Occurrence Of Malaria Research Articles

Assessment of malaria transmission in Kenya using multilevel logistic regression

Background: Kenya has a lower malaria incidence in comparison to other African malaria-endemic nations. Malaria is a significant public health concern in the country. The malaria indicator survey (MIS) data were analyzed using the logistic regression model. Nonetheless, independent data may be the cause of most MIS's hierarchical structure. This approach does not consider any association between data points within a cluster, as it assumes that the individual malaria statuses are independent of their causes. The approach may lead to biased analysis conclusions. The primary goal of this research is to determine the impact of sample enumeration areas (SEAs) and SEA features on individual malaria rapid diagnostic test (RDT) results. We are interested in identifying key factors influencing household members' malaria RDT findings or Kenya's malaria prevalence and assessing variation. Methods: Our study utilized the robust 2020 Kenya National Malaria Indicator Surveys (KMIS) dataset, which is representative of the entire nation. This dataset, comprising 301 clusters (134 urban and 167 rural areas), was instrumental in applying several multilevel models, including random sample and sample Enumeration Area (SEA) effects. We also considered the weights used in the s survey design, which is used to adjust uneven probabilities of choice within clusters, further enhancing the reliability and relevance of our findings. The methods used in this study involved a rigorous analysis of the KMIS dataset, including applying multilevel models and considering survey design weights to ensure the robustness and strength of our results. ResultsThis study's findings are significant and crucial in understanding the prevalence of malaria in Kenya. The findings reveal that factors such as region, place of residence, mosquito bed net use, water source location, wealth index, age, household size, and altitude are significantly associated with malaria's prevalence.After accounting for these variables, systematic changes across SEAs accounted for approximately 47.1% of the remaining variability in malaria occurrence in the study locations. In contrast, the remaining 52.9% was projected to be unmeasured differences between individuals or family units. These findings provide a detailed explanation of the various processes that influence malaria prevalence in Kenya. Conclusions: The study's multilevel logistic regression model, which includes random effects, identified two SEA-level and eight individual/household risk factors for malaria infection. Thus, increasing the availability of insecticide-treated bed nets is one crucial element that public health policymakers should consider. Furthermore, health planners can organize spatially targeted initiatives to prevent malaria transmission with the help of spatial clustering data.

A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

For many years’ malaria has been a health public concern in Kenya as well as many parts of Africa and other parts of the world. The purpose of this study is to develop and evaluate a supervised machine learning model to predict malaria occurrence (final malaria test results) in Kenya. The study investigated twelve predictor variables on the outcome variable (malaria test results), where five machine learning models namely; k-nearest neighbors, support vector machines, random forest, tree bagging, and boosting, were estimated. During the model evaluation, random forest emerged as the best overall model in the classification and prediction of final malaria test results. The model attained a higher classification accuracy of 97.33%, sensitivity of 71.1%, specificity of 98.4%, balanced accuracy of 84.7% and an area under the curve of 98.3%. From the final model, the presence of plasmodium falciparum emerged most important feature, followed by region, endemic zone and anemic level. The feature with the least importance in predicting final malaria test results was having mosquito nets. In conclusion, employing Machine learning algorithms enhances early detection, optimizing resource allocation for interventions, and ultimately reducing the incidence and impact of malaria in the Kenya. The study recommends allocation of resources and funds to areas with the presence of plasmodium falciparum, region susceptible to malaria, endemic zones and anemic prone areas.

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.

Plasmodium falciparum infection status in children less than 10 years old under seasonal malaria chemoprevention and risk of clinical malaria in the Koulikoro health district, Mali.

