined for parasites using the FECT and FA280 with a user audit.ResultsFor the first set of stool samples, there was no statistically significant difference in the pairwise agreements between the FECT and the FA280 with a user audit (exact binomial test, P = 1). However, there were statistically significant differences between the pairwise agreements for the FECT and the FA280 with the AI report (McNemar’s test, P < 0.001). The agreement for the species identification of parasites between the FA280 with AI report and FECT showed fair agreement (overall agreement = 75.5%, kappa [κ] = 0.367, 95% CI 0.248–0.486). On the other hand, the user audit for the FA280 and FECT showed perfect agreement (overall agreement = 100%, κ = 1.00, 95% CI 1.00–1.00). For the second set of samples, the FECT detected significantly more positive samples for parasites than the FA280 with a user audit (McNemar’s test, P < 0.001). The disparity in results may be attributed to the FECT using significantly larger stool samples than those used by the FA280. The larger sample size used by the FECT potentially contributed to the higher parasite detection rate. Regarding species identification, there was strong agreement between the FECT and the FA280 with a user audit for helminths (κ = 0.857, 95% CI 0.82–0.894). Similarly, there was perfect agreement for the species identification of protozoa between the FECT and the FA280 with user audit (κ = 1.00, 95% CI 1.00–1.00).ConclusionsAlthough the FA280 has advantages in terms of simplicity, shorter performance time, and reduced contamination in the laboratory, there are some limitations to consider. These include a higher cost per sample testing and a lower sensitivity compared to the FECT. However, the FA280 enables rapid, convenient, and safe stool examination of parasitic infections.Graphical

Hyperspectral Mapping of Human Primary and Stem Cells at Cell-Matrix Interfaces.

Extracellular matrices interface with cells to promote cell growth and tissue development. Given this critical role, matrix mimetics are introduced to enable biomedical materials ranging from tissue engineering scaffolds and tumor models to organoids for drug screening and implant surface coatings. Traditional microscopy methods are used to evaluate such materials in their ability to support exploitable cell responses, which are expressed in changes in cell proliferation rates and morphology. However, the physical imaging methods do not capture the chemistry of cells at cell-matrix interfaces. Herein, we report hyperspectral imaging to map the chemistry of human primary and embryonic stem cells grown on matrix materials, both native and artificial. We provide the statistical analysis of changes in lipid and protein content of the cells obtained from infrared spectral maps to conclude matrix morphologies as a major determinant of biochemical cell responses. The study demonstrates an effective methodology for evaluating bespoke matrix materials directly at cell-matrix interfaces.

The Seroepidemiology of Strongyloides stercoralis Infection in Jamaica.

Strongyloides stercoralis is a soil-transmitted helminth which is distributed predominately throughout tropical and subtropical regions and is considered a neglected tropical disease. Due to low larval output, traditional microscopic methods lack sensitivity, especially in areas of low endemicity. Serological assays present an opportunity to study the epidemiology of S. stercoralis in areas of low endemicity such as Jamaica. The current study evaluated the seroprevalence of S. stercoralis in a selected subpopulation in Jamaica. An analysis was conducted on 311 archived serum samples previously submitted for investigating viral infections during a fever epidemic between 2014 and 2015. Randomly selected, anonymized sera were tested for the presence of S. stercoralis IgG antibodies using the AccuDiag Strongyloides IgG ELISA Kit. Data including age, sex, clinical diagnosis, and the geographic location of sample submission were recorded to delineate trends in demographic variables. The seropositivity rate of S. stercoralis was 15.43%. The rate among females and males was 16.45% and 14.47%, respectively (χ 2 = 0.2339, p=0.629). The highest rate was found in middle adulthood (31-50 years) (26.53%; 13/49). The seroprevalence of S. stercoralis was significantly highest in a rural Regional Health Authority (33.33%; 14/42) and least within an urban Health Authority (9.71%; 17/175). Exposure to S. stercoralis appears to be highest in the rural Regional Health Authorities with an island-wide exposure rate of 15.43%. The rapid ELISA testing method for the detection of IgG antibodies to S. stercoralis used in this study may be useful as part of a combined approach to elucidate the epidemiology of this soil-transmitted parasite in Jamaica.

