Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Clinicians’ adherence to national pneumonia management guidelines at Kitale County Hospital, Kenya

Objectives: To determine adherence to national pneumonia guidelines in children aged 2-59 months admitted at Kitale County Hospital. Design: Retrospective Setting: Pediatric wards of Kitale County hospital in Trans Nzoia County, Kenya. Participants: Children under 5 years admitted with a diagnosis of pneumonia to Kitale County Hospital Pediatric ward. Interventions: Data were collected from the participant’s inpatient records upon discharge or death. All files were included till a sample size of 380 was achieved. Data on demographics and management was extracted from the pediatric admission form, daily ward round notes and treatment sheet. Data were then compared against the national guidelines to assess adherence. Main outcome measures: Adherence to the guidelines. Results: The median age was 12 months (IQR 7, 24). The males constituted 198 (52%) of the participants. The diagnosis was severe  pneumonia in 213 (56%) and pneumonia in 167 (44%) of the participants. Adherence at admission was 121 (32%) of the cases. Appropriate diagnosis was made in 202 (53%), correct drug chosen in 212 (56%) and correct dosage prescribed in 270 (71%) of theparticipants. The proportion of the patients correctly managed in accordance with guidelines during the inpatient stay was 2 (0.6%). Complete adherence from admission to discharge was in 8 (2%) of the cases. Conclusion: The level of adherence to the guidelines was low, markedly decreasing from admission to discharge

Economic impact of maize research in Tanzania

Maize was introduced in Tanzania in the 16th century but research on appropriate varieties and management practices did not get underway until the 1940s. In 1974, a National Maize Research Program (NA{RP) was established to co-ordinate maize research. During 1974-1994 the NM RP released 15 varieties. This study was conducted to assess the socio-economic impact of maize technology Development and transfer investment from 1974 to 1994. Standard pre-tested questionnaire and multi-stage sampling procedures were used for primary data collection. Data were collected from 978 farmers in 53 sites across seven agroecological zones. The sample survey revealed that the adoption rate of the improved varieties for the various zones were 28%, 66%, 44%, 24%, 66%, 81 % and 36% for the Central, Eastern, Lake, Northern, Southern, Southern Highlands and the Western Zones, respectively. The study demonstrated that farmers adopt the cheapest and low-risk technological components in a stepwise process reflecting the profitability and riskiness of each component. The estimated rate of return for the maize research and Development in Tanzania was 19%

HIV-1-specific mucosal IgA in a cohort of HIV-1-resistant Kenyan sex workers

Objectives: Most HIV-1 transmission is sexual; therefore, immune responses in the genital mucosa may be important in mediating protection against HIV infection. This study examined HIV-1-specific mucosal IgA in a cohort of HIV-1-resistant Kenyan female sex workers. Methods: HIV-1-specific immune responses were compared in HIV-1-resistant and HIV-1-infected sex workers, and in lower risk uninfected women. Cervical and vaginal samples from each group were tested for HIV-1-specific IgA and IgG by enzyme immunoassay. Systemic T-helper lymphocyte cell responses to HIV-1 envelope peptide epitopes were assayed using an interleukin 2 bioassay. HIV-1 risk-taking behaviours were assessed using standardized questionnaires. Results: HIV-1-specific IgA was present in the genital tract of 16 out of 21 (76%) HIV-1-resistant sex workers, five out of 19 (26%) infected women, and three out of 28 (11%) lower risk women (P < 0.0001). Among lower risk women, the presence of HIV-1-specific IgA was associated with HIV-1 risk-taking behaviour. Systemic T-helper lymphocyte responses to HIV-1 envelope peptides were present in 11 out of 20 (55%) HIV-1-resistant women, four out of 18 (22%) infected women, and one out of 25 (4%) lower risk women (P < 0.001). T-helper lymphocyte responses did not correlate with the presence or titre of virus-specific mucosal IgA in any study group. Conclusions: HIV-1-specific IgA is present in the genital tract of most HIV-1-resistant Kenyan sex workers, and of a minority of lower risk uninfected women, where it is associated with risk-taking behaviour. These data suggest a role for mucosal HIV-1-specific IgA responses in HIV-1 resistance, independent of host cellular responses

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

The past 2 years, during which waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants swept the globe, have starkly highlighted health disparities across nations. Tegally et al. show how the coordinated efforts of talented African scientists have in a short time made great contributions to pandemic surveillance and data gathering. Their efforts and initiatives have provided early warning that has likely benefited wealthier countries more than their own. Genomic surveillance identified the emergence of the highly transmissible Beta and Omicron variants and now the appearance of Omicron sublineages in Africa. However, it is imperative that technology transfer for diagnostics and vaccines, as well the logistic wherewithal to produce and deploy them, match the data-gathering effort

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran