Search Results (178)

Tuberculosis, malaria, and HIV are infectious diseases that have become major global health problems. Efforts to reduce the incidence and mortality of tuberculosis have undergone a long process, resulting in a significant annual decrease of up to 2%. In a single year, malaria cases can reach nearly 230,000,000, with up to 400,000 deaths worldwide. Meanwhile, approximately 37,000,000 people were living with HIV worldwide in 2020, with about 690,000 deaths due to AIDS reported in the same year. Within the framework of the Sustainable Development Goals (SDGs), particularly Goal 3 on good health and well-being, one of the key targets is to end the epidemics of tuberculosis, malaria, and HIV. This research examines the effects of various interventions on tuberculosis, malaria, and HIV coinfection. The interventions considered include preventive measures, mosquito nets, insecticides, contraception, tuberculosis treatment, malaria treatment, and antiretroviral (ARV) therapy for HIV. The mathematical model of tuberculosis, malaria, and HIV coinfection is well-defined, as it is proven to have non-negative solutions, to be bounded, and to remain within the positive invariant region. The tuberculosis, malaria, and HIV sub-models each have an asymptotically stable equilibrium when the basic reproduction number is less than one. Based on the results of numerical simulations of the sub-models, it can be observed that when the basic reproduction number exceeds one, the disease spreads throughout the population. Full article

Objectives: Malaria remains a major global health burden, particularly in endemic regions such as Djibouti, where Plasmodium falciparum and Plasmodium vivax co-circulate, complicating disease control strategies. Increasing resistance to current antimalarial drugs reduces treatment effectiveness and highlights the urgent need for new, safe, and affordable therapeutic agents. This study aimed to identify potential inhibitors from Djiboutian medicinal plants using an integrated in silico approach targeting key proteins from both parasite species. Methods: A library of 222 phytoconstituents was screened against Plasmodium vivax FK506-binding protein 35 (PDB ID: 3IHZ) and Plasmodium vivax dihydrofolate reductase–thymidylate synthase (PDB ID: 1J3K) using molecular docking. Top-ranked compounds were further analyzed for binding interactions and evaluated for drug-likeness and pharmacokinetic properties using QikProp in Maestro v11.5. Selected protein–ligand complexes were subjected to 100 ns molecular dynamics simulations, and their stability was assessed using multiple descriptors, including structural deviation, flexibility, compactness, solvent exposure, and hydrogen bond persistence. Results: Several phytoconstituents exhibited strong binding affinities, with docking scores ranging from −6.09 to −7.54 kcal/mol, outperforming the reference drug artemisinin. Interaction analysis revealed key hydrogen bonds and hydrophobic contacts with essential active-site residues. ADMET predictions indicated favorable pharmacokinetic profiles, including high oral absorption, good membrane permeability, and low predicted toxicity. Molecular dynamics simulations demonstrated stable behavior for most complexes, with compound 121 showing enhanced stability in the 1J3K system and compound 123 exhibiting consistent dynamic stability in the 3IHZ system. In contrast, compound 82 displayed greater structural fluctuations despite maintaining stable hydrogen bond interactions. Conclusions: The integration of molecular docking, ADMET prediction, and molecular dynamics simulations identified compounds 121 and 123 as the most promising antimalarial candidates, exhibiting an optimal balance of binding affinity, favorable pharmacokinetic properties, and dynamic stability. These findings highlight the potential of Djiboutian medicinal plants as a valuable source of novel antimalarial agents and provide a strong computational foundation for future experimental validation. Full article