Seasonal malaria chemoprevention (SMC) with Sulfadoxine pyrimethamine plus amodiaquine (SP + AQ) consist of a monthly administration of therapeutic dose to children under five years of age during the high risk of malaria in area where malaria is highly seasonal. According to SMC recommendation, both non-infected and asymptomatic Plasmodium falciparum infected children will receive similar treatment. The gap in our knowledge is how the effect of asymptomatic infection on the efficacy of SMC in preventing clinical malaria over a four-week period. Thus, this study aimed to assess the risk of clinical malaria and its association with children's infection status when SMC treatment is given. The study was carried out in the Koulikoro health district in Mali and concerned children under 10 years of age. A total of 726 and 1452 children were randomly selected and followed over the SMC campaign in the years 2019 and 2020 respectively. Prevalence of asymptomatic P. falciparum infection was determined each round by microscopy before SMC drugs intake. Children were passively followed over a four-week period to determine incidence of clinical malaria. R-Studio software was used for analysis. The risk of clinical malaria by infection status was estimated using a logistic regression. A Kaplan-Meier curve was used to determine the survival time between infected and uninfected children. The Pearson Chi-square test was used to compare proportions with the significant level at p< 0.05. The average prevalence of asymptomatic infection was 11.0% both years, and it was higher among children aged 5 to 9 years old in 2019 (p<0.001) and 2020 (p=0.016). The risk of clinical malaria was significantly higher among asymptomatic infected children 2019: (RR=3.05, CI [2.04-4.72]) and 2020 (RR=1.43, CI [1.04-1.97]) transmission seasons. Likewise, the time of the first malaria occurrence was statistically lower among infected children regardless the year (p<0.001 in 2019 and p=0.01 in 2020). Results show a high risk of clinical malaria in asymptomatic infected children during SMC delivery. Screening for P. falciparum infection before the SMC treatment could significantly enhance the impact of the strategy on malaria morbidity in endemic areas.

Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

Prevalence of Malaria among Patients attending Federal Medical Centre, Owerri, Imo State, Nigeria

The malaria parasite is a parasitic protozoan (a group of single-celled microorganisms) that inflicts humans with malaria. It is responsible for a significant proportion of the health burden of developing countries. Thus, this study aimed to evaluate the prevalence of malaria among patients attending the Federal Medical Centre Owerri, Imo State, Nigeria. A total of 150 patients consented to participate. Malaria diagnosis was performed using rapid diagnostic test kits and microscopic examination of Giemsa-stained blood film. Questionnaires were administered to gather socio-demographic data. The analysis revealed a 53% prevalence of malaria. Statistical analysis indicated associations between malaria occurrence and gender, educational level, and economic status. This study highlights the high prevalence of malaria among patients at FMC Owerri.

Infection vs. Reinfection: The Immunomodulation of Erythropoiesis.

Severe malarial anemia (SMA) increases the morbidity and mortality of Plasmodium, the causative agent of malaria. SMA is mainly developed by children and pregnant women in response to the infection. It is characterized by ineffective erythropoiesis caused by impaired erythropoietin (EPO) signaling. To gain new insights into the pathogenesis of SMA, we investigated the relationship between the immune system and erythropoiesis, conducting comparative analyses in a mouse model of malaria. Red blood cell (RBC) production was evaluated in infected and reinfected animals to mimic endemic occurrences. Higher levels of circulating EPO were observed in response to (re)infection. Despite no major differences in bone marrow erythropoiesis, compensatory mechanisms of splenic RBC production were significantly reduced in reinfected mice. Concomitantly, a pronounced immune response activation was observed in erythropoietic organs of reinfected animals in relation to single-infected mice. Aged mice were also used to mimic the occurrence of malaria in the elderly. The increase in symptom severity was correlated with the enhanced activation of the immune system, which significantly impaired erythropoiesis. Immunocompromised mice further support the existence of an immune-shaping regulation of RBC production. Overall, our data reveal the strict correlation between erythropoiesis and immune cells, which ultimately dictates the severity of SMA.

Correlation Of Malnutrition Status With Malaria Incidents In Children Under 5 Years Old

Malaria is a prevalent disease that continues to affect infants and results in the death of over 435,000 individuals worldwide, with children under the age of five accounting for 11-30% of these fatalities. Malnutrition afflicts millions of children worldwide, especially in Indonesia, and is the leading cause of mortality among toddlers. Investigate the correlation between malnutrition and the occurrence of malaria in children under the age of five. Additionally, explore the prevalence, factors that contribute to the risk, and underlying processes of both malaria and malnutrition. This study utilized 15 internationally recognized scholarly articles to investigate the correlation between malnutrition and the prevalence of malaria in children below the age of five. According to the analysis of 10 scholarly studies, the occurrence of malaria and malnutrition among children under the age of five remains significantly elevated, particularly in African nations, various countries in South Asia, Southeast Asia, the Middle East, Central America, and South America. The analysis of 3 journal papers examined the risk factors associated with malaria and malnutrition, specifically focusing on age, environment, sanitation, economy, education, food availability, and residing in refugee camps. After reviewing 9 journal publications, it has been shown that malnutrition is a risk factor for malaria. Additionally, one of the articles suggests that children who are malnourished have a reduced likelihood of contracting malaria. The findings of this scoping review indicate a bidirectional correlation between malnutrition and the occurrence of malaria in young children.

Geospatial Mapping of Malaria in the Lowland Forest Ecological Zone of Ondo State, Nigeria

Purpose: This study analyzed the spatial and temporal patterns of malaria occurrence in Ondo state, Nigeria, and to identify the major factors influencing the disease transmission and control.&#x0D; Methodology: The research collects primary data from questionnaires administered to hospital staff and workers in areas with high malaria cases while secondary data were sourced from the State’s ministry of health malaria cases gazette. The data was sorted, processed, and analyzed using Microsoft Excel and ArcGIS 10.2 software to produce maps and graphs showing the malaria occurrence and trends in the study area.&#x0D; Findings: The study analyzed the malaria occurrence and trends in Ondo State, Nigeria, from 2011 to 2018, using questionnaires and secondary data. The result revealed that mosquito, blood group, poverty, and communicability were significant factors for malaria incidence (p &lt; 0.001), while seasonality, time of day, and immunity status were non-significant (p &gt; 0.05). Result shows that malaria could be transmitted from mother to child during childbirth (p=0.008), but mortality was non-significant in the three Local Government Areas (LGAs) studied (p = 0.166). In addition, malaria cases and attack rate declined significantly from 2015 to 2018, due to the improved health facilities and preventive measures implemented by the State government and health authorities &#x0D; Unique contributor to theory, policy and practice: The study contributes to the existing knowledge and literature on malaria epidemiology and control in Nigeria, by providing a comprehensive and updated spatial and temporal analysis of the disease occurrence and factors in Ondo state. The study also provides useful recommendations for the state ministry of health and government to conduct further studies and develop strategies to interrupt malaria transmission in low susceptible settings, and to achieve the goal of malaria elimination in the state in line with global setting.

Utilization of malaria preventive strategies among pregnant women at Adeoyo Maternity Teaching Hospital, Ibadan, Nigeria

INTRODUCTION: Malaria in pregnancy is associated with a high rate of maternal and perinatal morbidity and mortality. Utilization of preventive strategies during pregnancy is an integral part of controlling the burden of malaria in pregnancy. This study evaluated the malaria preventive strategies utilized and the prevalence of asymptomatic malaria among pregnant women at Adeoyo Maternity Teaching Hospital (AMTH), Ibadan, Nigeria.&#x0D; METHODS: A facility-based descriptive cross-sectional study among 223 pregnant women in Adeoyo Maternity Teaching Hospital, Ibadan, Nigeria. Information on socio-demographic and obstetric characteristics, malaria symptoms, and the preventive strategies utilized were obtained using a structured questionnaire. Descriptive and bivariate analysis using chi-square test and multivariate analysis at 95% confidence interval and p&lt; 0.05 was used to determine the predictors of asymptomatic malaria infection in relation to socio-demographic characteristics and malaria preventive strategies utilized.&#x0D; RESULTS: The mean age was 30±5 years. More than half (53.4%) of them used insecticide-treated nets consistently, 49.3% sprayed the room regularly with insecticides, while the majority (88.3%) reported they maintained a clean environment. However, 11.7% tested positive for the malaria parasite. Consistency in the use of insecticide-treated net (ITN) and participants age were factors associated with malaria occurrence (p=0.01). The use of ITN and being &gt;30 years of age are protective factors against having malaria infection among pregnant women (AOR= 0.34; 95%, CI=0.14-0.84) and (AOR=0.31; 95% CI=0.13-0.76) respectively.&#x0D; CONCLUSION: Irrespective of all other malaria preventive strategies adopted by pregnant women, consistent use of ITN was independently a protective factor against developing malaria in pregnancy.

