Exploring the Association Between Age Groups and Success Patterns in Platelet-Rich Plasma Therapy: A Cohort Study.

Objective This study aimed to comprehensively examine the correlation between success trends in platelet-rich plasma (PRP) therapy and the advancing age of patients undergoing fertility interventions. Methods Female participants were categorized randomly into five age groups undergoing PRP or conventional hormone replacement therapy. Procedures included controlled ovarian stimulation, escalating estrogen dosage, gonadotrophin injections, and embryo transfer post-ovulation trigger. A pivotal PRP intervention was provided to half of the age sub-groups, and endometrial thickness was assessed 24 hours prior to embryo transfer. Statistical analysis employed SPSS 26.0 for Windows Student Version (IBM Inc., Armonk, New York), incorporating descriptive statistics, one-way analysis of variance (ANOVA), Tukey's honestly significant difference (HSD) test to explore age-PRP success relationships (p<0.05). Results The study, involving 60 participants, revealed a balanced patient distribution across age groups, with 20-30 age groups contributing 23.33% each. Baseline characteristics showed no significant differences between PRP and hormone replacement therapy (HRT) groups. Post-intervention, PRP demonstrated consistently higher endometrial thickness (p<0.001) and clinical pregnancy rates (63.33%) compared to HRT (40%). These findings suggest a positive association between PRP therapy and improved outcomes, particularly in younger age cohorts. Conclusion The study challenges traditional perspectives on hormonal influences in fertility, highlighting a potential link between PRP therapy and favorable outcomes among younger age groups. Improved endometrial thickness and clinical pregnancy rates in the PRP group emphasize the need for further exploration of PRP's mechanisms and applications in reproductive medicine.

Landscape of genomic structural variations in Indian population-based cohorts: Deeper insights into their prevalence and clinical relevance.

Structural variations (SV) are large (>50 base pairs) genomic rearrangements comprising deletions, duplications, insertions, inversions, and translocations. Studying SVs is important because they play active and critical roles in regulating gene expression, determining disease predispositions, and identifying population-specific differences among individuals of diverse ancestries. However, SV discoveries in the Indian population using whole-genome sequencing (WGS) have been limited. In this study, using short-read WGS having an average 42X depth of coverage, we identify and characterize 36,210 SVs from 529 individuals enrolled in population-based cohorts in India. These SVs include 24,574 deletions, 2,913 duplications, 8,710 insertions, and 13 inversions; 1.26% (456 out of 36,210) of the identified SVs can potentially impact the coding regions of genes. Furthermore, 56 of these SVs are highly intolerant to loss-of-function changes to the mapped genes, and five SVs impacting ADAMTS17, CCDC40, and RHCE are common in our study individuals. Seven rare SVs significantly impact dosage sensitivity of genes known to be associated with various clinical phenotypes. Most of the SVs in our study are rare and heterozygous. This fine-scale SV discovery in the underrepresented Indian population provides valuable insights that extend beyond Eurocentric human genetic studies.

The Omic Insights on Unfolding Saga of COVID-19.

The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.