Kamini Prajapati Prajapati

Immunotherapy for cancer treatment has proved to be a very useful technique that helps to boost the immune responses of the host against cancer cells. In this study, the effectiveness of atezolizumab is assessed in Programmed Death-Ligand 1(PD-L1) Positive advanced cancers of Indians. This study was performed using a retrospective analysis on the clinical data of 120 patients that received atezolizumab treatment between 2021 and 2025. Patients with advanced solid malignancies such as non-small cell lung carcinoma (NSCLC), hepaticellular carcinoma (HCC), and triple-negative breast cancer (TNBC) were included in this study. The RECIST 1.1 criteria and CTCAE Version 5.0 recommendations were used to assess the response to therapy, progression-free survival, overall survival, and adverse events linked to the immune system, respectively. Kaplan-Meier survival analysis, chi-square testing, and descriptive statistics were all part of the statistical package. The results demonstrated that patients exhibiting elevated levels of PD-L1 had a substantially better chance of surviving. In total, 76.6% of people were able to keep the sickness at bay, whereas only 48.3% responded. Treatment response is strongly correlated with PD-L1 over-expression (χ² = 9.64, p = 0.002). Adverse reactions experienced by subjects in this study were mostly mild-to-moderate. It was concluded in this study that atezolizumab is an effective and safe immunotherapy drug for treating PD-L1-positive malignancies in Indians.

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.

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.

Infertility is becoming an increasingly common reproductive health condition globally, leading to a dramatic increase in the use of assisted reproductive technologies, including intracytoplasmic sperm injection (ICSI) and in vitro fertilisation (IVF). Numerous factors, such as the mother, the embryo, and the IVF procedure, contribute to the success rate of in vitro fertilisation (IVF) and live births. Investigated here are in vitro fertilisation (IVF) success rates as a function of oocyte quality, maternal body mass index (BMI), embryo transfer methods, and ICSI. Female infertility patients undergoing in vitro fertilisation procedures at assisted reproduction centres were the subjects of the study, which used a quantitative methodology. Embryonic factors were considered alongside age, BMI, oocyte shape, fertilisation, embryo growth, embryo implantation rate, and pregnancy success rates. A chi-square test, descriptive statistics, regression models, and correlation analyses were all used to analyse the data statistically. The results show that the mother's oocyte quality and body mass index (BMI) significantly affect live birth rates, embryo growth, embryo implantation rate, and fertilisation success. There was a correlation between poor oocyte quality and high maternal BMI, lower rates of IVF success, and lower chances of live births.

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.