Detection Of Malaria Parasites Research Articles

Illuminating Malaria: Spectroscopy’s Vital Role in Diagnosis and Research

Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectroscopic methods in malaria research. From the identification of reliable biomarkers to the development of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This review will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leveraging the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global efforts towards the eradication of this devastating disease.

Malaria Infection in relation to parasitological indices, blood group and anaemia in pregnant women attending two health facilities in Adamawa State, Nigeria

Decline of maternal immunity and susceptibility to malaria had posed a detrimental effect on the maternal health and foetal outcomes. A cross-sectional study was carried out in two different secondary health facilities at General Hospital Ganye and Cottage Hospital Toungo with the aim of determining the malaria burden in relation to parasite species, parasite density, blood group and anaemia in pregnant women who booked for antenatal care as first timer irrespective of their gestational stage. A total of 471 randomly selected pregnant women who consented to the study were enrolled for the study. Blood samples were collected from each of the consented pregnant women. Thick and thin blood smear were prepared using Giemsa and examined microscopically to detect malaria parasite and infecting species, respectively. The overall prevalence of malaria was 22.9% (n=108). Plasmodium falciparum and P. malariae were the two malaria species encountered with a prevalence of 78.7% and 19.4%, respectively. Mixed infection had a 1.9% prevalence. Parasite density was recorded as low (67.6%), moderate (11.1%) and severe (21.3%) with a statistical significance (P=0.000). Prevalence of malaria in relation to blood group showed that pregnant women with blood group O (51.9%) had the highest burden of malaria infection and blood group AB (6.5%) had the least, although there was no statistical significance. Non-anaemic pregnant women recorded the highest infection prevalence (37.0%). This study revealed that malaria infection is significantly associated with the infecting parasite species and parasite density. It was discovered that P. falciparum was the most predominant species in the region during the period of this research.

Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images

Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms.

Feeding Behavior and Plasmodium Detection in Anopheles stephensi, a Malaria Vector in District Khyber, Khyber Pakhtunkhwa, Pakistan.

Anopheles stephensi is a significant malaria vector in Pakistan, and understanding its feeding behavior is necessary to control the spread of malaria. However, limited information is available on the host preferences of A. stephensi in Pakistan. Therefore, we aimed to explore the feeding behavior of A. stephensi, a malaria vector, in the District Khyber, Khyber Pakhtunkhwa, Pakistan. A total of 7462 mosquitoes were collected between March and September 2021, with 1674 (22.4%) identified as A. stephensi (952 female and 722 male). Among the female A. stephensi, 495 (52%) were blood-fed. DNA was extracted from the blood-fed female A. stephensi mosquitoes using the Ammonium Acetate Precipitation Method followed by PCR analysis, blood meal sources were identified. Nested PCR on 191 pooled samples was used to detect Plasmodium falciparum and Plasmodium vivax. Cattle blood meals were predominant (73%), followed by human (20%) and chicken (7%), with no dog blood meals detected. All individual mosquito samples were negative for Plasmodium falciparum, while two pooled samples (out of 191) tested positive for P. vivax. A. stephensi in Khyber District primarily displayed anthropophagic feeding behavior, with a small portion of the population infected with P. vivax. The results underscore the importance of targeted vector control strategies, environmental management, community engagement and continuous monitoring to suppress malaria transmission.

Image cropping for malaria parasite detection on heterogeneous data

Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite and transmitted by the bite of an infected female Anopheles mosquito. The parasite multiplies in the liver and then destroys the person's red blood cells until it reaches the severe stage, leading to death. The most used tools for diagnosing this disease are the microscope and the rapid diagnostic test (RDT), which have limitations preventing control of the disease. Computer vision technologies present alternatives by providing the means for early detection of this disease before it reaches the severe stage, facilitating treatment and saving patients. In this article, we suggest deep learning methods for earlier and more accurate detection of malaria parasites with high generalization capabilities using microscopic images of blood smears from many heterogeneous patients. These techniques are based on an image preprocessing method that mitigates some of the challenges associated with the variety of red cell characteristics due to patient diversity and other artifacts present in the data.For the study, we collected 65,970 microscopic images from 876 different patients to form a dataset of 33,007 images with a variety that enables us to create models with a high level of generalization. Three types of convolutional neural networks were used, namely Convolutional Neural Network (CNN), DenseNet, and LeNet-5, and the highest classification accuracy on the test data was 97.50% found with the DenseNet model.

Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.

A lightweight deep learning architecture for malaria parasite-type classification and life cycle stage detection

Malaria is an endemic in various tropical countries. The gold standard for disease detection is to examine the blood smears of patients by an expert medical professional to detect malaria parasite called Plasmodium. In the rural areas of underdeveloped countries, with limited infrastructure, a scarcity of healthcare professionals, an absence of sufficient computing devices, and a lack of widespread internet access, this task becomes more challenging. A severe case of malaria can be fatal within one week, so the correct detection of the malaria parasite and its life cycle stage is crucial in treating the disease correctly. Though computer vision-based malaria detection has been adequately explored lately, the malaria life cycle stage classification is still a relatively unexplored field. In this paper, we introduce a fast and robust deep learning methodology to not only classify the malaria parasite-type detection but also the life cycle stage identification of the infected cell. The proposed deep learning architecture is more than twenty times lighter than the widely used DenseNet and has less than 0.4 million parameters, making it a good candidate to be used in the mobile applications of such economically challenged states for malaria detection. We have used four different publicly available malaria datasets to test the proposed architecture and gained significantly better results than the current state of the art on malaria parasite-type and malaria life cycle classification.

Dépistage du paludisme : quel est l’apport de la biologie moléculaire ?

According to the World Health Organization, Microscopy is the gold standard for diagnosing malaria. However, the performance of this examination depends on the experience of the microscopist and the level of parasitemia. Thus, molecular biology detection of malaria could be an alternative technique. evaluate the contribution of molecular biology in detecting imported malaria. This was a descriptive, prospective study, including all students, from the Monastir region, and foreigners, from countries endemic to malaria. The study period was from September 2020 to April 2021. Each subject was screened for malaria by three methods: direct microscopic detection of Plasmodium, detection of plasmodial antigens, and detection of plasmodial DNA by nested PCR. Among the 127 subjects screened, only one had a positive microscopic examination for Plasmodium falciparum. Among the 126 subjects with a negative microscopic examination, twelve students had a positive nested PCR result, i.e. 9.5%. Molecular sequencing allowed the identification of ten isolates of Plasmodium falciparum, one Plasmodium malariae and one Plasmodium ovale. Our study showed that the results of nested PCR agreed with those of microscopy in 90.6% of cases. Nested PCR seems more sensitive for the detection of low parasitemias. Hence the importance of including molecular biology as a malaria screening tool to ensure better detection of imported cases.

Development and validation of a CNN-based model for Malaria Cell Detection

Malaria remains a great threat to the African continent and the world at large, and accurate Plasmodium detection and treatment are the most effective means of preventing the progression of mild malaria into severe disease and into death finally. However, manual operations and diagnosis face the challenge of individual and geographic heterogeneity. In order to solve this problem, this study proposed a Convolutional Neural Networks (CNN) based model for malaria cell detection, and the model can automatically recognize cell appearance and distinguish infected cells from uninfected cells, leading to an accurate and effective cell classification. The research results showed that CNN has better performance in malaria cell recognition compared with the traditional integrated learning model, and the optimized CNN algorithm achieved an accuracy of 98.29% compared with the average accuracy of integrated learning algorithms of 80.29%, which can effectively solve the problem of wrong and missed detection of in malaria cell detection

Development and validation of a CNN-based model for Malaria Cell Detection

Malaria remains a great threat to the African continent and the world at large, and accurate Plasmodium detection and treatment are the most effective means of preventing the progression of mild malaria into severe disease and into death finally. However, manual operations and diagnosis face the challenge of individual and geographic heterogeneity. In order to solve this problem, this study proposed a Convolutional Neural Networks (CNN) based model for malaria cell detection, and the model can automatically recognize cell appearance and distinguish infected cells from uninfected cells, leading to an accurate and effective cell classification. The research results showed that CNN has better performance in malaria cell recognition compared with the traditional integrated learning model, and the optimized CNN algorithm achieved an accuracy of 98.29% compared with the average accuracy of integrated learning algorithms of 80.29%, which can effectively solve the problem of wrong and missed detection of in malaria cell detection