Climate change is significantly altering the distribution, seasonality, and transmission dynamics of vector-borne diseases, posing an increasing public health concern, particularly in regions previously considered at low risk. Key climatic variables, including temperature, precipitation, and humidity, strongly influence vector survival, reproduction, and pathogen transmission capacity. This review provides an overview of current evidence on the relationship between climate change, vector ecology, and vector-borne disease epidemiology in Europe, based on evidence extracted from peer-reviewed literature published between 2010 and 2024 and retrieved from PubMed, Scopus, and Web of Science. Particular attention is given to vector-borne diseases with current or potential relevance to Europe. This includes infections that are already endemic, such as leishmaniasis and tick-borne encephalitis, as well as diseases currently imported, such as malaria, dengue, chikungunya, Zika, yellow fever, and Chagas disease, that may pose a future risk of local transmission due to climate-driven changes in vector distribution and abundance. The analysis highlights potential health and economic impacts, and discusses implications for public health preparedness, environmental sanitation, surveillance, and vector control strategies. Strengthening adaptive and mitigation policies, alongside targeted research efforts, is essential to enhance resilience of health systems against climate-driven epidemic risks. Full article

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

Malaria is a major global health concern caused by Plasmodium parasites, among which Plasmodium falciparum is responsible for the most severe and fatal cases. The emergence of drug resistance to existing antimalarial therapies necessitates the discovery of novel molecular targets and chemically distinct inhibitors. Current study employed an integrated in silico drug discovery pipeline combining high-throughput structure-based virtual screening of 1549 deep-sea marine microbial metabolites with MM-GBSA binding free-energy estimation, QikProp-based ADME/Tox profiling, and 100 ns molecular dynamics (MD) simulations to link rapid screening with dynamic verification of binding stability. Molecular docking against Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH; PDB ID: 7KZ4) yielded five top-ranked compounds with Glide scores ranging from −12.02 to −10.61 kcal·mol⁻¹, which is higher than the Primaquine (−6.920 kcal·mol⁻¹; a clinically approved antimalarial reference compound). MM-GBSA analysis further refined hit selection, producing binding free energies (ΔG_bind) between −63.28 and −31.37 kcal·mol⁻¹. The selected lead compounds included (±)-puniceusine P, aspergilol F, tersaphilone C, 4-carbglyceryl-3,3′-dihydroxy-5,5′-dimethyldiphenyl ether, and 15-O-methyl ML-236A. The top hits were subjected to 100 ns MD simulations in Desmond, demonstrating stable protein–ligand complexes, particularly for (±)-puniceusine P and 15-O-methyl ML-236A (protein backbone root mean square deviation (RMSD; ~0.8–1.0 Å). ADME profiling indicated acceptable predicted physicochemical and pharmacokinetic properties. Overall, these in silico findings highlight deep-sea marine microbial metabolites as promising PfDHODH inhibitor candidates requiring experimental validation. Full article

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. In this study, we present an integrative computational framework that combines ensemble machine learning, molecular docking, and molecular dynamics simulations to predict and characterize the antimalarial activity of compounds from the Malaria Box database. Initially, topographical and quantum mechanical descriptors were used to construct regression models for predicting pEC₅₀ values, but due to the limited predictive performance in the global regression, a classification strategy was adopted, categorizing compounds into “active” and “very active” classes. The best ensemble classifier achieved robust performance (Acc₁₀-fold = 0.738, Acc_ext = 0.675), with good sensitivity and specificity over individual models. Subsequent regression modeling within each class yielded high predictive accuracy, with ensemble models reaching Q²10-fold values of 0.810 and 0.793 for the very active and active classes, respectively. To explore potential mechanisms of action, molecular docking was performed against P. falciparum Cytochrome B, revealing strong binding affinities for most compounds, particularly those forming π–π stacking and hydrogen bonds with Glu272. Molecular dynamics simulations over 200 ns confirmed the stability of several ligand–protein complexes, including unexpected behavior from compound M31, which demonstrated stable binding despite poor docking scores, suggesting a possible competitive inhibition mechanism. Binding free energy calculations further validated these findings, highlighting several promising candidates for future experimental evaluation. This integrative approach offers a powerful platform for accelerating antimalarial drug discovery by combining predictive modeling with mechanistic insights. Full article

