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Antimicrobial resistance (AMR) was a leading cause of death globally in 2019. Sadly, COVID-19 has exacerbated AMR, nonetheless, the process of developing new antibiotics remains very challenging. This urgently requires the adoption of alternative approaches to treat multi-drug-resistant bacterial infections. This editorial introduces the ‘Bacteriophages against multi-drug resistant bacteria’ collection launched at BMC Infectious Diseases which highlights progress towards using bacteriophages to tackle AMR.

Drug-resistant Mycobacterium tuberculosis remains a significant global public health threat. While whole-genome sequencing (WGS) holds immense promise for understanding transmission dynamics and drug resistance mechanisms, its integration into routine surveillance remains limited. Additionally, insights from WGS are increasingly contributing to vaccine discovery by identifying novel antigenic targets and understanding pathogen evolution. The COVID-19 pandemic catalyzed an unprecedented expansion of genomic capacity in many low- and middle-income countries (LMICs), with public health institutions acquiring next-generation sequencing (NGS) platforms and developing local expertise in real-time pathogen surveillance. This hard-won capacity now represents a transformative opportunity to accelerate TB control enabling rapid detection of drug-resistant strains and high-resolution mapping of transmission networks that are critical for timely, targeted interventions. Furthermore, the integration of machine learning with genomic and clinical data offers a powerful avenue to improve the prediction of drug resistance and to tailor patient-specific TB management strategies. This article examines the practical challenges, emerging opportunities, and policy considerations necessary to embed genomic epidemiology within national TB control programs, particularly in high-burden, resource-constrained settings.

Background Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E . coli , targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs. Results Model performance varied across antibiotics. The Support Vector Machine excelled in predicting ciprofloxacin resistance (87% accuracy, F1 Score: 0.57), Light Gradient Boosting Machine for cefotaxime (92% accuracy, F1 Score: 0.42), and Gradient Boosting for ampicillin (58% accuracy, F1 Score: 0.66). In validation with data from Africa, Logistic Regression showed high accuracy for ampicillin (94%, F1 Score: 0.97), while Random Forest and Light Gradient Boosting Machine were effective for ciprofloxacin (50% accuracy, F1 Score: 0.56) and cefotaxime (45% accuracy, F1 Score:0.54), respectively. Key mutations associated with AMR were identified for these antibiotics. Conclusion As the threat of AMR continues to rise, the successful application of these models, particularly on genomic datasets from LMICs, signals a promising avenue for improving AMR prediction to support large AMR surveillance programs. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against AMR.

We propose a novel, non-discriminatory classification of monkeypox virus diversity. Together with the World Health Organization, we named three clades (I, IIa and IIb) in order of detection. Within IIb, the cause of the current global outbreak, we identified multiple lineages (A.1, A.2, A.1.1 and B.1) to support real-time genomic surveillance.

Background Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model’s generalizability on another dataset from South Africa. Results We trained ten machine learning algorithms on a dataset comprising of 182 MTB isolates with clinical data variables (age, sex, HIV status) and SNP mutations across the entire genome as predictor variables and phenotypic drug-susceptibility data for the four drugs as the outcome variable. Model performance varied across the four anti-TB drugs after a five-fold cross validation. The best model was selected considering the highest Mathews Correlation Coefficient (MCC) and Area Under the Receiver Operating Characteristic Curve (AUC) score as key metrics. The Logistic regression excelled in predicting rifampicin resistance (MCC: 0.83 (95% confidence intervals (CI) 0.73–0.86) and AUC: 0.96 (95% CI 0.95–0.98) and streptomycin (MCC: 0.44 (95% CI 0.27–0.58) and AUC: 0.80 (95% CI 0.74–0.82), Extreme Gradient Boosting (XGBoost) for ethambutol (MCC: 0.65 (95% CI 0.54–0.74) and AUC: 0.90 (95% CI 0.83–0.96) and Gradient Boosting (GBC) for isoniazid (MCC: 0.69 (95% CI 0.61–0.78) and AUC: 0.91 (95% CI 0.88–0.96). The best performing model per drug was only trained on the SNP dataset after excluding the clinical data variables because intergrating them with SNP mutations showed a marginal improvement in the model’s performance. Despite the high MCC (0.18 to 0.72) and AUC (0.66 to 0.95) scores for all the best models with the Uganda test dataset, LR model for rifampicin and streptomycin didn’t generalize with the South Africa dataset compared to the GBC and XGBoost models. Compared to TB profiler, LR for RIF was very sensitive and the GBC for INH and XGBoost for EMB were very specific on the Uganda dataset. TB profiler outperformed all the best models on the South Africa dataset. We identified key mutations associated with drug resistance for these antibiotics. HIV status was also identified among the top significant features in predicting drug resistance. Conclusion Leveraging machine learning applications in predicting antimicrobial resistance represents a promising avenue in addressing the global health challenge posed by antimicrobial resistance. This work demonstrates that integration of diverse data types such as genomic and clinical data could improve resistance predictions while using machine learning algorithms, support robust surveillance systems and also inform targeted interventions to curb the rising threat of antimicrobial resistance.

