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BACKGROUND: Verbal autopsy (VA), though imperfect, serves as a vital tool to determine cause-of-death, particularly for out-of-facility deaths, but challenges persist in integrating VA into Civil Registration and Vital Statistics systems. OBJECTIVE: To describe the challenges and successes of collecting a national sample of verbal autopsy interviews in South Africa to obtain the cause of death profile in 2017/18. METHODS: We recruited next of kin from 27 randomly selected sub-districts (10.5%) across South Africa between September 2017 and April 2018. Trained fieldworkers conducted face-to-face interviews using the WHO2016 VA instrument, with physicians certifying underlying causes of death. Feasibility was evaluated based on response rates, participation, and data quality. RESULTS: Of the total 36,976 deaths registered, only 26% were identified during recruitment, with a 55% overall response rate for VA interviews. Physician-reviewed VA data were deemed of good quality for assigning underlying causes of death in 83% of cases. By comparing cause-specific mortality fractions, physician-reviewed VA identified 22.3% HIV/AIDS and InterVA-5 identified 18.5%, aligning with burden of disease estimates, while Statistics South Africa reported 4.9% HIV/AIDS. CONCLUSIONS: The study demonstrated the feasibility of using VA on a national scale, but immense challenges in identifying and recruiting next of kin highlight the importance of formalising VAs within the country's death notification system.

• Main findings: Next of kin of 9 730 decedents were approached at the time of registration of death and 55% consented to be approached later and agreed to do a VA interview by a trained field-worker; 83% of physician-reviewed VA data were considered high-quality for determining underlying causes and 22.3% of all the deaths were due to HIV/AIDS, much higher than the proportion reported in the national statistical office.• Added knowledge: Implementing the VA on a national scale was achievable but significant challenges in recruiting next of kin, emphasising a need to formalise VAs within the country's death notification system.• Global health impact for policy and action: Accurate cause-of-death data are crucial for policymakers to make informed decisions about the country's health system and could be supported by using VAs, particularly for the deaths that occur outside health facilities.

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Even though hepatitis C virus (HCV) cDNA was characterized about 20 years ago, there is insufficient understanding of the molecular etiology underlying HCV infections. Current global rates of infection and its increasingly chronic character are causes of concern for health policy experts. Vast amount of data accumulated from biochemical, genomic, proteomic, and other biological analyses allows for novel insights into the HCV viral structure, life cycle and functions of its proteins. Biomedical text-mining is a useful approach for analyzing the increasing corpus of published scientific literature on HCV. We report here the first comprehensive HCV customized biomedical text-mining based online web resource, dragon exploratory system on hepatitis C virus (DESHCV), a biomedical text-mining and relationship exploring knowledge base was developed by exploring literature on HCV. The pre-compiled dictionaries existing in the dragon exploratory system (DES) were enriched with biomedical concepts pertaining to HCV proteins, their name variants and symbols to make it suitable for targeted information exploration and knowledge extraction as focused on HCV. A list of 32,895 abstracts retrieved via PubMed database using specific keywords searches related to HCV were processed based on concept recognition of terms from several dictionaries. The web query interface enables retrieval of information using specified concepts, keywords and phrases, generating text-derived association networks and hypotheses, which could be tested to identify potentially novel relationship between different concepts. Such an approach could also augment efforts in the search for diagnostic or even therapeutic targets. DESHCV thus represents online literature-based discovery resource freely accessible for academic and non-profit users via http://apps.sanbi.ac.za/DESHCV/ and its mirror site http://cbrc.kaust.edu.sa/deshcv/.

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Prostate cancer (PC) is one of the most commonly diagnosed cancers in men. PC is relatively difficult to diagnose due to a lack of clear early symptoms. Extensive research of PC has led to the availability of a large amount of data on PC. Several hundred genes are implicated in different stages of PC, which may help in developing diagnostic methods or even cures. In spite of this accumulated information, effective diagnostics and treatments remain evasive. We have developed Dragon Database of Genes associated with Prostate Cancer (DDPC) as an integrated knowledgebase of genes experimentally verified as implicated in PC. DDPC is distinctive from other databases in that (i) it provides pre-compiled biomedical text-mining information on PC, which otherwise require tedious computational analyses, (ii) it integrates data on molecular interactions, pathways, gene ontologies, gene regulation at molecular level, predicted transcription factor binding sites on promoters of PC implicated genes and transcription factors that correspond to these binding sites and (iii) it contains DrugBank data on drugs associated with PC. We believe this resource will serve as a source of useful information for research on PC. DDPC is freely accessible for academic and non-profit users via http://apps.sanbi.ac.za/ddpc/ and http://cbrc.kaust.edu.sa/ddpc/.

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Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics. Access is free for academic and non-profit users and database can be accessed at http://apps.sanbi.ac.za/ddoc/.

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