Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease.

Background and Aims: Osteopontin (OPN) is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the function of OPN in NAFLD is still inconclusive. Therefore, our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms. Methods: We analyzed the expression change of OPN in NAFLD by bioinformatic analysis, qRT-PCR, western blotting and immunofluorescence staining. To clarify the role of OPN in NAFLD, the effect of OPN from HepG2 cells on macrophage polarization and the involved mechanisms were examined by FACS and western blotting. Results: OPN was significantly upregulated in NAFLD patients compared with normal volunteers by microarray data, and the high expression of OPN was related with disease stage and progression. OPN level was also significantly increased in liver tissue samples of NAFLD from human and mouse, and in HepG2 cells treated with oleic acid (OA). Furthermore, the supernatants of OPN-treated HepG2 cells promoted the macrophage M1 polarization. Mechanistically, OPN activated the janus kinase 1(JAK1)/signal transducers and activators of transcription 1 (STAT1) signaling pathway in HepG2 cells, and consequently HepG2 cells secreted more high-mobility group box 1 (HMGB1), thereby promoting macrophage M1 polarization. Conclusions: OPN promoted macrophage M1 polarization by increasing JAK1/STAT1-induced HMGB1 secretion in hepatocytes.

Anxiety, Depression, and PTSD among College Students in the Post-COVID-19 Era: A Cross-Sectional Study.

In the post-COVID-19 era, significant changes have taken place regarding the epidemic, the economy, family and social life. However, it remains unclear how these changes encompass the psychological symptoms of college students. We carried out a cross-sectional study to investigate anxiety, depression, and post-traumatic stress disorder (PTSD) symptoms among college students from 10 November 2020, to 16 November 2020. The questionnaire included a self-designed canvas, Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), and Impact of Event Scale (IES-R). Factors associated with psychological symptoms were estimated by ordered and non-conditional logistic regression analysis. Of 4754 participants, 25.0%, 29.7%, 3.4%, 15.3%, 17.1%, and 2.9% reported anxiety, depression, PTSD symptoms, one, any two, and all three, respectively. In cases with anxiety or depression symptoms, there was a 9.11% comorbidity with PTSD. Factors associated with fears of being infected, social, family, and economic changes increased the risk of psychological symptoms in college students caused by COVID-19. Female college students, identified with anxiety or depression symptoms, were at a lower risk of developing PTSD symptoms (OR, 0.61, 95% CI: 0.43-0.86). Non-medical majors at university, rural residence, higher educational background, fear of taking public transport, and deterioration of family relationships increased the risk for PTSD symptoms among male respondents with anxiety or depression symptoms due to COVID-19. Factors correlated with psychological symptoms had expanded from the fear of being infected to extensive social, family, and economic changes caused by COVID-19. Therefore, screening and interventions for psychological symptoms should be consistently strengthened and more targeted to college students in the post-COVID-19 era.

Association between psychological symptoms and illegal driving behaviors in a sample of Chinese private car drivers.

Background: Findings on the associations between psychological symptoms and driving behaviors in private car drivers are inadequate. Method: The study consisted of 3,115 private car drivers in Yulin, China. The measurements included socio-demographic data, traffic violations, accidents, and Symptom Checklist-90 (SCL-90). In addition, an ordered logistic regression model was employed to examine the association between each psychological symptom and risky driving behaviors. Results: The overall prevalence rate of any self-reported psychological symptom was 10.24%, with 9.22% for males and 11.49% for females. Among them, obsessive-compulsive, interpersonal sensitivity, additional items, hostility, and depression were the five most common psychological symptoms, with prevalence rates of 7.90, 6.29, 6.00, 5.91, and 5.62%, respectively. Any psychological symptom factor was associated with a higher risk of traffic violations and accidents. However, the intensity of the correlations varied, with obsessive-compulsive symptoms the strongest in general traffic violations and anxiety symptoms in traffic accidents. All psychological symptoms except phobic anxiety and paranoid ideation contributed to a higher risk of failing the driver's license test. Conclusions: The prevalence rate of psychological symptoms was high in private car drivers. This study calls for an urgent need to establish a pilot tertiary prevention strategy to reduce risky driving behaviors through psychological symptom screening and interventions among private car drivers.

Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.

Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70-0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65-0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.

Effectiveness of CBT and its modifications for prevention of relapse/recurrence in depression: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: CBT and its modifications are effective for depression, but its efficacy for preventing depressive relapse/recurrence remains unclear. METHODS: A systematic review and meta-analysis was conducted scrutinizing the efficacy of CBT and its modifications for preventing depressive relapse/recurrence in randomized controlled trials. The retrieval database included PubMed, Web of Science, MEDLINE, and three major Chinese databases (CNKI, VIP, Wanfang), and the retrieval period was from January 1, 2009, to July 1, 2022. Data were analyzed by RevMan 5.4 and Stata 16. RESULTS: A total of 28 randomized controlled trials with 3938 participants were included. The relapse/recurrence rates in CBT and its modifications and the control group were 31.6 % and 41.3 %, respectively; CBT and its modifications significantly reduced the risk of developing a new depressive episode (RR, 0.73, 95%CI, 0.64-0.83). Stratified by follow-up time, both CBT and its modifications significantly reduced the relapse/recurrence of depression at ≤12 months (RR, 0.61, 95%CI, 0.47-0.79) and > 12 months (RR, 0.74, 95%CI, 0.59-0.93) compared with other active treatments. The beneficial effects of CBT and its modifications alone in reducing depressive relapse/recurrence were equivalent to those of antidepressant medication alone (RR, 1.01, 95 % CI, 0.79-1.28). Meta-regression results showed that the intervention method (P = 0.005) was one of the sources of heterogeneity. LIMITATIONS: Further subgroup analysis is limited by the literature. CONCLUSION: CBT and its modifications are reliable interventions for preventing relapse/recurrence in patients with depression. Further research is needed to determine the specific conditions for CBT and its applications in clinical practice.

Machine learning approaches for diagnosing depression using EEG: A review.

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.