Objectives: To determine whether relationship exists between overactive bladder (OAB) and sleep patterns through the cross-sectional study. Patients and methods: Patients from the National Health and Nutrition Examination Survey (NHANES) 2007–2014 were included in this study. Data were extracted through questionnaires, including demographics, dietary and health-related behaviors, body measurements and disease information. Three sleep factors were included to aggregate overall sleep scores, ranging from 0 to 3. A sleep score of 0 to 1, 2 or 3 was expressed as a bad, intermediate or healthy sleep pattern, respectively. The Overactive Bladder Symptom Score (OABSS) scale was applied to quantify the severity of OAB for each participant. Weighted logistic regression models were used to investigate the associations between sleep and OAB. Results: A total of 16,978 participants were enrolled in this study. The relationship between OAB and sleep patterns was statistically significant. After fully adjusting for confounding factors, the OAB risk of patients with intermediate and poor sleep patterns obviously increased by 26% and 38%, respectively, and mild (OR = 1.21, 95% CI [1.03,1.42]), moderate (OR = 1.45, 95% CI [1.27,1.66]) and severe (OR = 1.57, 95% CI [1.18,2.09]) OAB were significantly associated with sleep pattern grouping. The prevalence of OAB is significantly higher in patients with bad sleep patterns, and vice versa. Conclusion: This study indicated that there is a positive relationship between OAB and worse sleep-related issues.

Background: Kidney stone disease is a common surgical disease and a significant public health issue, which may be influenced by environmental factors such as domestic water hardness and its related minerals. Previous studies have shown inconsistent and controversial results regarding the impact of domestic water hardness on kidney stone formation. Methods: This prospective cohort study analyzed data from 288 041 participants in the UK Biobank with no prior history of kidney stones from 2006 to 2024. The exposures were domestic water hardness, calcium concentration, calcium carbonate concentration, and magnesium concentration. The main outcomes were the disease status and onset time of kidney stone diseases. The confounding factors of model adjustment included age, sex, ethnicity, economic level, education level, Townsend Deprivation Index, Index of Multiple Deprivation, assessment center, body mass index, drug history influencing the metabolism of calcium and magnesium, and water intake based on the directed acyclic graph of causal hypothesis. The association between domestic water hardness and kidney stones was assessed using the Cox regression models, sensitivity analyses, subgroup and interactive analyses. Results: During the follow-up period, 3298 participants (1.14%) developed kidney stones. In all participants, the mean concentration of calcium, calcium carbonate, and magnesium was 52.61, 135.01, and 4.66 mg/L, respectively. In Cox regression models, higher magnesium levels (Q4, > 5 mg/L) in natural water use can reduce the risk of kidney stones [HR and 95% CI: 0.88 (0.80–0.97) in model 3], but no significant correlation was found in domestic water hardness, calcium concentration, and calcium carbonate concentration in the overall models. Four sensitivity analyses further supported the overall results in the overall models. In subgroup analysis, hard water and calcium concentration in domestic water can increase the 18%–34% incidence risk of kidney stones in participants over 60 years old and female participants; high magnesium concentration (>5 mg/L) in domestic water can decrease the 10%–28% risk of kidney stones in males, participants ≤ 45 years old, and participants without renal failure. Magnesium interacted with other minerals, and its protective effects were more significant in hard water (HR: 0.73, 95% CI: 0.61–0.87), a high concentration of CaCO₃ (HR: 0.62, 95% CI: 0.50–0.78), and calcium (HR: 0.48, 95% CI: 0.33–0.71) in domestic water. Conclusion: Our findings suggested that magnesium levels in water can decrease kidney stone risk, but in the overall population, domestic water hardness, calcium concentration, and calcium carbonate concentration have no significant impact on the formation of kidney stones. Interestingly, hard water and its calcium concentration can promote the formation risk of kidney stones in participants > 60 years old and females, while high magnesium concentration in domestic water can reduce the risk in males, ≤ 45 years old, and those without renal failure. The protective effects of magnesium interacted with other minerals and were more obvious in the population intake of hard water and high concentrations of CaCO₃ and calcium. This study contributes to the complex understanding of environmental factors in kidney stone etiology and suggests a need for focusing on mineral-specific effects in different populations and interaction with other minerals, which hope to provide some evidence of water’s role in public health and clinical management of kidney stones.

Background. Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology. Methods. Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status. Results. Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival. Conclusion. A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.

The primary subtypes of renal cell carcinoma (RCC) include clear cell, papillary and chromophobe RCC. RCC occurs often due to loss of von Hippel‑Lindau (VHL) and accumulation of lipids and glycogen, and RCC cells may exhibit sensitivity to the disruption of normal metabolism or homologous recombination gene defect. Although the application of molecular‑targeted drugs (tyrosine kinase inhibitors) and immune checkpoint inhibitors has been recommended for the treatment of advanced RCC, more targets of DNA damage repair (DDR) signaling pathway involved in the synthetic lethal effect have been investigated. However, although achievements has been made in the exploration of the roles of DDR genes on RCC progression, their association has not been systematically summarized. Poly (ADP‑ribose) polymerase (PARP) 1 inhibitors are used in tumors with BRCA1/2 DNA repair‑associated mutations. PARP family enzymes perform post‑translational modification functions and participate in DDR and cell death. Inhibitors of PARP, ataxia telangiectasia mutant gene and polymerase θ serve key roles in the treatment of specific RCC subtypes. PARP1 may serve as an important biological marker to predict the therapeutic effect of immune checkpoint inhibitors and evaluate the prognosis of patients with ccRCC with polybromo 1 mutation. Therefore, the roles of DDR pathway on RCC progression or treatment may hold promises for the treatment of certain specific types of RCC.

Objective. This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms’ tumor (WT). Patients and methods. Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox’s regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms’ reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices). Results. 1613 WT patients under 18 were involved in the study and randomly divided into the training (n=1210) and validation (n=403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction. Conclusion. Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.