Background: Periodontitis is a common chronic inflammatory disease. However, drug‐related risks and underlying molecular mechanisms remain underexplored from large real‐world data. Methods: We first mined the US Food and Drug Administration Adverse Event Reporting System (FAERS) database to identify drugs disproportionately associated with periodontitis, using four signal detection algorithms and logistic regression for confounder adjustment. Identified drugs were then mapped to their protein targets via DrugBank, followed by pathway enrichment and protein–protein interaction (PPI) network analysis to explore biological relevance. To assess potential causality, we conducted Mendelian randomisation (MR) using cis‐pQTLs from UKB‐PPP and deCODE cohorts. Finally, we used single‐cell RNA sequencing (scRNA‐seq) data from gingival tissue and peripheral blood of periodontitis patients to evaluate cell type–specific expression of candidate causal genes. Results: Five drugs (actonel, aclasta, aredia, amlodipine and avastin) were significantly positively associated with periodontitis based on FAERS. VEGFA showed an association with disease risk (OR = 1.043, p = 0.049) after meta‐analysis of two cohorts. scRNA‐seq data identified high VEGFA expression in monocytes in both gingival and blood samples of periodontitis patients. Conclusion: This study uncovered the association between drug and periodontitis and highlighted VEGFA as a potential molecular mediator. Further studies are needed to confirm causality.

Oral ulcers are common, painful lesions that significantly affect quality of life. Certain medications, especially antimetabolites and mTOR inhibitors, have been implicated in their development. However, drug-induced oral ulcers have not been systematically evaluated using large-scale real-world data. The study employed a dual-validation strategy combining pharmacovigilance signal mining and multivariate logistic regression analysis. By querying specific 'preferred terms' (PTs), adverse event reports in the FAERS database of the Food and Drugs Adminstration of USA, related to oral ulcers were identified. Signal detection was conducted using four disproportionality analysis methods. For the initially screened candidate drugs, a multivariate logistic regression model was constructed, adjusting for potential confounders such as age, gender, weight, reporter type, reporting country and year, to further validate their independent association with oral ulcer reports (P_FDR < .05). Reports of oral ulcerations have shown a significant and sustained increase since 2010, peaking in 2020. The multi-method validation framework identified 289 statistically significant drug-oral ulceration association signals. Drugs with the highest signal strength included cyclizine, mycophenolate sodium and aredia. Other drugs with significant signals included lactulose, acetaminophen codeine phosphate, leflunomide, omalizumab, everolimus and atezolizumab. Integrating signal detection results, logistic regression analysis further confirmed that multiple drugs were significantly associated with the risk of oral ulcer reports. Drugs with extremely high odds ratios (ORs) included estriol and docetaxel sagent. Strongly associated drugs also included immunosuppressants, antihistamines, combination analgesics, bisphosphonates, sartans and antibiotics. This study systematically identified approximately 289 drugs significantly associated with oral ulcer risk, including immunosuppressants, cardiovascular drugs and bronchodilators. Based on pharmacovigilance signal detection and regression analysis, patients on long-term treatment with these drugs should receive enhanced oral monitoring. As this is an observational study, further prospective and experimental research is needed to confirm causality.

Introduction: Metastatic breast cancer (MBC) remains a major clinical challenge despite recent advances in HER2-targeted therapies such as trastuzumab deruxtecan (T-DXd). While clinical trials have demonstrated its efficacy, concerns about treatment-emergent toxicities persist, highlighting the need for real-world pharmacovigilance to capture a broader spectrum of adverse events (AEs). Methods: We analyzed reports from the US FDA Adverse Event Reporting System (FAERS, 2004–2024), including trastuzumab deruxtecan reports recorded since its market introduction in 2019. Disproportionality analyses were performed to identify potential AE signals. Time-to-onset distributions were assessed, and a clinical prioritization score was developed to rank AEs based on signal strength, frequency, mortality, and classification as important or designated medical events. Results: Disproportionality analyses revealed significant AE signals associated with T-DXd across multiple organ systems, including injury and procedural complications, respiratory, gastrointestinal, and social circumstances. Time-to-onset analysis revealed substantial heterogeneity in the timing of T-DXd–associated AEs. Early-onset events included dyspnea and eye disorders, whereas cardiac and nervous system toxicities showed delayed onset. Among twelve high-priority AEs, peritonitis received the highest priority scores (scores = 11). Interstitial lung disease demonstrated the most prominent disproportionality signal. Several other pulmonary-related events, including pneumonitis, pulmonary fibrosis, and pulmonary toxicity, also ranked as high priority. Conclusion: This study highlights respiratory and gastrointestinal toxicities as major risks associated with T-DXd in metastatic breast cancer. Careful monitoring, early intervention, and patient education are essential to balance benefits and risks, while future research should focus on molecular mechanisms to enable precision medicine.

