Significance of Machine Learning Algorithms to Improve Predictive Analytics in Chronic Disease Management through Pharmacogenomics

The treatment of chronic diseases is an important concern in global health. Machine learning (ML)-based disease prediction models are becoming more important for making informed medical decisions in light of the paradigm shift towards preventative care. Integrating genetic, pharmacogenomic, personal health, and psychosocial data can greatly assist healthcare practitioners in making treatment-related decisions for patients with chronic diseases. This study utilizes a Deep Convolutional Neural Network-assisted Chronic Disease Management (DCNN-CDM) through pharmacogenomics and an improved predictive analytics model to enable informed real-time decision-making at the point of care. Data augmentation in terms of feature space allows the DCNN model to avoid over-fitting while effectively capturing high-level features submerged in chronic disease datasets. Afterwards, this work suggests an attention-empowered DCNN model to enhance sick case diagnosis accuracy, which augments data regarding sample space, thereby alleviating the class imbalance issue. Electronic health information data mining is now using predictive analytics to determine individuals at risk of acquiring chronic disease problems. The suggested model may aid in the early and accurate diagnosis of chronic diseases. The numerical outcomes demonstrate that the recommended DCNN-CDM model increases the accuracy rate of 98.7%, patient monitoring rate of 97.5%, F1-score rate of 96.3% and predictive performance rate of 95.1% compared to other existing methodologies

Analysis of AI-based Tools Challenges and Opportunities for Public Health Care Solutions

Healthcare Systems are essential to maintaining people\u27s health. Determining precise diagnoses is an essential step in this procedure. As sources point out, missed and incorrect diagnoses as a widespread problem, a solution needs to be found. Emergency rooms are known to be stressful workplaces, and diagnostic errors are frequently made there. Systems, products, and services are changing quickly due to technology advancements that today\u27s companies must adapt to. One such technology that can help with diagnosis is artificial intelligence (AI), but it also presents ethical, legal, and technical difficulties. Therefore, the purpose of this study is to examine how AI can impact diagnosis accuracy and how the technical, ethical, and legal issues of its incorporation into healthcare are related. This study looks at the idea of patient empowerment and how it might be applied to enhance the provision of patient health care

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems

Development of Region Specific Hybrid Goat and their Performance Evaluation under High Altitude Condition

Goat meat (chevon) comprises an important source of protein to provide essential amino acids in addition to other meat and plant sources of proteins. Therefore, demands for chevon are huge from civil and defence sector in this region. However, there is limited availability of fresh tender chevon in Ladakh region round the year. Hence, there was a need of augmenting local availability of fresh goat meat by developing animal technology for fast growing region-specific crossbred goat for meat purpose that can efficiently perform under adverse climatic conditions prevailing in this region. The present crossbred goat was developed by using mixing genes of adaptive and meat traits through cross breeding between local goats (Changthangi and Gaddi breeds of goats) and Sirohi/Black Bengal goats. To develop this technology, we introduced Black Bengal and Sirohi from plain areas and native breed of goats viz. Gaddi and Changthangi goats for further adaptation and growth performance studies at Leh-Ladakh. After initial studies goats were divided into high altitude resistant/adapted and susceptible groups. High altitude resistant/adapted goats were taken for further cross breeding and pure breeding. All the kids produced out of this breeding were studied for physiological responses, growth performance, and blood biochemical parameters to know their adaptive and growth performance at high altitude. Crossbred kids of Sirohi ♂/Black Bengal ♂ X Changthangi ♀ had significantly (P<0.05) higher weight gain, adaptive physiological responses and blood biochemicals level as compared to exotic pure bred and other cross bred kids. These crossbred kids attained market weight faster than local as well as breeds from plain areas (Sirohi and Black Bengal goats). Average meat yield is 7-10 kg per adult crossbred goat if slaughtered at 9-12 month age. These cross bred (broiler goat) may be reared at Leh-Ladakh for meat purpose. Hence, this animal technology may help in increasing of fresh goat meat (chevon) supply to meet army’s and civil requirements in Leh-Ladakh

Effect of heat shock protein 70 polymorphism on thermotolerance in Tharparkar cattle

Abstract Aim: Out of various members of heat shock protein (HSP) superfamily which act a molecular chaperon by binding to the denaturing protein thus stabilizing them and preserving their activity, HSP70 are of major importance in thermotolerance development. Thus, present investigation aimed at a screening of HSP70 gene for polymorphisms and possible differences in thermotolerance in Tharparkar breed of cattle. Materials and Methods: A 295 bp fragment of HSP70 gene was subjected to polymerase chain reaction-singlestrand conformation polymorphism (SSCP) followed by sequencing of different SSCP patterns in 64 Tharparkar cattle. A comparative thermotolerance of identified genotypes was analyzed using heat tolerance coefficients (HTCs) of animals for different seasons. Results: Three SSCP patterns and consequently two alleles namely A and B were documented in one fragment of HSP70 gene. On sequencing, one single-nucleotide polymorphism with G > T substitution was found at a position that led to a change of amino acid aspartate to tyrosine in allele A. It was found that in maintaining near normal average rectal temperature, genotype AA was superior (p≤0.01). Genotype AA, thus, was found to be most thermotolerant genotype with the highest HTC (p≤0.01). Conclusion: The polymorphism at HSP70 is expected to be a potent determinant for heat tolerance in cattle, which may aid in selection for thermotolerance in cattle

Association of ATP1A1 gene polymorphism with thermotolerance in Tharparkar and Vrindavani cattle

Aim: One of the major biochemical aspects of thermoregulation is equilibrium of ion gradient across biological membranes. Na+/K+-ATPase, a member of P type-ATPase family, is a major contributor to the mechanism that actively controls crossmembrane ion gradient. Thus, we examined ATP1A1 gene that encodes alpha-1 chain of Na+/K+-ATPase, for genetic polymorphisms. Materials and Methods: A total of 100 Vrindavani (composite cross strain of Hariana x Holstein-Friesian/Brown Swiss/Jersey) and 64 Tharparkar (indigenous) cattle were screened for genetic polymorphism in ATP1A1 gene, using polymerase chain reaction single-strand conformation polymorphism and DNA sequencing. For association studies, rectal temperature (RT) and respiration rate (RR) of all animals were recorded twice daily for 3 seasons. Results: A SNP (C2789A) was identified in exon 17 of ATP1A1 gene. Three genotypes namely CC, CA, and AA were observed in both, Vrindavani and Tharparkar cattle. The gene frequencies in Tharparkar and Vrindavani for allele A were 0.51 and 0.48, and for allele C were 0.49 and 0.52, respectively, which remained at intermediate range. Association study of genotypes with RT and RR in both cattle population revealed that the animals with genotype CC exhibited significantly lower RT and higher heat tolerance coefficient than CA and AA genotypes. Conclusion: Differential thermoregulation between different genotypes of ATP1A1 gene indicate that the ATP1A1 gene could be potentially contributing to thermotolerance in both, Tharparkar, an indigenous breed and Vrindavani, a composite crossbred cattle