Applied Science and Biotechnology Journal for Advanced Research
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Enhancing Cyber Defense Mechanisms for Genomic Data in Personalized Healthcare Systems
Get PDFIn 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
Evaluating the Role of Large Language Models Detection: A Comparative Analysis of Noninvasive Testing Methods and AI-Generated Diagnoses
Get PDFNonalcoholic 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
Public Key Encryption and Keyword Search Mapping
Get PDFThis work addresses a difficult method for searching for crucial keywords for mixed cloud statistics (MRSE), which is a first step towards enabling secure cloud data processing. Among the different meanings of multiple terms, we choose the relevant concept of "relational coherence". Businesses can now more easily and affordably outsource a greater variety of products and services to community clouds thanks to cloud computing. To guarantee that sensitive personal data is kept enclosed before being supplied for their work. Given the enormous number of users of data and records in a search engine, it must be able to search for a specific phrase using numerous keyword searches and show a connection between them measure in order to successfully satisfy the demand of obtaining data. First, we suggest using the simple-to-use MRSE technique to protect a computer that contains internal objects. Real-world dataset experiments demonstrate that the suggested approaches do not significantly reduce the associated computing and communication expenses. One of the ways we effectively represent user information needs through optimization is by mapping feedback sessions to pseudo (fake) documents. Average precision (CAP) is a new metric used to assess the quality of the reconstructed online search results. Experiments with real data demonstrate that the suggested strategies have very little effect on transmission and computation. We extend these two methods to include more search semantics in order to enhance the search experience provided by the data search service
A Setting Technique for Comparative Protein Modelling for Web based SMART Tool
Get PDFWhen the "hairless protein" linked to the hairless gene, which is necessary for hair growth, stops working, the result will be total hairlessness. This gene is located on chromosome 8 at locations 22027873-22045326. The hairless gene, which similarly aids in histone demethylation, is a member of the JmjC domain superfamily. With 1189 residues in the hairless protein, the domain sequence spans positions 946 to 1157 and is 212 amino acids long. JmjC domains have been identified in over 100 bacterial and eukaryotic sequences due to significant sequence similarity. Among them the human hairless gene, which is mutated in alopecia universalis sufferers. We have attempted to use the bioinformatics method to homology model the JmjC domain in the hairless protein. The tools and programmes used in this work are NCBI-BLASTP, EBIClustalW, SMART, 3D-PSSM, DeepView/Switzerland-PDB Viewer, PyMOL, and WhatCheck. The structure of the JmjC domain is predicted using the template crystal structure of the probable antibiotic biosynthesis protein from Thermus thermophilus HB8. The minimised energy value of the modelled domain structure was -3394.570 KJ/mol. The WHAT IF-Proteins Model Check tool was used to verify the simulated domain structure.
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A Study on Factors Attributed to Failure of Pharmaceutical Products
Get PDFThe Pharmaceutical Industry plays a pivotal role in healthcare by developing, manufacturing, and marketing drugs to treat a myriad of medical conditions. However, despite rigorous research, development, and testing processes, a significant number of pharmaceutical products fail to meet the expected standards or gain market acceptance. This study aims to explore the multifaceted factors that contribute to the failure of pharmaceutical products, focusing on the product life cycle as a framework for analysis. The product life cycle of a pharmaceutical product encompasses several stages, including research and development, clinical trials, regulatory approval, launch, and post-marketing surveillance. At each stage, various factors can influence the success or failure of a product. These factors range from scientific challenges and regulatory hurdles during the R&D phase to manufacturing issues, competitive pressures, and post-market safety concerns.
Through a comprehensive theoretical reviews and authors understanding of the subject, this study identifies key factors attributed to the failure of pharmaceutical products. These include but are not limited to Scientific Challenges: Inherent complexities in drug discovery and development, including target identification, drug design, and optimization, can lead to unforeseen efficacy or safety issues. Regulatory Hurdles: Stringent regulatory requirements and evolving guidelines can delay approvals, increase development costs, and limit market access for new drugs. Manufacturing Issues: Quality control failures, supply chain disruptions, and manufacturing inconsistencies can compromise the integrity, efficacy, and safety of pharmaceutical products. Competitive Pressures: Intense competition from generic drugs, biosimilars, and innovative therapies can erode market share and profitability, especially for products with limited differentiation. Post-Market Safety Concerns: Adverse events, drug interactions, and long-term side effects discovered after product launch can result in recalls, litigation, and damage to the brand reputation. By understanding and addressing these factors proactively, pharmaceutical companies can mitigate risks, enhance product quality, and improve the likelihood of success throughout the product life cycle. This study underscores the importance of comprehensive risk management, continuous monitoring, and adaptive strategies to navigate the complexities and challenges inherent in the pharmaceutical industry
A Review Study on Insecure Food Habits and its Impact on Health & Healthcare
Get PDFHealth is primary matter for every individual, to large extent it has to be a primary matter. Healthcare is immensely required elements for human being. It has the potential of scaling up towards business avenues. Health is such an issue that gets attention of States and Central government primarily to the extent that the local authorities are also involved. Healthcare Entrepreneurship has always been the massive support to the Economic growth. Healthcare Entrepreneurship wasn’t potentially the preferred choice however the advent of the Pharmacy Courses and the avenue of online operations in Pharmacy sector gave a good boost to the Healthcare sector. Food habits eventually have an impact on the health and insecure food habit destroys the physical & mental health of human being. Insecure food habits is characterized by limited access to nutritious and safe food, have emerged as a pressing public health concern worldwide.