Research has demonstrated that hypoxia, a decrease in intracellular oxygen, is linked to multiple processes in the skin, including metabolic adaptation, cell survival, pathogenic microbe infection, immune response, tumorigenesis, and various diseases. This paper aims to perform a thorough bibliometric analysis of studies of hypoxia in skin health and disease to identify the frontiers, hotspots, and evolution of these studies. Articles about hypoxia in skin health and disease in the Web of Science Core Collection were gathered as of 31 December 2023. To find bursting keywords and references, VOSviewer was used to look at co-authorship and phrase co-occurrences and make visualizations of them. A total of 1,511 papers on hypoxia in skin health and disease were examined in this study. China, the United States, and Japan were the top three countries contributing to this topic, while the International Journal of Molecular Sciences was the journal with the most articles. In terms of prolific authors in the publications, Yuesheng Huang from China contributed the most articles. The terms “hypoxia,” “angiogenesis,” and “wound healing” were the ones that were used the most frequently. In this study, a bibliometric analysis was performed on the last ten years’ worth of research on hypoxia in skin health and disease. The analysis identified the publications, nations, authors, institutions, and journals that were involved in this subject. These data offer a more comprehensive understanding of the area of research that has been conducted on hypoxia in skin health and disease.

Androgenetic alopecia (AGA) is the most common form of hair loss globally. Despite its prevalence, only two FDA-approved drugs-minoxidil and finasteride-are currently available, underscoring the urgent need to discover novel biomarkers and therapeutic targets for AGA diagnosis, treatment, and monitoring. This study aimed to explore novel therapeutic targets and biomarkers for AGA and investigate the causal role of plasma metabolites mediating therapeutic targets in AGA. Cis-expression quantitative trait loci (cis-eQTL) data were derived from the eQTLGen Consortium (31,684 samples). Genetic associations with AGA were obtained from the FinnGen cohort (220 cases, 219,249 controls), and the UK Biobank (66,172 cases and 140,864 controls). Colocalization and summary-data-based MR (SMR) analyses were performed to rank these candidate therapeutic targets. Additionally, Transcriptome-Wide Association Study (TWAS) and Multi-marker Analysis of GenoMic Annotation (MAGMA) analyses were conducted to prioritize potential biomarkers, while Phenome-wide association study (PheWAS), and drug prediction were used to identify therapeutic targets. Lastly, mediation analysis was applied to explore the role of metabolites in AGA treatment. Ten genes emerged as promising targets after multi-layer validation. Among them, SOD1 and KL were classified as tier 1 targets with strong evidence. NT5E, SQLE, and ADM were designated tier 2 with moderate support, while PAM, LAMC1, LAMC3, PRLR, and BRAF were tier 3 candidates. Polymerase Chain Reaction (qPCR) validation supported these findings. Our integrative multi-omics analysis identified 10 potential therapeutic targets and biomarkers for AGA. SOD1 and KL stand out as strong candidates, offering promising directions for future AGA therapies.

Background: Drug-induced psoriasis has been increasingly recognized, but the spectrum of potential triggering medications remains incompletely characterized. We aimed to identify the drugs potentially associated with psoriasis using a large-scale and real-world pharmacovigilance analysis. Methods: Firstly, disproportionality analyses were used to detect psoriasis-related drug signals. Furthermore, Multivariable logistic regression and Bayesian shrinkage models were applied to evaluate the consistency of the detected associations after adjustment for available demographic and reporting-related factors. Finally, stratified analyses by sex, age, and reporter type were conducted to assess signal robustness. Results: A total of 30 drugs showed significant associations with psoriasis. These signals involved several pharmacological categories, including anti-infective agents (e.g., Abacavir, Fluconazole, Moxifloxacin, Sulfamethoxazole), anti-inflammatory and immunomodulatory drugs (e.g., Celecoxib, Tofacitinib), cardiovascular agents (e.g., Acebutolol, Telmisartan, Clonidine), neuropsychiatric drugs (e.g., Nortriptyline, Temazepam, Zopiclone, Wellbutrin), hormonal agents (e.g., Progesterone, Prometrium), and respiratory medications (e.g., Ventolin). Bayesian correction generally attenuated extreme effect estimates but confirmed the robustness of most signals. The stratified analysis also approve the above results. Conclusion: Our findings suggest that multiple drug classes are associated with psoriasis signals in pharmacovigilance data. These results highlight the importance of early recognition of drug-associated psoriasis and may assist clinicians in medication review and risk stratification in routine practice.