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Background: Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. Methods: To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. Results: This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. Conclusions: An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears. [ABSTRACT FROM AUTHOR]

Malaria in pregnancy causes adverse consequences and prompt and accurate diagnosis is essential for case management. In malaria endemic countries, diagnosis is mainly based on rapid diagnostic tests (RDT) and microscopy. However, increasing reports of false negatives caused by low parasitemia and pfhrp2/3 deletions raise concerns about HRP2-based RDT usefulness. This study aimed to assess RDT and microscopy performance and to describe pfhrp2/3 deletions in a cohort of 418 pregnant women in Burkina Faso. Malaria was diagnosed using RDT and microscopy and blood samples were collected during antenatal care visits. Diagnostic results were compared to PCR as gold standard. Pfhrp2 and pfhrp3 deletions were characterized for patients with confirmed P. falciparum infection. RDT had better sensitivity (76%) but lower specificity (83%) than microscopy (sensitivity = 57%; specificity = 98%). Low parasitemia (<150 parasites/µL), especially in multigravidae, was the principal factor causing false negatives by both methods. Moreover, pfhrp2 deletion frequency among overall false negatives by RDT was 21.43%. Higher frequency of deletions was found among all samples, independently of RDT result, for example around 2% of samples had double deletions meaning that the majority of deletions had no effect on RDT testing. Finally, it was found higher pfhrp2 deletion in women with lower uterine height during the first trimester. Wider and National surveillance study of deletions is recommended among pregnant women and in Burkina Faso. [ABSTRACT FROM AUTHOR]

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints. [ABSTRACT FROM AUTHOR]

Abstract Background Battling malaria’s morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. Methods To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. Results This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. Conclusions An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears.

Abstract Background False negative rapid diagnostic tests (RDTs) accruing to the non-detection of Plasmodium falciparum histidine-rich protein 2/3 (Pfhrp2/3) is threatening the diagnosis and management of malaria. Although regular monitoring is necessary to gauge the level of efficacy of the tool, studies in Cameroon remain limited. This study assessed Plasmodium spp. prevalence and Pfhrp2/3 gene deletions across ecological and transmission zones in Cameroon. Methods This is a cross-sectional, multi-site, community- and hospital- based study, in 21 health facilities and 14 communities covering all five ecological settings in low seasonal (LS) and intense perennial (IPT) malaria transmission zones between 2019 and 2021. Participants were screened for malaria parasite using Pfhrp2 RDT and light microscopic examination of thick peripheral blood smears. DNA was extracted from dried blood spot using chelex®-100 and P. falciparum confirmed using varATS real-time quantitative Polymerase Chain Reaction (qPCR), P. malariae and P. ovale by real-time qPCR of Plasmepsin gene, and P. vivax using a commercial kit. Isolates with amplified Pfcsp and Pfama-1 genes were assayed for Pfhrp 2/3 gene deletions by conventional PCR. Results A total of 3,373 participants enrolled, 1,786 Plasmodium spp. infected, with 77.4% P. falciparum. Discordant RDT and qPCR results (False negatives) were reported in 191 (15.7%) P. falciparum mono-infected samples from LS (29%, 42) and IPT (13.9%, 149). The Pfhrp2+/Pfhrp3 + genotype was most frequent, similar between LS (5.5%, 8/145) and IPT (6.0%, 65/1,076). Single Pfhrp2 and Pfhrp3 gene deletions occurred in LS (0.7%, 1/145 each) and IPT (3.6%, 39/1,076 vs. 2.9%, 31/1,076), respectively. Whilst a single sample harboured Pfhrp2-/Pfhrp3- genotype in LS, 2.4% (26/1,076) were double deleted at IPT. Pfhrp2+/Pfhrp3- (0.3%, 3/1,076) and Pfhrp2-/Pfhrp3+ (1.2%, 13/1,076) genotypes were only observed in IPT. Pfhrp2, Pfhrp3 deletions and Pfhrp2-/Pfhrp3- genotype accounted for 78.8% (26), 69.7% (23) and 63.6% (21) RDT false negatives, respectively. Conclusion Plasmodium falciparum remains the most dominant and widely distributed Plasmodium species across transmission and ecological zones in Cameroon. Although the low prevalence of Pfhrp2/3 gene deletions supports the continued use of HRP2-based RDTs for routine malaria diagnosis, the high proportion of false-negatives due to gene deleted parasites necessitates continued surveillance to inform control and elimination efforts.

