Blood Smear Microscopy Research Articles

RDW-SD - A more sensitive marker of anisocytosis than RDW-CV in patients with macrocytosis

Background: Anisocytosis is a variation in RBC size. This can be conventionally observed on peripheral blood smear microscopy and also by red cell distribution width, a RBC parameter obtained on automated hematology analyzers. Red cell distribution width is considered a quantitative measure of anisocytosis and does not carry subjective variation associated with microscopy and it is reported as RDW-CV % and RDW-SD. Both these values are calculated differently. Although it is one of the most frequently used hemogram parameters for anisocytosis, it is not well understood. This study aimed to compare the sensitivity of RDW-CV and RDW-SD as markers of anisocytosis in macrocytosis. Material and methods: This was a cross-sectional prospective observational study conducted in a laboratory of a tertiary care hospital in western India. The sample size of 138 was calculated at a precision 0.1 and 90% confidence interval. 138 blood samples each from patients with macrocytosis and normocytosis control group were analysed. Results: The range for RDW-CV & RDW-SD was 12.59%-15.13% & 40.05-47.81fl respectively in our population. Gender-wise distribution showed 72% male & 28% female. The Z score value of 4.12 (>1.96 is significant) reflected a significant difference in anisocytosis detection using RDW-SD compared to RDW-CV in macrocytosis. Conclusions: RDW-SD is a more sensitive marker of RBC anisocytosis than RDW-CV in macrocytosis.

Molecular and microscopic detection of haemoprotozoan diseases in dogs from Haryana, India.

Haemoparasitic infections are frequently observed in dogs from tropical regions, including India. The present investigation combined microscopic blood smear examination and PCR assays to assess the occurrence of canine tick-borne diseases (CTBD) from suspected dogs in and around Hisar, Haryana. Using the Giemsa-stained peripheral thin blood smear examination, 15 (12.5%) of the 120 dogs were infected with CTBD, with 5.8%, 3.3%, 2.5%, and 0.8% dogs testing positive for Hepatozoon canis, Ehrlichia canis, Babesia vogeli, and Babesia gibsoni, respectively. Using the PCR assay, CTBD was found to be 64.16% (77/120) in examined dogs. Of the 77 PCR-positive canines, 56 were infected with a single haemoparasite, while 21 were infected with two or more species. H. canis was the most abundant tick-borne pathogen, representing 35%, followed by E. canis 25.8%, B. vogeli 20%, and B. gibsoni 2.5%. The most common co-infection was with H. canis along with E. canis (7.5%). The PCR assay was proven to be more efficient for detecting haemoparasites in dogs compared to blood smear examinations. The study suggests that canine tick-borne diseases are common in Haryana and recommends using PCR-based molecular tests in addition to conventional microscopic examination to diagnose these infections for effective treatment and management of infected canines.

Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.

Ensemble learning using Gompertz function for leukemia classification

Leukemia, a life-threatening disease primarily affecting children below 15 and individuals aged 55 and above, is characterized by the overproduction of abnormal blood cells. Timely detection of leukemia plays a crucial role in significantly reducing the associated mortality rate. Given the recent developments in deep learning, the development of automated leukemia detection techniques using blood smear images has emerged as an important research area. This manuscript introduces a novel algorithm to classify microscopic blood smear images into five categories: AML, CML, ALL, CLL and normal. The study investigates the use of pre-trained convolutional neural networks powered by a fuzzy ensemble-based classifier. The fuzzy-based ensemble learning technique employs adaptable weights according to the confidence scores derived from the base learner models instead of using fixed weights for the base learner models. Gompertz function with parameters, α, β and γ are considered here. The parameters of the Gompertz function are further optimized using a grid search approach to achieve the most favorable classification outcomes. Conducting a 5-class classification, the proposed algorithm achieved an accuracy of 88.80%, surpassing the performance of traditional ensemble techniques such as average, weighted average and majority voting by a significant margin of 1.83%, 1.83% and 5.71%, respectively.

