Summary Background Mobile telephones (henceforth ‘phones’) have become an essential part of everyday life in both healthcare and community settings. However, the widespread use of mobile phones in healthcare facilities is of concern because they can act as vehicles for transmitting pathogenic bacteria. This study aimed to investigate the bacterial contamination of mobile phones of healthcare workers (HCWs) at the University Teaching Hospital, Lusaka, Zambia. Methods This cross-sectional study, from May to July 2019, involved 117 HCWs. A self-administered questionnaire was used to gather sociodemographic and phone usage data. The mobile phones of HCWs were swabbed for culture and antimicrobial susceptibility testing. Results The overall prevalence of mobile phone contamination was 79%. The predominant isolates were coagulase-negative staphylococci (50%), Staphylococcus aureus (24.5%) and Bacillus spp. (14.3%). Other isolates were Escherichia coli , Acinetobacter spp. , Pseudomonas spp. , Klebsiella sp. and Proteus sp. Most isolates were susceptible to tetracycline, gentamicin and cotrimoxazole, while all Gram-positive organisms were resistant to penicillin. Meticillin resistance was detected in 25% and 48% of S. aureus and coagulase-negative staphylococci isolates, respectively. No significant association was found between mobile phone contamination and age group, gender, profession, mobile phone disinfection or work area. Conclusion Mobile phones of HCWs carry potentially pathogenic bacteria and can be a source of healthcare-associated infections in healthcare settings. Hence, regulations regarding the use of mobile phones need to be developed, especially in critical areas, to reduce the dissemination of pathogenic bacteria from hands to phones and, potentially, to patients.

The mitogen-activated protein kinase (MAPK) pathways are crucial regulators of the cellular processes that fuel the malignant transformation of normal cells. The molecular aberrations which lead to cancer involve mutations in, and transcription variations of, various MAPK pathway genes. Here, we examine the genome sequences of 40,848 patient-derived tumours representing 101 distinct human cancers to identify cancer-associated mutations in MAPK signalling pathway genes. We show that patients with tumours that have mutations within genes of the ERK-1/2 pathway, the p38 pathways, or multiple MAPK pathway modules, tend to have worse disease outcomes than patients with tumours that have no mutations within the MAPK pathways genes. Furthermore, by integrating information extracted from various large-scale molecular datasets, we expose the relationship between the fitness of cancer cells after CRISPR mediated gene knockout of MAPK pathway genes, and their dose-responses to MAPK pathway inhibitors. Besides providing new insights into MAPK pathways, we unearth vulnerabilities in specific pathway genes that are reflected in the re sponses of cancer cells to MAPK targeting drugs: a revelation with great potential for guiding the development of innovative therapies.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

In the recent past, sub-Saharan Africa has not escaped the devastating effects of avian influenza virus (AIV) in poultry and wild birds. This systematic review describes the prevalence, spatiotemporal distribution, and virus subtypes detected in domestic and wild birds for the past two decades (2000–2019). We collected data from three electronic databases, PubMed, SpringerLink electronic journals and African Journals Online, using the Preferred Reporting Items for Systematic reviews and Meta-Analyses protocol. A total of 1656 articles were reviewed, from which 68 were selected. An overall prevalence of 3.0% AIV in birds was observed. The prevalence varied between regions and ranged from 1.1% to 7.1%. The Kruskal–Wallis and Wilcoxon signed-rank sum test showed no significant difference in the prevalence of AIV across regions, χ2(3) = 5.237, p = 0.1553 and seasons, T = 820, z = −1.244, p = 0.2136. Nineteen hemagglutinin/neuraminidase subtype combinations were detected during the reviewed period, with southern Africa recording more diverse AIV subtypes than other regions. The most detected subtype was H5N1, followed by H9N2, H5N2, H5N8 and H6N2. Whilst these predominant subtypes were mostly detected in domestic poultry, H1N6, H3N6, H4N6, H4N8, H9N1 and H11N9 were exclusively detected in wild birds. Meanwhile, H5N1, H5N2 and H5N8 were detected in both wild and domestic birds suggesting circulation of these subtypes among wild and domestic birds. Our findings provide critical information on the eco-epidemiology of AIVs that can be used to improve surveillance strategies for the prevention and control of avian influenza in sub-Saharan Africa.