Variations in Plasma Glucose, Testosterone, and Tumor Necrosis Factor-alpha in Relationship with Polycystic Ovary Syndrome and Viral Seromarkers in Infertile Women: A Case–Control Observational Study

Study Background: Polycystic ovary syndrome (PCOS) is a common cause of infertility in Nigeria which is associated with insulin resistance. Insulin resistance may be induced by elevated inflammatory cytokines, androgen excess (testosterone), and irregular menstruation or anovulation. Aim and Objective: This work was designed to determine possible variations in plasma glucose, testosterone, and tumor necrosis factor-alpha (TNFα) in relationship with PCOS and viral seromarkers in infertile women. Materials and Methods: Two hundred and fourteen (214) primary infertility women with PCOS aged 37 ± 4.93 years diagnosed through ultrasound were recruited as test subjects while one hundred (100) age-matched primary infertility women without PCOS were recruited as control. All subjects were negative to Giemsa thick blood film staining for the detection of Plasmodium and Ziehl–Neelsen staining for the detection of acid–fast bacilli. HIVp24 antigen and antibody, plasma testosterone, antibody to hepatitis C virus (anti-HCV), and envelope antigen to hepatitis B Virus (HBeAg) were determined in the subjects by enzyme-linked immunosorbent assay while blood glucose was measured spectrophotometrically. Results: The results obtained showed a higher frequency of 0.93% (2) HIVP24antigen-antibody, 6.5% (17) anti-HCV, 14.5% (31) HBeAg, and 0.5% (1) HIVP24 + HBeAg (except for HIVP24 antigen and antibody) in PCOS women than the frequency of viral seromarkers of 1% (1) HIVP24 antigen and antibody, 6% (6) anti-HCV, 8% (8) HBeAg, and 0% (0) HIVP24 + HBeAg in non-PCOS control women. There was a significant association between PCOS and expression of viral seromarkers; there was a significantly higher fasting plasma glucose and testosterone level in all PCOS women with or without viral seromarkers (P < 0.05). There was a significantly higher plasma TNFα in PCOS women that expressed viral seromarkers than in PCOS women who do not express any of the viral seromarkers (P < 0.05). There was a significantly higher plasma TNFα in PCOS and non-PCOS women although more in PCOS women that expressed viral seromarkers than in women without any viral seromarker (P < 0.05). Conclusion: PCOS may be characterized by elevated plasma fasting glucose, testosterone, and expression of viral seromarkers while viral infection could also trigger higher plasma TNFα in PCOS women than in non-PCOS women infected with viral agent.

Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case–control study

BackgroundMicroscopic detection of malaria parasites is labour-intensive, time-consuming, and expertise-demanding. Moreover, the slide interpretation is highly dependent on the staining technique and the technician’s expertise. Therefore, there is a growing interest in next-generation, fully- or semi-integrated microscopes that can improve slide preparation and examination. This study aimed to evaluate the clinical performance of miLab™ (Noul Inc., Republic of Korea), a fully-integrated automated microscopy device for the detection of malaria parasites in symptomatic patients at point-of-care in Sudan.MethodsThis was a prospective, case–control diagnostic accuracy study conducted in primary health care facilities in rural Khartoum, Sudan in 2020. According to the outcomes of routine on-site microscopy testing, 100 malaria-positive and 90 malaria-negative patients who presented at the health facility and were 5 years of age or older were enrolled consecutively. All consenting patients underwent miLab™ testing and received a negative or suspected result. For the primary analysis, the suspected results were regarded as positive (automated mode). For the secondary analysis, the operator reviewed the suspected results and categorized them as either negative or positive (corrected mode). Nested polymerase chain reaction (PCR) was used as the reference standard, and expert light microscopy as the comparator.ResultsOut of the 190 patients, malaria diagnosis was confirmed by PCR in 112 and excluded in 78. The sensitivity of miLab™ was 91.1% (95% confidence interval [CI] 84.2–95.6%) and the specificity was 66.7% (95% Cl 55.1–67.7%) in the automated mode. The specificity increased to 96.2% (95% Cl 89.6–99.2%), with operator intervention in the corrected mode. Concordance of miLab with expert microscopy was substantial (kappa 0.65 [95% CI 0.54–0.76]) in the automated mode, but almost perfect (kappa 0.97 [95% CI 0.95–0.99]) in the corrected mode. A mean difference of 0.359 was found in the Bland–Altman analysis of the agreement between expert microscopy and miLab™ for quantifying parasite counts.ConclusionWhen used in a clinical context, miLab™ demonstrated high sensitivity but low specificity. Expert intervention was shown to be required to improve the device’s specificity in its current version. miLab™ in the corrected mode performed similar to expert microscopy. Before clinical application, more refinement is needed to ensure full workflow automation and eliminate human intervention.Trial registration ClinicalTrials.gov: NCT04558515

