We develop a machine learning (ML) approach for improving the accuracy of the horizontal dis-tribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry-transport model over Northern Africa using Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. These observations are used during the training phase of the ML method for deriving a regional bias correction of AODs simulated by CHIMERE. The results are daily maps of regional bias corrected AODs with full horizontal coverage over Northern Africa. We test four types of ML models: multiple linear regression (MLR), random forests (RF), gradi-ent boosting (XGB), and multiple layer perceptron networks (NN). We perform comparisons with satellite and independent ground-based observations of AOD that are not used in the training phase. They suggest that all models have overall comparable performances with a slight advantage of the RF model which expresses less spatial artifacts. While the method slightly underestimates the very high AODs, it significantly reduces biases and absolute errors, and clearly enhances linear correlations with respect to independent observations. This im-provement for deriving the AOD is particularly relevant for high dust pollution regions like the Sahara Desert, which dramatically lack ground-based measurements for validations of chemis-try-transport modeling which currently remains challenging and imprecise.

COVID-19 differential spread and impacts across regions is a major focus for researchers and policy makers. Africa has attracted tremendous attention due to predictions of catastrophic impacts that have not yet materialized. Early in the pandemic, the seemingly low African case count was largely attributed to low testing and case reporting. However, there is also reason to consider that many African countries got out ahead of the virus early on. Factors explaining low spread include early government mandated lockdowns, community-wide actions, population distribution, social contacts, and ecology of human habitation. While recent data from seroprevalence studies posit more extensive circulation of the virus, continuing low COVID-19 burden may be explained by the demographic pyramid, prevalence of pre-existing conditions, trained immunity, genetics, and broader sociocultural dynamics. Though all these prongs contribute to the observed profile of COVID-19 in Africa, some provide stronger evidence than others. This review is important to expand what is known about the differential impacts of pandemics enhancing scientific understanding and gearing appropriate public health responses. Also, highlighting potential lessons the world may draw from Africa for global health on assumptions regarding deadly viral pandemics given its long experience with infectious diseases.

This study reports the use of Nanotrap® Microbiome A Particles (NMAPs) to capture and concentrate viruses from diluted samples/specimens to improve their recovery and sensitivity of detection by real-time PCR/RT-PCR (qPCR/RT-qPCR). Five highly infectious animal disease viruses including goat pox virus (GTPV), sheep pox virus (SPPV), lumpy skin disease virus (LSDV), peste des petits ruminants virus (PPRV), and African swine fever virus (ASFV) were used in this study. After capture, the viruses remained viable and recoverable by virus isolation (VI) using susceptible cell lines. To assess efficacy of recovery, the viruses were serially diluted in phosphate buffered saline (PBS) or Eagle’s Minimum Essential Medium (EMEM) and then subjected to virus capture using NMAPs. The NMAPs and the captured viruses were clarified on a magnetic stand, reconstituted in PBS or EMEM, and analyzed separately by VI and virus-specific qPCR/RT-qPCR. The results of the qPCR/RT-qPCR showed up to 100-fold increase in the sensitivity of detection of the viruses following virus capture compared to the untreated viruses from the same dilutions. NMAPs were used to capture and concentrate viruses from clinical specimens to determine diagnostic sensitivity (DSe) that were comparable (100%) to the untreated specimens. NMAPs were also used to capture spiked viruses from EDTA whole blood (EWB). Virus capture from EWB was partially blocked, most likely by hemoglobin (HMB), which also binds NMAPs and outcompetes the viruses. The effect of HMB could be removed by either dilution (in PBS) or using HemogloBind™ (Biotech Support Group; Monmouth Junction, NJ) that specifically binds and precipitates HMB. Enhanced recovery and detection of viruses using NMAPs can be applicable to other highly pathogenic animal viruses of agricultural importance.

Pteridine reductase 1 is a trypanosomatid multifunctional enzyme that provides a mechanism for escape of Dihydrofolate reductase (DHFR) inhibition. This is because PTR1 can reduce pterins and folates. Trypanosomes require folates and pterins for survival and are unable to synthesize them de novo. Currently there are no anti-folate based Human African Trypanosomiasis (HAT) chemotherapeutics in use. Thus, successful dual inhibition of TbDHFR and TbPTR1 has implications in the exploitation of anti-folates. We carried out molecular docking of a ligand library of 5742 compounds against TbPTR1 and identified 18 compounds showing promising binding modes. The protein-ligand complexes were subjected to Molecular dynamics to characterize their molecular interactions and energetics followed by in vitro testing. In this study, we identified five potential TbPTR1 inhibitors that showed low micromolar Trypanosome growth inhibition in in vitro experiments with no significant human cell cytotoxicity. Compounds RUBi004, RUBi007, RUBi014, and RUBi018 displayed moderate to strong antagonism when used in combination with the known TbDHFR inhibitor, WR99210. This gave an indication that the compounds might inhibit both TbPTR1 and TbDHFR. RUBi016 showed an additive effect in the isobologram assay. Our results provide a basis for scaffold optimization for further studies in the development of HAT antifolates.

Chagas disease, caused by Trypanosoma cruzi, presents a significant challenge in global health due to the parasite's complex life cycle and its persistence in human tissues. In contrast, advances in treating Trypanosoma brucei infections, notably through the use of irreversible enzyme inhibitors like eflornithine, offer valuable insights into therapeutic strategies. This article explores how artificial intelligence (AI) can aid in identifying crucial metabolic and biochemical pathways in T. cruzi that could serve as targets for irreversible enzymatic inhibitors, drawing parallels to the successful inhibition of T. brucei’s ornithine decarboxylase. By employing AI for data mining, drug-target interaction prediction, pathway modelling, and drug repurposing, researchers can accelerate the discovery of novel treatments. We compare the biological and biochemical differences between T. cruzi and T. brucei, highlighting how AI can bridge the gap in drug discovery and offer new possibilities for Chagas disease treatment.