Environmental DNA in a Biofilm Can Be Used to Assess Diatom Ecological Health in Stream Water Ecology

In urban and agricultural streams, assessing aquatic ecosystem health is critical due to widespread pollution. Traditional methods for evaluating attached diatoms crucial for ecosystem monitoring face limitations such as species misidentification and sample damage. This study was conducted in the Miho River within the Geum River system and highlights the effectiveness of environmental DNA (eDNA) techniques for more accurate and efficient genetic-based analysis than conventional microscopic analysis methods. When eDNA-based assessments were compared with traditional microscopic methods, this study found that eDNA analysis often revealed poorer ecosystem health. Notably, eDNA assessments showed a stronger correlation with phosphorus concentrations, underlining their precision and importance in ecological studies. These findings suggest that eDNA has potential as a valuable tool for comprehensive biomonitoring. However, the use of international genetic barcode databases in eDNA analysis could lead to the identification of unrecorded species in Korea. Therefore, this study recommends developing a localized genetic barcode database and constructing eDNA information through meta-barcoding, focusing on native species. This approach is crucial for enhancing the accuracy and applicability of eDNA-based health assessments in national biomonitoring efforts.

Dientamoeba fragilis associated with microbiome diversity changes in acute gastroenteritis patients.

This study examined the correlation between intestinal protozoans and the bacterial microbiome in faecal samples collected from 463 patients in New Zealand who were diagnosed with gastroenteritis. In comparison to traditional microscopic diagnosis methods, Multiplexed-tandem PCR proved to be more effective in detecting intestinal parasites. Among the identified protozoans, Blastocystis sp. and Dientamoeba fragilis were the most prevalent. Notably, D. fragilis was significantly associated with an increase in the alpha-diversity of host prokaryotic microbes. Although the exact role of Blastocystis sp. and D. fragilis as the primary cause of gastroenteritis remains debatable, our data indicates a substantial correlation between these protozoans and the prokaryote microbiome of their hosts, particularly when compared to other protists or patients with gastroenteritis but no detectable parasitic cause. These findings underscore the significance of comprehending the contributions of intestinal protozoans, specifically D. fragilis, to the development of gastroenteritis and their potential implications for disease management.

High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.

High-throughput digital pathology offers considerable advantages over traditional semiquantitative and manual methods of counting pathology. We used brain tissue from 5 clinical-pathologic cohort studies of aging; the Religious Orders Study, the Rush Memory and Aging Project, the Minority Aging Research Study, the African American Clinical Core, and the Latino Core to (1) develop a workflow management system for digital pathology processes, (2) optimize digital algorithms to quantify Alzheimer disease (AD) pathology, and (3) harmonize data statistically. Data from digital algorithms for the quantification of β-amyloid (Aβ, n = 413) whole slide images and tau-tangles (n = 639) were highly correlated with manual pathology data (r = 0.83 to 0.94). Measures were robust and reproducible across different magnifications and repeated scans. Digital measures for Aβ and tau-tangles across multiple brain regions reproduced established patterns of correlations, even when samples were stratified by clinical diagnosis. Finally, we harmonized newly generated digital measures with historical measures across multiple large autopsy-based studies. We describe a multidisciplinary approach to develop a digital pathology pipeline that reproducibly identifies AD neuropathologies, Aβ load, and tau-tangles. Digital pathology is a powerful tool that can overcome critical challenges associated with traditional microscopy methods.

Development of a Droplet Digital PCR Assay for Detection and Quantification of Stubby Root Nematode, Paratrichodorus allius, in Soil.