Malaria remains a major global health burden, requiring rapid and reliable diagnostic tools to complement or replace labor-intensive manual microscopy. Although deep learning methods have demonstrated strong potential for automated malaria diagnosis, many existing approaches depend on computationally expensive transfer learning architectures or exhibit sensitivity to suboptimal hyperparameter configurations. This study proposes a lightweight automated framework for binary classification of malaria cell images using a custom Convolutional Neural Network (CNN) optimized by a novel Adaptive Marine Predators Algorithm (AMPA). The proposed AMPA integrates a state-aware adaptive control factor that dynamically adjusts step size based on population loss, thereby improving search efficiency and reducing susceptibility to local optima. The framework was evaluated on the NIH Malaria Cell Image Dataset containing 27,558 single-cell images. Experimental results show that the AMPA-optimized CNN achieves a testing accuracy of 95.00% and an Area Under the Curve of 0.986. Comparative experiments indicate that the proposed model outperforms several reported lightweight architectures, including MobileNetV2 (92.00%) and YOLO-based detectors (94.07%), while achieving performance comparable to deeper networks such as VGG-16 (94.88%), with substantially lower computational complexity. The model further attains high sensitivity (0.94) and precision (0.96), supporting its suitability as a robust and resource-efficient approach for automated malaria screening research. Full article

Yellow fever (YF) and malaria co-infections are real public health concerns in Africa, especially in countries such as Nigeria, where mosquitoes carrying both pathogens (Aedes for YF, Anopheles for malaria) coexist. A mathematical model that considers the critical factors influencing the transmission dynamics and control interventions of YF and malaria co-infections is formulated and used to analyse the problem. The essential dynamical features of the model, such as the basic reproduction number and disease-free equilibrium, are determined and analysed. The qualitative analysis of the model illustrates the conditions under which the disease can be eradicated or persists. Further analysis, supported by numerical simulations, reveals the intrinsic dynamics of the model and the impact of control interventions such as yellow fever vaccination, use of insecticide-treated mosquito nets, treatment of malaria-infected humans, and use of insecticides. The results of the analysis demonstrate the impact of interventions; specifically, effective implementations of interventions such as yellow fever vaccination, use of insecticide-treated mosquito nets, and use of insecticides appear to have a significant impact in eradicating YF and malaria co-infections in endemic areas. Effective treatment of malaria-infected humans may lead to a decrease in infections but might not necessarily lead to eradicating infections in endemic areas. These findings are expected to aid in improving the management of YF and malaria co-infections in endemic regions for expeditious disease eradication. Full article

Background and Objectives: Malaria, a mosquito-borne parasitic disease caused by Plasmodium species, remains a public health concern in many tropical and subtropical regions. In Saudi Arabia, sustained control efforts have substantially reduced malaria transmission; however, regional heterogeneity and the growing contribution of imported infections continue to shape national malaria epidemiology. This study aimed to provide a comprehensive national assessment of malaria patterns in Saudi Arabia within a prevention-of-reintroduction framework. Materials and Methods: National malaria surveillance data reported by the Ministry of Health from 2010 to 2024 were analyzed to describe temporal trends in malaria burden and incidence, parasite species distribution, age structure, and seasonality across 20 health regions. Spatial heterogeneity was assessed using regional heatmaps and endemicity mapping. Transmission classification, parasite species, and age distribution were examined nationally and in greater detail for the Aseer and Jazan regions during 2021–2024. Future malaria trends through 2030 were projected using autoregressive integrated moving average (ARIMA) models. Results: Between 2010 and 2024, a total of 52,627 malaria cases were reported nationally, with marked interannual variability and an increase in incidence observed after 2020. Malaria burden was historically concentrated in seven endemic regions and subsequently became largely restricted to Aseer and Jazan. Plasmodium falciparum and Plasmodium vivax/Plasmodium ovale accounted for the majority of infections, with cases predominantly occurring among individuals aged ≥10 years. From 2021 onward, no indigenous malaria transmission was recorded; introduced cases were uncommon, and most infections were classified as imported. Forecasting analyses indicated stable national and regional malaria trends through 2030. Conclusions: Malaria in Saudi Arabia during 2010–2024 was characterized by pronounced regional heterogeneity and predominantly importation-driven dynamics. These findings underscore the importance of sustained surveillance, targeted interventions in high-burden regions, and continued vigilance to prevent the re-establishment of local transmission. Full article