Nontuberculous mycobacteria (NTM) are environmental opportunistic pathogens of humans and animals that are emerging with a serious public health impact particularly in individuals with Acquired Immunodeficiency Syndromes. Recent scientific evidence is shifting from NTMs being known as traditional environmental organisms to serious pathogenic organisms in both animals and humans. In humans, factors attributable to this rise have been linked mainly to Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome pandemic. In recent years there has been an increase in multidrug resistant Tuberculosis in Zambia and it is thought that NTMs could possibly be the cause. This study was therefore formulated to review available information on the prevalence of NTM in humans, animals and the environment, species distribution, zoonotic potential and public health importance in Zambia. This review was conducted in accordance with PRISMA guidelines. A literature search was done in PubMed and Google scholar using predefined search terms such as ‘nontuberculous mycobacteria’, ‘atypical mycobacteria’, ‘mycobacteria other than mycobacterium tuberculosis’ and ‘Zambia’, in combination with Boolean operators (AND, OR). This particular systematic review draws findings based on literature search between 2000 and 2020. Through literature search 243 papers were identified, 23 duplicates were identified and removed and 206 articles were excluded as they did not meet the inclusion criteria. The full text of the remaining 14 articles were considered for this review. The overall prevalence of NTM in humans was 24.39%, in water 21.5%, in animals 16.05% of which the prevalence in cattle was 14.81% and Kafue Lechwe 1.23%. Mycobacterium intracellulare was the most common isolated nontuberculous mycobacteria in humans and cattle while Mycobacterium gordonae was the most common in water, and Mycobacterium stomatepiae sp Nov in Kafue Lechwe. Nontuberculous mycobacteria are an emerging public health threat in Zambia both in humans and animals and this calls for the need for molecular information on the zoonotic transmission of nontuberculous mycobacteria. Increased awareness of nontuberculous mycobacteria diseases among clinicians and laboratory personnel is crucial for patient management and an essential step for facilitating the identification of nontuberculous mycobacteria species in laboratories.

The malignant phenotype of tumour cells is fuelled by changes in the expression of various transcription factors, including some of the well-studied proteins such as p53 and Myc. Despite significant progress made, little is known about several other transcription factors, including ELF4, and how they help shape the oncogenic processes in cancer cells. To this end, we performed a bioinformatics analysis to facilitate a detailed understanding of how the expression variations of ELF4 in human cancers are related to disease outcomes and the cancer cell drug responses. Here, using ELF4 mRNA expression data of 9,350 samples from the Cancer Genome Atlas pan-cancer project, we identify two groups of patient’s tumours: those that expressed high ELF4 transcripts and those that expressed low ELF4 transcripts across 32 different human cancers. We uncover that patients segregated into these two groups are associated with different clinical outcomes. Further, we find that tumours that express high ELF4 mRNA levels tend to be of a higher-grade, afflict a significantly older patient population and have a significantly higher mutation burden. By analysing dose-response profiles to 397 anti-cancer drugs of 612 well-characterised human cancer cell lines, we discover that cell lines that expressed high ELF4 mRNA transcript are significantly less responsive to 129 anti-cancer drugs, and only significantly more response to three drugs: dasatinib, WH-4-023, and Ponatinib, all of which remarkably target the proto-oncogene tyrosine-protein kinase SRC and tyrosine-protein kinase ABL1. Collectively our analyses have shown that, across the 32 different human cancers, the patients afflicted with tumours that overexpress ELF4 tended to have a more aggressive disease that is also is more likely more refractory to most anti-cancer drugs, a finding upon which we could devise novel categorisation of patient tumours, treatment, and prognostic strategies.

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes.We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

Since the earliest reports of the Coronavirus disease - 2019 (COVID-19) in Wuhan, China in December 2019, the disease has rapidly spread worldwide, attaining pandemic levels in early March 2020. However, the spread of COVID-19 has differed in the African setting compared to countries on other continents. To predict the spread of COVID-19 in Africa and within each country on the continent, we applied a Susceptible-Infectious-Recovered mathematical model. Here, our results show that, overall, Africa is currently (May 29, 2020) at the peak of the COVID-19 pandemic, after which we predict the number of cases would begin to fall in June 2020. Furthermore, we predict that the ending phase of the pandemic would be in Mid-August 2020 and that decreasing cases of COVID-19 infections would be detected until around December 2020 and January 2021. Our results also reveal that of the 51 countries with reported COVID-19 cases, only nine, including South Africa, Egypt and Ethiopia, are likely to report higher monthly COVID-19 cases in June 2020 than those reported in the previous months. Overall, at the end of this pandemic, we predict that approximately 279,000 (about 154,000 future cases) individuals in Africa would have been infected with the COVID-19 virus. Here, our predictions are data-driven and based on the previously observed trends in the spread of the COVID-19 pandemic. Shifts in the population dynamics and/or changes in the infectiousness of the COVID-19 virus may require new forecasts of the disease spread.