AI DRIVEN BLOOD CELL ANALYSIS FOR MALARIA AND BLOOD CANCER SCREENING

Malaria and blood Cancer is one of the deadliest diseases cross the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe.Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. It Was Designed to Identify Malaria Parasites and Cancerous Cell Presence in Blood Using Images of Blood Samples which got Tested with Giemsa-stain. Convolutional neural networks (CNNs) are then trained on these extracted features to accurately classify blood cell images into disease positive or disease-negative categories. The CNN detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The proposed model consists of three convolutional layers and fully connected layers each. The neural network presented is a cascade of several convolutional layers having multiple filters present in layers, which yields the exceptionally good accuracy as per the available resources. The experiment results demonstrate a significant improvement in malaria parasite and blood cancer recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient solutions by leveraging state-of-the-art technologies to combat malaria and blood cancer. Keywords : Deep Learning, CNN, Image analysis , InceptionresnetV2

Real-time fluorescence loop-mediated isothermal amplification assays for detection of zoonotic malaria Plasmodium parasites

BackgroundNatural human infections by Plasmodium cynomolgi and P. inui have been reported recently and gain the substantial attention from Southeast Asian countries. Zoonotic transmission of non-human malaria parasites to humans from macaque monkeys occurred through the bites of the infected mosquitoes. The objective of this study is to establish real-time fluorescence loop-mediated isothermal amplification (LAMP) assays for the detection of zoonotic malaria parasites by combining real-time fluorescent technology with the isothermal amplification technique. MethodsBy using 18S rRNA as the target gene, the primers for P. cynomolgi, P. coatneyi and P. inui were newly designed in the present study. Four novel real-time fluorescence LAMP assays were developed for the detection of P. cynomolgi, P. coatneyi, P. inui and P. knowlesi. The entire amplification process was completed in 60 min, with the assays performed at 65 °C. By using SYTO-9 as the nucleic acid intercalating dye, the reaction was monitored via real-time fluorescence signal. ResultsThere was no observed cross-reactivity among the primers from different species. All 70 field-collected monkey samples were successfully amplified by real-time fluorescence LAMP assays. The detection limit for P. cynomolgi, P. coatneyi and P. knowlesi was 5 × 109 copies/µL. Meanwhile, the detection limit of P. inui was 5 × 1010 copies/µL. ConclusionThis is the first report of the detection of four zoonotic malaria parasites by real-time fluorescence LAMP approaches. It is an effective, rapid and simple-to-use technique. This presented platform exhibits considerable potential as an alternative detection for zoonotic malaria parasites.

Assessment of the selected hematological profiles among malaria and Schistosoma mansoni co-infected patients, Northwest Ethiopia.

Malaria and schistosomiasis are infectious diseases that cause hematological profiles abnormalities. Malaria and Schistosoma mansoni co-infection causes exacerbation of health consequences and co-morbidities. The aim of this study was to assess the selected hematological profiles among malaria and S. mansoni co-infected patients at Dembiya Selected Health Institutions. An institutional-based comparative cross-sectional study was conducted from March 30 to August 10, 2022. A total of 140 individuals were enrolled in the study using a convenient sampling technique. Wet mount and Kato Katz technique were conducted to detect S. mansoni in Stool sample. Blood films were prepared for the detection of plasmodium. The data was coded and entered into EpiData version 3.1 before being analyzed with SPSS version 25. A P-value of less than 0.05 was considered statistically significant. Mean of WBC, neutrophil, lymphocyte, eosinophil, RBC, hemoglobin, and hematocrit [4.IU/L,2.2IU/L, 1.4IU/L, 0.1IU/L, 3.13IU/L, 9.5g/dL, and 28.7%, resepectively] in co-infected were significantly lower than [7.5IU/L, 4.6IU/L, 2.1IU/L, 0.38IU/L, 4.8IU/L, 14.6g/dL, and 43.7%, resepectively] in the healthy control participants. Mean of RBC, hemoglobin, and hematocrit [3.13IU/L, 9.5g/dL, 28.7%, resepectively] in co-infected were significantly lower compared to [3.8IU/L, 11.5g/dL, 33.9%, resepectively] in the malaria monoinfected participants and [3.7IU/L,11.5g/dL, 33.6%, resepectively] in the S. mansoni monoinfected participants. The result of hematological profiles in healthy participants had no significant difference compared to light,moderate and heavy S. mansoni infection intensity in coinfection. The number of S. mansoni eggs per gram of stool had been negatively correlated with hematological profiles of co-infected participants except lymphocyte and monocyte which correlated positively. Hematological profiles status in coinfection were significantly altered compared to malaria monoinfection, S. mansoni monoinfection, and healthy participants.Therefore, hematological tests should be used to monitor and manage coinfection related complications, and to reduce coinfection associated morbidity and mortality.