Vector-borne diseases are strongly influenced by climate, yet the magnitude and temporal variability of climate–disease relationships remain poorly quantified. Outbreaks occur abruptly, and responses are typically delayed, underscoring the need for predictive tools that can support proactive interventions. This study applies Temporal Fusion Transformers (TFTs) to long-term, high-resolution climate datasets and to weekly malaria and dengue case records from South Africa and Vietnam to assess predictive performance and identify key environmental drivers. The models incorporated diverse climatic predictors and large-scale climate indices and were trained using multi-horizon forecasting with novel loss functions and physics-based constraints. The best malaria model achieved an R² of 0.95 and an MAE of 4.98, while leading dengue models reached R² values up to 0.90. Variable-importance analyses derived from model-learned weights showed that extreme temperature and rainfall metrics were consistently the strongest predictors, with ENSO (El Niño Southern Oscillation) and IOD (Indian Ocean Dipole) improving longer-range malaria forecasts. Furthermore, climate–disease risk dynamics were explored, revealing specific temperature and rainfall thresholds associated with elevated transmission and highlighting non-stationary relationships across decades. These findings demonstrate accurate, interpretable forecasting offered by TFTs and represent a valuable tool for early warning and understanding of complex climate–disease interactions. Full article

Background: Malaria continues to be a significant global health concern, particularly in tropical and subtropical areas. Timely and accurate diagnosis is crucial in minimizing the disease’s mortality. The standard method, microscopic diagnosis, which represents the gold standard, is heavily reliant on skilled interpretation, labor-intensive, and prone to human error. Methods: To address these challenges, we propose the NOVA (Novel Multi-Scale Adaptive Vision Architecture) for the diagnosis of malaria. NOVA is based on an innovative dynamic channel attention and Learnable Temperature Spatial Pyramid Attention to achieve more powerful feature representation and better classification performance. In addition, adaptive feature refinement and enhanced transformer blocks are used to obtain multi-scale feature extraction and contextual reasoning. Furthermore, a multi-strategy pooling mechanism that fuses average, max, and attention-based aggregation is developed to enhance the model’s discriminative capability. Results: We conduct experiments on a publicly accessible dataset of 15,031 microscopic thin blood smear images to validate the effectiveness of the proposed approach. The model is assessed and compared on a benchmark malaria microscopy dataset, achieving an accuracy of 97.00%, a precision of 96.00%, and an F1-score of 97.00%, outperforming other existing models. Conclusions: The experimental results demonstrate the feasibility of the proposed approach as a potential research prototype for the automated diagnosis of malaria. Before clinical deployment, further multi-site clinical evaluation on a large patient cohort is required for validation. Full article

Background/Objectives: Malaria is a tropical disease mainly caused by Plasmodium falciparum and represents a global public health problem, with over 200 million cases and 500 thousand deaths reported worldwide. Considering its treatment limitations, it is essential to develop new compounds against malaria. In this context, acridine derivatives are privileged structures. Methods: Thus, new spiroacridines containing N-acylhydrazone (AMTAC) and N-phenylacetamide (ACMD) were synthesized and evaluated in malaria and cytotoxicity assays, as well as in silico studies. Results: As a result, five spiroacridines showed inhibitory activity over 70% against the P. falciparum 3D7-GFP strain at 10 μM, along with an IC₅₀ range of 2–4 μM. After a brief Structure–Activity Relationship (SAR) analysis, it was observed that the spiroacridine structure must be associated with the hydrazone moiety to successfully inhibit parasite growth. In addition, these molecules presented promising resistance profile, with selectivity for the parasite. After computational studies, spiroacridines showed better affinity with dihydrofolate reductase (DHFR), overcoming the quadruple mutant resistance to pyrimethamine, with more stability in complex with the enzyme. Conclusions: Therefore, the potential of spiroacridines against malaria, with moderate resistance and selectivity profile, as well as DHFR inhibition greater than pyrimethamine, was confirmed. Full article

