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Abstract Background Several variants of SARS-CoV-2 have a demonstrated impact on public health, including high and increased transmissibility, severity of infection, and immune escape. Therefore, this study aimed to determine the SARS-CoV-2 lineages and better characterize the dynamics of the pandemic during the different waves in Guinea. Methods Whole genome sequencing of 363 samples with PCR cycle threshold (Ct) values under thirty was undertaken between May 2020 and May 2023. The Illumina iSeq 100 technology was used. The sequences were then analyzed using the GeVarli pipeline to generate consensus sequences and variant calling. All sequences isolated in Guinea and available on GISAID were included in the analysis for phylogenetic tree and phylodynamic determination. Nextstain tools were used for these analyses. Statistical analysis was done using GraphPad Prism version 10. Results The circulation of SARS-CoV-2 in Guinea can be distributed in three different periods. The first, lasting from May to June 2020, was characterized by lineages B1 and B.1.1. The second period, from January 2021 to July 2021, was characterized by the lineages B.1.1.7 (Alpha), AY.122, B.1.1.318, R1, B.1.525 and B.1.629. The third period, between December 2021 and May 2023, was characterized by the Omicron variant, with nine subvariant majorities found. In addition, detecting variants in the period out of their circulation was documented. The importation and exportation investigation showed the strong movement viral association between Guinea and Senegal on the one hand and Guinea and Nigeria on the other. Conclusion In summary, this study contributes to understanding the epidemic dynamics of the disease by describing the significant variants that circulated in Guinee and the distribution of this variant in the population. It also shows the importation and exportation of the virus during the pandemic. Sub-sampling and degradation of samples for sequences were observed. Organization and collaboration between laboratories are needed for a good sample-collecting and storage system for future direction.

Human Mpox cases are increasingly reported in Africa, with the highest burden in the Democratic Republic of Congo (DRC). While case reporting on a clinical basis can overestimate infection rates, laboratory confirmation by PCR can underestimate them, especially on suboptimal samples like blood, commonly used in DRC. Here we used a Luminex-based assay to evaluate whether antibody testing can be complementary to confirm cases and to identify human transmission chains during outbreak investigations. We used left-over blood samples from 463 patients, collected during 174 outbreaks between 2013 and 2022, with corresponding Mpox and VZV PCR results. In total, 157 (33.9%) samples were orthopox-PCR positive and classified as Mpox+; 124 (26.8%) had antibodies to at least one of the three Mpox peptides. The proportion of antibody positive samples was significantly higher in Mpox positive samples (36.9%) versus negative (21.6%) (p < 0.001). By combining PCR and serology, 66 additional patients were identified, leading to an Mpox infection rate of 48.2% (223/463) versus 33.9% when only PCR positivity is considered. Mpox infections were as such identified in 14 additional health zones and 23 additional outbreaks (111/174 (63.8%) versus 88/174 (50.6%)). Our findings highlight the urgent need of rapid on-site diagnostics to circumvent Mpox spread.

Abstract Background Plasmodium falciparum malaria remains a major health problem in Africa. The mechanisms of pathogenesis are not fully understood. Transcriptomic studies may provide new insights into molecular pathways involved in the severe form of the disease. Methods Blood transcriptional levels were assessed in patients with cerebral malaria, non-cerebral malaria, or mild malaria by using microarray technology to look for gene expression profiles associated with clinical status. Multi-way ANOVA was used to extract differentially expressed genes. Network and pathways analyses were used to detect enrichment for biological pathways. Results We identified a set of 443 genes that were differentially expressed in the three patient groups after applying a false discovery rate of 10%. Since the cerebral patients displayed a particular transcriptional pattern, we focused our analysis on the differences between cerebral malaria patients and mild malaria patients. We further found 842 differentially expressed genes after applying a false discovery rate of 10%. Unsupervised hierarchical clustering of cerebral malaria-informative genes led to clustering of the cerebral malaria patients. The support vector machine method allowed us to correctly classify five out of six cerebral malaria patients and six of six mild malaria patients. Furthermore, the products of the differentially expressed genes were mapped onto a human protein-protein network. This led to the identification of the proteins with the highest number of interactions, including GSK3B, RELA, and APP. The enrichment analysis of the gene functional annotation indicates that genes involved in immune signalling pathways play a role in the occurrence of cerebral malaria. These include BCR-, TCR-, TLR-, cytokine-, FcεRI-, and FCGR- signalling pathways and natural killer cell cytotoxicity pathways, which are involved in the activation of immune cells. In addition, our results revealed an enrichment of genes involved in Alzheimer’s disease. Conclusions In the present study, we examine a set of genes whose expression differed in cerebral malaria patients and mild malaria patients. Moreover, our results provide new insights into the potential effect of the dysregulation of gene expression in immune pathways. Host genetic variation may partly explain such alteration of gene expression. Further studies are required to investigate this in African populations.

In September 2022, deaths of pigs manifesting pox-like lesions caused by swinepox virus were reported in Tshuapa Province, Democratic Republic of the Congo. Two human mpox cases were found concurrently in the surrounding community. Specific diagnostics and robust sequencing are needed to characterize multiple poxviruses and prevent potential poxvirus transmission.