Frequency of Blood Donation and Malaria Occurrence among Blood Donors in a Tertiary Hospital, Nigeria

Context: Malaria is a significant cause of morbidity and mortality in Sub-Saharan Africa (SSA). Transfusion transmitted malaria contributes significantly to the burden of malaria in SSA. The safety of blood transfusion as it relates to frequency of blood donation and malaria occurrence on the part of donors is an aspect that has not been properly investigated hence this study.&#x0D; Objectives: This study was conducted to assess the frequency of blood donation and occurrence of malaria among blood donors at OAUTHC, Ile-Ife.&#x0D; Materials and methods: This was a cross-sectional study. Ethical approval was obtained. One hundred and thirty-three consenting blood donors aged between 18-50 years were recruited for the study. Two millilitres of blood were collected from each study participant and immediately transported to the laboratory for processing. Giemsa-stained films of the samples were viewed under the oil immersion objective of the microscope. Questionnaires were administered to the study participants to obtain relevant information. Data generated were analysed using descriptive and inferential statistics with SPSS software version 20. The level of significance was set at p &lt; 0.05.&#x0D; Results: The prevalence of malaria among the blood donors was 21.1% with the highest rate among commercial donors (33.3%) followed by family donors (12.9%) then voluntary donors (11.9%). Evaluation of the frequency of donation showed that malaria occurred more in recurring donors (77.78%) than first time donors (22.22%).&#x0D; Conclusion: Malaria is highly prevalent among blood donors and occurs more in recurring blood donors than first time donors.

Joint quantile disease mapping with application to malaria and G6PD deficiency.

Statistical analysis based on quantile methods is more comprehensive, flexible and less sensitive to outliers when compared to mean methods. Joint disease mapping is useful for inferring correlation between different diseases. Most studies investigate this link through multiple correlated mean regressions. We propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of malaria and the gene deficiency G6PD, where medical scientists have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of malaria. Thus, the need for flexible joint quantile regression in a disease mapping framework arises. Our model can be used for linear and nonlinear effects of covariates by stochastic splines since we define it as a latent Gaussian model. We perform Bayesian inference using the R integrated nested Laplace approximation, suitable even for large datasets. Finally, we illustrate the model's applicability by considering data from 21 countries, although better data are needed to prove a significant relationship. The proposed methodology offers a framework for future studies of interrelated disease phenomena.

Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.

Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022

ObjectivesFollowing the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali. MethodsCohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria. ResultsA total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season. ConclusionDetermining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.

Linear and Nonlinear Associations between Meteorological Variables and the Incidence of Malaria in Zahedan District Southwest of Iran 2000-2019

Introduction: Iran is one of the malaria-prone countries. Malaria transmission is likely to be affected by many factors, including meteorological variables. This study aimed to evaluate the effect of climate variables on malaria incidence. Methods: A secondary analysis was conducted to examine the relation between malaria and meteorological variables in Zahedan district from 2000 to 2019. We built univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM)/ Generalized Additive Mixed Models (GAMM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results: The SARIMA multivariate (1,0,1) (0,1,1)12 model, including the mean temperature and minimum humidity variables without lag, was the best fit. In nonlinear analysis, the number of malaria cases positively correlated with the month from January and peaked in May (edf=6.29). There was a generally negative correlation between malaria and time in years (edf=8.41). The mean temperature, between 20 to 30°C had the highest and slightly positive relation with the incidence of malaria (edf=7.55). Rainfall showed a negative association with small fluctuations between 20 and 45 mm and a positive association over 50 mm (edf=7.52). Mean relative humidity from above 50% had a negative relation with the number of cases (edf=6.93). The hours of sunshine in a month, until 235 hours, had a negative correlation and above 340 hours had a positive correlation with the incidence of malaria (edf=7.4). Conclusion: Meteorological variables can affect malaria occurrence.

Identification of disease-related genes in Plasmodium berghei by network module analysis

BackgroundPlasmodium berghei has been used as a preferred model for studying human malaria, but only a limited number of disease-associated genes of P. berghei have been reported to date. Identification of new disease-related genes as many as possible will provide a landscape for better understanding the pathogenesis of P. berghei.MethodsNetwork module analysis method was developed and applied to identify disease-related genes in P. berghei genome. Sequence feature identification, gene ontology annotation, and T-cell epitope analysis were performed on these genes to illustrate their functions in the pathogenesis of P. berghei.Results33,314 genes were classified into 4,693 clusters. 4,127 genes shared by six malaria parasites were identified and are involved in many aspects of biological processes. Most of the known essential genes belong to shared genes. A total of 63 clusters consisting of 405 P. berghei genes were enriched in rodent malaria parasites. These genes participate in various stages of parasites such as liver stage development and immune evasion. Combination of these genes might be responsible for P. berghei infecting mice. Comparing with P. chabaudi, none of the clusters were specific to P. berghei. P. berghei lacks some proteins belonging to P. chabaudi and possesses some specific T-cell epitopes binding by class-I MHC, which might together contribute to the occurrence of experimental cerebral malaria (ECM).ConclusionsWe successfully identified disease-associated P. berghei genes by network module analysis. These results will deepen understanding of the pathogenesis of P. berghei and provide candidate parasite genes for further ECM investigation.

