in-utero-valproic-acid-exposure-cryptorchidism-reports-integrating

In Utero Valproic Acid Exposure in Cryptorchidism Reports: Integrating FAERS, Toxicogenomics, Transcriptomics, and Mendelian Randomization

To screen the FDA Adverse Event Reporting System (FAERS) for valproic acid (VPA)-associated cryptorchidism reports, restrict developmental interpretation ...

The Affiliated Hospital of Chengde Medical University

Psoriasis is a chronic inflammatory skin disorder related to dyslipidemia, with decreased high-density lipoprotein (HDL). Various cell types express phospholipid transfer protein (PLTP) as well as cholesteryl ester transfer protein (CETP). Their elevated levels among transgenic (Tg) mice led to reduced HDL and a higher risk of atherosclerosis (AS). This study examined whether elevated CETP and PLTP could aggravate psoriasis in a psoriasis vulgaris mouse model.

Elevated cholesteryl ester transfer and phospholipid transfer proteins aggravated psoriasis in imiquimod-induced mouse models.

Recent research has underscored the critical role of long non-coding RNAs (lncRNAs) in tumorigenesis and malignancy development. Nevertheless, the role of lncRNA cytoskeleton regulator RNA (CYTOR) in the progression of melanoma remains only partially elucidated. This research seeks to explore the impact of CYTOR on melanoma development and to elucidate the molecular mechanisms involved.

Long non-coding RNA CYTOR promotes the progression of melanoma  the miR-485-5p/GPI axis.

Keloid is a fibroproliferative disorder that poses a challenge in clinical management. This study aims to identify and functionally annotate differentially expressed genes (DEGs) in keloid and explore the potential role of SLC6A15. The data were obtained from GEO (GSE218922 and GSE7890), and the DEGs and module genes were obtained with Limma and WGCNA. KEGG and GO enrichment analysis, and machine learning algorithms (Random Forest, Boruta, and XGBoost) were conducted to explore the keloid-related key genes. Finally, qRT-PCR was carried out to detect SLC6A15 mRNA expression, and CCK-8 and flow cytometry were employed to assess cell proliferation and apoptosis. We obtained 147 DEGs between keloid fibroblasts and normal fibroblasts, and 193 DEGs between keloid stem cells and normal stem cells, followed by acquisition of 40 intersection DEGs. These intersection DEGs were mainly enriched in external encapsulating structure organization, extracellular matrix organization, and were closely related to cytoskeleton in muscle cells and arrhythmogenic right ventricular cardiomyopathy (ARVC). WGCNA analysis identified five modules, with the blue modules showing a significant negative correlation with keloid. Afterwards, three machine learning methods were applied to analyze DEGs in keloid, identifying SLC6A15 as the most important gene. Further validation demonstrated that SLC6A15 was lowly expressed in keloid tissues and fibroblasts, and SLC6A15 overexpression inhibited proliferation and facilitated apoptosis in keloid fibroblasts. This study identified SLC6A15 as a potential biomarker for keloid, providing new research clues for the treatment target of this disorder.

Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning.

Pemphigus is a rare yet serious autoimmune skin disease. This study aimed to identify the drugs most commonly associated with pemphigus risk using the Federal drug administration adverse event reporting system (FAERS) database. Data on drugs and adverse events between 2004 and the second quarter of 2024 were obtained from the FAERS database. The medical dictionary for regulatory activities (MedDRA) was used to identify pemphigus cases, whereas the DrugBank database was used to determine the generic names of the medications. Four signal quantification methods were used for the analysis: odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, and empirical Bayesian geometric mean. Additionally, the timing of drug-induced pemphigus was calculated using Weibull shape parameters. Finally, potential factors affecting pemphigus were identified in the FAERS database using univariate and multivariate logistic regression analyses. Of the 7315 adverse event reports linked to pemphigus, diclofenac sodium was involved in 1227 cases, which was the highest number. Disproportionality analysis Disproportionality analysis identified the top 5 drugs that caused adverse reactions: diclofenac sodium, abatacept, tocilizumab, and alendronate sodium. Based on the time to onset of drug-induced pemphigus, the Weibull shape parameter (0.60) indicated that pemphigus induced by this class of drugs was classified as an early failure. Seven independent risk factors related to drugs were identified through multivariable analysis, including diclofenac sodium, abatacept, tocilizumab, alendronate sodium, secukinumab, cetirizine, and chlorhexidine, odds ratio >1 for each. FAERS database was analyzed to identify drugs with strong pemphigus signals, which can be used as a guide for standardizing drug use in clinical practice.

Article	Year
Elevated cholesteryl ester transfer and phospholipid transfer proteins aggravated psoriasis in imiquimod-induced mouse models. Lipids in health and disease\| PubMed ID: 35982498	2022
Long non-coding RNA CYTOR promotes the progression of melanoma the miR-485-5p/GPI axis. PeerJ\| PubMed ID: 40292095	2025
Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning. Biochemical genetics\| PubMed ID: 40742424	2026
Exploring drug-induced pemphigus findings from the FDA adverse event reporting system database. Medicine\| PubMed ID: 40958229	2025

Yandong Niu

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