Abstract Background Accurate diagnosis and timely treatment are crucial in combating malaria. Methods A total of 449 samples were screened for Plasmodium falciparum infection by expert microscopy, qPCR, and three RDTs, namely Rapigen Biocredit Malaria Ag Pf (detecting HRP2 and pLDH on separate bands), Abbott NxTek Eliminate Malaria Ag Pf (detecting HRP2), and SD Bioline Malaria Ag Pf (detecting HRP2). hrp2/3 deletion typing was done by digital PCR. Results 45.7% (205/449) individuals tested positive by qPCR for P. falciparum with a mean parasite density of 12.5 parasites/μL. Using qPCR as reference, the sensitivity of microscopy was 28.3% (58/205), the Biocredit RDT was 52.2% (107/205), the NxTek RDT was 49.3% (101/205), and the Bioline RDT was 39.5% (81/205). When only samples with densities > 20 parasites/μL were included (n = 89), sensitivity of 62.9% (56/89) by microscopy, 88.8% (79/89) by Biocredit, 88.8% (79/89) by NxTek, and 78.7% (70/89) by Bioline were obtained. All three RDTs demonstrated specificities > 95%. The limits of detection (95% probability that a sample tested positive) was 4393 parasites/μL (microscopy), 56 parasites/μL (Biocredit, considering either HRP2 or pLDH), 84 parasites/μL (NxTek), and 331 parasites/μL (Bioline). None of the three qPCR-confirmed P. falciparum positive samples, identified solely through the pLDH target, or eight samples negative for all RDTs but qPCR-positive at densities > 20 parasites/µL carried hrp2/3 deletions. Conclusion The Biocredit and NxTek RDTs demonstrated comparable diagnostic efficacies. All three RDTs performed better than microscopy.

Summary Background: Children under five years of age constitute the “at risk” population for fatal complications of P. falciparum malaria infection. Early diagnosis and prompt treatment are key features in the current Roll Back Malaria Programme . Objective: To assess the diagnostic performance of the Immunochromatographic test (ICT) in the rapid diagnosis of falciparum malaria infection in children aged less than five years with a presumptive clinical diagnosis of malaria. Method: This prospective study was conducted at the Children Emergency Room of the Lagos University Teaching Hospital between January and October 2001. Febrile children aged less than five years and were suspected to have malaria, were enrolled. Blood samples from all patients were tested using the ICT-Malaria Pfo test kit as well as the conventional blood film microscopy after Giemsa staining Results: The results showed that the diagnosis of falciparum malaria was achieved within eight minutes using the ICT-Malaria P f o test kit. The sensitivity of the test kit was 69.6 percent while the specificity was 98.4 percent. However, among patients with severe malaria, the ICT had a sensitivity of 90.3 percent. The test kit was unable to detect antigenaemia at parasite density below 400/ml. An increase in body temperature was associated with higher rate of ICT test positivity. Conclusion: The low sensitivity of the ICT technique obtained in this study makes it a poor screening tool for the routine diagnosis of falciparum malaria in children aged 0-5yrs. It may be useful in the rapid diagnosis of suspected severe malaria in febrile children. The high cost of the test kit is likely to prove a major limitation to its widespread use.

Background: Accurate diagnosis and timely treatment are crucial in combating malaria. Methods: A total of 449 samples were screened for Plasmodium falciparum infection by expert microscopy, qPCR, and three RDTs, namely Rapigen Biocredit Malaria Ag Pf (detecting HRP2 and pLDH on separate bands), Abbott NxTek Eliminate Malaria Ag Pf (detecting HRP2), and SD Bioline Malaria Ag Pf (detecting HRP2). hrp2/3 deletion typing was done by digital PCR. Results: 45.7% (205/449) individuals tested positive by qPCR for P. falciparum with a mean parasite density of 12.5 parasites/μL. Using qPCR as reference, the sensitivity of microscopy was 28.3% (58/205), the Biocredit RDT was 52.2% (107/205), the NxTek RDT was 49.3% (101/205), and the Bioline RDT was 39.5% (81/205). When only samples with densities > 20 parasites/μL were included (n = 89), sensitivity of 62.9% (56/89) by microscopy, 88.8% (79/89) by Biocredit, 88.8% (79/89) by NxTek, and 78.7% (70/89) by Bioline were obtained. All three RDTs demonstrated specificities > 95%. The limits of detection (95% probability that a sample tested positive) was 4393 parasites/μL (microscopy), 56 parasites/μL (Biocredit, considering either HRP2 or pLDH), 84 parasites/μL (NxTek), and 331 parasites/μL (Bioline). None of the three qPCR-confirmed P. falciparum positive samples, identified solely through the pLDH target, or eight samples negative for all RDTs but qPCR-positive at densities > 20 parasites/µL carried hrp2/3 deletions. Conclusion: The Biocredit and NxTek RDTs demonstrated comparable diagnostic efficacies. All three RDTs performed better than microscopy. [ABSTRACT FROM AUTHOR]