Recent immunological studies of vaginal candidiasis in African populations have revealed complex host‒pathogen interactions with implications for therapeutic development and HIV acquisition risk. This scoping review synthesized evidence from Uganda, Zambia, South Africa, and Kenya between 2020 and 2024, focusing on immune responses, cellular dynamics, and tissue effects due vulvovaginal candidiasis. Analysis revealed a coordinated inflammatory response marked by elevated levels of the proinflammatory cytokines IL-1β and IL-6 and increased chemokine IL-8-mediated immune cell recruitment. Compared with those in control individuals, distinct T-cell population patterns in colonized individuals show reduced Th17-like CD4+ T-cell activation, with concurrent increases in Th1/Th2-enriched CD4+ T cells. Molecular analysis revealed that 162 differentially expressed genes were involved primarily in neutrophil-mediated immunity and cytokine signaling pathways. Despite robust immune activation, tissue integrity remained intact, accompanied by elevated antimicrobial peptides SLPI and BD-2. Notably, Candida-colonized individuals presented reduced frequencies of HIV target cells (CCR5 + HLA-DR + CD4 + T cells). These findings advance our understanding of population-specific immune responses to vaginal candidiasis and identify promising therapeutic targets, highlighting the need for longitudinal studies to characterize vulvovaginal candidiasis immunopathogenesis fully in African populations.

COVID-19 is a respiratory disease caused by the SARS-CoV-2 virus, which put the entire world on hold for two years from 2020 to March 2022 before the rapid roll-out of vaccines. This virus claimed many lives, 6,331,727 deaths globally, and 172,390 people in Africa died from the disease, while Uganda lost 3,620 people in the same period. The study aimed to analyse the spread patterns of SARS-CoV-2 in Uganda and neighbouring countries and understand the community transmission within Uganda. The study utilised genomic sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) repository. Bayesian Phylogeography methods were used via the BEAST software. The first case of COVID-19 in Uganda likely originated from Kenya. The virus spread from Kenya to other East African countries. Cross-border movements, especially by truck drivers and refugees, played a significant role in the spread. The study highlights the importance of cross-border collaborations to control infectious disease spread. The spread pattern was influenced by trade routes and the movement of people, emphasising the need for robust border control measures during pandemics. Understanding the origin and spread of SARS-CoV-2 can inform better public health strategies and preparedness for future pandemics.

Childhood HBV immunization remains globally fundamental to the elimination of hepatitis B virus (HBV). However, monitoring proportions of HBV vaccine seroprotection and their determinants among African Pediatric recipients is crucial. This study sought to verify extent of immune protection accorded by the HBV vaccine in African children of up to 17 years of age by pooling the prevalence of seroprotection reported by primary studies conducted in the Northern, Western, and Southern African regions. We included 19 eligible articles out of the 197 initially downloaded, published from 1999 to 2021 from African Journals Online (AJOL), EMBASE, Scopus, and PubMed. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO), University of York Centre for Reviews and Dissemination, under the registration number CRD42022361277. Significantly higher ( p  < 0.0001) proportion of HBV vaccine seroprotection (69.07%) was found among children under 15 years of age than children 15–17 years (32.368%), 95% CI [34.2454–39.0847%]. Whereas successful integration of the HBV vaccine on the extended programs on immunizations (EPI) has been a major achievement in the reduction of HBV infection in Africa, markedly reduced HBV vaccine seroprotection is persistently demonstrated among adolescent children 15–17 years of age. Future studies are required to clarify the need for booster dose vaccination in most at risk populations and age groups.

Background Candida africana is an emergent variant that has been listed as a new species or variety within the Candida albicans complex since 2001. It has a worldwide intra- albicans complex pooled prevalence of 1.67% and varies between 0 and 8% depending on geographical region. We present the results of a pilot study on its prevalence in Uganda. Methodology We conducted a cross-sectional study between March and June 2023. We recruited 4 pregnant women from Mulago Specialized Women and Neonatal Hospital, 102 from Kawempe National Referral Hospital, and 48 from Sebbi Hospital. Vaginal swabs were tested using microscopy, culture and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF). Results The prevalence of C. africana was zero. Out of the 103 isolates, the majority (81.553%) were identified as Candida albicans , followed by Nakeseomyces glabrata (13.592%) and Pichia kudriavzevii (1.942%). Cyberlindnera jadinii , Candida tropicalis , and Candida parapsilosis each accounted for 0.971% of the isolates. Conclusion The prevalence of C. africana in Uganda is zero. However, large-scale cross-sectional studies, including studies involving the collection of vaginal samples from both urban and rural settings in Uganda and the use of both MALDI-TOF- and PCR-based laboratory methods, are needed to fully describe the public health burden of C. africana infections.

Susceptibility testing for pyrazinamide (PZA), a cornerstone anti-TB drug is not commonly done in Uganda because it is expensive and characterized with technical difficulties thus resistance to this drug is less studied. Resistance is commonly associated with mutations in the pncA gene and its promoter region. However, these mutations vary geographically and those conferring phenotypic resistance are unknown in Uganda. This study determined the prevalence of PZA resistance and its association with pncA mutations. Using a cross-sectional design, archived isolates collected during the Uganda national drug resistance survey between 2008-2011 were sub-cultured. PZA resistance was tested by BACTEC Mycobacterial Growth Indicator Tube (MGIT) 960 system. Sequence reads were downloaded from the NCBI Library and bioinformatics pipelines were used to screen for PZA resistance-conferring mutations. The prevalence of phenotypic PZA resistance was found to be 21%. The sensitivity and specificity of pncA sequencing were 24% (95% CI, 9.36-45.13%) and 100% (73.54% - 100.0%) respectively. We identified four mutations associated with PZA phenotypic resistance in Uganda; K96R, T142R, R154G and V180F. There is a high prevalence of phenotypic PZA resistance among TB patients in Uganda. The low sensitivity of pncA gene sequencing confirms the already documented discordances suggesting other mechanisms of PZA resistance in Mycobacterium tuberculosis.