The stubby root nematode Paratrichodorus allius is an important plant-parasitic nematode species within the Trichodoridae family. It can directly harm the plants by feeding on the roots or indirectly by transmitting Tobacco rattle virus. These nematodes are mostly diagnosed either by traditional microscopic methods or a polymerase chain reaction (PCR)-based method. Droplet digital PCR (ddPCR) is a novel PCR technique which is sensitive and precise in quantifying DNA templates of the test samples. In this study, we developed a ddPCR assay to detect and quantify P. allius in soil. The specificity and sensitivity of the assay was first determined using P. allius nematode DNA or DNA from sterilized soil artificially inoculated with P. allius, and the assay was used to quantify P. allius populations in field soils. The assay did not detect nematodes other than P. allius, thus showing high specificity. It was able to detect P. allius equivalent to a 0.01 and 0.02 portion of a single nematode in soil DNA and nematode DNA extracts, respectively. Highly linear relationships between DNA copy numbers from ddPCR and serial dilutions of known concentrations were observed with DNA from P. allius nematodes (R2 = 0.9842) and from artificially infested soil (R2 = 0.9464). The P. allius populations from field soils determined by ddPCR were highly correlated with traditional microscopic counts (R2 = 0.7963). To our knowledge, this is the first report of applying ddPCR to detect and quantify stubby root nematode in soil. The results of this study support the potentiality of a ddPCR assay as a new research tool in diagnostics of plant-parasitic nematodes.

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

AbstractThe emergence of smart cities is set to transform transportation systems by leveraging real‐time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real‐time traffic conditions. A framework for near‐real‐time city‐scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi‐level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high‐dimensional real‐time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.

Mold in Paradise: A Review of Fungi Found in Libraries.

Libraries contain a large amount of organic material, frequently stored with inadequate climate control; thus, mold growth represents a considerable threat to library buildings and their contents. In this essay, we review published papers that have isolated microscopic fungi from library books, shelving, walls, and other surfaces, as well as from air samples within library buildings. Our literature search found 54 published studies about mold in libraries, 53 of which identified fungi to genus and/or species. In 28 of the 53 studies, Aspergillus was the single most common genus isolated from libraries. Most of these studies used traditional culture and microscopic methods for identifying the fungi. Mold damage to books and archival holdings causes biodeterioration of valuable educational and cultural resources. Exposure to molds may also be correlated with negative health effects in both patrons and librarians, so there are legitimate concerns about the dangers of contact with high levels of fungal contamination. Microbiologists are frequently called upon to help librarians after flooding and other events that bring water into library settings. This review can help guide microbiologists to choose appropriate protocols for the isolation and identification of mold in libraries and be a resource for librarians who are not usually trained in building science to manage the threat molds can pose to library holdings.

Assessing the trophic ecology of Southern Ocean Myctophidae: the added value of DNA metabarcoding

Lanternfishes (Myctophidae) are key components of mesopelagic fish communities globally. In the Southern Ocean, incomplete information on myctophid diets limits our understanding of their energetics, interactions, and wider ecosystem impact. Traditional microscopic methods of diet analysis have relatively coarse prey resolution and possible taxonomic and observer biases. Deoxyribonucleic acid (DNA) metabarcode sequencing promises higher taxonomic and temporal resolution, but uncertainty remains in comparing this with microscopy-based analyses. Here, we applied 18S DNA metabarcode sequencing to stomach contents from 20 Electrona antarctica individuals that had previously been examined via microscopic analysis. Across all fish analysed, crustacean and gastropod taxa dominated the prey identified via both methods, with broad agreement between methods on the relative abundance of different prey items. DNA metabarcode sequencing recovered greater taxonomic diversity and resolution, particularly for soft-bodied prey items and small crustaceans. DNA sequencing results also more clearly differentiated diet between individuals collected from different environments. Overall, our findings illustrate how DNA-based methods are complementary to, and consistent with, traditional methods and can provide additional, high-resolution data on a range of trophic interactions.