This review study comprehensively examines the intricate relationship between insecure food habits and their profound impact on health and healthcare systems. Drawing upon secondary data investigations, this study explores the multifaceted repercussions of food insecurity on various dimensions of health, encompassing nutritional deficiencies, chronic diseases, mental health disorders, and overall well-being. Through a systematic analysis of existing literature, this review underscores the urgent need for targeted interventions and policy measures to alleviate food insecurity and mitigate its adverse health outcomes. This study provides valuable insights to inform evidence-based strategies aimed at promoting health equity and enhancing the resilience of healthcare systems in the face of food insecurity challenges
Assessment of Nutritional Knowledge and Practices Regarding Canteen Snacks among Youth in Maharashtra
Get PDFIntroduction- 1/5th 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
A Study on Deep Learning Architectures and Dimensionality Reduction Techniques on Gene Expression Data
Get PDFGenomics, driven by the evolution of high-throughput sequencing and microarray technologies, has become one of the key inventions of cracking the secrets of complex biological systems. The deep learning architecture not only provides with a powerful tool to derive the hidden insights from the huge amount of genomic data, but also enables to mine meaningful information. In this study, we will examine the application of deep learning methods in the analysis of genomics data, specifically on dimensionality reduction and predictive modeling for binary phenotypes. We focus on the problems with the existing strategies, spot the avenues for the further research, and provide you with a glimpse of the dramatic influence of deep learning on genomics. In this study, we delve into the application of deep learning methods in the analysis of genomic data, with a specific focus on two crucial aspects: dimensionality reduction and predictive modeling for binary phenotypes. Dimensionality reduction techniques are essential for tackling the high-dimensional nature of genomic data, where thousands or even millions of features (e.g., gene expressions, genetic variants) are measured for each sample. Deep learning models can effectively capture the complex relationships and patterns within this high-dimensional space, enabling the extraction of lower-dimensional representations that preserve the most salient information. Throughout this study, we critically examine the existing strategies and approaches in the field of genomics, identifying their limitations and highlighting the avenues for further research. We explore how deep learning can address these challenges and provide a glimpse into the dramatic influence this technology is poised to have on the field of genomics
Genetic Variability, Heritability and Genetic Advance of Dry Matter Yield and Yield Contributing Characters in Rhodes Grass (Chloris gayana) Genotypes
Get PDFInformation on the communal association of traits is important for effective selection in forage-breeding program. Twenty four genotypes of Rhodes grass and one check were evaluated at Mechara Agricultural Research site (Onstation) with lattice design in 2023/24 main rainy season to evaluate Genetic variability, heritability and genetic advance of dry matter yield and yield contributing characters in Rhodes grass genotypes. The mean sum of squares of genotypes showed significant differences (p < 0.05) for stand vigor, days to 50% emergence, date to 50% flowering and Plant height and highly significant (p < 0.001) for biomass yield, dry matter and number of leaf per plant. Maximum phenotypic variance and genotypic variance value was recorded for days to maturity. The range observed for heritability (H2bs) was from (0.0%) to (55%). Stand vigor exhibited highest value of genetic advance as percentage of mean followed by number of leaf per plant. Highest genotypic coefficient variation were recorded from days to maturity (89.8%) flowed by Plant height (62.3%) and Highest phenotypic coefficient variation were recorded from plot cover (184.9%) followed by days to maturity (225.4%). Phenotypically and genotypically dry matter yield was highly positive significant associated with of Plot cover (0.546**), stand vigor (0.566**), leaf per plant (0.439**) and showed highly negative significant with days to 50% emergence. The results of phenotypic path coefficient analysis showed that stand vigor (0.378) and leaf per plant had exerted moderate positive direct effect on dry matter. stand vigor followed by plant height, plot cover and leaf per plant had exerted high and positive direct effect on dry matter yield and genotypic path analysis showed stand vigor followed by plant height, plot cover and leaf per plant had exerted high and positive direct effect on dry matter yield. This indicates that selection based on these traits could be more effective to maximize dry yield
LLM Connection Graphs for Global Feature Extraction in Point Cloud Analysis
Get PDFGraph convolutional networks (GCNs) have effectively utilized local connections for point cloud analysis. How- ever, capturing distant dependencies (i.e., global features) with a single local connection graph, such as the Euclidean k-nearest neighbor graph, remains challenging. To ad- dress this, we introduce the Multi-Space Graph Convolutional Network (PointGCNN), which leverages reinforcement learning to adaptively construct connection graphs in multiple latent spaces, integrating both local and non-local dependencies. Initially, we encode and concatenate low- level local features from Euclidean and Eigenvalue spaces. Convolution layers are then hierarchically built, with each layer forming dynamic connection graphs to guide the propagation of low-level features. [1,2,3,4,11,14,16]These implicitly constructed graphs enable our model to uncover hidden dependencies. The assorted connections from different graphs support the extraction of fine-grained features from various perspectives, enhancing complex scene recognition. Thus, our model can capture multiple global contexts beyond the local scope of a single space, providing strong robustness against perturbations. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two major public point cloud benchmarks