The glutathione S-transferase (GST) gene superfamily encodes for enzymes involved in conjugation of electrophilic compounds to glutathione. Several polymorphisms in the GST genes have been implicated as risk factors for prostate cancer. We did a meta-analysis of 11 studies with GSTM1 genotyping (2,063 prostate cancer cases and 2,625 controls), 10 studies with GSTT1 genotyping (1,965 cases and 2,554 controls), and 12 studies with GSTP1 genotyping (2,528 cases and 3,076 controls). The random effects odds ratio was 1.08 [95% confidence interval (95% CI), 0.93-1.25, no significant between-study heterogeneity] for the GSTM1 null versus nondeleted genotype and 0.90 (95% CI, 0.73-1.12; P = 0.03 for heterogeneity) for the GSTT1 null versus nondeleted genotype. Overall, the random effects odds ratio was 1.05 (95% CI, 0.90-1.21; P < 0.01 for heterogeneity) for the GSTP1-Val versus GSTP1-Ile allele. For all three polymorphisms, there was a trend for the presence of an association in the earliest published studies, but this did not seem to be validated in subsequent research. For GSTT1, larger studies gave different results than smaller ones. The meta-analysis shows that these three polymorphisms are unlikely to be major determinants of susceptibility to prostate cancer on a wide population basis.

In most western countries prostate cancer is the most commonly diagnosed non-skin cancer in men. Despite its high morbidity and mortality the etiology of prostate cancer remains obscure. The involvement of androgens has been examined extensively in prostate carcinogenesis but the results of most epidemiological studies remain inconclusive. This review focuses on current perspectives of androgen levels and polymorphisms in androgen-related genes. Racial differences in genetic polymorphisms that have a role in the biosynthesis and metabolism of androgens may partly account for racial differences in prostate cancer risk. Reasons are also given for inconsistent results in molecular epidemiological studies and insights and directions for future research are discussed. The development of a polygenic model for prostate cancer, incorporating multiple loci from the individual genes, may maximize the chance of identifying individuals with high-risk genotypes, resulting in better preventive, diagnostic, and therapeutic strategies.

We aimed to investigate the influence of class I and class II HLA specificities and of the concordance between maternal and infant HLA on vertical HIV-1 transmission. HLA typing of samples from mothers and infants enrolled in the Ariel study, a perinatal HIV-1 transmission cohort including 203 mother-infant pairs, was performed by serological and molecular methods. HLA effects were evaluated alone and by multivariate modeling considering also other known predictors of perinatal HIV-1 transmission (maternal viral load, antiretroviral therapy, duration of rupture of membranes, and histological chorioamnionitis). Modest associations were seen with specific HLA markers (increased risk with infant B67 and B58 and maternal DR1; decreased risk with maternal B12), but these were not statistically significant after adjusting for multiple comparisons. Mother-infant concordance at any class I locus was a strong predictor of transmission (odds ratio [OR], 4.16; p = 0.028). Transmission was not associated with class II concordance. Class I HLA concordance retained its importance after adjusting for maternal viral load, antiretroviral therapy, duration of rupture of membranes or histological chorioamnionitis. In multivariate modeling, only class I concordance (OR, 3.59; p = 0.069) and chorioamnionitis (OR, 3.79; p = 0.030) were retained as independent predictors of transmission. HLA alleles, and in particular the class I concordance between maternal and neonatal HLA, may regulate the risk of perinatal HIV-1 transmission.

A C-to-T polymorphism in exon 2 of the cathepsin D gene encoding cathepsin D (CTSD) has been implicated as a risk factor for Alzheimer’s disease. The authors performed a meta-analysis of 14 studies (16 comparisons) with CTSD genotyping (3,174 Alzheimer’s disease cases and 3,298 controls). Overall, the random effects odds ratio for the T versus the C allele was 1.17 (95% confidence interval (CI): 0.95, 1.44), with some between-study heterogeneity (p < 0.01). There was significant between-study heterogeneity but no evidence of a significant association when the first hypothesis-generating study was excluded from the calculations (odds ratio (OR) = 1.11, 95% CI: 0.91, 1.35; p = 0.29). The summary odds ratio for T carriers versus T noncarriers was similar in subjects carrying or not carrying an apolipoprotein E ε4 allele (APOE*4). The increased susceptibility to Alzheimer’s disease conferred by APOE*4 carriage tended to be more prominent in the presence of the T allele (random effects OR = 6.07, 95% CI: 4.19, 8.79, and OR = 4.09, 95% CI: 3.15, 5.31, in T carriers and noncarriers, respectively). The meta-analysis shows that the CTSD polymorphism is not a major risk factor for Alzheimer’s disease, although a small effect or an enhancement of the APOE*4 effect cannot be excluded.

Background/Objectives: In recent years, knowledge management and digital innovation have become pivotal in transforming healthcare systems, driving efficiency and enhancing patient outcomes. This study presents a bibliometric analysis of research trends at the intersection of knowledge management and digital innovation in healthcare, examining the scope, impact, and evolution of scholarly work in this domain. Methods: Using a comprehensive dataset from the Scopus database, we analyzed 419 publications from 1985–2023 to identify influential authors, journal collaborations, and emerging topics and methodologies in the field. Results: Our findings reveal a significant increase in research interest, highlighting themes such as data-driven healthcare, artificial intelligence in clinical decision support, and knowledge-sharing platforms’ role in improving healthcare delivery. The analysis also underscores the growing importance of interdisciplinary collaboration between healthcare providers, technologists, and policy-makers. Conclusions: By mapping the intellectual structure of knowledge management and digital innovation in healthcare, this study provides valuable insights for academics, practitioners, and policy-makers seeking to harness knowledge management practices and digital technologies to foster innovation and resilience in healthcare systems.