Baobab Index

The distribution of African great apes has remained unconfirmed regarding their southern limit, particularly on the western side of the continent. IUCN maps include the Mayombe forest of Angola as part of the estimated distribution of western lowland gorillas (Gorilla gorilla gorilla) and central chimpanzees (Pan troglodytes troglodytes). However, until now no published evidence-based records had confirmed the continued presence of both species. The Mayombe forest is a key biodiversity hotspot and a potentially important stronghold for the conservation of great ape populations in Africa. Here, we report the first systematic evidence of both species in the Mayombe National Park, Cabinda, Angola. In 2023, a grid of camera traps was systematically deployed, producing the first visual records of gorillas and chimpanzees. Building on these findings, in 2024, a pilot survey including ad libitum field observations was carried out along exploratory trails to maximise data collection. The combination of these records identified a hotspot of great ape activity where six transects were established, and systematic direct and indirect evidence was documented. Chimpanzees were recorded more times across a broader range of evidence categories, while gorillas appeared less and seemed more spatially restricted. Notably, both species were detected at overlapping sites but never simultaneously, indicating sympatric coexistence with spatio-temporal partitioning. These findings confirm the southernmost predicted distribution of both species for this part of Africa, filling critical gaps in the understanding of great ape evolution and biogeography, and providing a baseline for the first demographic and ecological census of great apes in Angola.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the data set is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently used as pseudolabels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methods, such as hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN), are used, with results visualized through principal component analysis-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underlines the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable method for analyzing complex genomic data sets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

A metagenomic analysis was performed by high-throughput sequencing (HTS) to identify viruses infecting a wild weed collected in Seles (Angola), which exhibited clear yellowing symptoms. The analysis led to the discovery of a putatively novel carlavirus, tentatively named 'Seles weed carlavirus'. The complete genome sequence, consisting of 8,597 nucleotides, poly-A tail excluded, exhibited the typical organization of members of the genus Carlavirus, including the replicase polyprotein (ORF1); the triple gene block (ORFs 2-4); the coat protein (ORF5) and an RNA-binding protein (ORF6). The replicase polyprotein and coat protein gene regions of the newly described virus shared the highest amino acid sequence identity with the corresponding sequences of cowpea mild mottle virus (51.40%) and Hainan betaflexivirus (63.08%), respectively. The infection was further confirmed by RT-PCR with multiple specific targeted primer pairs, whose related amplicons were cloned and sequenced.

This review aimed to explore how artificial intelligence can be integrated with global genomic resources and to examine its implications for advancing precision medicine, with particular attention to population diversity, predictive modeling, and clinical translation. A narrative review design was adopted, drawing on literature from major databases and supplementary search sources including PubMed, Scopus, Web of Science, IEEE Xplore, Embase, and Google Scholar. Studies were selected based on relevance to artificial intelligence applications in genomic data analysis and precision medicine. Key information from included studies was extracted using a structured narrative extraction framework and synthesized thematically to identify key patterns and emerging insights. A total of 54 studies were included in this review. The synthesis identified five recurring application areas: genomic data integration, variant and disease association detection, disease susceptibility and risk prediction, treatment response prediction, and clinical decision support. Findings indicate that artificial intelligence can support the integration and analysis of multi-omics data, support the identification of genetic variants and disease associations, and improve predictive modeling for precision medicine. Incorporating diverse population data was also reported to improve model generalizability and reduce bias. However, challenges related to data standardization, interoperability, ethical governance, overfitting in small or biased cohorts, limited prospective clinical validation, reproducibility, and clinical implementation remain significant barriers. The integration of artificial intelligence with global genomic resources holds substantial promise for advancing precision medicine by enabling more accurate, inclusive, and individualized precision medicine. However, this promise should be interpreted cautiously because many AI genomic models remain dependent on retrospective datasets, limited external validation, and variable reproducibility.

Copy number variation in multigene families composed of tandemly arrayed repeat units is shaped in part by recombination events such as unequal homologous chromosome exchange (HCE) and sister chromatid exchange (SCE). To explore the relative contribution of intrachromosomal and interchromosomal recombination to multigene family copy number dynamics in apomictic organisms, we conducted computational simulations using empirically determined parameters obtained by measuring recombination rate in the 45S rDNA of mutation accumulation lines of

Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (-11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by -29·9% (-33·6 to -25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Gates Foundation, St Jude Children's Research Hospital.

The emergence of multidrug-resistant (MDR) Staphylococcus aureus strains poses significant challenges for effective management and treatment, often harboring genes associated with antimicrobial resistance (AMR) and virulence factors contributing to their pathogenicity, establishing them as a global health threat. This study aims to perform a comprehensive genomic investigation of a Libyan MDR S. aureus strain to assess its resistance profile and virulence mechanisms. Whole genome sequencing and bioinformatics analyses, including comparative genome analysis, phylogenetics and the screening of key genetic elements involved in pathogenicity and AMR, were conducted to explore the genomic features of this strain. Several resistance detection tools and databases were applied to evaluate variations in genomic annotation and prediction. Our strain presented a 2.77 Mb genome with 2,650 coding sequences, sharing 94.04% orthologous genes with the CC239-MRSA-III strain. Key resistance and virulence factors, such as mecA, blaZ, and efflux pumps, were identified, along with point mutations in metabolic and replication enzymes. Additionally, an alanine racemase gene (alr) homologous to the vanT in vanG cluster was identified, suggesting a potential evolving resistance variant and pointing to a potential under-characterized gene related to early stages of resistance development. Variations among resistance prediction tools also highlighted the need for integrating multiple analysis approaches. This study provides a detailed genomic analysis of a Libyan MDR S. aureus strain, revealing an extensive array of resistance and virulence factors. The discrepancies across resistance detection tools underscore the importance of standardized genomic investigation approaches for improved management of resistant infections.

Alazami syndrome is a neurodevelopmental disorder characterized by postnatal growth retardation, moderate to severe intellectual disability, and facial dysmorphology. It is caused by biallelic variants in the transcriptional regulator La ribonucleoprotein 7 (LARP7), where frameshift variants accounted for the majority of cases. The current study presents 7 new patients, including 3 males and 4 females from 3 unrelated families. Careful and thorough clinical examination identified novel oro-dental disease abnormalities, including a prominent premaxilla and enamel defects. The detected variants (c.1113_1116del, c.997 + 2T > C and c.518T > C) were not reported in the previous studies. The substitution c.518T > C represented the second missense variant to be identified in patients with Alazami syndrome. Male patients from the three families fulfilled ≥ 2 clinical warning signs of primary immunodeficiency. Lymphocyte subset counts and immunoglobulin levels were estimated in patients from two families. The values were within reference ranges, with only minor non-significant alterations in cytotoxic T-cell counts. A functional assay of B lymphocyte response was performed in one family, demonstrating impaired Streptococcus pneumoniae IgG antibody production following Pneumovax vaccination in the male patient, while his female sibling mounted an adequate response. In conclusion, the disease has a wide range of symptoms, which vary greatly among the affected patients. Our study expanded the clinical and molecular spectrum of the disorder and highlighted immunodeficiency as an underrecognized disease feature, potentially with a male sex predilection.

BACKGROUND: Safe anesthesia is fundamental to safe surgery and depends on facility readiness, team behaviors, and consistent adherence to safety standards such as the World Health Organization (WHO) Surgical Safety Checklist. Across the Arab region, variability in workforce capacity, equipment and supplies, and checklist implementation may contribute to uneven safety performance, yet multicountry data describing readiness and routine anesthesia safety practices remain limited. We therefore assessed facility readiness and clinicians’ knowledge, attitudes, and self-reported safety practices, and examined how checklist use and perceived barriers relate to these domains across fourteen Arab countries. METHODS: We conducted a multicountry online cross-sectional survey of anesthesia team members working in hospital operating theatres and procedural anesthesia locations across fourteen Arab countries. The survey evaluated facility readiness, knowledge, attitudes, and self-reported safety practices, and captured frequency of surgical safety checklist use and perceived barriers to safe anesthesia practice. RESULTS: Overall, facility readiness and participants’ knowledge, attitudes, and self-reported safety practices were generally high; however, routine checklist use was not universal. Commonly reported barriers included understaffing and workload pressure, as well as financial constraints affecting equipment and supplies. More frequent checklist use was associated with better safety-related performance across assessed domains, whereas a higher barrier burden was associated with poorer readiness and self-reported practice. CONCLUSIONS: Although anesthesia safety performance was generally favorable across surveyed settings, important implementation and systems gaps remain, particularly in routine checklist use and resource-related constraints. Strengthening workforce capacity, ensuring access to essential equipment and supplies, and embedding meaningful, high-fidelity checklist use may help reduce residual safety gaps and support safer anesthesia care across diverse hospitals in the Arab region. TRIAL REGISTRATION: Not applicable. (This study is an observational cross-sectional survey and did not involve prospective assignment to a health-related intervention.)

The protozoan pathogen