Background: With growing use of parasitological tests to detect malaria and decreasing incidence of the disease in Africa; it becomes necessary to increase the understanding of causes of non-malaria acute febrile illness (NMAFI) towards providing appropriate case management. This research investigates causes of NMAFI in pediatric out-patients in rural Guinea-Bissau. Methods: Children 0-5 years presenting acute fever (≥38°) or history of fever, negative malaria rapid diagnostic test (mRDT) and no signs of specific disease were recruited at the out-patient clinic of 3 health facilities in Bafatá province during 54 consecutive weeks (dry and rainy season). Medical history was recorded and blood, nasopharyngeal, stool and urine samples were collected and tested for the presence of 38 different potential aetiological causes of fever. Results: Samples from 741 children were analysed, the protocol was successful in determining a probable aetiological cause of acute fever in 544 (73.61%) cases. Respiratory viruses were the most frequently identified pathogens, present in the nasopharynx samples of 435 (58.86%) cases, followed by bacteria detected in 167 (22.60%) samples. Despite presenting negative mRDTs, P. falciparum was identified in samples of 24 (3.25%) patients. Conclusions: This research provides a description of the aetiological causes of NMAFI in West African context. Evidence of viral infections were more commonly found than bacteria or parasites. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to analyze trends in the timely diagnosis of malaria cases over the past 10 years in relation to the utilization of different types of healthcare facilities. Methods: The study included 3,697 confirmed and suspected cases of malaria reported between January 1, 2013, and December 31, 2022, in the national integrative disease and healthcare management system. Some cases lacking a case report or with information missing from the case report were excluded from the analysis. A generalized linear model with a Poisson distribution was constructed to estimate risk ratios and 95% confidence intervals adjusted for other variables, such as distance. Results: When cases involving diagnosis >5 days after symptom onset in confirmed patients (5DD) were examined according to the type of healthcare facility, the risk ratio of 5DD cases was found to be higher for tertiary hospitals than for public health facilities. Specifically, the risk ratio was higher when the diagnosis was established at a tertiary hospital, even after a participant had visited primary or secondary hospitals. In an analysis adjusted for the distance to each participant's healthcare facility, the results did not differ substantially from the results of the crude analysis. Conclusion: It is imperative to improve the diagnostic capabilities of public facilities and raise awareness of malaria at primary healthcare facilities for effective prevention and control. [ABSTRACT FROM AUTHOR]

Locally acquired malaria is rare in the US; however, in 2023, cases were reported in Florida, Texas, Maryland, and Arkansas. Prompt diagnosis and treatment of malaria are essential to prevent severe malaria disease. This article details malaria and offers treatment guidelines. [ABSTRACT FROM AUTHOR]

Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLab™ device incorporating the solid hydrogel staining method was proposed for consistent blood film preparation, eliminating the use of complex equipment and liquid reagent maintenance. The miLab™ ensures consistent, high-quality, and reproducible blood films across various hematocrits by leveraging deformable staining patches. Embedded-deep-learning-enabled miLab™ was utilized to detect and classify malarial parasites from autofocused images of stained blood cells using an internal optical system. The results of this method were consistent with manual microscopy images. This method not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission. This method can set a new paradigm for on-site malaria diagnosis. The miLab™ algorithm for malaria detection achieved a total accuracy of 98.86% for infected red blood cell (RBC) classification. Clinical validation performed in Malawi demonstrated an overall percent agreement of 92.21%. Based on these results, miLab™ can become a reliable and efficient tool for decentralized malaria diagnosis.

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as “black boxes” with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model’s decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system’s performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.

The article informs about that artemether-lumefantrine is now recommended as the primary treatment for uncomplicated falciparum malaria, including during pregnancy, based on international consensus and historical data. Topic include the revised CDC recommendations align with the WHO's endorsement of artemisinin combination therapies for all individuals with this type of malaria.

