Baobab Index

Post-translational modifications (PTMs) play a crucial role in increasing proteomic diversity. N-linked glycosylation acts as a key regulatory layer that influences protein stability, trafficking, circulation, and immune responses. Unlike conventional inflammatory biomarkers that measure individual proteins, nuclear magnetic resonance (NMR) spectroscopy identifies the combined signals GlycA and GlycB from glycoproteins, offering an overall view of systemic glycoprotein changes. These signals represent the N-glycosylation patterns of several abundant acute-phase proteins (APPs), giving detailed molecular insights. This review offers a detailed assessment of GlycA and GlycB as mechanistically grounded indicators of liver glycoprotein remodeling and systemic inflammation. GlycA mainly indicates the levels and structural complexity of N-acetylglucosamine (GlcNAc) and N-acetylgalactosamine (GalNAc) residues linked to acute-phase glycoproteins and glycan branching. In contrast, GlycB reflects changes in terminal sialylation, which influences glycoprotein half-life, immune recognition via lectins, and inflammatory signaling. Collectively, these biomarkers combine measurements of hepatic APP production with variations in glycan structure, offering mechanistically anchored reporters of hepatic glycoprotein remodeling. We explore the enzymatic pathways responsible for N-glycan branching, fucosylation, and sialylation, as well as the roles of major APP scaffolds in the GlycA and GlycB resonances. We also highlight the emerging clinical significance of these signals across infectious, autoimmune, cardiovascular, metabolic, neurodegenerative, and cancer-related diseases. Rather than serving simply as markers of inflammation, GlycA and GlycB provide mechanistically interpretable readouts of cytokine-driven hepatic glycoprotein remodeling and systemic immune activation, supporting their application in disease risk stratification, longitudinal monitoring, therapeutic response assessment, and precision medicine.

East African health systems face dual challenges of infectious disease burdens and rising non-communicable diseases within resource-constrained environments. Artificial Intelligence (AI), Big Data, and Cloud Computing offer transformative potential for healthcare delivery through enhanced diagnostics, optimized resource allocation, and improved care continuity. We conducted a systematic review following PRISMA 2020 guidelines, searching PubMed, IEEE Xplore Web of Science, and Google Scholar for studies on AI, Big Data, and Cloud Computing applications in East-African healthcare systems (2014-2024). Risk of bias was assessed using the ROBIS-I tool, with narrative synthesis used to analyze findings. Thematic analysis was guided by the Consolidated Framework for Implementation Research (CFIR). From 129 identified studies, 19 met inclusion criteria. Findings revealed promising pilot implementations demonstrating preliminary evidence of improved diagnostic accuracy and operational efficiency. However, based on the available evidence, no interventions achieved sustainable scale-up using predefined scaling criteria. Five key barriers emerged: inadequate infrastructure (reported in 74% of studies), limited workforce digital literacy (58%), incomplete governance frameworks (47%), poor system interoperability (42%), and unsustainable financing models dependent on donor funding (37%). A significant policy-practice gap exists between comprehensive national digital health strategies and ground-level implementation. While pilot projects show measurable benefits, systematic barriers prevent scaling to routine practice. Based on the available evidence, success may require coordinated investments across infrastructure, workforce development, governance, interoperability, and sustainable financing, supported by implementation science approaches. However, the moderate risk of bias in included studies and the developing state of implementation research in the region warrant cautious interpretation of these findings.

Partitioning of genetic variance into additive and non-additive components using the pedigree-based best linear unbiased prediction (P-BLUP) model is possible because of the family structure and replicated clones in clonally propagated crops, but this model may overestimate these components. However, the genomic best linear unbiased prediction (G-BLUP) method, which integrates the genetic relationship through molecular marker information reduces the overestimation. Alternatively, a combination of the P-BLUP and G-BLUP, sourcing to create a hybrid matrix that estimates hybrid best linear unbiased prediction (H-BLUP), is proposed. We investigated if integrating molecular information into the clonal model could improve the partitioning of the variance components leading to more accurate estimates of genetic parameters and prediction accuracy of breeding values of 14 key traits in diploid banana. In this study, we used clones of 14 full-sib families from a factorial mating design of four female and five diploid male banana (

