Applied Science and Biotechnology Journal for Advanced Research

Assessment of Nutritional Knowledge and Practices Regarding Canteen Snacks among Youth in Maharashtra

Kanishka Singh, Maseera Khan, Mayuri Gaikwad — Mon, 16 Sep 2024 00:00:00 +0530

Introduction- 1/5^th Indian populas is adolescents & 66% are U5, making India a young country. Due to increasing urbanization & industrialization the reproductive young population in India is undergoing dramatic physical, financial, social, food behavior and cultural transitions which dramatically are impacting the general & nutritional health and well-being of individuals. In latest studies, non-nutritious institutional food supply, food menu and deficient food choices of youth are the top causative factors for making LSDs epidemic along with other DD. NCDs in India will cost national loss ~3.6 trillion and heart wrenching 63% preventable NCDs deaths by 2030. All this makes the present study an exigency towards food serving & health care sector.

Methodology- For present study single sample, pre-test and non-experimental developmental research design was adapted to select sample size of 50 of 15 – 45 yrs. (male & female) based on inclusion & exclusion criteria through purposive random sampling from the study area at Aurangabad. For data collection structured interview schedule which consisted of 3 sections namely – Sociodemographic profile, assessment of knowledge and assessment of practices was developed & validated from experts before field administration. Data was tabulated in MS-Excel 2007 version and statistical analysis was done using IBM SPSS advanced statistics 29.0 (5725-A54) version.

Objectives- 1. To assess the nutritional knowledge regarding canteen snacks of college students. 2. To assess the practices regarding canteen snacks of college students.

Results & Discussion- The overall assessment of respondent’s nutritional knowledge on category basis shows, 30% had knowledge of food groups, 34% knows about nutrients, 64% had knowledge related to cooking methods, 76% had food choices knowledge, 95% had knowledge of my plate, and 80% had knowledge of food menu.

It shows that in 14% prefer eating in canteen daily, 14% prefer alternately, 32% weekly and 40% sometimes/ never; in type of snacks preferred most 64% prefer fried foods, 24% prefer packed foods, and 10% prefer drinks like cola, etc.; 20% consume millets daily in meals, 8% alternately, 26% weekly and 46% sometimes/never; 26% consumes vegetables daily in snack items, 6% alternately, 18% weekly, 50% sometimes/ never; 38% were consuming fruits daily, 26% alternately, 32% weekly and 4% some/never; 48% preferred eating sprouts daily and 52% did not preferred eating sprouts on daily basis; 24% consume packed fruit juices, 54% consume fresh fruit juices, 14% aerated drinks and 8% consume flavored; 46% always smell food for food spoilage, 34% smell sometimes, 14% never and 6% can’t say; 32% eat fast food 1 time/day, 20% eat 2 times/day, 4% eat 3 times/day & 44% responded none; and regarding the taste 18% prefer sweet taste, 47% prefer sour and 78% prefer spicy.

Evaluating the Role of Large Language Models Detection: A Comparative Analysis of Noninvasive Testing Methods and AI-Generated Diagnoses

Yue Zhu, Ziwei Wang, Xiaoyi Zhang, Yuchen Zhang, Jiaqi Hong — Fri, 20 Sep 2024 00:00:00 +0530

Nonalcoholic fatty liver disease (NAFLD) has become a global epidemic. The coexistence of NAFLD and type 2 diabetes mellitus (T2DM) is common, and their interaction significantly heightens the risk of adverse clinical outcomes. Despite advancements in medicine, diagnosing NAFLD remains a critical challenge. Large language models (LLMs) have shown exceptional capabilities in various medical applications. However, their potential in diagnosing NAFLD has yet to be fully explored.

Enhancing Cyber Defense Mechanisms for Genomic Data in Personalized Healthcare Systems

Ammar Alzaydi, Kahtan Abedalrhman, Siti Nurhaliza, Mohd Ismail — Sat, 28 Sep 2024 00:00:00 +0530

In the era of personalized medicine, genomic data emerges as a cornerstone for tailored healthcare solutions, offering unprecedented opportunities for disease prediction and prevention. However, this sensitive data is increasingly vulnerable to cyber threats that compromise patient privacy and system integrity. Addressing this critical issue, our research introduces a novel cybersecurity framework specifically designed to protect genomic information within healthcare systems. We develop and implement advanced cryptographic methods, real-time intrusion detection systems, and secure data sharing protocols to construct a robust defense mechanism. Through extensive simulations, we evaluate the efficacy of our framework against a range of cyber threats, demonstrating significant enhancements in security measures. Our findings reveal that the proposed solution not only fortifies the security of genomic data but also ensures compliance with regulatory standards and ethical guidelines. This paper contributes a methodologically sound approach to cybersecurity in healthcare, proposing a scalable and efficient framework that paves the way for safer genomic data handling in the realm of personalized medicine.

LLM Machine Learning for Predicting Cardiovascular Mortality in Patients

Yue Zhu, Xiaoyi Zhang, Yuechen Zhang — Sun, 29 Sep 2024 00:00:00 +0530

Patients with chronic kidney disease (CKD) face a high risk of cardiovascular death, yet accurately predicting this risk remains challenging. This study aims to develop an interpretable machine learning (ML) model to predict 10-year cardiovascular mortality in CKD patients using SHAP explainers. [1]Six ML models were created and tested, with the best model selected for prediction and patient categorization. Survival rates were analyzed using log-rank tests on Kaplan-Meier curves, and Cox regression was employed to explore the relationship between ML-predicted risk scores and mortality. The chosen autoencoder (AE) model demonstrated superior performance, with higher ML scores[2] significantly correlating with increased cardiovascular mortality risk. Key determinants such as age, high blood pressure, C-reactive protein, and serum creatinine were identified. The ML-driven tool showcased high accuracy in determining the 10-year cardiovascular mortality risk for CKD patients, offering valuable insights for individual risk assessments.