Human NCR3 gene variants rs2736191 and rs11575837 alter longitudinal risk for development of pediatric malaria episodes and severe malarial anemia

BackgroundPlasmodium falciparum malaria is a leading cause of pediatric morbidity and mortality in holoendemic transmission areas. Severe malarial anemia [SMA, hemoglobin (Hb) < 5.0 g/dL in children] is the most common clinical manifestation of severe malaria in such regions. Although innate immune response genes are known to influence the development of SMA, the role of natural killer (NK) cells in malaria pathogenesis remains largely undefined. As such, we examined the impact of genetic variation in the gene encoding a primary NK cell receptor, natural cytotoxicity-triggering receptor 3 (NCR3), on the occurrence of malaria and SMA episodes over time.MethodsSusceptibility to malaria, SMA, and all-cause mortality was determined in carriers of NCR3 genetic variants (i.e., rs2736191:C > G and rs11575837:C > T) and their haplotypes. The prospective observational study was conducted over a 36 mos. follow-up period in a cohort of children (n = 1,515, aged 1.9–40 mos.) residing in a holoendemic P. falciparum transmission region, Siaya, Kenya.ResultsPoisson regression modeling, controlling for anemia-promoting covariates, revealed a significantly increased risk of malaria in carriers of the homozygous mutant allele genotype (TT) for rs11575837 after multiple test correction [Incidence rate ratio (IRR) = 1.540, 95% CI = 1.114–2.129, P = 0.009]. Increased risk of SMA was observed for rs2736191 in children who inherited the CG genotype (IRR = 1.269, 95% CI = 1.009–1.597, P = 0.041) and in the additive model (presence of 1 or 2 copies) (IRR = 1.198, 95% CI = 1.030–1.393, P = 0.019), but was not significant after multiple test correction. Modeling of the haplotypes revealed that the CC haplotype had a significant additive effect for protection against SMA (i.e., reduced risk for development of SMA) after multiple test correction (IRR = 0.823, 95% CI = 0.711–0.952, P = 0.009). Although increased susceptibility to SMA was present in carriers of the GC haplotype (IRR = 1.276, 95% CI = 1.030–1.581, P = 0.026) with an additive effect (IRR = 1.182, 95% CI = 1.018–1.372, P = 0.029), the results did not remain significant after multiple test correction. None of the NCR3 genotypes or haplotypes were associated with all-cause mortality.ConclusionsVariation in NCR3 alters susceptibility to malaria and SMA during the acquisition of naturally-acquired malarial immunity. These results highlight the importance of NK cells in the innate immune response to malaria.

Risk factors of malaria transmission in mining workers in Muara Enim, South Sumatra, Indonesia

Eliminating malaria by 2030 is stated as goal three in the UN’s Sustainable Development Goals (SDGs). However, malaria still remains a significant public health problem. This study aims to identify the factors determining malaria transmission in artisanal or small-scale miner (ASM) communities in three villages: Tanjung Agung, Tanjung Lalang, and Penyandingan, located in the Tanjung Enim District, Muara Enim, South Sumatra, Indonesia. Researchers conducted a cross-sectional study involving 92 participants from the study area. They used a logistic regression model to investigate the risk factors related to malaria occurrence. The multivariable analysis revealed that age (Adjusted Prevalence Ratio (APR) = 7.989 with 95% CI 1.724–37.002) and mosquito breeding (APR = 7.685 with 95% CI 1.502–39.309) were risk factors for malaria. On the other hand, higher education (APR = 0.104 with 95% CI 0.027–0.403), the use of mosquito repellent (APR = 0.138 with 95% CI 0.035–0.549), and the condition of house walls (APR = 0.145 with 95% CI 0.0414–0.511) were identified as protective factors. The current study highlights age and mosquito breeding sites as risk factors for malaria. Additionally, higher education, insect repellent use, and the condition of house walls are protective factors against malaria. Therefore, reducing risk factors and increasing protective measures through effective communication, information, and education are highly recommended to eliminate malaria in mining areas.