Introduction: Malaria is one of the most prevalent infectious diseases in subSaharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Malaria exists as an endemic in many countries including Bangladesh and the malaria vaccine is not yet available here. The study aimed to assess the level of knowledge and acceptance of the malaria vaccination among the parents of children under the age of five in Bangladesh's malaria‐endemic areas and the sociodemographic, behavioural, and household factors associated with the acceptance and knowledge of the malaria vaccine. Methods: From January to March 2022, a cross‐sectional study was conducted in all five malaria‐endemic districts of Bangladesh, involving 405 parents of children under the age of 5 who met the inclusion criteria. Multiple logistic regression was used to analyze the factor affecting parents' acceptance and knowledge of malaria vaccination in children under five and other variables. Results: Majority (54%) of the respondents were mothers. Almost half (49%) of the respondents were aged between 26 and 35 years old and around 90% were from rural areas. A small portion (20%) of the participants were housewives and 46% of them completed primary education. Overall, 70% of the study participants reported that they would accept malaria vaccination independently. About one‐fourth (25%) heard about the malaria vaccine and 48% of them mentioned health professionals as the source of information. Knowledge of malaria vaccination was found associated with residence, income, and family size. Acceptance and knowledge were both associated with residence, education, occupation, income, and family size. In a multivariable analysis, housing structure, house wall, house window, knowledge of malaria, testing for malaria, and being diagnosed with malaria were all associated with knowledge of and acceptance of getting vaccinated against malaria. Conclusions: The present study highlights the necessity of creating awareness of malaria vaccines in epidemic areas of Bangladesh. This study offers crucial data to develop a policy for a novel malaria vaccine, supporting its adoption in Bangladesh. Public Contribution: This study was based on interviews. The interviewees were recruited as public representatives from the malaria‐endemic area to assist us in building an understanding of knowledge and acceptance of the malaria vaccine among parents of under‐five children in Bangladesh. [ABSTRACT FROM AUTHOR]

This article, published in Frontiers in Epidemiology, discusses the current situation of malaria molecular epidemiology in Africa. The authors emphasize the importance of molecular surveillance in guiding evidence-based decision-making and improving malaria surveillance strategies. The article presents findings from studies conducted in Ghana and Kenya, which highlight the prevalence of parasites with gene deletions that evade detection by current diagnostic methods. The authors also discuss the emergence and spread of drug-resistant malaria parasites and the need for sustained molecular surveillance to mitigate the risk. The article concludes by emphasizing the value of malaria molecular epidemiology in guiding strategic planning and surveillance strategy implementation for malaria elimination efforts. [Extracted from the article]

The article presents a case study of an 8-year-old patient with symptoms of malaria and it discusses the infected red blood cells flag (InR) of the Mindray BC 6800 plus. It reports on the identification of malaria-infected red blood cells, which aids in diagnosis, and it mentions the use of rapid malaria testing to detect malaria antigens. It highlights the importance of monitoring parasitemia during treatment to confirm the patient's response to therapy.

Introduction: According to the guidelines (GL) valid in Germany, persons born or raised in a malaria-endemic area or had continuously stayed in a malaria-endemic area for more than 6 months may only be admitted donating blood if, among other things, validated and quality-assured laboratory diagnostics show that there is no evidence of infectivity. In a statement of the Working Group "Blood" of the Federal Ministry of Health (WGB), a reduction of the deferral period from 4 to 3 years and an antibody test after the deferral period are recommended. Methods: In accordance with the GL, nucleic acid testing (NAT) by means of PCR is carried out at our institution after a retention period of 4 years. In addition to the validated molecular biological testing, an infection serological examination was performed. Case Presentation: In the present cases, Plasmodia genome was detected in the respective single PCR in two blood donors originating from malaria-endemic areas after the expiry of the deferral period. However, one donor tested negative for antibodies against Plasmodia. Discussion/Conclusion: This observation is discussed in the context of a recommendation of the WGB. The question is addressed whether PCR testing is dispensable or whether a combination of infection serological testing and NAT should be favored. [ABSTRACT FROM AUTHOR]

Background: Pregnancy poses specific challenges in the diagnosis of Plasmodium falciparum infection due to parasite sequestration in the placenta. The diagnosis of Plasmodium falciparum infection in pregnant mothers therefore requires highly sensitive methods in order to detect the presence of parasites. These include those that detect the presence of antigens and those that detect and quantify the presence of the malaria parasites. Objective: The study assessed the performance of mRDT diagnostic test ((PfHRP2 -RDT) in the detection of malaria infection in blood samples from nulliparous pregnant women within the first trimester of pregnancy in Western Kenya. Methods: This was a prospective study on blood specimens collected from pregnant women in a malaria-endemic region in Kenya. m-polymerase chain reaction (mPCR) and mRDT tests were performed. The diagnostic accuracy of m-RDT was compared with mPCR as the gold standard for the purpose of this study. Setting: Twelve primary health facilities in Busia, Bungoma and Kakamega Counties in Kenya Results: Out of 264 mPCR positive samples, 130 were mRDT positive (true positives) while 134 were mRDT negative (false negative). And out of 441 mPCR negative samples, 41 were positive on mRDT (false positive). Thus, in comparison with mPCR, the sensitivity and specificity of mRDT to detect malaria infection in nulliparous pregnant mothers in first trimester was 49.2% and 88.9% respectively Conclusions: The sensitivity of mRDT to detect Plasmodium falciparum infections in nulliparous pregnant mothers in the first trimester was not satisfactory compared to mPCR tests. [ABSTRACT FROM AUTHOR]