Evaluation of Malaria Rapid Diagnostic Test Performance and pfhrp2 Deletion in Tanzania School Surveys, 2017.

As part of malaria nationwide monitoring and evaluation initiatives, there is an increasing trend of incorporating malaria rapid diagnostic tests (mRDTs) in surveys conducted within primary schools to detect malaria parasites. However, mRDTs based on the detection of histidine-rich protein 2 (HRP2) are known to yield false-positive results due to persistent antigenemia, and false-negative results may result from low parasitemia or Plasmodium falciparum hrp2/3 gene deletion. We evaluated diagnostic performance of an HRP2 and pan-parasite lactate dehydrogenase (HRP2/pLDH) mRDT against polymerase chain reaction (PCR) for detection of P. falciparum among 17,051 primary school-age children from eight regions of Tanzania in 2017. According to PCR, the prevalence of P. falciparum was 19.2% (95% CI: 18.6-19.8). Using PCR as reference, the sensitivity and specificity of mRDT was 76.2% (95% CI: 74.7-77.7) and 93.9% (95% CI: 93.5-94.3), respectively. Test agreement was lowest in low transmission areas, where true-positive mRDTs were outnumbered by false-negatives due to low parasitemia. Discordant samples (mRDT-negative but PCR-positive) were screened for pfhrp2/3 deletion by real-time PCR. Among those with a parasite density sufficient for analysis, pfhrp2 deletion was confirmed in 60 samples, whereas pfhrp3 deletion was confirmed in two samples; one sample had both pfhrp2 and pfhrp3 deletions. The majority of samples with gene deletions were detected in the high-transmission Kagera region. Compared with mRDTs, PCR and other molecular methods offer increased sensitivity and are not affected by pfhrp2/3 deletions, making them a useful supplement to mRDTs in schools and other epidemiological surveys.

Web-based Deep Learning System for Malaria Parasite Detection in Granular Blood Samples

Abstract: Malaria is a serious health concern for modern humans, affecting people of all ages. Infected mosquitoes carry the fatal parasites responsible for malaria. Malaria can be diagnosed by examining a sample of the patient's blood under a microscope for parasites. The project comprises creating a web tool that employs deep learning to detect malaria parasites in blood smear photos. Convolutional neural network (CNN) models such as ResNet50, VGG19, and Customized CNN can be used to collect and categorize a set of blood smear images in order to identify patterns and characteristics. Convolutional layers, maxpooling layers, entirely linked layers, and a SoftMax layer are all utilized to create a Convolutional Neural Network (CNN) model. This technique can improve the accuracy of parasite diagnosis, increase detection rates, and reduce the disease's impact on global health.

Prevalence, Associated Factors and Treatment Outcomes of Laboratory-confirmed Pregnancy Malaria at Antenatal Care in Three Healthcare Facilities of Douala, Cameroon

