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Background: Several research efforts have found that older schizophrenia patients are at higher risk for suicide. Related risk factors for suicide attempts in the elderly with schizophrenia are poorly understood. This study sought to assess the prevalence of suicide attempts and related risk variables in schizophrenia patients over the age of 50. Methods: We recruited 591 elderly patients over the age of 50. Demographic, clinical and blood parameters of all patients were collected. Patients with schizophrenia were evaluated for psychopathological symptoms using the Positive and Negative Syndrome Scale (PANSS). Results: The rate of suicide attempts among elderly Chinese schizophrenia patients was 15.2%. In these elderly schizophrenia patients, those who with suicide attempts had significantly higher scores for insomnia, depression and smoking addiction, and higher levels of red blood cells, Hc1 glycosylated hemoglobin and free T3, but significantly lower levels of hypersensitive C‐reactive protein and ‐globulin. Binary logistic regression analysis showed that higher depression scores and smoking addiction were risk factors for suicide attempts in these patients. Additionally, multiple regression analyses showed that higher BMI and insomnia scores were associated with suicide risk scores in elderly schizophrenia patients. Conclusions: Our research shows that there is a high incidence of suicide attempts in people over 50 years of age with schizophrenia. A number of clinically relevant factors and metabolic markers are associated with suicide attempts among elderly patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: The psychological well‐being of lung cancer patients is critical in‐patient care but frequently overlooked. Methods: This study, employing a cross‐sectional, questionnaire‐based design, aimed to elucidate the prevalence of depressive and anxiety symptoms among lung cancer patients and identify associated risk factors. Participants' demographic, medical history, disease stage, and pathology were systematically collected. Psychological assessment was conducted using the general anxiety disorder‐7 (GAD‐7), patient health questionnaire‐9 (PHQ‐9), and hospital anxiety and depression scale (HADS). Statistical analyses were performed using SPSS software (version 25.0). Results: Out of 294 distributed questionnaires, 247 lung cancer patients were included in the final analysis, with an average completion time of 9.08 min. Notably, 32.4% exhibited depressive symptoms, while 30% displayed signs of anxiety. A significant correlation was found between both depressive and anxiety symptoms and a history of tobacco and alcohol consumption. Specifically, increased nicotine dependence and greater cumulative tobacco use were linked to higher rates of depressive symptoms, whereas cumulative alcohol consumption was associated with increased risks of anxiety symptoms. Conclusion: The study affirms the feasibility of GAD‐7, PHQ‐9, and HADS as screening tools for depressive and anxiety symptoms in lung cancer patients. It further highlights tobacco and alcohol consumption as significant risk factors for poor psychological health in this population. [ABSTRACT FROM AUTHOR]

Accurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. In this study, a refined prediction and early warning method system for urban flood and waterlogging processes based on deep learning methods is proposed. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I (a common used statistic for spatial autocorrelation). For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by mean absolute error (MAE), root mean square error (RMSE), Nash efficiency coefficient (NSE) and correlation coefficient (CC). The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a 0.001 m RMSE of the predicted and measured ponding depth) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real‐time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. The research results can provide theoretical support for urban flood prevention and control. [ABSTRACT FROM AUTHOR]

Background and Objectives: Comorbidity of substance use disorders and depression is a common phenomenon. It is well known that opioid addiction is neurobiologically distinct from psychostimulant addiction. However, direct comparisons of comorbid depressive symptoms in patients with methamphetamine (METH) use disorder (MAUD) and heroin use disorders (HUD) have been lacking until now. Methods: A total of 353 patients with methamphetamine use disorder, 76 patients with HUD, and 203 healthy controls were recruited. The Beck Depression Inventory (BDI‐SF), the Desires for Drug Questionnaire (DDQ) and the short form of the Childhood Trauma Questionnaire (CTQ‐SF) were used to measure participants' depressive symptoms, drug craving, and childhood abuse or neglect, respectively. Results: The prevalence of depressive symptoms was 35.41% (125/353) in MAUD and 56.57% (43/76) in MAUD, significantly higher than the 22.66% (46/203) in healthy controls. Furthermore, there was a significant difference in the total BDI score between the MAUD and HUD groups (F = 5.02, df = 1, 372, p =.026). Among MAUD, years of education, history of incarceration, month of abstinence and negative reinforcement scores were associated with depressive symptoms (all p <.05). Among HUD, duration of drug use, childhood emotional abuse and sexual abuse were associated with depressive symptoms (all p <.05). Conclusions and Scientific Significance: The prevalence and correlates of depressive symptoms differ between MAUD and HUD, adding to the existing literature. Therefore, treatment and intervention programs should be designed to address these unique correlates in HUD and MAUD patients. [ABSTRACT FROM AUTHOR]

Rechargeable Zn‐air batteries (ZAB) have drawn extensive attention due to their eco‐friendliness and safety. However, the lack of high‐performance and low‐cost oxygen redox reactions (OER and ORR) catalysts has become one of the main stumbling blocks in their development. Herein, we successfully fabricate a CoFe nanobubble encapsulated in nitrogen‐doped carbon nanocage on wood carbon support (CoFe@NC/WC) via pyrolysis of a novel Prussian blue analog (PBA)/spruce precursor. The hierarchical CoFe@NC/WC catalyst exhibits an excellent potential difference of 0.74 V between the OER potential at 10 mA cm−2 and half‐wave potential of ORR in 0.1 M KOH, comparable to recently reported preeminent electrocatalysts. Further, CoFe@NC/WC shows outstanding electrochemical performance in liquid ZAB, with a peak power density of 138.9 mW cm−2 and a specific capacity of 763.5 mAh g−1. More importantly, a bacterial cellulose nanofiber reinforced polyacrylic acid (BC‐PAA) hydrogel electrolyte shows ultrahigh tensile‐breaking stress of 1.58 MPa. In conjunction with the as‐prepared CoFe@NC/WC catalyst, BC‐PAA‐based wearable ZAB displays impressive rechargeability and foldability, and can power portable electronics, such as electronic timer and mobile phone, in bent states. This work provides a new approach toward high‐activity and low‐cost catalysts for ZAB. [ABSTRACT FROM AUTHOR]