Background: Immune cells play a pivotal role in maintaining ovarian function. However, the specific contributions of different immune cell phenotypes to the pathogenesis of specific ovarian-related diseases remain poorly understood. The authors aim to investigate the correlation between 731 immunophenotypes and ovarian-related diseases. Materials and methods: Utilizing publicly available genetic data, the authors undertook a series of quality control measures to identify instrumental variables associated with exposure. Subsequently, we conducted two-sample Mendelian randomization (MR) using inverse variance weighting to explore the causal relationships between 731 immune cell features and six ovarian-related diseases: ovarian cysts, ovarian dysfunction, premature ovarian failure (POF), polycystic ovary syndrome (PCOS), benign neoplasm of ovary, and malignant neoplasm of ovary at the genetic level. Sensitivity analyses, including leave-one-out and other MR analysis models, were performed. Finally, Bayesian colocalization (COLOC) analysis was employed to identify specific co-localized genes, thereby validating the MR results. Results: At the significance level corrected by Bonferroni, four immune phenotypes, including CD25 on IgD- CD38- B cells, were associated with ovarian cysts; four immune phenotypes, including CD39+ CD4+ T cell Absolute Count, were associated with ovarian dysfunction; eight immune phenotypes, including SSC-A on HLA DR+ CD8+ T cells, were associated with POF; five immune phenotypes, including CD20- CD38- B cell Absolute Count, were associated with PCOS; five immune phenotypes, including CD4+ CD8dim T cell Absolute Count, were associated with benign ovarian tumors; and three immune phenotypes, including BAFF-R on IgD- CD38+ B cells, were associated with malignant ovarian tumors. Sensitivity analysis indicated robust results. COLOC analysis identified four immune cell co-localized variants (rs150386792, rs117936291, rs75926368, and rs575687159) with ovarian diseases. Conclusion: Our study elucidates the close genetic associations between immune cells and six ovarian-related diseases, thereby providing valuable insights for future research endeavors and clinical applications.

Hypothalamic hamartomas (HHs) are congenital developmental malformations located in the hypothalamus. They are associated with a characteristic clinical manifestation known as gelastic seizures (GS). However, the traditional understanding of HHs has been limited, resulting in insufficient treatment options and high recurrence rates of seizures after surgery. This is consistent with the network hypothesis of focal epilepsy that the epileptogenic zone is not only limited to HH but may also involve the distant cerebral cortex external to the HH mass. The epilepsy network theory, on the other hand, provides a new perspective. In this study, we aim to explore HH-related epilepsy as a network disease, challenging the conventional notion of being a focal lesional disease. We analyze various aspects of HHs, including genes and signaling pathways, local circuits, the whole-brain level, phenotypical expression in terms of seizure semiology, and comorbidities. By examining HHs through the lens of network theory, we can enhance our understanding of the condition and potentially identify novel approaches for more effective management and treatment of epilepsy associated with HHs.

Background: Osteomyelitis (OM) is a severe bone infection with rising incidence rates, particularly among elderly and diabetic patients. The immune–inflammatory axis is implicated in OM pathogenesis, but its complex interplay remains poorly defined. This study aims to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM using Mendelian randomization (MR) and bioinformatics. Methods: Utilizing publicly available genetic data, we undertook a series of quality control measures to identify instrumental variables (IVs) associated with exposure. Subsequently, we conducted MR using inverse variance weighting to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM. Bioinformatics tools were applied to explore the functional enrichment and protein–protein interaction networks of the implicated genes. Results: The MR analysis identified 13 immune cell phenotypes and 2 plasma inflammatory proteins associated with risk of OM. Notably, higher levels of HLA DR on plasmacytoid dendritic cells, memory B cell absolute counts, and CD8dim T cell percentages were associated with increased OM risk. Additionally, elevated levels of CD6 and IL‐12 subunit B were correlated with OM risk. Bioinformatics analysis revealed the enrichment of related genes in immune‐related pathways and highlighted the complex interaction networks of the implicated proteins. Conclusions: This study provides novel insights into the immune–inflammatory axis in OM and identifies potential biomarkers for risk assessment. The findings warrant further validation in diverse populations and pave the way for developing targeted preventive and therapeutic strategies for OM.

Rensch’s rule describes that sexual size dimorphism (SSD) increases with body size (hyperallometry) when males are larger, and decreases with body size (hypoallometry) when males are smaller. In this paper, on the basis of mean adult body size resulting from 18 populations of the common frogRana temporariaand 24 populations of the Tibetan frogNanorana parkeri, we tested the consistency of allometric relationships between males and females with Rensch’s rule. Our results show that the variation in degree of female-biased SSD increased with increasing mean size at intraspecific levels in two species, which is consistent with the inverse of Rensch’s rule. Furthermore, we tested the hypothesis that the degree of SSD decreased with increasing altitudes. Inconsistent with the predications of our hypothesis, we found no relationships between the degree of SSD and altitude for the two species investigated. These findings suggest that females living in adverse climates in high altitudes cannot adjust their body size as plastically as males.

This study aims to determine hub genes related to the incidence and prognosis of EGFR-mutant (MT) lung adenocarcinoma (LUAD) with weighted gene coexpression network analysis (WGCNA). From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we used 253 EGFR-MT LUAD samples and 38 normal lung tissue samples. At the same time, GSE19188 was additionally included to verify the accuracy of the predicted gene. To discover differentially expressed genes (DEGs), the R package “limma” was used. The R packages “WGCNA” and “survival” were used to perform WGCNA and survival analyses, respectively. The functional analysis was carried out with the R package “clusterProfiler.” In total, 1450 EGFR-MT–specific DEGs were found, and 7 tumor-related modules were marked with WGCNA. We found 6 hub genes in DEGs that overlapped with the tumor-related modules, and the overexpression level of B3GNT3 was significantly associated with the worse OS (overall survival) of the EGFR-MT LUAD patients (p < 0.05). Functional analysis of the hub genes showed the metabolism and protein synthesis–related terms added value. In conclusion, we used WGCNA to identify hub genes in the development of EGFR-MT LUAD. The established prognostic factors could be used as clinical biomarkers. To confirm the mechanism of those genes in EGFR-MT LUAD, further molecular research is required.