High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of epithelial ovarian cancer, often diagnosed at advanced stages with a poor prognosis. Paclitaxel (PTX), a standard chemotherapeutic agent for HGSOC, exerts cytotoxic effects on cancer cells and modulates the tumor microenvironment. This study aimed to elucidate the molecular mechanisms of PTX in HGSOC using bioinformatics, machine learning, network pharmacology, and molecular docking, to identify potential diagnostic biomarkers and therapeutic targets. We identified differentially expressed genes (DEGs) between HGSOC and normal ovarian tissues using the GSE54388 dataset from the Gene Expression Omnibus database. The intersection of these DEGs with PTX targets, identified from the Swiss Target Prediction database, yielded 15 overlapping genes. These genes were analyzed via protein–protein interaction (PPI) network analysis to identify significant interaction relationships. Kaplan–Meier survival analysis was then performed to assess the prognostic significance of these genes. Their protein expression patterns in HGSOC tissues were validated using the Human Protein Atlas (HPA) database. Functional enrichment analysis was conducted using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. A combined diagnostic model was developed using LASSO regression and validated in two independent external datasets (GSE26712 and GSE12470). Molecular docking experiments were conducted to confirm the binding affinity of PTX to key proteins. Immune infiltration analysis was performed to assess the tumor microenvironment, revealing significant differences in immune cell composition between normal and tumor tissues. A total of 2267 DEGs were identified, with 15 overlapping genes related to PTX targets. After PPI network analysis, Kaplan–Meier survival analysis, and HPA validation, five key genes (AURKA, CBX7, CCNA2, HSP90AA1, and TUBB3) were identified as associated with HGSOC progression. The combined diagnostic model demonstrated high accuracy in distinguishing HGSOC from normal tissues, with AUC values of 0.9892 and 0.9465 in the GSE26712 and GSE12470 validation datasets, respectively. Molecular docking confirmed stable binding of PTX to these key proteins, suggesting their role in PTX’s therapeutic effects. Immune infiltration analysis revealed significant differences in immune cell composition between normal and tumor tissues, highlighting the potential impact of these genes on the tumor microenvironment. In summary, our findings provide a theoretical basis for improving clinical diagnosis and elucidating the underlying mechanisms of HGSOC.

Edentulism concentrates in older adults and is associated with poorer diet, elevated malnutrition risk, and adverse health trajectories. Interpretable, well-calibrated tools applicable in routine care are needed to identify those at risk and support timely, tooth-preserving management. We analyzed data from the nationally representative China Health and Retirement Longitudinal Study with baseline in 2011 to 2012 and follow-ups in 2013, 2015, and 2018. Adults with baseline dentition status and all prespecified predictors were eligible. Incident edentulism was ascertained at each wave. Predictors defined a priori included age, sex, body mass index, blood pressure, lipid profile, log-transformed C-reactive protein (ln-CRP), and estimated glucose disposal rate (eGDR). Four algorithms were trained in a development set and evaluated in an independent validation set: multivariable logistic regression, LASSO-penalized logistic regression, random forest, and extreme gradient boosting. Validation included area under the receiver-operating-characteristic curve (AUC) with nonparametric comparisons, calibration with logistic recalibration, decision-curve analysis across clinically relevant thresholds, and time-dependent AUC over follow-up. Of 6130 adults, 5231 were dentate at baseline and formed the risk set; incident edentulism occurred in 2013 (n = 338), 2015 (n = 253), and 2018 (n = 308). Logistic regression yielded the highest discrimination (AUC 0.695) with stable performance across waves and over 2-7 years. Calibration closely matched the ideal after simple recalibration, and decision curves showed consistent net benefit versus treat-all and treat-none strategies. In the final model, age and C-reactive protein (ln-CRP) were dominant independent predictors; men had lower risk, and total cholesterol showed a modest inverse association. A nomogram was derived to enable point-of-care risk stratification. In a large, nationally representative cohort, an internally validated logistic regression model based on routinely available data showed moderate discrimination, good calibration, and potential clinical utility for predicting incident edentulism. Age and low-grade systemic inflammation were the main contributors to risk stratification and may help guide early, tooth-preserving care.

Background: The urothelial carcinoma (UC) trial landscape has expanded over two decades, alongside a shift from chemotherapy to immunotherapy and targeted approaches. We characterized temporal patterns in trial phase, interventions, sponsorship, geographic footprint, and the uptake of biomarker specification and artificial intelligence (AI) terminology. Methods: We conducted a trial registry‐based analysis of 823 interventional UC studies first posted on ClinicalTrials.gov during 2005–2024. Trials were classified by phase, recruitment status, and intervention, and further stratified by sponsor type and geographic region. Registry text was mined to flag biomarker‐specified and AI‐enabled registrations. Trends were summarized overall and for two periods: 2005–2014 and 2015–2024. Results: Annual registrations increased after 2015, peaking in 2018. Early‐phase studies predominated overall, with expansion of Phase 3 activity in 2015–2024. Drug interventions remained dominant as immunotherapy displaced chemotherapy; pembrolizumab was the leading agent in the later period. The sponsor mix shifted toward industry, and the geographic footprint broadened: the United States/Canada remained the largest contributor, with growth in multinational activity and increased participation from East Asia. Biomarker‐specified language rose to roughly one quarter of registrations in 2015–2024, whereas explicit AI terminology was rare. Early‐phase termination rates were notable. Conclusions: UC trials have scaled and globalized while consolidating immunotherapy and advancing genomically directed strategies. Priorities include precise preregistration of biomarker and computational endpoints, inclusive multinational accrual, and adequately powered comparative trials against current standards to accelerate durable clinical benefit.

Objectives: Complete tooth loss is a late-stage oral health endpoint that may reflect cumulative oral, systemic, behavioral, and social risk processes. We examined whether two-wave patterns of the modified cardiometabolic index (MCMI) from 2011 to 2015 were associated with incident edentulism from 2015 to 2018 among Chinese adults. Methods: We analyzed participants aged 45 years or older from the China Health and Retirement Longitudinal Study who reported having natural teeth in 2015 and had MCMI measured in 2011 and 2015. Using wave-specific medians, we grouped participants into four 2011–2015 MCMI patterns: stable high, high-to-low, low-to-high, and stable low. Incident edentulism was defined as self-reported complete tooth loss in 2018 among those at risk in 2015. Risk ratios were estimated using modified Poisson regression with robust variance, with stepwise adjustment for sociodemographic and behavioral factors; fully adjusted models additionally included baseline MCMI. Cumulative MCMI and simple change in MCMI were also examined as tertiles. Results: Among 3,626 adults, 231 developed edentulism by 2018, yielding a 6.4% cumulative incidence. Compared with the stable high pattern, participants who moved from high to low MCMI had a lower risk of incident edentulism in the fully adjusted model (adjusted RR, 0.57; 95% CI, 0.34 to 0.97). The low-to-high and stable low patterns showed no clear associations after adjustment. In parallel analyses, neither cumulative MCMI nor simple change tertiles demonstrated a consistent gradient in edentulism risk. Conclusions: In this longitudinal CHARLS analysis, a high-to-low MCMI change pattern was associated with lower subsequent risk of incident edentulism compared with persistently high MCMI, whereas cumulative MCMI and absolute change showed no consistent gradient. Because edentulism is a late-stage cumulative endpoint and baseline oral health measures were unavailable, these findings should be interpreted as observational associations rather than evidence of a direct cardiometabolic effect on tooth loss.