Knowledge, attitudes and perceptions about rabies among the people in the community, healthcare professionals and veterinary practitioners in Bangladesh

It is crucial to explore knowledge, attitudes and perceptions (KAP) about rabies among the people in the community, the personnel dealing with animal bite management and suspected rabies patients, including humans and animals, to facilitate intervention in improving rabies elimination strategies. In 2016, we conducted an interactive face-to-face survey in three different districts of Bangladesh to understand the extent of KAP towards rabies in the community peoples (CPs), human healthcare professionals (HCPs) and veterinary practitioners (VPs). A set of prescribed questions was employed to measure what proportion of each group possessed sufficient knowledge, positive attitudes and adequate perceptions about rabies. A total of 1133 CPs, 211 HCPs and 168 VPs were interviewed by using a standard questionnaire comprising both closed and open-ended questions. Of the CPs, 49% identified the disease correctly (i.e. rabies is caused by an animal bite or a scratch). Only 29% of the CPs were aware that a wound should be washed immediately with soap and water after an animal bite or a scratch. However, only 49% of the CPs, 65% of the HCPs and 60% of the VPs felt that it is important to consult a physician and receive post-exposure vaccine as the first line of treatment following an animal exposure. Among the HCPs, 23% of the respondents did not possess sufficient knowledge about animal bites as categorised by the World Health Organization (WHO), and 12% of the respondents did not possess the knowledge on how to manage an animal bite properly. Out of 52% of the VPs who previously treated suspected rabid animals, only 29% had a history of taking rabies pre-exposure prophylaxis (PEP). Lack of formal education and rural subsistence were found to largely contribute to poor rabies KAP level among the CPs (P ≤ 0.01). There has been a high demand for proper training to be provided to HCPs and VPs for the effective management of an animal bite incidence in human and animals, respectively. Multi-sectoral collaboration through integrated One Health initiatives including community education, awareness programmes, facilitation of rabies PEP, and dog vaccination as well as its population control are critical in the way forward to control rabies in Bangladesh

The Pathological Role of miRNAs in Endometriosis

Association studies investigating miRNA in relation to diseases have consistently shown significant alterations in miRNA expression, particularly within inflammatory pathways, where they regulate inflammatory cytokines, transcription factors (such as NF-κB, STAT3, HIF1α), and inflammatory proteins (including COX-2 and iNOS). Given that endometriosis (EMS) is characterized as an inflammatory disease, albeit one influenced by estrogen levels, it is natural to speculate about the connection between EMS and miRNA. Recent research has indeed confirmed alterations in the expression levels of numerous microRNAs (miRNAs) in both endometriotic lesions and the eutopic endometrium of women with EMS, when compared to healthy controls. The undeniable association of miRNAs with EMS hints at the emergence of a new era in the study of miRNA in the context of EMS. This article reviews the advancements made in understanding the pathological role of miRNA in EMS and its association with EMS-associated infertility. These findings contribute to the ongoing pursuit of developing miRNA-based therapeutics and diagnostic markers for EMS

Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery

Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. This research investigates the application of robust machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), and K-nearest neighbor (KNN), to the prediction of cerebral strokes. Stroke data is collected from Harvard Dataverse Repository. The data includes—clinical, physiological, behavioral, demographic, and historical data. The Synthetic Minority Oversampling Technique (SMOTE), adaptive synthetic sampling (ADASYN), and the Random Oversampling Technique (ROSE) are used to address class imbalances to improve the accuracy of minority classes. To address the challenge of forecasting strokes from partial and imbalanced physiological data, this study introduces a novel hybrid ML approach by combining a machine learning method with an oversampling technique called ADASYN_RF. ADASYN is an oversampling technique used to resample the imbalanced dataset then RF is implemented on the resampled dataset. Also, other oversampling techniques and ML models are implemented to compare the results. Notably, the RF algorithm paired with ADASYN achieves an exceptional performance of 99% detection accuracy, exhibiting its dominance in stroke prediction. The proposed approach enables cost-effective, precise stroke prediction, providing a valuable tool for clinical diagnosis