lncRNA-CIR regulates cell apoptosis of chondrocytes in osteoarthritis.

BACKGROUND AND OBJECTIVES: Osteoarthritis (OA) is a complex chronic degenerative joint disease involving oxidative stress, inflammation, and apoptosis of chondrocytes. As decoys of micro RNAs, long non-coding RNAs (lncRNAs) play important roles in various biological processes. This study was designed to investigate the interactions between lncRNA-CIR, chondrocyte apoptosis, and the molecular mechanisms underlying OA. METHODS: Primary cultured chondrocytes were stressed using H2 O2 , IL-1ß, or TNF-ɑ to simulate conditions found in OA. Quantitative real-time PCR was performed to detect miR-130a, lncRNA-CIR, and Bim mRNA expression levels. Western blot analysis was used to detect Bim protein expression levels. Reactive oxygen species (ROS) levels were assayed by detecting the fluorescent signal of 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA). Cell apoptosis was measured with combined staining of PI and DAPI. lncRNA-CIR knockdown and miR-130a over-expression or inhibition were performed using small interfering RNAs, and miR-130 mimics or inhibitors, respectively. RESULTS: lncRNA-CIR is significantly upregulated in OA patients, accompanied by downregulation of miR-130a and upregulation of Bim. Bio-informatics analysis predicted miR-130a as a target of both lncRNA-CIR and Bim. While lncRNA-CIR knockdown significantly increased the expression of Bim, miR-130a significantly suppressed Bim expression, with accompanying increases of ROS level, inflammatory mediator release, cell apoptosis, and relative luciferase activity. CONCLUSIONS: The present findings demonstrated that the lncRNA-CIR/miR-130a/Bim axis is involved in oxidative stress-related apoptosis of chondrocytes in OA.

Exploring the symptom patterns of depression and anxiety among teachers during COVID-19: A latent profile analysis.

OBJECTIVE: The present study used the latent profile analysis (LPA) approach to explore the symptom patterns of depression and anxiety among Chinese teachers during COVID-19 and its relationship with fear of COVID-19 and suicidal ideation. METHOD: A sample of 6,121 teachers from primary and secondary schools in a district-level administrative unit in southern China was used. The LPA was employed to identify different symptom patterns of depression and anxiety. We subsequently used logistic regression to analyze the effects of demographic variables on the different profiles. The Bolck-Croon-Hagenaars method assessed the relationships between each profile and fear of COVID-19 and suicidal ideation. RESULTS: The study identified five significant latent profiles and two subtypes in the moderate psychological disorder group. We also found that gender, professional title, and age significantly influenced the distribution of the profiles. The risk for both fear of COVID-19 and suicidal ideation was highest in the severe psychological disorder group. The high anxiety subtype had a significantly greater fear of the new coronavirus epidemic than the high depression subtype, which had a significantly higher level of suicidal ideation than the high anxiety subtype. CONCLUSION: The profiles we identified have distinct features that confirm their unique patterns of symptom endorsement. Our study may have important implications for early warning and intervention in teacher mental health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

'Can we predict aggression?'-Determining the predictors of aggression among individuals with substance use disorder in China undergoing enforced detoxification through machine learning.

BACKGROUND: The general aggression model has shown that both individual and situational factors can predict aggression. However, past research has tended to discuss these two factors separately, which might lead to inconsistency. This study addresses this gap by examining the importance of each predictor of aggression in a Chinese compulsory drug treatment population and further explores the predictors of aggression in various substance use disorder populations. METHOD: Analyses were conducted using a sample of 894 male participants (mean = 38.30, SD = 8.38) in Chinese compulsory drug rehab. A machine learning model named LightGBM was employed to make predictions. We then used a game-theoretic explanatory technique, SHAP, to estimate the effect of predictors. RESULTS: In the full-sample model, psychological security, parental conflict, and impulsivity were the top 3 predictors. Depression, childhood abuse, and alexithymia positively predicted aggression, whereas psychological security, family cohesion, and gratitude negatively predicted aggression. There were significant differences in the predictive effects of depressants and stimulants. Although the importance of predictors varied between drug-use groups, several individual and situational factors were consistently the most important predictors. LIMITATIONS: All participants in this study were male, and the data were acquired through self-reports from the participants. Domestic and nondomestic aggression are not distinguished. Additionally, our findings cannot support causal conclusions. CONCLUSION: This study tested a series of classical theories of the predictors of aggression in China's compulsory drug treatment context and extended the ideas of the GAM to various substance use disorder groups. The findings have important implications for aggression treatment.

Factors Affecting the Public Acceptance of Extramarital Sex in China.

There is a lack of quantitative studies on the acceptance of extramarital sex in China. Based on data from the Chinese General Social Survey 2013 (CGSS2013), this paper used a zero-inflated Poisson regression model to analyze the factors influencing the public's attitudes toward extramarital sex. When other variables were controlled, groups of younger ages, higher educational levels, and stronger tendencies toward "liberalization" and non-Islamic beliefs were more tolerant toward extramarital sex, whereas gender and Christian beliefs had no significant influence. In this regard, family and marriage counseling, and society's moral tolerance and social control of religion are discussed, and further research on cross-cultural verification is needed.

Loan default prediction of Chinese P2P market: a machine learning methodology.

Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.

Tracking Study on the Relapse and Aftercare Effect of Drug Patients Released From a Compulsory Isolated Detoxification Center.

BACKGROUND AND AIMS: There are no accurate statistical data on the relapse rate of drug abstainers after compulsory detoxification in China. This study aimed to collect relapse data for drug abstainers through follow-up visits, verify the effectiveness of professional social worker services and explore significant factors affecting relapse. DESIGN AND SETTING: The drug abstainers released from Guangzhou T Compulsory Isolated Detoxification Center were randomly divided into two groups. The difference between the experimental group and the control group is that assistance services were provided by social workers to the former. PARTICIPANTS: The study included 510 drug abstainers released from T Center, including 153 in the experimental group and 357 in the control group. MEASUREMENTS: Demographic information, history of drug abuse, and motivation for drug rehabilitation (SOCRATES) were collected 1 month prior to drug abstainer release from compulsory detoxification. Then, the relapse situation after their release was tracked according to fixed time points. FINDINGS: The overall relapse rate of 510 drug abstainers after their release from compulsory detoxification was 47.6%. The average survival time to relapse based on survival analysis was 220 days (N = 486), as calculated with Bayesian estimation by the MCMC method. The average survival times to relapse of the experimental group and control group were 393 and 175 days, respectively. By taking the specific survival time as the dependent variable and the group as the control variable (OR = 25.362), logistic regression analysis showed that marital status (OR = 2.666), previous compulsory detoxification experience (OR = 2.329) and location of household registration (OR = 1.557) had a significant impact on the survival time to relapse. CONCLUSIONS: The occurrence of relapse among drug patients released from compulsory detoxification can be delayed effectively through the intervention of professional social worker services. Regardless of whether patients receive aftercare after compulsory detoxification, drug-using patients who are single, have multiple detoxification experiences and whose households are registered in other provinces deserve special attention. Relevant suggestions to avoid relapse are provided.