Non-suicidal self-injury (NSSI) in adolescents is a significant mental health problem around the world. Here, we performed a meta-analysis to systematically delineate the risk factors for NSSI. We searched Medline, Embase, Web of Science and Cochrane for relevant articles and abstracts published prior to 12 November 2021. Pooled odds ratios (ORs) and 95% confident intervals (CIs) were used to assess various risk factors, and publication bias was assessed by Egger's test, the trim and fill method and meta-regression. This study is registered with PROSPERO, CRD42021265885. A total of 25 articles were eventually included in the analysis. Eighty risk factors were identified and classified into 7 categories: mental disorders (ORs, 1·89; 95% CI, 1·60–2·24), bullying (ORs, 1·98; 95% CI, 1·32–2·95), low health literacy (ORs, 2·20; 95% CI, 1·63–2·96), problem behaviours (ORs, 2·36; 95% CI, 2·00–2·77), adverse childhood experiences (ORs, 2·49; 95% CI, 1·85–3.34), physical symptoms (ORs, 2·85; 95% CI, 1·36–5·97) and the female gender (ORs, 2·89; 95% CI, 2·43–3·43). The range of heterogeneity (I²) was from 20·3% to 99·2%. This meta-analysis found that mental disorders, low health literacy, adverse childhood experiences, bullying, problem behaviours, the female gender and physical symptoms appear to be risk factors for NSSI.

(Purpose) Early diagnosis or detection of Alzheimer’s disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions. (Method) First, we used maximum inter-class variance to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch’s t-test. Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC. (Results) The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36±0.94) was better than the linear kernel of 91.47±1.02 and the radial basis function (RBF) kernel of 86.71±1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions. The results were coherent with existing literatures. (Conclusion) The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

Sustained stress triggers series of changes in the brain and the body. At the early stage of stress, the activated hypothalamus-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS) axis can upregulate the levels of glucocorticoid (GCs) and catecholamines (CAs), respectively, and then they in turn inhibit the secretion of proinflammatory cytokines directly or indirectly while promoting the secretion of anti-inflammatory cytokines. At the prolonged stage, the sustained activated HPA demonstrates cortisol-resistance. At the same time, the inflammation related transcription pathway, such as nuclear-factor kappa-B (NF-κB) signaling, may be inhibited. Additionally, the inflammatory cytokines mediate a negative feedback regulation on themselves. Collectively, these regulations may increase the proinflammatory cytokines while decreasing the anti-inflammatory cytokines. This may further activate NF-κB and increase the proinflammation cytokines, which in turn reduce the inflammatory responses, contributing to various diseases.

Repetitive transcranial magnetic stimulation (rTMS) has been widely used in non-invasive treatments for different neurological disorders. Few biomarkers are available for treatment response prediction. This study aims to analyze the correlation between changes in long-term potentiation (LTP)-like cortical plasticity and cognitive function in patients with Alzheimer's disease (AD) that underwent rTMS treatment. A total of 75 AD patients were randomized into either 20 Hz rTMS treatment at the dorsolateral prefrontal cortex (DLPFC) group (n = 37) or a sham treatment group (n = 38) for 30 sessions over six weeks (five days per week) with a three-month follow-up. Neuropsychological assessments were conducted using the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment-Cognitive Component (ADAS-Cog). The cortical plasticity reflected by the motor-evoked potential (MEP) before and after high-frequency repetitive TMS to the primary motor cortex (M1) was also examined prior to and after the treatment period. The results showed that the cognitive ability of patients who underwent the MMSE and ADAS-Cog assessments showed small but significant improvement after six weeks of rTMS treatment compared with the sham group. The cortical plasticity improvement correlated to the observed cognition change. Cortical LTP-like plasticity could predict the treatment responses of cognitive improvements in AD patients receiving rTMS intervention. This warrants future clinical trials using cortical LTP as a predictive marker.