Due to the difficulty of obtaining blood samples, which is the invasive method that is currently used for the detection of Plasmodium spp., alternative diagnostic sampling methods that are effective and non-invasive are needed, particularly for long-term studies. Saliva and stool samples from malaria-infected individuals contain trace amounts of Plasmodium DNA and therefore could be used as alternatives. Malaria was screened using rapid diagnosis tests and confirmed via microscopy. Nested PCR tests targeting the Plasmodium falciparum-specific STEVOR gene were performed for blood, saliva and stool samples that were positive for malaria. Three hundred sixty-seven (367) children were enrolled and eighty (22.22%) were confirmed to be positive for malaria. Matched blood, saliva and stool samples were available for 35 children. By using blood smears as the gold standard for the diagnosis of malaria, our study indicates that Plasmodium DNA was more detectable in blood (100%) than in saliva (22.86%) and stools (14.29%). Applying qPCR to the STEVOR gene to detect Plasmodium falciparum DNA in saliva and stool samples cannot be considered as an alternative to the current malaria detection processes using blood specimens. [ABSTRACT FROM AUTHOR]

After the lifting of COVID-19 restrictions, international travel has demonstrated recovery to prepandemic levels. Travel, particularly to tropical regions, can be associated with contracting various infectious diseases. For this reason, collecting a travel history is a necessity when assessing any patient with vague infectious symptoms, most notably fever. Early suspicion, identification, and treatment of tropical illnesses can be lifesaving. This case study concerns a patient who recently traveled to Africa and is under evaluation for fever in the emergency department. The proper approach to the febrile traveler and the pathophysiology, diagnosis, and treatment of malaria are reviewed. [ABSTRACT FROM AUTHOR]

Infectious diseases and their imperative awareness gain major relevance through global warming and multi-continent refugee crises. Here, we demonstrate the challenges of malaria diagnosis, disease course, and treatment, including post-artesunate hemolysis in a Syrian refugee with severe falciparum malaria, most probably infected during migrant smuggling from Türkiye to Germany. [ABSTRACT FROM AUTHOR]

Purpose: Malaria is a life-threatening mosquito-borne disease caused by Plasmodium parasites, mainly in tropical and subtropical countries. Plasmodium falciparum (P. falciparum) is the most prevalent cause on the African continent and responsible for most malaria-related deaths globally. Important medical needs are biomarkers for disease severity or disease outcome. A potential source of easily accessible biomarkers are blood-borne small extracellular vesicles (sEVs). Methods: We performed an EV Array to find proteins on plasma sEVs that are differentially expressed in malaria patients. Plasma samples from 21 healthy subjects and 15 malaria patients were analyzed. The EV array contained 40 antibodies to capture sEVs, which were then visualized with a cocktail of biotin-conjugated CD9, CD63, and CD81 antibodies. Results: We detected significant differences in the protein decoration of sEVs between healthy subjects and malaria patients. We found CD106 to be the best discrimination marker based on receiver operating characteristic (ROC) analysis with an area under the curve of > 0.974. Additional ensemble feature selection revealed CD106, Osteopontin, CD81, major histocompatibility complex class II DR (HLA-DR), and heparin binding EGF like growth factor (HBEGF) together with thrombocytes to be a feature panel for discrimination between healthy and malaria. TNF-R-II correlated with HLA-A/B/C as well as CD9 with CD81, whereas Osteopontin negatively correlated with CD81 and CD9. Pathway analysis linked the herein identified proteins to IFN-γ signaling. Conclusion: sEV-associated proteins can discriminate between healthy individuals and malaria patients and are candidates for future predictive biomarkers. Trial registration: The trial was registered in the Deutsches Register Klinischer Studien (DRKS-ID: DRKS00012518). [ABSTRACT FROM AUTHOR]