The linear-quadratic (LQ) model and its derived ratio, α/β, have served as the cornerstone of radiotherapy dose-fractionation decisions. The period from 2015 to 2026 has witnessed a substantial re-evaluation of this paradigm, driven by the clinical success of hypofractionation in prostate and breast cancer, stereotactic body radiation therapy (SBRT), and radiogenomics. A systematic review with narrative synthesis was conducted to evaluate quantitative estimates of α/β derived from clinical and preclinical studies over the last decade, updating classical assumptions using modern trial data. Extensive Phase III data in prostate cancer consistently define an α/β of 1.2 to 2.0 Gy. Microscopic models in breast cancer align with an α/β of ∼2.7 Gy. Conversely, lung SBRT data present a high modeled α/β driven by hypoxia artifacts. Genomic integration via the Genomic Adjusted Radiation Dose (GARD) reveals that α/β operates as a dynamic, patient-specific phenotype. In the molecular era, static α/β assumptions must be integrated with disease-specific kinetics, microenvironmental data, and genomic intrinsic radiosensitivity.

Human metapneumovirus (hMPV) is a major contributor of acute respiratory infections (ARI) in childhood and vulnerable adults. It comprises two antigenically distinct lineages (A and B), with multiple sub-lineages. Genomic analyses of hMPV strains enable monitoring of viral evolution and transmission to inform future interventions but remain underutilized in Africa. We generated 52 near-complete hMPV genomes from respiratory samples collected in Kilifi, Coastal Kenya, using a tiled-amplicon approach and Oxford Nanopore Technologies sequencing. These samples had been identified as hMPV positive by quantitative PCR during (a) a multi-facility outpatient ARI surveillance in nine health facilities in Kilifi between 2016 and 2017, and 2021 to 2023 and (b) a community-based respiratory infection cohort surveillance study between 2023-2024 that sampled enrolled participants irrespective of symptom status. Of the 192 positive samples analyzed from the two studies, children under 5 years accounted for most hMPV cases (134/186, 72%). 52 samples were sequenced (>70% genome coverage), and hMPV-A (27/52, 53.8%) and hMPV-B (25/52, 46.2%) lineages were identified. The recovered sequences mapped into sub-lineages A2c (27/52, 53.8%), B1 (12/52, 21.2%), and B2b (13/52, 25%). A shift in the predominant sub-lineage was observed from B2b (2016) to B1 (2021), and finally to A2c-wild type (2023). In February 2021, for the first time, we detected a single A2c strain with a 111-nucleotide duplication in the G gene among Kenyan samples. Our study expands the global nucleotide sequence database for hMPV by adding new whole-genome sequences from Kenya collected over the last decade. It highlights the ongoing replacement of locally predominant hMPV lineages and the importation and local transmission of globally circulating strains. These findings underscore the importance of sustained hMPV genomic surveillance to detect emerging variants and monitor lineage circulation patterns that may impact viral transmission, molecular detection, and future control measures. Human metapneumovirus (hMPV) infection can result in mild-to-severe acute respiratory illness, especially in young children, the elderly and individuals with weakened body defenses. Analyzing the genetic code of hMPV can enhance understanding on how this virus spreads and adapts to host populations over time. However, such investigations are scarce in Africa. Here, we generated and analyzed the genome sequences from 52 hMPV-positive samples, collected from individuals enrolled in our outpatient clinics/community surveillance studies in Kilifi, Coastal Kenya, between 2016-2017 and 2021-2024. Using the Oxford Nanopore Technologies (ONT) sequencing approach, we recovered hMPV sequences belonging to both known viral lineages, hMPV-A and hMPV-B, which were further classified into three distinct genetic sub-lineages (A2c, B1 and B2b). Over the years, the predominant hMPV sub-lineage in the region changed from B2b in 2016, to B1 in 2021, and then to A2c in 2023. Notably, for the first time in Kenya, we detected a sequence belonging to a newly characterized variant of hMPV, A2c-111nt-dup, that has a 111-nucleotide duplication in the G (glycoprotein) gene. Our findings demonstrate the sub-lineage dynamics of hMPV in Coastal Kenya. We recommend continuous monitoring of hMPV sequence characteristics to track its spread and inform the design of future interventions.

The increasing appearance of drug-resistant and zoonotic influenza strains highlights an urgent need for host-directed antivirals that offer broad-spectrum activity and a higher barrier to resistance. Here, we describe the characterization of M4, a small-molecule identified from a high-throughput screen that potently inhibits influenza A and B viruses. Mechanistic studies reveal that M4 suppresses influenza virus replication by preventing formation of export-competent viral ribonucleoprotein (vRNP) complexes in the nucleus. Chemoproteomic profiling identified the lipid transfer protein STARD7 as the primary cellular target, and genetic depletion of STARD7 phenocopies the antiviral effects of M4. Additional studies localized the M4 binding site to cysteine 302 within the lipid-binding domain of STARD7, supporting a model in which STARD7-dependent lipid transfer activity promotes efficient vRNP assembly and nuclear export. Combining M4 with baloxavir enhances antiviral efficacy in a murine infection model, providing in vivo support for a host-directed therapeutic strategy. Together, these results identify STARD7 as a metabolic checkpoint licensing vRNP nuclear export and they establish a proof of concept for therapeutic intervention with small molecule inhibitors.