Pregnancy malaria is a life-threatening condition to the mother, the fetus and the newborn. Since the implementation of the World Health Organization’s recommendations of specific malaria control strategies to pregnant women in malaria endemic countries, evaluation studies are needed to assess the prevalence of malaria in pregnancy. This cross-sectional prospective study was therefore set to determine the prevalence and associated factors as well as the treatment outcomes of Plasmodium infection among pregnant women attending antenatal care in three hospitals of Douala in Cameroon.
 Each volunteered pregnant woman received for antenatal care was questioned according to the study questionnaire to collect sociodemographic data, use of malaria prevention tools and pregnancy history. Then a laboratory test was carried for Plasmodium detection in the peripheral blood using microscopy and a HRP2-based rapid diagnostic test. Plasmodium carrying pregnant women were treated according to the national malaria program scheme followed by post-treatment follow-up. Data were processed and analyzed using SPSS.20 software. Association of Plasmodium infection with risk factors was analyzed as univariate using Pearson Chi-square and Fisher Exact tests considering a P-value < 0.05 as statistically significant.
 A total of 487 pregnant women aged between 18 years and 42 years were included in the study. The prevalence of Plasmodium infection was 3.5% and 4.3% by malaria rapid diagnostic test and microscopy respectively. Only Plasmodium falciparum asexual stage was detected. Parasite loads were low. Factors associated with Plasmodium falciparum higher prevalence were being less than 21 years old (0.02), not sleeping under mosquitoe net daily (p=0.04) and having fever during the pregnancy (p=0.04). Parenteral treatment with either artesunate, artemether or quinine sulfate showed good efficacy.
 Conclusion: Pregnant women attending the antenatal cares harbored low Plasmodium infection prevalence and loads. Being of young age, not sleeping under mosquitoe bednet daily and having fever during the pregnancy were predictive Plasmodium falciparum. Plasmodium infected pregnant women were successfully treated with antimalarial medications recommended by the national Ministry of Public Health. The authors recommend increasing efforts by health authorities to strengthen malaria prevention in pregnant women through more adherence to sleeping under ITNs daily and taking IPT-sp as well as systematic detection of Plasmodium infection at each antenatal visit.

MALARIA DETECTION USING CNN

The primary objective of this project is to develop a robust and efficient malaria detection system based on Convolutional Neural Networks (CNNs). CNNs have demonstrated remarkable performance in image recognition tasks, making them ideal for analyzing medical images such as blood smears for the presence of malaria parasites. By harnessing the power of CNN architecture, our goal is to create a highly accurate and reliable system capable of detecting malaria parasites with precision and consistency. Furthermore, we aim to ensure the efficiency of the developed system, allowing for the rapid processing of large volumes of blood smear images. This efficiency is crucial for facilitating timely diagnosis and treatment, particularly in regions where malaria is prevalent. By optimizing the computational processes involved in malaria detection, we seek to streamline the diagnostic workflow and enhance the overall effectiveness of malaria control efforts.In addition to accuracy and efficiency, adaptability is another key objective of this project. We aim to design a malaria detection system that can effectively handle variations in image quality and parasite morphology. This adaptability ensures that the system remains robust and reliable across different datasets and real-world scenarios, enabling healthcare professionals to confidently rely on its diagnostic capabilities.

Recombinase-Aided Loop-Mediated Isothermal Amplification on Human Plasmodium knowlesi.

Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification technique that can amplify specific nucleic acids at a constant temperature (63-65°C) within a short period (<1 hour). In this study, we report the utilization of recombinase-aided LAMP to specifically amplify the 18S sRNA of Plasmodium knowlesi. The method was built on a conventional LAMP assay by inclusion of an extra enzyme, namely recombinase, into the master mixture. With the addition of recombinase into the LAMP assay, the assay speed was executed within a time frame of less than 28 minutes at 65°C. We screened 55 P. knowlesi samples and 47 non-P. knowlesi samples. No cross-reactivity was observed for non-P. knowlesi samples, and the detection limit for recombinase-aided LAMP was one copy for P. knowlesi after LAMP amplification. It has been reported elsewhere that LAMP can be detected through fluorescent readout systems. Although such systems result in considerable limits of detection, the need for sophisticated equipment limits their use. Hence, we used here a colorimetric detection platform for the evaluation of the LAMP assay's performance. This malachite green-based recombinase-aided LAMP assay enabled visualization of results with the naked eye. Negative samples were observed by a change in color from green to colorless, whereas positive samples remained green. Our results demonstrate that the LAMP assay developed here is a convenient, sensitive, and useful diagnostic tool for the rapid detection of knowlesi malaria parasites. This method is suitable for implementation in remote healthcare settings, where centralized laboratory facilities, funds, and clinicians are in short supply.