Background and Aim: Malaria is a vector-borne disease that has become a global concern. The researchers used a model of the spread of malaria that involved the coexistence of two populations: humans and mosquitoes. The main objective of this work is to analyze the sensitivity index of each parameter in the model. Materials and Methods: Three compartments were used in the model's construction for the human population and two for the mosquito population. The main objective of this work is to analyze the sensitivity index of each parameter in the model. This analysis is crucial to understanding the variance in model output brought on by changes in input. This analysis provides information to researchers about the parameters that most influence the dynamics of the spread of malaria in the human population. Results: The results of this study inform that the parameter of an individual mosquito's average number of bites on all human hosts is the most dominant factor in increasing the spread of malaria in humans. On the other hand, the number of mosquitoes that naturally die is the most important parameter in reducing the spread of malaria in humans. Conclusion: The sensitivity index shows that the parameter of an individual mosquito's average number of ites on all human hosts (rr) is e most dominant in increasing the spread of malaria in humans. On the other hand, thnaturally die open paren mu sub v, die (μv) is the most important parameter in reducing the spread of malaria in humans. [ABSTRACT FROM AUTHOR]

Introduction: Malaria is a major public health problem in worldwide and causes high morbidity and mortality, despite global efforts to eradicate the disease. The purpose of the research is to analyse the perception and behavioural practices towards malaria detection among healthcare workers. Aim -To analyze the Knowledge & awareness among the healthcare professionals. Material & Method: The pilot cross-sectional survey was carried out for the period of two months. Study included health care professionals, doctors and community workers who were working in private hospitals. Convenience sampling was used and total 500 participants were included in the study. Results: The least knowledge and awareness were found in Category 3 workers. Conclusion The public health professionals must play a very important role by increasing awareness among population through appropriate education & counselling. [ABSTRACT FROM AUTHOR]

Abstract Background Prompt and appropriate clinical management of malaria is critical for reducing the continued high burden of malaria among children under five years in sub-Saharan countries. However, more remains to be known about how a patient’s socioeconomic status (SES) would affect the access to diagnosis of malaria. Methods In this cross-sectional study using the Demographic and Health Survey and Malaria Indicators Survey, we pooled the data of 38,567 febrile under-five children in 2016–2018 from 19 sub-Saharan countries. Multivariable logistic regression was used to assess the associations between SES and two binary outcomes: the visit to a health facility and a blood test for fever. Stratified analyses were further conducted by the type of health facilities (public hospitals/public primary healthcare facilities/private hospitals/private primary healthcare facilities) for the latter outcome. Results Fifty-eight percent of the febrile children were taken to health facilities, among whom only 55% took blood tests. Compared to children from households in the highest wealth quintile, children in the lowest quintile were less likely to be taken to medical facilities [adjusted odds ratio (aOR) = 0.775, 95% confidence interval (CI): 0.675–0.889]. Parents with more than secondary education were more likely to seek care (aOR = 1.830, 95% CI: 1.561–2.145) and to have blood tests (aOR = 1.729, 95% CI: 1.436–2.082) for their febrile children than parents without formal education. The probabilities of receiving blood tests at public hospitals and public primary healthcare facilities stayed relatively high across parental education levels and wealth quintiles, while these probabilities remained the lowest at private primary healthcare facilities, ranging from 0.100 (95% CI: 0.074–0.127) to 0.139 (95% CI: 0.083–0.194) across parental education levels and from 0.104 (95% CI: 0.078–0.130) to 0.125 (95% CI: 0.090–0.160) across wealth quintiles. Conclusions Significant socioeconomic disparities existed both in the access to health facilities and laboratory diagnosis of malaria in children in sub-Saharan African countries. These disparities were particularly evident in the private sector. Universal health coverage needs to be further strengthened to make formal healthcare in general and the laboratory diagnosis of malaria more accessible and affordable. Graphical abstract

IntroductionMalaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.MethodsIn this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide.ResultsComparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application.ConclusionThe coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.

