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Journal of Pharmaceutical Research and Integrated Medical Sciences

Shivani Singh

Author Profile
22
Publications
1
Years Active
9
Collaborators
26
Citations

Publications by Shivani Singh

22 publications found (showing 21-22) • Active 2026-2026

2026

2 publications

Modulating The Epigenetic Clock, Senolytic Therapies for Human Longevity: Age Tissue Regeneration, Synergistic Effect of Nad+ Precursors and Telomerase, Human Age Enhancement

with Yash Srivastav Srivastav, Stuti Verma Verma, Anup Kumar Sirbaiya Sirbaiya, Shivani Singh Singh, Amita Singh Singh, Deepshi Srivastava Srivastava, Kamini Prajapati Prajapati
2026

The human aging process can be characterized by progressive cellular degeneration, mitochondria malfunctioning, inflammatory responses, epigenetics modifications, and loss of tissue regenerative ability. The recent progress made in the field of longevity has revealed several promising treatment opportunities for prolonging a healthy lifespan in humans through epigenetics regulation, senolytics application, NAD+ precursors' intake, telomerase activation, and regenerative treatments. This review considers evidence from human studies about the impact of DNA methylation, cell senescence elimination, mitochondria recovery, enhanced immunity, tissue renewal, and cognitive reserve increase in human aging biology. According to findings based on human research, interventions involving senolytic compounds, Nicotinamide Riboside (NR), Nicotinamide Mononucleotide (NMN), and telomerase-linked regenerative treatment have the ability to contribute to improved metabolism, vascular functions, immunological resilience, and cognitive efficiency while reducing inflammatory processes and decreasing the number of senescent cells in a human body. In addition, comprehensive longevity approaches consisting of the mentioned interventions seem to possess combined benefits in terms of human longevity improvement. However, there are certain drawbacks that must be addressed when applying these interventions into clinical practice; namely, small sample sizes used in studies, lack of long-term safety testing, ethical issues, and inadequate biomarkers. Future directions in the research are discussed.

Large Language Models (LLMs) in Hypnosis, Leveraging Machine Learning to Map and Induce Hypnotic Trance States via Real-Time EEG, DORAs, VRH, HRV: Human-Led Hypnosis vs Algorithmically Hypnotherapy for Pain Management

with Yash Srivastav Srivastav, Raman Srivastava Srivastava, Stuti Verma Verma, Anup Kumar Sirbaiya Sirbaiya, Shivani Singh Singh, Kamini Prajapati Prajapati, Vasu Tiwari Tiwari
2026

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.