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BACKGROUND HIV/AIDS remains one of the major global human health challenges, especially in resource-limited environments. By 2017, over 77.3 million people were infected with the disease, and approximately 35.4 million individuals had already died from AIDS-related illnesses. Approximately 21.7 million people were accessing ART with significant clinical outcomes. However, numerous challenges are experienced in the delivery and accurate interpretation of data on patients with HIV data by various health care providers at different care levels. Mobile health (mHealth) technology is progressively making inroads into the health sector as well as medical research. Different mobile devices have become common in health care settings, leading to rapid growth in the development of downloadable software specifically designed to fulfill particular health-related purposes. OBJECTIVE We developed a mobile-based app called ARVPredictor and demonstrated that it can accurately define HIV-1 drug-resistance mutations in the HIV pol gene for use at the point of care. METHODS ARVPredictor was designed using Android Studio with Java as the programming language and is compatible with both Android and iOS. The app system is hosted on Nginx Server, and network calls are built on PHP’s Laravel framework handled by the Retrofit Library. The DigitalOcean offers a high-performance and stable cloud computing platform for ARVPredictor. This mobile app is enlisted in the Google Play Store as an “ARVPredictor” and the source code is available under MIT permissive license at a GitHub repository. To test for agreement between the ARVPredictor and Stanford HIV Database in detecting HIV subtype and NNRT and NRTI mutations, a total of 100 known HIV sequences were evaluated. RESULTS The mobile-based app (ARVPredictor) takes in a set of sequences or known mutations (protease, reverse transcriptase and integrase). It then returns inferred levels of resistance to selected nucleoside, nonnucleoside protease, and integrase inhibitors for accurate HIV/AIDS management at the point of care. The ARVPredictor identified similar HIV subtypes in 98/100 sequences compared with the Stanford HIV Database (κ=0.98, indicating near perfect agreement). There were 89/100 major NNRTI and NRTI mutations identified by ARVPredictor, similar to the Stanford HIV Database (κ=0.89, indicating near perfect agreement). Eight mutations classified as major by the Stanford HIV Database were classified as others by ARVPredictor. CONCLUSIONS The ARVPredictor largely agrees with the Stanford HIV Database in identifying both major and minor proteases, reverse transcriptase, and integrase mutations. The app can be conveniently used robustly at the point of care by HIV/AIDS care providers to improve the management of HIV infection.

Background HIV/AIDS remains one of the major global human health challenges, especially in resource-limited environments. By 2017, over 77.3 million people were infected with the disease, and approximately 35.4 million individuals had already died from AIDS-related illnesses. Approximately 21.7 million people were accessing ART with significant clinical outcomes. However, numerous challenges are experienced in the delivery and accurate interpretation of data on patients with HIV data by various health care providers at different care levels. Mobile health (mHealth) technology is progressively making inroads into the health sector as well as medical research. Different mobile devices have become common in health care settings, leading to rapid growth in the development of downloadable software specifically designed to fulfill particular health-related purposes. Objective We developed a mobile-based app called ARVPredictor and demonstrated that it can accurately define HIV-1 drug-resistance mutations in the HIV pol gene for use at the point of care. Methods ARVPredictor was designed using Android Studio with Java as the programming language and is compatible with both Android and iOS. The app system is hosted on Nginx Server, and network calls are built on PHP’s Laravel framework handled by the Retrofit Library. The DigitalOcean offers a high-performance and stable cloud computing platform for ARVPredictor. This mobile app is enlisted in the Google Play Store as an “ARVPredictor” and the source code is available under MIT permissive license at a GitHub repository. To test for agreement between the ARVPredictor and Stanford HIV Database in detecting HIV subtype and NNRT and NRTI mutations, a total of 100 known HIV sequences were evaluated. Results The mobile-based app (ARVPredictor) takes in a set of sequences or known mutations (protease, reverse transcriptase and integrase). It then returns inferred levels of resistance to selected nucleoside, nonnucleoside protease, and integrase inhibitors for accurate HIV/AIDS management at the point of care. The ARVPredictor identified similar HIV subtypes in 98/100 sequences compared with the Stanford HIV Database (κ=0.98, indicating near perfect agreement). There were 89/100 major NNRTI and NRTI mutations identified by ARVPredictor, similar to the Stanford HIV Database (κ=0.89, indicating near perfect agreement). Eight mutations classified as major by the Stanford HIV Database were classified as others by ARVPredictor. Conclusions The ARVPredictor largely agrees with the Stanford HIV Database in identifying both major and minor proteases, reverse transcriptase, and integrase mutations. The app can be conveniently used robustly at the point of care by HIV/AIDS care providers to improve the management of HIV infection.

