Unfolding the reality of the smoking paradox in a South Asian cohort of patients presenting with ST-elevation acute coronary syndrome undergoing primary percutaneous coronary intervention.

Objectives: Smoking is a potent risk factor for coronary artery disease, but there is controversy about its protective nature in terms of prognosis in ST-elevation acute coronary syndrome patients undergoing primary percutaneous coronary intervention. So, the main objective of this study is to unfold this controversy in a South Asian population in terms of clinical angiographic parameters and its in-hospital outcomes. Methods: In this study, we included 1756 consecutive patients diagnosed with ST-elevation acute coronary syndrome undergoing primary percutaneous coronary intervention. Patients were classified into smokers and non-smokers, and the in-hospital mortality rate was compared. Multivariable logistic regression analysis was performed to evaluate the paradoxical role of smoking. Results: Smokers were younger (53.78 ± 11.16 years vs 56.43 ± 11.17 years; p < 0.001) and more frequently men (98.7% vs 69.9%; p < 0.001) and had less diabetes (19.6% vs 44.8%; p < 0.001) and hypertension (38.5% vs 64.9%; p < 0.001). Smokers presented less frequently in Killip III (5.6% vs 8.1%; p < 0.001) and Killip IV (2.5% vs 4.8%; p < 0.001). Smokers mostly had single vessel disease (41.7% vs 34.4%; p = 0.013), whereas non-smokers had the multi-vessel disease and frequently presented with total occlusion of the culprit vessel (64.6% vs 58.8%; p = 0.040). Smokers have significantly lesser mortality (1.8% vs 4.3%; p = 0.009) compared to non-smokers with an odds ratio of 0.41 (95% confidence interval (CI): 0.21-0.82, p = 0.011); however, adjusted odds ratio on multivariable analysis was 0.67 (95% CI: 0.31-1.41, p = 0.290). Conclusions: The paradoxical protective role of smoking is the confounding effect of mainly younger age, less coronary artery disease burden, lower prevalence of diabetes and hypertension, and lower Killip III/IV at presentation.

Increasing Incidence of ST-Elevation Acute Coronary Syndrome in Young South Asian Population, a Challenge for the World? An Assessment of Clinical and Angiographic Patterns and Hospital Course of Premature Acute Myocardial Infarction.

The incidence of premature ischemic heart disease (IHD) is increasing because of urbanization, a sedentary lifestyle, and various other unexplored factors, especially in South Asia. This study aimed to assess the distribution of premature ST-elevation acute coronary syndrome (STE-ACS) with its clinical and angiographic pattern along with hospital course in a contemporary cohort of patients who underwent primary percutaneous intervention at a tertiary care center in the South Asian region. We included consecutive patients of either gender diagnosed with STE-ACS and who underwent primary percutaneous intervention. Patients were stratified based on age as ≤40 years (young) and >40 years (old). Clinical characteristics, angiographic patterns, and hospital course were compared between the 2 groups. Of the total of 4,686 patients, 466 (9.9%) were young (≤40 years). Young patients had a lower prevalence of hypertension (40.8% vs 54.5%, p <0.001), diabetes (26.6% vs 36.4%, p <0.001), metabolic syndrome (14.8% vs 24%, p <0.001), history of IHD (5.8% vs 9.3%, p = 0.013) and a higher frequency of smoking (33% vs 24.7%, p <0.001), positive family history (8.2% vs 3.2%, p <0.001), and single-vessel involvement (60.1% vs 33.2%, p <0.001). The composite adverse clinical outcome occurrence was significantly lower in young patients (14.2% vs 19.5%, p = 0.006). On multivariable analysis, history of IHD in young, whereas age, Killip class III/IV, intubated, arrhythmias on arrival, diabetes, history of IHD, pre-procedure left ventricular end-diastolic pressure, ejection fraction <40%, and slow flow/no-reflow during the procedure were found to be the independent predictors of adverse clinical outcome in old patients. In conclusion, we have a substantial burden of premature STE-ACS, mostly in male patients potentially driven by smoking and positive family history. Despite favorable pathophysiology, with mostly single-vessel hospital courses of STE-ACS in the young equally lethal in nature.

Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine Learning.

Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. The in hand research work focuses on students' grade and marks prediction utilizing supervised ML approaches. The data-set utilized in this research work has been obtained from the Board of Intermediate & Secondary Education (B.I.S.E) Peshawar, Khyber Pakhtunkhwa. There are 7 areas in BISEP i.e., Peshawar, FR-Peshawar, Charsadda, Khyber, Mohmand and Upper and Lower Chitral. This paper aims to examine the quality of education that is closely related to the aims of sustainability. The system has created an abundance of data which needs to be properly analyzed so that most useful information should be obtained for planning and future development. Grade and marks forecasting of students with their historical educational record is a renowned and valuable application in the EDM. It becomes an incredible information source that could be utilized in various ways to enhance the standard of education nationwide. Relevant research study reveals that numerous methods for academic performance forecasting are built to carryout improvements in administrative and teaching staff of academic organizations. In the put forwarded approach, the acquired data-set is pre-processed to purify the data quality, the labeled academic historical data of student (30 optimum attributes) is utilized to train regression model and DT-classifier. The regression will forecast marks, while grade will be forecasted by classification system, eventually analyzed the results obtained by the models. The results obtained show that machine learning technology is efficient and relevant for predicting students performance.

Mannich bases derivatives of 2-Phenyl-5-Benzimidazole sulfonic acid; Synthesis, Characterization, Computational studies and Biological evaluation

Abstract A new series of N-Mannich bases of 2-Phenyl-5-benzimidazole sulfonic acid have been synthesized through amino methylation reaction with secondary amines. The two moieties were held together through a methylene bridge, which comes from formaldehyde (Formalin Solution 37%) used in the reaction. Chemical structures of the newly synthesized compounds have been confirmed using FT-IR, 1HNMR and 13CNMR. Different in vitro assays including Anti-oxidant, Enzyme inhibition, Anti-microbial and Cytotoxicity assay were performed to evaluate the biological potential with reference to the standard drug. Among the synthesized library, compound 3a shows maximum alpha-glucosidase inhibition with an IC50 value of 66.66 µg/ml, compound 3d was found most toxic with LC50 value of 10.17 µg/ml. ADME evaluation studies were performed with the help of Molinspiration online software. Docking calculations were also performed. Given the importance of the nucleus involved, the synthesized compound might find extensive medicinal applications as reported in the literature.

Impact analysis of keyword extraction using contextual word embedding.

A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques.

Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.

Gaze and Eye Tracking: Techniques and Applications in ADAS.

Tracking drivers' eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers' community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver's eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver's eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

A Comprehensive Survey of Driving Monitoring and Assistance Systems.

Improving a vehicle driver's performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience. The studies focused on the three main elements of the driving process, viz. driver, vehicle, and driving environment are analytically reviewed in this work, and a comprehensive framework of DMAS, major research areas, and their interaction is explored. A well-designed DMAS improves the driving experience by continuously monitoring the critical parameters associated with the driver, vehicle, and surroundings by acquiring and processing the data obtained from multiple sensors. A discussion on the challenges associated with the current and future DMAS and their potential solutions is also presented.

Status of tick distribution in Bangladesh, India and Pakistan.

On a global basis, ticks transmit a greater variety of pathogenic microorganisms, protozoa, rickettsiae, spirochaets, and viruses than any other arthropods and are among the most important vectors of diseases affecting livestock, humans, and companion animals. Ticks and tick-borne diseases (TTBDs) affect 80% of the world cattle population and are widely distributed throughout the world, particularly in tropical and subtropical countries including India, Pakistan, and Bangladesh. Ticks and tick-transmitted infections have coevolved with various wild animal hosts, which constitute the reservoir hosts for ticks and tick-borne pathogens of livestock, pets, and humans. In this region, the livestock sector is suffering from a number of disease problems caused by bacteria, viruses, fungi, and parasites. Among the parasitological problems, the damage caused by TTBDs is considered very high, and the control of TTBDs has been given priority.