Livestock reference genomes have transformed the discovery of variants associated with production, reproduction, health, and environmental adaptation. Nevertheless, a single linear reference represents only one mosaic haplotype and incompletely captures sequence diversity within a species, particularly structural variants, copy-number changes, repeat-rich regions, and breed-specific sequences. Pangenomes address this limitation by integrating multiple high-quality assemblies or population-scale variants into a unified sequence or graph representation. Concurrently, multi-omics approaches connect genomic variation with transcriptomic, epigenomic, manuscriptproteomic, metabolomic, and microbiome responses, thereby improving biological interpretation of genotype-phenotype relationships. This review synthesizes recent progress in livestock pangenomics and multi-omics, with emphasis on cattle, goats, sheep, water buffalo, and chickens. It describes advances in long-read and haplotype-resolved sequencing, graph construction, structural-variant discovery and genotyping, functional annotation, and integrative analysis. Recent pangenome studies have uncovered substantial non-reference sequence, reduced reference bias, identified breed- and population-specific structural variants, and resolved candidate variants underlying pigmentation, body size, tail morphology, cashmere production, altitude adaptation, and other economically relevant traits. However, translation into routine breeding remains constrained by uneven population representation, inconsistent structural-variant definitions, limited functional annotation, computational demands, and insufficient validation across environments. Future progress will depend on diverse near-complete assemblies, graph-aware imputation and genomic prediction, long-read transcriptomics, single-cell and spatial omics, rigorous causal validation, and open, interoperable resources. Together, these developments can support more accurate, resilient, and biologically informed livestock improvement. Importantly, current dairy-cattle evidence indicates that pangenome-derived structural variants can substantially improve variant discovery and functional interpretation while yielding only marginal average gains in routine genomic prediction, favoring targeted augmentation rather than wholesale replacement of established SNP-based evaluations.

The escalating challenge of antimicrobial resistance require a continuous search for novel compounds with therapeutic potential. The approach of linking traditional practices and knowledge with evidence-based preclinical studies can help minimize the resource-intensive process of drug screening. Rumex abyssinicus is an important plant species used in Ethiopian traditional medicine, and this study aims to conduct a phytochemical analysis of root extracts of R. abyssinicus and determine its antimicrobial activity. R. abyssinicus roots were extracted in six solvents of different polarities. Agar well-diffusion assays of the extracts were performed against type culture bacterial strains, clinically bacterial isolates, and fungi, while the tube dilution method was used to determine the minimum inhibitory concentration of the extracts. The ground root powder was refluxed with acetonitrile (1:10 w/v) and analyzed using reverse-phase high-performance liquid chromatography combined with mass-spectrometry. A one-way analysis of variance followed by post hoc multiple comparisons was performed, and differences were considered statistically significant at p < 0.05. The root extract of R. abyssinicus showed antimicrobial activity against type culture bacteria and clinical isolates, and fungi. Extraction with different solvents yielded extracts with varying antimicrobial activities. The extracts showed a varied range of antimicrobial activities against the test organisms, with inhibition zones of 11-25 mm for bacteria and 9-24 mm for fungi. The MIC range fell between 12.5 and 100 mg/mL. The phytochemical analysis revealed the presence of major compounds such as emodin (30%), chrysophanol (18%), physcion (16%), helminthosporin (12%), citreorosein (11%) and emodic acid (8%) which are envisaged to have functional roles in chemical defense against the tested organisms. The susceptibilities of the tested bacteria and fungi varied depending on the type of solvent used for extraction and extract concentration. Enhanced antimicrobial activities were observed against the entire panel of bacteria compared to those against the test fungi. The results support the traditional use of R. abyssinicus for treating infectious diseases.

This systematic review compared genetic evidence of pancreatic steatosis across African and non-African populations to illuminate ancestry-specific mechanisms and precision-prevention opportunities. Following PRISMA guidelines for reporting, a search was conducted across PubMed, Scopus, Web of Science, and NHGRI-EBI GWAS Catalog for studies spanning 2011 to 31