Background Increasing trends of antimicrobial resistance are observed around the world, driven in part by excessive use of antimicrobials. Limited access to diagnostics, particularly in low- and middle-income countries, contributes to diagnostic uncertainty, which may promote unnecessary antibiotic use. We investigated whether introducing a package of diagnostic tools, clinical algorithm, and training-and-communication messages could safely reduce antibiotic prescribing compared with current standard-of-care for febrile patients presenting to outpatient clinics in Uganda. Methods This was an open-label, multicenter, 2-arm randomized controlled trial conducted at 3 public health facilities (Aduku, Nagongera, and Kihihi health center IVs) comparing the proportions of antibiotic prescriptions and clinical outcomes for febrile outpatients aged ≥1 year. The intervention arm included a package of point-of-care tests, a diagnostic and treatment algorithm, and training-and-communication messages. Standard-of-care was provided to patients in the control arm. Results A total of 2400 patients were enrolled, with 49.5% in the intervention arm. Overall, there was no significant difference in antibiotic prescriptions between the study arms (relative risk [RR]: 1.03; 95% CI:.96–1.11). In the intervention arm, patients with positive malaria test results (313/500 [62.6%] vs 170/473 [35.9%]) had a higher RR of being prescribed antibiotics (1.74; 1.52–2.00), while those with negative malaria results (348/688 [50.6%] vs 376/508 [74.0%]) had a lower RR (.68;.63–.75). There was no significant difference in clinical outcomes. Conclusions This study found that a diagnostic intervention for management of febrile outpatients did not achieve the desired impact on antibiotic prescribing at 3 diverse and representative health facility sites in Uganda. [ABSTRACT FROM AUTHOR]

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Tropical and potentially fatal malaria is brought on by the parasite Plasmodium spp which spreads through infected female anopheles mosquitoes within the human populations. In Ghana, malaria is endemic and perennial, with distinct seasonal fluctuations in the northern part. Children aged below five years are among the population most vulnerable to malaria in Ghana. This study's goal is to establish how suspected malaria cases, tested malaria cases, and climatic conditions impact confirmed malaria cases in children under five years in the Sunyani Municipality, Bono Region, Ghana. The dependent variable, monthly number of confirmed cases of malaria in children under five years, was modelled with the independent variables, monthly number of tested cases of malaria in children under five years, mean monthly relative humidity, mean monthly rainfall, and mean monthly temperature, in the Sunyani Municipality. We employed multiple linear regression after data transformation, exploratory data analysis, and correlation analysis. Results show that tested malaria cases and climatic factors significantly influence confirmed malaria cases in children under 5 years. About 41.8% of variations in confirmed malaria cases among children under 5 years is attributed to climatic factors and the number of tested cases. Moreover, results show that increase in tested cases and rainfall leads to more confirmed malaria cases among children under 5 years, while increase in temperature reduces malaria infections. To reduce the incidence of malaria in children under five years, the government and its stakeholders should encourage parent to let their children sleep in treated mosquito nets, distil stagnant waters during raining seasons, spray bushes with antimosquito insecticides, and destroy all breeding grounds of mosquitoes at all times. We proposed that all malaria cases should be laboratory tested and properly confirmed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities. [ABSTRACT FROM AUTHOR]

This case study examined current trends in the prevalence of vector-borne diseases and the impact of climate change on disease distribution. Our findings indicate that the dynamics of the Anopheles mosquito population in particular has changed dramatically in the past decade and now poses an increasing threat to human populations previously at low risk for malaria transmission. Given their geographic location and propensity for sustaining vector-borne disease outbreaks, southeastern states are particularly vulnerable to climate-induced changes in vector populations. We demonstrate the need to strengthen our hospital and laboratory infrastructure prior to further increases in the incidence of vector-borne diseases by discussing a case of uncomplicated malaria in a patient who arrived in one of our hospitals in Louisiana. This case exemplifies a delay in diagnosis and obtaining appropriate treatment in a timely manner, which suggests that our current health care infrastructure, especially in areas heavily affected by climate change, may not be adequately prepared to protect patients from vector-borne diseases. We conclude our discussion by examining current laboratory protocols in place with suggestions for future actions to combat this increasing threat to public health in the United States. [ABSTRACT FROM AUTHOR]

Background Microscopy is the gold standard for malaria diagnosis but is dependent on trained personnel. Rapid diagnostic tests (RDTs) form the mainstay of diagnosis in endemic areas without access to high-quality microscopy. We aimed to evaluate whether RDT alone could rule out imported malaria in children presenting to UK emergency departments (EDs). Methods UK-based, multi-center, retrospective, diagnostic accuracy study. Included : any child <16 years presenting to ED with history of fever and travel to a malaria-endemic country, between 01/01/2016 and 31/12/2017. Diagnosis: microscopy for malarial parasites (clinical reference standard) and RDT (index test). UK Health Research Authority approval: 20/HRA/1341. Results There were 47 cases of malaria out of 1,414 eligible cases (prevalence 3.3%) in a cohort of children whose median age was 4 years (IQR 2–9), of whom 43% were female. Cases of Plasmodium falciparum totaled 36 (77%, prevalence 2.5%). The sensitivity of RDT alone to detect malaria infection due to any Plasmodium species was 93.6% (95% CI 82.5–98.7%), specificity 99.4% (95% CI 98.9–99.7%), positive predictive value 84.6% (95% CI 71.9–93.1%) and negative predictive value 99.8% (95% CI 99.4–100.0%). Sensitivity of RDT to detect P. falciparum infection was 100% (90.3–100%), specificity 98.8% (98.1–99.3%), positive predictive value 69.2% (54.9–81.2%, n = 46/52) and negative predictive value 100% (99.7–100%, n = 1,362/1,362). Conclusions RDTs were 100% sensitive in detecting P. falciparum malaria. However, lower sensitivity for other malaria species and the rise of pfhrp2 and pfhrp3 (pfhrp2/3) gene deletions in the P. falciparum parasite mandate the continued use of microscopy for diagnosing malaria. [ABSTRACT FROM AUTHOR]