Malaria control in sub-Saharan Africa lies at the intersection of changing climate suitability and the scale-up of vector control and case management. Drawing on recent evidence from Kenya, we argue that climate variability already exerts effects on malaria outcomes comparable to, and sometimes stronger than, those of commonly measured interventions at local scales. Transmission dynamics display non-linear, lagged relationships with temperature and rainfall. As a result, climate change is expected to alter prevailing conditions and extremes, reshaping the geography and seasonality of malaria risk. At the same time, socio-economic development and vector control intervention such as insecticide-treated bed nets (ITNs) and timely case management continue to reduce malaria incidence and deaths, especially among young children. However, their population-level impact depends on when and where interventions are deployed relative to climate-favoured windows of transmission. We propose a practical agenda for “climate-smart” malaria control in Kenya advocating for dynamic targeting of interventions according to observed climate lags and thresholds, sustaining protection for the youngest, and innovating approaches for school-age reservoirs of infection. Access to effective care should be re-conceptualized as a climate-adaptation strategy, and short-term, locally tailored forecasts should be embedded into routine planning to support anticipatory and equitable malaria control. Full article

Microsporidia MB is a natural symbiont of Anopheles arabiensis Patton, 1905 that blocks Plasmodium transmission without compromising mosquito fitness. Developing this symbiont for malaria control requires a thorough understanding of its transmission dynamics across generations under environmentally relevant conditions. This study aimed to evaluate the transgenerational spread of Microsporidia MB in semi-field settings. From field-collected infected females (G0), several independent replicates of Microsporidia MB-infected An. arabiensis were established and maintained under semi-field conditions for six generations (F1–F6). All the mosquitoes were screened for Microsporidia MB prevalence and infection intensity using qPCR after death. Temperature and humidity and wing size were recorded. Two replicates sustained through F6. Microsporidia MB prevalence and intensity increased from F1 to F3–F4 before declining. A weak positive correlation was observed between prevalence and temperature, but not humidity. Female mosquitoes exhibited higher infection intensities. Female wing size varied across generations, while male size remained consistent. This first study on Microsporidia MB transgenerational transmission under semi-field conditions revealed a pattern of initial increase followed by decline in prevalence and intensity. The stability of male size supports the potential use of infected males in release programs. These findings underscore additional investigations into environmental drivers for mosquito symbiont prevalence. Full article

Plasmodium parasites are the causative agents of malaria and can infect humans and other vertebrates, impacting socioeconomic development and causing significant health issues globally. Plasmodium falciparum causes the most severe type of infection, which can lead to chronic morbidity and other severe complications like anemia and cerebral malaria. The onset of infection is marked by the injection of sporozoites into the skin through the bite of a female Anopheles mosquito. This triggers a cascade of reactions elicited both by the host immune system in response to infection and by the parasite in a bid to evade the host immune system, survive, and replicate. The dynamics of this host–parasite relationship have prompted extensive research in an attempt to understand and exploit it in the fight against malaria. Thus, understanding the temporal and spatial dimensions of adaptation in host–parasite relationships is critical for forecasting parasite evolution and spread within and between host populations. One such relationship is the complex interplay between malaria and cellular aging processes. Understanding this dynamic will provide novel insights into the pathophysiology of the disease. This comprehensive review takes us on that journey by providing an overview of the interaction between the Plasmodium parasite and its host and the interplay between infection mechanisms, host immune response, and parasite evasion strategies, narrowing it down to how it affects cellular aging biomarkers and how this can be explored as a platform in the fight against the disease. Full article