Cervical malignancy afflicts women of all societies. In Kenya, 4,802 women are diagnosed with cervical malignancy and almost 2500 die annually with only 3.2% of cervical screening uptake. The Main goal of this study was to find out the factors that contribute to the uptake of cervical screening at Mama Lucy Kibaki Hospital. This was a descriptive and cross-sectional study that used a purposeful sampling method. An interview-administered questionnaire was used to collect data from women and hospital key informants. Multivariate regression was used to analyse associations between study variables. A total of 246 participants were recruited. Uptake of cervical screening was 23.1%, with 83.6% being aware of cervical cancer. Fear of results (69.5%), lack of information (69.8%) and fear of the screening procedure (65.2%) were major cervical screening barriers. Free cervical screening (93.5%) comprehensive cancer health education (90.2%), voluntary cervical screening centres (84.9%), mass media cervical cancer campaigns (83.3%) and cervical cancer screening mobile clinics (81.7%) to be the likely motivators to cervical screening uptake. Multivariate regression showed that older women participated more in uptake than young women (p = 0.001), those who had used contraceptives (p=0.001) and those with higher income (p = 0.03). In conclusion, there was a low uptake of screening for cervical cancer disease. A comprehensive and appropriate sensitization program is required, which eventually may increase uptake of cervical screening.

Background Information about HBV sero-markers, infection stages and genotypes in HIV-1 infected and uninfected injection and non-injection drug users (IDUs) in Kenya remains elusive. Methods A cross-sectional study examining HBV sero-marker, infection stages and genotypes was conducted among HIV-1 infected and uninfected, respectively, IDUs (n = 157 and n = 214) and non-IDUs (n = 139 and n = 48), and HIV-1 uninfected non-drug using controls (n = 194) from coastal, Kenya. HBV sero-marker and infection stages were based on HBV 5-panel rapid test plasma sero-reactivity. DNA was extracted from acute and chronic plasma samples and genotypes established by nested-PCR and direct sequencing. Results HBsAg positivity was higher in HIV-1 infected IDUs (9.6 %) relative to HIV-1 uninfected IDUs (2.3 %), HIV-1 infected non-IDUs (3.6 %), HIV-1 uninfected non-IDUs (0.0 %) and non-drug users (2.6 %; P = 0.002). Contrastingly, HBsAb positivity was higher in HIV-1 uninfected IDUs (14.6 %) and non-IDUs (16.8) in comparison to HIV-1 infected IDUs (8.3 %), and non-IDUs (8.6 %), and non-drug users (8.2 %; P = 0.023). HBcAb positivity was higher in HIV-1 infected IDUs (10.2 %) compared to HIV-1 uninfected IDUs (3.3 %), HIV-1 infected non-IDUs (6.5 %), HIV-1 uninfected non-IDUs (2.1 %) and non-drug users (4.6 %; P = 0.038). Acute (5.7 %, 1.4 %, 0.0 %, 0.0 % and 1.5 %) and chronic (5.1 %, 0.9 %, 3.6 %, 0.0 % and 1.5 %) stages were higher in HIV-1 infected IDUs, compared to HIV-1 uninfected IDUs, HIV-1 infected and uninfected non-IDUs and non-drug users, respectively. However, vaccine type response stage was higher in HIV-1 uninfected IDUs (15.4 %) relative to HIV-1 infected IDUs (6.4 %), and HIV-1 infected (6.5 %), and uninfected (10.4 %) non-IDUs, and non-drug users (5.7 %; P = 0.003). Higher resolved infection rates were also recorded in HIV-1 uninfected IDUs (11.2 %) compared to HIV-1 infected IDUs (8.3 %), and HIV-1 infected (7.2 %), uninfected (6.3 %) non-IDUs, and non-drug users (6.7 %; P = 0.479), respectively. Only A1 genotype showing minimal diversity was detected among the study participants. Conclusion HBV sero-markers and infection staging are valuable in diagnosis and genotyping of HBV infections. Among IDUs, higher HBsAg and HBcAb positivity in HIV-1 infected and higher HBsAb positivity in HIV-1 negative IDUs suggests frequent exposure. Additionally, HBV genotype A is the dominant circulating genotype in both high and low risk populations of Kenya.