Vasu Tiwari Tiwari

The investigation assessed the potential applications of LLMs, EEG neurofeedback, HRV analysis, DORAS systems, and VRH in the development of AI-powered hypnotherapy solutions for pain therapy. The results proved that AI-based hypnotherapy platforms had higher levels of customization, ability to monitor the states of trance, maintain consistency of sessions, and promote physiological adaptations compared to conventional hypnotherapy approaches based on human hypnotherapists. The quantitative analysis revealed that hypnotherapy sessions assisted by VRH delivered the most effective pain relief outcomes, whereas the EEG and HRV assessments revealed enhanced levels of autonomic relaxation and emotional control in the context of hypnotherapy. The researchers found that despite obvious strengths in terms of scalability, incorporation of neurofeedback, and responsiveness to individual conditions of patients, AI systems lack some qualities inherent to humans such as emotional empathy and rapport building. Overall, it can be concluded that future hypnotherapy systems are more likely to become hybrid human-AI solutions.

This current study focuses on the impact of Artificial Intelligence (AI) on the rapid identification of new molecules to suppress aging processes, which increases the proportion of healthspan relative to lifespan. The research approach taken involved a quantitative method, where artificial intelligence-based machine learning, bioinformatics, and statistical analysis were used alongside computational molecular docking. Biochemical information from databases such as PubChem, DrugBank, and ChEMBL was leveraged to screen and analyze molecular data. It was observed that AI-assisted predictive models, especially Deep Learning Neural Networks, offered highly accurate predictions concerning the biological activity of anti-aging compounds. The selected molecules showed considerable decreases in oxidative stress, inflammation, and cellular senescence markers, coupled with improved mitochondrial function and cell repair. Moreover, quantitative results showed that the use of AI for predicting the efficacy of anti-aging agents led to more significant healthspan enhancements than lifespan increases.

The modern repair of hernia disease has undergone tremendous transitions in the past where open surgeries are still used to repair the hernia, to sophisticated minimally invasive and robotic assisted techniques through animal-based research being very essential in the understanding of the structure of the anatomy, biomaterials functions and the healing process. This review is a systematic analysis of the existing literature performed on animal models like rodents, rabbits, pigs, and canines to indicate the efficacy of various types of mesh, surgical methods and new technologies in enhancing clinical outcomes and recurrence. The results suggest that, biologic and composite mesh stimulates tissue integration, neovascularisation and organized collagen deposition and less inflammatory response and adhesion formation in comparison with synthetic mesh. As well, less invasive and robotic-assisted surgery methods enhance surgical accuracy, less tissue trauma, and outcome reproducibility. Nevertheless, interspecies variability, absence of standardized models, and inadequate long-term data are also limitations to direct translational applicability. The review notes the significance of the incorporation of advanced technologies including bioengineered meshes, artificial intelligence, and 3D bio printing and the necessity of standardized and long-term and interdisciplinary research to enhance the safety and efficacy of hernia repair approaches