Background: Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults. Methods: This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017–2012). In particular, NHANES (2007–2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011–2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms. Results: The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69–0.73) and 0.85 (95% CI, 0.83–0.86), and an externally validated C index of 0.71 (95% CI, 0.68–0.74) and 0.83 (95% CI, 0.81–0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10–17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions. Conclusions: This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized.Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas.Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell’s concordance index (0.87; 95% CI: 0.84–0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors.Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.

Background: Depression is a prevalent mental health disorder with a complex etiology and substantial public health implications. Early identification of individuals at risk for depression is crucial for effective intervention and prevention efforts. This study aimed to develop a predictive model for depression by integrating demographic factors (age, race, marital status, income), lifestyle factors (sleep duration, physical activity), and physiological measures (hypertension, blood lead levels). A key objective was to explore the role of physical activity and blood lead levels as predictors of current depression risk. Methods: Data were extracted from the 2007–2014 National Health and Nutrition Examination Survey (NHANES). We applied a logistic regression analysis to these data to assess the predictive value of the above eight factors for depression to create the predictive model. Results: The predictive model had bootstrap-corrected c-indexes of 0.68 (95% CI, 0.67–0.70) and 0.66 (95% CI, 0.64–0.68) for the training and validation cohorts, respectively, and well-calibrated curves. As the risk of depression increased, the proportion of participants with 1.76 ~ 68.90 µg/L blood lead gradually increased, and the proportion of participants with 0.05 ~ 0.66 µg/L blood lead gradually decreased. In addition, the proportion of sedentary participants increased as the risk of depression increased. Conclusions: This study developed a depression risk assessment model that incorporates physical activity and blood lead factors. This model is a promising tool for screening, assessing, and treating depression in the general population. However, because the corrected c-indices of the predictive model have not yet reached an acceptable threshold of 0.70, caution should be exercised when drawing conclusions. Further research is required to improve the performance of this model.

Background The admission time of patients with ST-segment elevation myocardial infarction (STEMI) may affect the quality of care they receive. This study aimed to explore the pattern and magnitude of variation in quality of care for patients with STEMI in both the process and outcome domains. Methods We performed a retrospective study based on STEMI data from China. We estimated the adjusted ORs of six process indicators and one outcome indicator of STEMI care quality by fitting multilevel multivariable regression models across 42 4hour time periods per week. Results The study cohort comprised 98 628 patients with STEMI. Care quality varied by time of arrival to the emergency department. We identified three main patterns of variation, which were consistent across days of the week. In the first pattern, which applied to electrocardiographic examination within 10 min of arrival and to aspirin or clopidogrel use within 10 min of arrival, quality was lowest for arrivals between 08:00 and 12:00, rose through the day and peaked for arrivals between 24:00 and 04:00. Percutaneous transluminal coronary intervention treatment within 90 min showed the same pattern but with maximal performance for those arriving 20:00–24:00. In the third pattern, applying to lipid function evaluation within 24 hours and beta blocker use within 24 hours, quality was best for arrivals between 04:00–08:00 and 16:00–19:00 and worst for arrivals between 24:00–04:00 and 12:00–16:00. Conclusions The quality of care for STEMI shows three patterns of diurnal variation. Detecting the times at which quality is relatively low may lead to quality improvement in healthcare. Quality improvement should focus on reducing the weekend effect and off-hour effect and the diurnal temporal variation.