Women of sub-Saharan African descent have disproportionately higher incidence of triple-negative breast cancer (TNBC) and TNBC-specific mortality across all populations. Population studies show racial differences in TNBC biology, including higher prevalence of basal-like and quadruple-negative subtypes in African Americans (AA). However, previous investigations relied on self-reported race (SRR) of primarily U.S. populations. Due to heterogeneous genetic admixture and biological consequences of social determinants, the true association of African ancestry with TNBC biology is unclear. To address this, we conducted RNA sequencing on an international cohort of AAs, as well as West and East Africans with TNBC. Using comprehensive genetic ancestry estimation in this African-enriched cohort, we found expression of 613 genes associated with African ancestry and 2,000+ associated with regional African ancestry. A subset of African-associated genes also showed differences in normal breast tissue. Pathway enrichment and deconvolution of tumor cellular composition revealed that tumor-associated immunologic profiles are distinct in patients of African descent. Significance: Our comprehensive ancestry quantification process revealed that ancestry-associated gene expression profiles in TNBC include population-level distinctions in immunologic landscapes. These differences may explain some differences in race–group clinical outcomes. This study shows the first definitive link between African ancestry and the TNBC immunologic landscape, from an African-enriched international multiethnic cohort. See related commentary by Hamilton et al., p. 2496. This article is highlighted in the In This Issue feature, p. 2483

Introduction: The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than dialysis, graft failure is possible after transplantation. Hence, this study aimed to predict the risk of graft failure among post-transplant recipients in Ethiopia using the selected machine learning prediction models. Methodology: The data was extracted from the retrospective cohort of kidney transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. In response to the imbalanced nature of the data, we performed hyperparameter tuning, probability threshold moving, tree-based ensemble learning, stacking ensemble learning, and probability calibrations to improve the prediction results. Merit-based selected probabilistic (logistic regression, naive Bayes, and artificial neural network) and tree-based ensemble (random forest, bagged tree, and stochastic gradient boosting) models were applied. Model comparison was performed in terms of discrimination and calibration performance. The best-performing model was then used to predict the risk of graft failure. Results: A total of 278 completed cases were analyzed, with 21 graft failures and 3 events per predictor. Of these, 74.8% are male, and 25.2% are female, with a median age of 37. From the comparison of models at the individual level, the bagged tree and random forest have top and equal discrimination performance (AUC-ROC = 0.84). In contrast, the random forest has the best calibration performance (brier score = 0.045). Under testing the individual model as a meta-learner for stacking ensemble learning, the result of stochastic gradient boosting as a meta-learner has the top discrimination (AUC-ROC = 0.88) and calibration (brier score = 0.048) performance. Regarding feature importance, chronic rejection, blood urea nitrogen, number of post-transplant admissions, phosphorus level, acute rejection, and urological complications are the top predictors of graft failure. Conclusions: Bagging, boosting, and stacking, with probability calibration, are good choices for clinical risk predictions working on imbalanced data. The data-driven probability threshold is more beneficial than the natural threshold of 0.5 to improve the prediction result from imbalanced data. Integrating various techniques in a systematic framework is a smart strategy to improve prediction results from imbalanced data. It is recommended for clinical experts in kidney transplantation to use the final calibrated model as a decision support system to predict the risk of graft failure for individual patients.

Wastewater surveillance systems have become an important component of COVID-19 outbreak monitoring in high-income settings. However, its use in most low-income settings has not been well-studied. This study assessed the feasibility and utility of wastewater surveillance system to monitor SARS-CoV-2 RNA in Addis Ababa, Ethiopia. The study was conducted at nine Membrane Bio-reactor (MBR) wastewater processing plants. The samples were collected in two separate time series. Wastewater samples and known leftover RT-PCR tested nasopharyngeal swabs were processed using two extraction protocols with different sample conditions. SARS-CoV-2 wastewater RT-PCR testing was conducted using RIDA GENE SARS-CoV-2 RUO protocol for wastewater SARS-CoV-2 RNA testing. Wastewater SARS-CoV-2 RNA RT-PCR protocol adaptation, optimization, and detection were conducted in an Addis Ababa, Ethiopia context. Samples collected during the first time series, when the national COVID-19 case load was low, were all negative. Conversely, samples collected during the second time series were all positive, coinciding with the highest daily reported new cases of COVID-19 in Ethiopia. The wastewater-based SARS-CoV-2 surveillance approach is feasible for Addis Ababa. The COVID-19 wastewater based epidemiological approach can potentially fill the evidence gap in distribution and dynamics of COVID-19 in Ethiopia and other low-income settings.

BACKGROUND: The aim of this study was to assess the healthrelated quality of life of patients with T2DM and hypertension attending public health facilities in Addis Ababa, Ethiopia.METHODS: A cross-sectional study was conducted from 1st through 30th August 2020 at the selected institutions. Health facilities were chosen purposively based on patient load. Participants were drawn after proportional to size allocation. A translated EQ-5D-3L, and EQ-VAS instrument was used to collect the data. Analysis was done using SPSS v.26.0. Both parametric and non-parametric models were applied in the analysis.RESULTS: Of the 409 participants included, the majority were in the age group of 46-60 (36.0%), females (56.0%), from hospitals (54.8%), jobless (25.4%), and married (63.3%). Over two-thirds of the patients reported no problems with self-care, usual activity, and depression/anxiety. All dimensions showed an increasing proportion of moderate to severe problems in the age group beyond 45. Facility type, comorbid condition and age showed a statistically significant score difference for QoL. The overall prevalence of any problem was 59.0%. Education level, visit to a health center, and marriage showed lower odds of affected HRQoL whereas, lower monthly income and presence of comorbidities were opposite.CONCLUSION: HRQoL of patients in the study settings was suboptimal and below the general population. Attributes, such as education, facility type, marital status, income level, and comorbid status have a statistically significant association with HRQoL. Arrangement of a safe and quality health services is paramount, especially, during the COVID-19 pandemic.

BACKGROUND: Worldwide, the supply of organs continues to be much less than the demand. Many studies identify poor knowledge and negative attitude about organ donation for this. The purpose of this study was to assess knowledge and attitude towards organ donation and associated factors among people who accompany patient during hospital visit at tertiary hospital in Ethiopia. METHODS: A hospital-based cross-sectional study was conducted from March 1st to May 31, 2019, at a tertiary hospital and the only transplant center in Ethiopia. The patient companions were interviewed with structured questionnaires. Descriptive statistics were presented and factors associated with good knowledge and favorable attitude were assessed using Binary logistic regression. RESULTS: A total of 414 patient companion participated in the study. The mean age of the respondents was 35+13years, and 252(60.9%) were males. Forty-four (10.6%) of the study participants had good knowledge about organ donation. Male gender was significantly associated with improved knowledge (AOR=2.127 95% CI: 1.036, 4.368). A favorable attitude towards donation was found in 219(52.9%) of the study participants. Participants who had completed secondary education were more likely to have unfavorable attitude towards organ donation (AOR=0.498, 95% CI: 0.290, 0.857). CONCLUSION: Knowledge about organ donation was found to be poor, and attitudes towards organ donation were found to be unfavorable among patient companions at a major medical center in Ethiopia. Strategies focused on education of the general public and broad dissemination of information on the benefits of organ donation will be critical for improving the organ donor pool.