Quantification of arbuscular mycorrhizal fungi root colonization in wheat, tomato, and leek using absolute qPCR

Arbuscular mycorrhizal fungi (AMF) form symbioses with most terrestrial plants and are known to have a positive effect on plant growth and health. Different methodologies have been developed to assess the AMF-plant symbiosis. The most applied method, which involves staining of roots and microscopic observation of the AMF structures, is tedious and time-consuming and the results are highly dependent on the observer. Using quantitative polymerase chain reaction (qPCR) to quantify AMF root colonization represents a reliable, high-throughput technique that allows the assessment of numerous samples. Quantification with qPCR can be performed through two methods: relative quantification and absolute quantification. In relative quantification, the target gene is normalized with a reference gene. On the other hand, absolute quantification involves the use of a standard curve, for which template DNA is serially diluted. In a previous paper, we validated the primer pair AMG1F and AM1 for a relative quantification approach to assess AMF root colonization in Petunia. Here, we tested the same primers with an absolute quantification approach and compared the results with the traditional microscopy method. We evaluated the qPCR method with three different crops, namely, wheat (cv. Colmetta and Wiwa), tomato, and leek. We observed a strong correlation between microscopy and qPCR for Colmetta (r = 0.90, p < 0.001), Wiwa (r = 0.94, p < 0.001), and tomato (r = 0.93, p < 0.001), but no correlation for leek (r = 0.27, p = 0.268). This highlights the importance of testing the primer pair for each specific crop.

Microscopic Prevalence of Intestinal Parasites in Gastrointestinal Disordered Patients in Wasit Province, Iraq

Background: Intestinal parasites (IPs) have been a big concern for low-income countries as they are the major cause of high morbidity and mortality. Aims: Identification the prevalence of IPs in gastrointestinal disordered patients, with estimation relationship of these pathogens to hematological and some epidemiological parameters. Materials and Methods: A total of 200 individuals with a history of abdominal pain, anal itching, colic, diarrhea, dysentery, and nausea or vomiting were selected, and asked to obtain of fecal and blood samples. Traditional microscopic methods were used to detect the parasites; while, Automated Hematology Analyser was served for blood counting. Results: There were 59.5% positive patients with intestinal parasites; in which, 68.91% with single infection (particularly Entamoeba histolytica, Enterobius vermicularis and Giardia lamblia), and 31.09% with mixed infections (particularly Enterobius vermicularis and Ascaris lumbricoides as well as Giardia lamblia and Entamoeba histolytica). Concerning risk factors, significant higher values of positivity were showed in patients of 3-7 years old, males more than females, and in rural more than urban areas. Regarding hematology, insignificant variation was recorded between infested and non-infested individuals, but macrocytic type of RBCs was highly prevalent in infested individuals. Subsequently, macrocytic type was increased significantly prevalent in patients with Ascaris lumbricoides; while, normocytic and microcytic types were seen in Balantidium coli. In patients with mixed infections, macrocytic type was recorded significantly in patients having Enterobius vermicularis and Ascaris lumbricoides as well as in those having Giardia lamblia and Entamoeba histolytica; while, microcytic type was identified in patients with Enterobius vermicularis and Ascaris lumbricoides, and those with Enterobius vermicularis, Giardia lamblia and Balantidium coli. However, normocytic type was seen significantly in patients with Entamoeba histolytica and Balantidium coli; Entamoeba histolytica and Enterobius vermicularis; and Giardia lamblia and Entamoeba histolytica. Conclusions: The findings imply that intestinal parasites among gastrointestinal patients were prevalent as single or mixed infections. Age, sex and areas were related significantly with the existence of parasitic infection, in addition to the effect of these parasites on shape of RBCs. However, annual surveillance appears of great importance to detect the prevalence of intestinal parasites in different individuals.