Background: Non-suicidal self-injury (NSSI) behavior among college students is a focus of attention in current society. In the information era, the Internet serves as a public health concern and as an effective pathway for prevention. In order to reduce NSSI behavior, we explore its influence factors, especially the relations between neuroticism, emotion regulation (ER), depression, and NSSI behavior. Methods: A total of 450 college students were surveyed with the Big Five Inventory-2, Emotion Regulation Questionnaire, Self-Rating Depression Scale, and Adolescent Non-Suicidal Self-Injury Assessment Questionnaire. Results: Regression analysis showed that neuroticism significantly negatively predicted emotion regulation, while it positively predicted depression and NSSI. Multiple mediation modeling demonstrated that neuroticism and emotion regulation had no significant direct effects on NSSI. However, neuroticism could indirectly affect NSSI through four pathways of multiple mediating effects, including depression, cognitive reappraisal-depression, expressive suppression-depression, and cognitive reappraisal-expressive suppression-depression. Conclusions: Neuroticism positively predicts depression and NSSI behavior, and affects NSSI through the mediating effect of ER and depression. Therefore, amelioration of neuroticism from the perspectives of emotion regulation and depression is recommended, so as to reduce NSSI behavior among college students with highly neurotic personalities.

Background Alzheimer’s disease (AD) is a neurodegenerative disorder with multiple pathological features. Therefore, multi-target-directed ligands (MTDLs) strategy has been developed to combat this disease. We have previously designed and synthesized dimeric tacrine (10)-hupyridone (A10E), a novel tacrine derivative with acetylcholinesterase (AChE) inhibition and brain-derived neurotrophic factor (BDNF) activation activity, by linking tacrine and a fragment of huperzine A. However, it was largely unknown whether A10E could act on other AD targets and produce cognition-enhancing ability in AD animal models. Methods Behavioral and biochemical methods were applied to evaluate multi-target cognitive-enhancing effects and mechanisms of A10E in APP/PS1 transgenic mice and β-amyloid (Aβ) oligomers-treated mice. The neuroprotective mechanisms of A10E were explored in SH-SY5Y cells. And the anti-aggregation effects of A10E on Aβ were directly investigated in vitro. Results A10E could prevent cognitive impairments in both APP/PS1 mice and Aβ oligomers-treated mice, with higher potency than tacrine and huperzine A. Moreover, A10E could effectively inhibit Aβ production and deposition, reduce neuroinflammation, enhance brain derived brain-derived neurotrophic factor (BDNF) expression, and elevate cholinergic neurotransmission in vivo. A10E, at nanomolar concentrations, could also inhibit Aβ oligomers-induced neurotoxicity via the activation of the TrkB/Akt pathway. Furthermore, Aβ oligomerization and fibrillization could be directly disrupted by A10E. Conclusion A10E could produce anti-AD neuroprotective effects via multi-target mechanisms, including the inhibition of Aβ aggregation, the activation of the BDNF/TrkB pathway, the reduction of neuroinflammation and the decrease of AChE activity. As MTDLs could produce additional benefits, such as overcoming the deficits of drug combination and enhancing the compliance of AD patients, our results suggested that A10E might be developed as a promising MTDL lead for the treatment of AD.

Background The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. Methods Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. Results A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  − 2.75, p < .001, R2adj = 0.40; r =  − .84, 95% CI: − .41 to − .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0–87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2–56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9–90.8%)/availability (80.88% of models, 95% CI: 77.3–84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. Conclusions Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Objectives Police officers are generally under long‐term occupational stress. Good mental health ability enables them to better deal with emergencies and enhance their combat effectiveness. We aimed to develop the Police Mental Health Ability Scale (PMHAS) to provide a reference for police selection and ability training. Methods Through literature analysis, individual interviews, half‐open and half‐closed questionnaire surveys, and expert consultations, the components of police mental health ability (PMHA) were theoretically constructed. Then, we enrolled 824 in‐service police officers who participated in the training in Chongqing City and Sichuan Province from November 2018 to January 2019 and recovered 767 valid questionnaires (recovery rate, 93.08%). Results Exploratory factor analysis generated five factors for PMHAS, including cognitive intelligence, emotional catharsis, swift decisiveness, behavioral drive, and reward pursuit, accounting for 58.904% of the variance. Confirmatory factor analysis demonstrated that the model fit well (χ2/df = 1.117, RMSEA = 0.020, GFI = 0.948, CFI = 0.990, IFI = 0.990, TLI = 0.987). The correlation coefficients of factors (r = −0.023 ~ 0.580) were lower than that of each factor and total score (r = 0.477 ~ 0.819). The Cronbach's α coefficients of PMHAS and its factors were 0.606–0.863, and the test–retest reliabilities were 0.602–0.732. Conclusion These results suggest that PMHAS is reliable and valid enough for measuring PMHA, which shows that it is a potentially valuable tool for assessing the mental health ability of police officers.