Patterns of Perception of Cardiac Symptoms by Patients Presenting with ST-Segment Elevation Myocardial Infarction and their Knowledge of Coronay Artery Disease Risk Factors.

BACKGROUND: Proper knowledge regarding Coronary Artery Disease and their risk factors is essential for the early recognition of the disease and its presentation. This study was conducted to identify pattern of clinical symptoms and knowledge regarding Coronary Artery Disease risk factors among ST-Elevation myocardial infarction (STEMI) patients. METHODS: This cross-sectional, observational study was conducted among 340 ST-Elevation myocardial infarction patients in the inpatient Cardiology Department of Shahid Gangalal National Heart Centre Nepal, from November 2020 to February 2021. Baseline clinical characteristics, knowledge regarding Coronary Artery Disease risk factors, patterns of symptoms, and prehospital delay were collected and evaluated. RESULTS: In our study, 299 (87.9%) had typical ischemic chest pain during the symptom onset, however, only 81 (23.8%) perceived chest pain as cardiac disease, and 311 (91.5%) of the patients presented to the nearby health care center within the recommended time of less than 12 hours for the reperfusion therapy of ST-Elevation myocardial infarction. Perception of symptoms as a cardiac origin and typical chest pain were not significantly associated with earlier presentation. Also, the typical chest pain was not significantly associated with the perception of the symptom as a cardiac origin. The history of Coronary Artery Disease was considered as a Coronary Artery Disease risk factor by 184(54.1%) of the study population and 137(40.3%), 132(38.8%), 110(32.4%), 105(30.9%) and 71(20.9%) considered hypertension, smoking, age, obesity, and diabetes mellitus as a Coronary Artery Disease risk factor respectively. CONCLUSIONS: Though most patients presented with typical chest pain, identification of the chest pain as a cardiac origin and the awareness of the Coronary Artery Disease risk factors was low.

CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention.

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug-target affinity is crucial. The proposed model, the prediction of drug-target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug-target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.