South African Medical Research Council (SAMRC) Repository
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WHO consolidated guidelines on tuberculosis module 5: Management of tuberculosis in children and adolescents
No full text
The decline in femicide in South Africa: Evidence from three national femicide studies across 18 years
No full textSVRI Forum Conference, 19-23 September 2022, Cancun, Mexico
"Advances in combating HIV with broadly neutralizing antibodies"
No full textThe 24th International AIDS Conference, Symposium, Canada
Depression and post-traumatic stress symptoms two years post-rape and the role of early counselling: Rape Impact Cohort Evaluation (RICE) study
No full textSVRI Forum Conference 19-23 September 2022, Cancun, Mexico
Decrease in femicide in South Africa: Three national studies across 18 years
No full textMurder of women and girls, in acts of femicide, is the most extreme form of gender-based violence (GBV)
Plant foods consumption and its association with cardiovascular disease risk profile in South Africans at risk of diabetes
No full textPoster presented at the International Congress of Nutrition (ICN), Tokyo, Japan, 6-11 December 2022.Plant foods differ in their nutrient content and can be classified as healthy (i.e., fruits and vegetables) or less healthy (i.e., sugar-sweetened beverages). Nutrient-rich plant foods are healthier and contain bioactive compounds such as polyphenols with antioxidant properties that may protect against cardiovascular disease (CVD). We assessed the distribution of healthy plant foods and its association with CVD risk factors in South African adults at high risk of diabetes. Methods: This cross-sectional study utilized baseline screening data from the South African Diabetes Prevention Programme (SA-DPP). Participants identified as being at high risk of diabetes underwent data collection including a non-quantified 24-hour dietary recall, physical examination, and biochemical analysis. Group comparisons used appropriate statistical tests to explore differences in the distribution and associations of common CVD risk factors by plant foods consumption. Results: Among 693 participants (81% females), the mean age was 51 years (SD=8.95). The prevalence of obesity was higher in consumers of cereals than in non-consumers (86% vs. 14%, p=0.018). Compared with non-consumers, consumers of maize had lower fasting insulin (7.8 vs. 9.6 mIU/L, p<0.001), lower LDL-cholesterol (3.0 vs. 3.2 mmol/L, p=0.011), lower triglycerides (1.2 vs. 1.3 mmol/L, p=0.023) and lower fibrinogen (3.6 vs. 3.8 g/L, p=0.005) levels; consumers of yellow coloured vitamin A rich vegetables and tubers had lower systolic blood pressure (125 vs. 128 mmHg, p=0.030) and lower triglycerides (1.2 vs. 1.3 mmol/L, p=0.028), while consumers of vitamin A rich fruits had lower fasting plasma glucose (5.0 vs. 5.4 mmol/L, p=0.001). Regression analysis revealed a negative association between body mass index ≥30 kg/m2 and white roots and tubers consumption (adjusted odds ratio: 0.64, p=0.048). Conclusions: Significant differences were apparent in the distribution of some CVD risk factors between consumers and non-consumers of certain plant foods. The association of healthy plant foods consumption and CVD risk reduction needs further investigations in this setting
Automatic tuberculosis and COVID-19 cough classification using deep learning
No full textProc. of the International Conference on Electrical, Computer and Energy Technologies (ICECET 2022) 20-22 July 2022, Prague-Czech Republic.We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant symptom and claim thousands of lives each year. The cough audio recordings were collected at both indoor and outdoor settings and also uploaded using smartphones from subjects around the globe, thus containing various levels of noise. This cough data include 1.68 hours of TB coughs, 18.54 minutes of COVID-19 coughs and 1.69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50. These three deep architectures were also pre-trained on 2.14 hours of sneeze, 2.91 hours of speech and 2.79 hours of noise for improved performance. The class-imbalance in our dataset was addressed by using SMOTE data balancing technique and using performance metrics such as F1-score and AUC. Our study shows that the highest F1-scores of 0.9259 and 0.8631 have been achieved from a pre-trained Resnet50 for two-class (TB vs COVID-19) and three-class (TB vs COVID-19 vs healthy) cough classification tasks, respectively. The application of deep transfer learning has improved the classifiers’ performance and makes them more robust as they generalise better over the cross-validation folds. Their performances exceed the TB triage test requirements set by the world health organisation (WHO). The features producing the best performance contain higher order of MFCCs suggesting that the differences between TB and COVID-19 coughs are not perceivable by the human ear. This type of cough audio classification is non-contact, cost-effective and can easily be deployed on a smartphone, thus it can be an excellent tool for both TB and COVID-19 screening
Nqoba Sibindi! Rape survivors’ experiences of shame, self-blame and self-stigma in eThekwini, South Africa
No full textSVRI Forum Conference 19-23 September 2022, Cancun, Mexico
