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Abstract Objective To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. Design We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided. Results Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel $$\upmu$$ μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution $$\upmu$$ μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using $$\upmu$$ μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented. Conclusion The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Abstract Background Nef performs multiple cellular activities that enhance HIV-1 pathogenesis. The role of Nef-mediated down-regulation of the host restriction factor SERINC5 in HIV-1 pathogenesis is not well-defined. We aimed to investigate if SERINC5 down-regulation activity contributes to HIV-1 subtype C disease progression, to assess the relative contribution of this activity to overall Nef function, and to identify amino acids required for optimal activity. We measured the SERINC5 down-regulation activity of 106 subtype C Nef clones, isolated from individuals in early infection, for which the Nef activities of CD4 and HLA-I down-regulation as well as alteration of TCR signalling were previously measured. The relationship between SERINC5 down-regulation and markers of disease progression, and the relative contribution of SERINC5 down-regulation to a Nef fitness model-derived E value (a proxy for overall Nef fitness in vivo), were assessed. Results No overall relationship was found between SERINC5 down-regulation and viral load set point (p = 0.28) or rate of CD4+ T cell decline (p = 0.45). CD4 down-regulation (p = 0.02) and SERINC5 down-regulation (p = 0.003) were significant determinants of E values in univariate analyses, with the greatest relative contribution for SERINC5 down-regulation, and only SERINC5 down-regulation remained significant in the multivariate analysis (p = 0.003). Using a codon-by-codon analysis, several amino acids were significantly associated with increased (10I, 11V, 38D, 51T, 65D, 101V, 188H and, 191H) or decreased (10K, 38E, 65E, 135F, 173T, 176T and, 191R) SERINC5 down-regulation activity. Site-directed mutagenesis experiments of selected mutants confirmed a substantial reduction in SERINC5 down-regulation activity associated with the mutation 173T, while mutations 10K, 135F, and 176T were associated with more modest reductions in activity that were not statistically significant. Conclusions These results suggest that SERINC5 down-regulation is a significant contributor to overall Nef function and identify potential genetic determinants of this Nef function that may have relevance for vaccines or therapeutics.

Emerging highly transmissible viral infections such as SARS-CoV-2 pose a significant global threat to human health and the economy. Since its first appearance in December 2019 in the city of Wuhan, Hubei province, China, SARS-CoV-2 infection has quickly spread across the globe, with the first case reported on the African continent, in Egypt on February 14th, 2020. Although the global number of COVID-19 infections has increased exponentially since the beginning of the pandemic, the number of new infections and deaths recorded in African countries have been relatively modest, suggesting slower transmission dynamics of the virus on the continent, a lower case fatality rate, or simply a lack of testing or reliable data. Notably, there is no significant increase in unexplained pneumonias or deaths on the continent which could possibly indicate the effectiveness of interventions introduced by several African governments. However, there has not yet been a comprehensive assessment of sub-Saharan Africa’s (SSA) preparedness and response to the COVID-19 pandemic that may have contributed to prevent an uncontrolled outbreak so far. As a group of early career scientists and the next generation of African scientific leaders with experience of working in medical and diverse health research fields in both SSA and resource-rich countries, we present a unique perspective on the current public health interventions to fight COVID-19 in Africa. Our perspective is based on extensive review of the available scientific publications, official technical reports and announcements released by governmental and non-governmental health organizations as well as from our personal experiences as workers on the COVID-19 battlefield in SSA. We documented public health interventions implemented in seven SSA countries including Uganda, Kenya, Rwanda, Cameroon, Zambia, South Africa and Botswana, the existing gaps and the important components of disease control that may strengthen SSA response to future outbreaks.