Non O1, non O139 vibrio cholerae bacteraemia in an infant; case report and literature review

Non 01, Non O139 Vibrio cholerae bacteraemia is a rare but potentially fatal occurrence. There have been very few incidents of this infection from around the world. The treatment regimen of antibiotics also varies in literature. We present a case of bacteraemia caused by Non O1, Non O139 Vibrio cholerae along with associated risk factors, disease manifestations, laboratory diagnosis and treatment regimen. This serves to add additional information regarding symptoms and signs of this infection along with management of patient. Knowledge regarding this topic shall be highly useful to professionals if further cases are detected. In the discussion section, a review of literature of previous cases is also presented

Comparing hyperparameter optimized support vector machine, multi-layer perceptron and bagging classifiers for diabetes mellitus prediction

Diabetes Mellitus (DM) is a chronic metabolic disorder that affects the way body processes blood glucose levels. Within the medical field, Machine Learning (ML) has significant potential for accurately forecasting and diagnosing a range of chronic conditions. If an accurate prognosis is achieved early, the risk to health and intensity of DM can be significantly mitigated. In this study, a robust methodology for DM prognosis was proposed, which included anomaly replacement, data normalization, feature extraction, and K-fold cross-validation. Three machine learning methods, Support Vector Machine, Multilayer Perceptron and Bagging, were employed to predict Diabetes Mellitus using the National Health and Nutritional Examination Survey (NHANES) 2011-2012 dataset. Accuracy, AUC and Recall were chosen as the evaluation metrics and subsequently optimized during hyperparameter tweaking. From all the comprehensive tests, Bagging outperformed the other two models with an Accuracy of 96.67, AUC score of 99.2 and Recall of 97.0. The proposed methodology surpasses other approaches for forecasting DM

Community conversation : addressing mental health stigma with ethnic minority communities

Stigma associated with mental health problems is a significant public health issue. Patterns of stigma and discrimination vary between and within communities and are related to conceptualisations of, and beliefs about, mental health. Population approaches to addressing stigma rarely consider diverse cultural understandings of mental health. 257 members of the major black and minority ethnic communities in Scotland participated in 26 mental health awareness workshops that were designed and delivered by community organisations. Questionnaires measuring knowledge, attitudes and behavioural intent were completed before and after the intervention. Community led approaches that acknowledge cultural constructs of mental health were received positively by community groups. The study found significant reported stigma in relation to public protection, marriage, shame and contribution, but also high levels of recovery optimism. The workshops resulted in significant positive change in relation to knowledge, attitudes and behavioural intent amongst participants, with most aspects of stigma showing significant improvement, with the exception of dangerousness. The paper argues community approaches to tackling stigma are more valuable than top-down public education and could form the basis of national initiatives. Refinements to the evaluation framework are considered