High prevalence and risk factors associated with asymptomatic malaria among children in Nkwen village, Northwest Region, Cameroon

BackgroundIn endemic locations, asymptomatic malaria is a major contribution to the rise in clinical malaria. In order to achieve the goal of interrupting malaria transmission, control programmes should take into consideration carriers of asymptomatic malaria parasite. Hence, the purpose of this study was to look at the prevalence and risk factors of asymptomatic malaria in children in Nkwen village.MethodsUsing a cross-sectional and community-based design, conducted between June and December 2022, a total of 246 children were enrolled after obtaining informed and signed consent from parents and/ or guardians. To collect data, pre-tested, closed-ended, structured questionnaires were used, ensuring the accuracy and reliability of the information gathered. A digital thermometer with infrared forehead capability was used to take participants’ body temperature, providing precise measurements and respondents with temperature < 37.5 °C, and not presenting any symptoms or indicators of malaria were included in the study, ensuring the focus on asymptomatic cases. Blood samples were collected by venipuncture and screened for the presence of asymptomatic parasitaemia using blood smear microscopy and nested polymerase chain reaction (PCR). Data was entered into Microsoft Excel worksheet and analysed using SPSS version 23 software. Logistic regression models were carried out to explore the risk factors associated with asymptomatic malaria at household and individual levels and statistically significant association was considered at a p-value < 0.05.ResultsA total of 246 healthy children were examined for asymptomatic malaria infection using microscopy and PCR. Of the examined children, 65.9% (162/246) were malaria positive by PCR while 59.3% (146/246) were malaria positive by microscopy. Considering both diagnostic methods, females had a greater prevalence of asymptomatic malaria than males. In logistic analysis, the risk of developing asymptomatic malaria was associated several factors: previous malaria episode (OR = 5.14; CI 2.94–9.01), family history of malaria (OR = 3.86; CI 2.21–6.74), homestead near swampy areas (OR = 3.56; CI 2.10–10.61), non-utilization of insecticide treated nets (OR = 4.36; CI 2.53–7.5), non-usage of indoor residual spray (IRS) (OR = 6.67; CI 3.75–11.86) and opened eaves (OR = 3.86; CI 2.21–6.74). No associations were established between asymptomatic malaria and the following factors: age group (p > 0.05), gender (p > 0.05) and type of wall construction (p > 0.05).ConclusionThe high rate of asymptomatic malaria in this study is a significant problem and may jeopardize the current malaria control effort. Personal and house-level risk factors were linked with asymptomatic malaria. Therefore, it should be considered when evaluating and restructuring more successful malaria elimination tactics to accomplish the intended goals of malaria control.

Spatiotemporal analysis of malaria in the state of Mato Grosso-Legal Amazon, Brazil: identification of high-risk áreas

The objective of the study was to analyze temporal and geographic data to identify high-risk areas for malaria occurrence in the state of Mato Grosso within the Legal Amazon. High-risk areas were identified through scan statistics, and the relative risk (RR) was characterized using cluster maps. Statistical temporal trend analysis was conducted using Prais-Winsten autoregression. A total of 20,819 cases were identified, with higher incidence rates among men. Malaria was predominantly diagnosed via thick blood smear microscopy, with Plasmodium vivax being the most prevalent species. The average incidence ranged from 0.01 to 2,849.85, with higher averages observed in the North, West, and Northwest regions. The Global Moran analysis revealed a positive correlation (0.47) between incidence rates and regions with a higher number of cases, while Local Moran identified distinct infection clusters. Eight risk clusters were delineated using scan statistics, with the highest RR recorded at 90.09. The spatiotemporal scan classified three statistically significant high-risk clusters exhibiting stationary temporal trends. These findings highlight the delineation of high-risk areas for malaria infection in Mato Grosso.

Acute Leukemia Subtype Recognition in Blood Smear Images with Machine Learning

Acute leukemia is a swiftly progressing blood cancer affecting white blood cells which poses a significant threat to the immune system and often leads to fatal outcomes if not detected and treated promptly. The current manual diagnostic method, being time-consuming and prone to errors, necessitates an urgent shift toward a comprehensive automated system. This paper presents an innovative approach to automatically identify acute leukemia cells and their subtypes by analyzing microscopic blood smear images. The proposed methodology involves the segmentation of clustered lymphocytes, isolation of nuclei, and extraction of diverse features from each nucleus. A random forest classifier is then trained to categorize nuclei into healthy or cancerous, with further precision in classifying cancerous nuclei into specific subtypes. The method achieves an impressive 97% accuracy across all evaluations, holding profound implications for pathologists and medical practitioners in their decision-making processes

Malaria prevalence and associated population and ecological risk factors among women and children under 5 years in Rwanda

BackgroundMalaria continues to pose a substantial public health concern in Rwanda, despite substantial progress in recent years. Little is known about effect of ecological factors and their interaction in malaria transmission. Understanding the prevalence and identifying risk factors, both population-based and ecological such as zooprophylaxis and irrigation are crucial for targeted intervention strategies. MethodsThis study analyzed the 6th iteration of the Demographic and Health Survey conducted in Rwanda between 2019 and 2020. The study employed a nationally representative sample, utilizing rapid diagnostic tests and blood smear microscopy to determine malaria prevalence among women and under 5 years old children. Logistic regression analysis was used in R version 4.3.1 to evaluate population and ecological risk factors that are associated with malaria prevalence. Additionally, interactive effects of ecological factors on malaria were evaluated. ResultsThe analysis revealed a notable malaria prevalence in Rwanda, emphasizing the continued significance of malaria control efforts. Approximately 1.79 % of the population tested positive for malaria. Proximity to irrigation sites and lowland were identified as significant risk factors to malaria with adjusted odds ratio (AOR) 1.47(1.00–2.15) and AOR 5.44(4.01–8.61) respectively however cattle ownership exhibited a protective effect AOR 0.41(0.23–0.72). Interactive effects of livestock and irrigation on malaria prevalence were revealed. Additionally, population-based risk factors, including age, household wealth, utilization of Insecticide Treated Nets, were associated with varying malaria risks. ConclusionThis study underscores the persistent challenge of malaria in Rwanda and the importance of tailored intervention strategies. To effectively combat malaria, efforts must consider the interplay of ecological factors, such as high cattle density, and demographic factors, targeting high-risk populations, especially those living in proximity to lowlands and irrigation areas. These findings provide critical insights for advancing malaria elimination efforts in Rwanda and serve as a basis for comprehensive public health planning and action.

Correlation Between I/T Ratio, S100B, Interleukin-10 Serum, and NEWS2 Score in Sepsis

Immature/total neutrophils (I/T) ratio is a simple and inexpensive method used as an early sign of bacterial infection. Meanwhile, the S100B protein is found in glial cells and functions as a neuroprotection. Then, the interleukin-10 cytokine acts as an anti-inflammatory cytokine during infection. In addition, the NEWS2 score is used to see the early and fast changes in patients with sepsis. The purpose of the study was to prove the correlation between the I/T ratio, S100B, IL-10, and NEWS2 score in patients with sepsis. A cross-sectional study was conducted on 34 patients with sepsis from September 2020 to February 2021. I/T ratio was calculated in 100 leukocytes from microscopic blood smear preparation. Levels of S100B and IL-10 serum were measured using the ELISA method. Pearson correlation test was used for normally distributed data, and the Spearman correlation test was utilized for abnormally distributed data. Correlation test between I/T ratio, S100B, and IL-10 with NEWS2 score respectively showed values of r=0.58; P=0.01, r=0.36; P=0.03, and r=0.39; P=0.02, in which P&lt;0.05. There was a moderate positive correlation between I/T ratio and NEWS2 score, a weak positive correlation between S100B and NEWS2 score, and a weak positive correlation between IL-10 and NEWS2 score.

Exploring the Impact of Batch Size on Deep Learning Artificial Intelligence Models for Malaria Detection.

Introduction Malaria is a major public health concern, especially in developing countries. Malaria often presents with recurrent fever, malaise, and other nonspecific symptoms mistaken for influenza. Light microscopy of peripheral blood smears is considered the gold standard diagnostic test for malaria. Delays in malaria diagnosis can increase morbidity and mortality. Microscopy can be time-consuming and limited by skilled labor, infrastructure, and interobserver variability. Artificial intelligence (AI)-based tools for diagnostic screening can automate blood smear analysis without relying on a trained technician. Convolutional neural networks (CNN), deep learning neural networks that can identify visual patterns, are being explored for use in abnormality detection in medical images. A parameter that can be optimized in CNN models is the batch size or the number of images used during model training at once in one forward and backward pass. The choice of batch size in developing CNN-based malaria screening tools can affect model accuracy, training speed, and, ultimately, clinical usability. This study explores the impact of batch size on CNN model accuracy for malaria detection from thin blood smear images. Methods We used the publicly available "NIH-NLM-ThinBloodSmearsPf" dataset from the United States National Library of Medicine, consisting of blood smear images forPlasmodium falciparum. The collection consists of 13,779 "parasitized" and 13,779 "uninfected" single-cell images. We created four datasets containing all images, each with unique randomized subsets of images for model testing. Using Python, four identical 10-layer CNN models were developed and trained with varying batch sizes for 10 epochs against all datasets, resulting in 16 sets of outputs. Model prediction accuracy, training time, and F1-score,an accuracy metric used to quantify model performance, were collected. Results All models produced F1-scores of 94%-96%, with 10 of 16 instances producing F1-scores of 95%. After averaging all four dataset outputs by batch size, we observed that, as batch size increased from 16 to 128, the average combined false positives plus false negatives increased by 15.4% (130-150), and the average model F1-score accuracy decreased by 1% (95.3%-94.3%). The average training time also decreased by 28.11% (1,556-1,119 seconds). Conclusion In each dataset, we observe an approximately 1% decrease in F1-score as the batch size was increased. Clinically, a 1% deviation at the population level can create a relatively significant impact on outcomes. Results from this study suggest that smaller batch sizes could improve accuracy in models with similar layer complexity and datasets, potentially resulting in better clinical outcomes. Reduced memory requirement for training also means that model training can be achieved with more economical hardware. Our findings suggest that smaller batch sizes could be evaluated for improvements in accuracy to help develop an AI model that could screen thin blood smears for malaria.