Application of geographical information system and remote sensing for investigating malaria’s footprint: a case study of Iwo Metropolis, Nigeria

On a worldwide scale, the challenge posed by malaria fever has long been acknowledged as a significant peril to the global population. Nigeria, being situated within a tropical climate, has experienced an especially profound impact. The imperative to closely monitor this lethal disease cannot be overstated. Therefore, this research employs a comprehensive methodology that combines Geographic Information System (GIS) techniques with Epidemiology. The primary objective is to create a malaria risk map specifically for Iwo Metropolis while also gauging the extent of susceptibility within the region. Ultimately, the aim is to diminish the occurrence of malaria by implementing suitable medical interventions. The objectives of this study are the creation of spatial database for the entities in the study area, performing spatial analyses on the database created; and analyzing the spread of malaria disease based on environmental factors in Iwo Local government and finally to generate Malaria Risk Map. This research work was designed to examine and analyze the incidence of malaria and clearer understanding of geographic distribution of malaria-infected people and locations of such inhabitants to the health facility being essential for monitoring the prevalence and for providing treatment, care and support services to the affected area. The study adopted the primary method of data collection using Global positioning system (GPS) to pick point locations of areas where malaria is prevalent and the locations of the different hospitals within Iwo metropolis. Also, topographic maps were collected from SAS planet and Google Earth and secondary data from the State General Hospital, Iwo, where malaria data of adults above 18 years were collected which is divided into old and new cases. In the build- up of this project, the data collected were then uploaded to ArcGIS 10.8, this software was used to perform spatial analysis like buffering operation, service area, trend analysis and neighborhood operation. This project is aimed at producing digital or hard copy maps showing malaria patterns in form of high and low risk areas in order to determine areas that require quick intervention programs.

Coenzyme Q10 exhibits anti-inflammatory and immune-modulatory thereby decelerating the occurrence of experimental cerebral malaria

Cerebral Malaria (CM) is associated with the complex neurological syndrome, whose pathology is mediated by severe inflammatory processes following infection with Plasmodium falciparum. Coenzyme-Q10 (Co-Q10) is a potent anti-inflammatory, anti-oxidant, and anti-apoptotic agent with numerous clinical applications. The aim of this study was to elucidate the role of oral administration of Co-Q10 on the initiation or regulation of inflammatory immune response during experimental cerebral malaria (ECM). For this purpose, the pre-clinical effect of Co-Q10 was evaluated in C57BL/6 J mice infected with Plasmodium berghei ANKA (PbA). Treatment with Co-Q10 resulted in the reduction of infiltrating parasite load, greatly improved the survival rate of PbA-infected mice that occurred independent of parasitaemia and prevented PbA-induced disruption of the blood-brain barrier (BBB) integrity. Exposure to Co-Q10 resulted in the reduction of infiltration of effector CD8 + T cells in the brain and secretion of cytolytic Granzyme B molecules. Notably, Co-Q10-treated mice had reduced levels of CD8 +T cell chemokines CXCR3, CCR2, and CCR5 in the brain following PbA-infection. Brain tissue analysis showed a reduction in the levels of inflammatory mediators TNF- α, CCL3, and RANTES in Co-Q10 administered mice. In addition, Co-Q10 modulated the differentiation and maturation of both splenic and brain dendritic cells and cross-presentation (CD8α+DCs) during ECM. Remarkably, Co-Q10 was very effective in decreasing levels of CD86, MHC-II, and CD40 in macrophages associated with ECM pathology. Exposure to Co-Q10 resulted in increased expression levels of Arginase-1 and Ym1/chitinase 3–like 3, which is linked to ECM protection. Furthermore, Co-Q10 supplementation prevented PbA-induced depletion of Arginase and CD206 mannose receptor levels. Co-Q10 abrogated PbA-driven elevation in pro-inflammatory cytokines IL-1β, IL-18, and IL-6 levels. In conclusion, the oral supplementation with Co-Q10 decelerates the occurrence of ECM by preventing lethal inflammatory immune responses and dampening genes associated with inflammation and immune-pathology during ECM, and offers an inimitable opening for developing an anti-inflammatory agent against cerebral malaria.