Background: Prompt and appropriate clinical management of malaria is critical for reducing the continued high burden of malaria among children under five years in sub-Saharan countries. However, more remains to be known about how a patient's socioeconomic status (SES) would affect the access to diagnosis of malaria. Methods: In this cross-sectional study using the Demographic and Health Survey and Malaria Indicators Survey, we pooled the data of 38,567 febrile under-five children in 2016–2018 from 19 sub-Saharan countries. Multivariable logistic regression was used to assess the associations between SES and two binary outcomes: the visit to a health facility and a blood test for fever. Stratified analyses were further conducted by the type of health facilities (public hospitals/public primary healthcare facilities/private hospitals/private primary healthcare facilities) for the latter outcome. Results: Fifty-eight percent of the febrile children were taken to health facilities, among whom only 55% took blood tests. Compared to children from households in the highest wealth quintile, children in the lowest quintile were less likely to be taken to medical facilities [adjusted odds ratio (aOR) = 0.775, 95% confidence interval (CI): 0.675–0.889]. Parents with more than secondary education were more likely to seek care (aOR = 1.830, 95% CI: 1.561–2.145) and to have blood tests (aOR = 1.729, 95% CI: 1.436–2.082) for their febrile children than parents without formal education. The probabilities of receiving blood tests at public hospitals and public primary healthcare facilities stayed relatively high across parental education levels and wealth quintiles, while these probabilities remained the lowest at private primary healthcare facilities, ranging from 0.100 (95% CI: 0.074–0.127) to 0.139 (95% CI: 0.083–0.194) across parental education levels and from 0.104 (95% CI: 0.078–0.130) to 0.125 (95% CI: 0.090–0.160) across wealth quintiles. Conclusions: Significant socioeconomic disparities existed both in the access to health facilities and laboratory diagnosis of malaria in children in sub-Saharan African countries. These disparities were particularly evident in the private sector. Universal health coverage needs to be further strengthened to make formal healthcare in general and the laboratory diagnosis of malaria more accessible and affordable. [ABSTRACT FROM AUTHOR]

The article describes the case of locally acquired mosquito-transmitted Plasmodium falciparum Malaria in a patient in Maryland in August 2023. It discusses the patient's medical history, results of the patient's laboratory tests, the diagnosis of Plasmodium falciparum, treatment given to the patient and the implementation of active case finding to identify other malaria cases in Maryland following the confirmation of malaria in the patient.

Abstract Background Malaria related mortality is associated with significant deregulation of host inflammatory factors such as interferon-inducible protein 10, a member of the CXC or α-subfamily (CXCL10), and host angiogenic factors such as angiopoietin 1 (Ang-1) and angiopoietin 2 (Ang-2). However, detection of these factors in malaria patients requires the drawing of blood, which is invasive and increases the risk of accidental blood-borne infections. There has been an increased interest in the use of saliva as the body fluid of choice for the diagnosis of many infectious diseases including malaria. Here, saliva levels of CXCL10, Ang-1, and Ang-2 previously shown to be predictive of severe malaria in malaria patients in Ghana were assessed in malaria patients. Methods This study was conducted in the Shai-Osudoku District Hospital in Dodowa, Accra, Ghana and the study population comprised 119 malaria patients and 94 non-malaria subjects. The non-malaria subjects are healthy community participants with no malaria infection. Plasma and saliva levels of CXCL10, Ang-1 and Ang-2 of the study participants were measured using an enzyme-linked immunoassay. Complete blood counts of each participant were measured with a haematology autoanalyzer. Pearson correlation was used to evaluate the correlation between plasma and saliva levels of each biomarker in malaria patients. A p-value of