An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning

Leukemia, a global health challenge characterized by malignant blood cell proliferation, demands innovative diagnostic techniques due to its increasing incidence. Among leukemia types, Acute Lymphoblastic Leukemia (ALL) emerges as a particularly aggressive form affecting diverse age groups. This study proposes an advanced mechanized system utilizing Deep Neural Networks for detecting ALL blast cells in microscopic blood smear images. Achieving a remarkable accuracy of 97% using MobileNetV2, our system demonstrates high sensitivity and specificity in identifying multiple ALL sub-types. Furthermore, we introduce cutting-edge telediagnosis software facilitating real-time support for clinicians in promptly and accurately diagnosing various ALL subtypes from microscopic blood smear images. This research aims to enhance leukemia diagnosis efficiency, which is crucial for the timely intervention and managing this life-threatening condition.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review

Despite the persistency of World Health Organization to eliminate malaria since 1987, malaria disease continues to pose a significant threat to global health. As the severity of malaria persists over the years, there is a critical need for an automated diagnosis system for more efficient diagnosis and effective treatment administration. To mitigate the increase in mosquito-borne diseases, there has been a heightened interest in the application of Artificial intelligence (AI), specifically deep learning. With the assistance of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework, an extensive review of current state of automated malaria diagnosis systems utilizing machine learning and deep learning approaches was performed across eight scientific databases, with 50 articles shortlisted from the years 2015–2023. Besides, identifying the research gaps, we synthesise the existing literature, analyse the outcomes, and explore the critical parameters that influence model performance. From the review, the prevailing models primarily focus on binary classification while disregarding cross-dataset validations and multi-stage classification. This gap challenges the delivering effective treatments, especially considering potential drug resistance. Established protocols and classification models are needed to anticipate the specific malaria species. The keywords in automated malaria diagnosis that we identified include machine learning, deep learning, transfer learning, and convolutional neural networks. Through examinations of the constraints in current methodologies, we provide valuable suggestions that could propel the field of automated malaria diagnosis. This systematic review provides a comprehensive overview, critical insights, and a roadmap for future research endeavours in this vital domain of healthcare.

Detection of Leukemia Disease Using Image Processing and Machine Learning

Abstract: This paper presents a project that addresses Detection Of Leukemia Disease Using Image Processing and Machine Learning. This research proposes an approach that combines image processing techniques with machine learning algorithms to detect leukemia in microscopic blood smear images at a stage. The proposed method involves enhancing the images through preprocessing identifying the regions of interest through segmentation and extracting features using image processing algorithms. Afterward a machine learning model trained on a dataset of annotated images is employed to classify the samples as either positive or negative, for leukemia. The effectiveness of this approach is evaluated using metrics like accuracy, sensitivity and specificity. Experimental results show promising outcomes in detecting leukemia which could serve as a tool for healthcare professionals, in early diagnosis and treatment planning. By integrating this framework into practice it has the potential to improve efficiency and accuracy in diagnosing leukemia while ultimately leading to patient outcomes and enhanced healthcare management.

Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Challenges of diagnosing severe malaria with complications in adult patients: a case report

BackgroundMalaria is known to be the main cause of death in malaria-endemic areas. The authors report a case of severe malaria in an adult with no history of travel from an endemic area with good outcomes after hospitalization.Case presentationA 46-year-old man was brought to the Emergency Room (ER) because of fever and chills for 6 days. Complaints were accompanied by nausea and vomiting three times a day. The patient also experienced headaches, weakness, coughing, and a runny nose after two days of admission. The patient had no history of traveling from a malaria-endemic area. The patient was transferred from the Emergency Department (ED) to the High Care Unit (HCU), and during 1 day of intensive care at the HCU, there was a clinical deterioration characterized by dyspnea, icteric sclerae, acral edema, tenderness in both calves, and rash in the abdominal area. Due to worsening respiratory function, the patient was placed on a ventilator. During intensive treatment, the patient continued to show deterioration. The clinical findings suggested a possible feature of Weil’s disease or fulminant hepatitis, and although the patient was in intensive care, there was no clinically significant improvement. Furthermore, microscopic blood smear examination and rapid diagnostic tests (RDTs) for malaria were carried out on the 4th day of treatment with negative results. As there was no clinically significant improvement, it was decided to take a blood smear and repeat RDT on the twelfth day, which showed a positive result for falciparum malaria. Subsequently, artesunate was administered intravenously, and the patient’s condition began to improve with a negative parasite count the following day. The patient was discharged in good clinical condition on day 25 of treatment.ConclusionGood quality malaria diagnostic techniques are essential to diagnose malaria. A timely diagnosis of malaria has the potential to save the patient. Because Jakarta is not a malaria endemic area, it was concluded that this case was an introduced malaria case.

Automated Life Stage Classification of Malaria Using Deep Learning

INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low. OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources. METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique. RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score. CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.

Epidemiology and risk factors of malaria among pregnant women attending antenatal clinics in Osun State, Nigeria

Globally, malaria in pregnancy leads to severe complications such as abortion, stillbirth, premature delivery, low birth weight, and fetal underdevelopment. This study examined the prevalence and risk factors of malaria among pregnant women attending antenatal care in Osun State, Nigeria. Between July and December 2020, 555 pregnant women from ten primary health centres in Osogbo, Ejigbo and Ilobu Local Government Areas participated in the study. Employing pre-tested questionnaires, qualitative data on the demographic and risk behaviours of participants were obtained. Malaria prevalence, determined by rapid diagnostic tests and blood smear microscopy was 15% caused by Plasmodium falciparum. The highest malaria prevalence was found among women who didn't use insecticides (23.1%), followed by those who did not use insecticide-treated nets (16.5%). The lowest prevalence was among women in their second and third trimesters who had taken Intermittent Preventive Treatment (IPT) (6.3%). The highest prevalence occurred among women aged 17-24 years (17.4%), while the lowest was among women aged 25-34 years (12.9%). All participants were aware of malaria but their knowledge about transmission (67%), symptoms (64%) and prevention (44%) varied. The study highlights the vulnerability of pregnant women who do not use insecticide-treated nets and emphasizes the need for improved malaria preventive practices. Control efforts should focus on the distribution of insecticide-treated nets, health education during antenatal visits and increased usage of IPT as recommended by the World Health Organization.

Detection of the infective Plasmodium falciparum gametocytes by RT-qPCR assay from a malaria-endemic region of Northeastern India

IntroductionThe diagnosis of infectious reservoirs in malaria (gametocytes) is necessary, especially in low-density infections and asymptomatic malaria patients. The gametocyte stage is a surrogate marker for infection of P. falciparum malaria in healthy individuals. The early detection of infectious gametocytes and treatment will strengthen our efforts in curbing transmission. The nested PCR and real-time quantitative PCR (RT-qPCR) methods have been demonstrated for the diagnosis of infectious gametocyte reservoirs. In this study, RDT, blood smear microscopy, and nested-PCR were used for the detection of P. falciparum and P. vivax, and compared with RT-qPCR detection of Pfg27 gametocyte biomarker gene.MethodsIn the present cross-sectional study, 356 human blood samples were collected from endemic areas of Kokrajhar Assam (asymptomatic and symptomatic malaria patients) for malaria diagnosis.ResultsA total of 8.42%(30/356) incidence of malaria was observed. Malaria patients were observed to be both symptomatic, 80%(24/30; 13Pf+11Pv), and asymptomatic, 20%(6 (4Pf +2Pv)). More than 64%(11/17) of Pf and 92.3%(12/13) of Pv infections were observed in children and the adolescent population (age &amp;lt;20 years) by RDT, microscopy, nested PCR, and RT-qPCR methods. The prevalence of Pf infection was 4.77%(17/356) by RT-qPCR method. Of 16 the Pf positive samples 81.25%(13/16) were symptomatic and 18.75%(3/16) were asymptomatic. One asymptomatic individual was found positive for Pf infection by the RT-qPCR method.ConclusionThe findings from this research study revealed that the routine microscopy and RDT methods are insufficient for detecting all asymptomatic malaria and gametocyte infectious reservoirs. The early detection of infectious P. falciparum gametocytes and the treatment of patients will be helpful in preventing the transmission of malaria.

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images