Adolescent depression is a common and serious mental disorder with unique characteristics that are distinct from adult depression. The adult non-human primate stress-induced model of depressive-like behavior is an excellent model for the study of mechanisms; however, an adolescent nonhuman primate model is still lacking. Ten male adolescent cynomolgus monkeys were divided into a chronic unpredictable mild stress (CUMS, n = 5) group and a control (CON, n = 5) group by age and weight-matched pairs. The CUMS group was exposed to multiple unpredictable mild stressors for five cycles over 55 days. At baseline, there were no differences between CUMS and CON groups. At endpoint, the CUMS group demonstrated significantly higher depressive-like behavior (huddle posture), and significantly lower locomotion compared with the CON group. Furthermore, depressive-like behavior increased from baseline to endpoint in the CUMS group, but not changed in the CON group. In the attempt for apple test, the CUMS group made significantly fewer attempts for the apple than the CON group. In the human intruder test, the CUMS group showed significantly higher anxiety-like behaviors in the stare phase than the CON group. Hair cortisol level was significantly higher in the CUMS group than the CON group at endpoint, and was also elevated from baseline to endpoint. Metabolic profiling of plasma at endpoint identified alterations in metabolite pathways which overlapped with those of adolescent depression patients. CUMS can induce depressive-like and anxiety-like behaviors, hypercortisolemia, and metabolic perturbations in adolescent cynomolgus monkeys. This is a promising model to study the mechanisms underlying adolescent depression.

BackgroundAvailable evidence on the comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder (PTSD) in children and adolescents remains uncertain.ObjectiveWe aimed to compare and rank the different types and formats of psychotherapies for PTSD in children and adolescents.MethodsWe searched eight databases and other international registers up to 31 December 2020. The pairwise meta-analyses and frequentist network meta-analyses estimated pooled standardised mean differences (SMDs) and ORs with random-effects model. Efficacy at post-treatment and follow-up, acceptability, depressive and anxiety symptoms were measured.FindingsWe included 56 randomised controlled trials with 5327 patients comparing 14 different types of psychotherapies and 3 control conditions. For efficacy, cognitive processing therapy (CPT), behavioural therapy (BT), individual trauma-focused cognitive–behavioural therapy (TF-CBT), eye movement desensitisation and reprocessing, and group TF-CBT were significantly superior to all control conditions at post-treatment and follow-up (SMDs between −2.42 and −0.25). Moreover, CPT, BT and individual TF-CBT were more effective than supportive therapy (SMDs between −1.92 and −0.49). Results for depressive and anxiety symptoms were similar to the findings for the primary outcome. Most of the results were rated as ‘moderate’ to ‘very low’ in terms of confidence of evidence.ConclusionsCPT, BT and individual TF-CBT appear to be the best choices of psychotherapy for PTSD in young patients. Other types and different ways of delivering psychological treatment can be alternative options. Clinicians should consider the importance of each outcome and the patients’ preferences in real clinical practice.

To rescue and preserve an endangered language, this paper studied an end-to-end speech recognition model based on sample transfer learning for the low-resource Tujia language. From the perspective of the Tujia language international phonetic alphabet (IPA) label layer, using Chinese corpus as an extension of the Tujia language can effectively solve the problem of an insufficient corpus in the Tujia language, constructing a cross-language corpus and an IPA dictionary that is unified between the Chinese and Tujia languages. The convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) network were used to extract the cross-language acoustic features and train shared hidden layer weights for the Tujia language and Chinese phonetic corpus. In addition, the automatic speech recognition function of the Tujia language was realized using the end-to-end method that consists of symmetric encoding and decoding. Furthermore, transfer learning was used to establish the model of the cross-language end-to-end Tujia language recognition system. The experimental results showed that the recognition error rate of the proposed model is 46.19%, which is 2.11% lower than the that of the model that only used the Tujia language data for training. Therefore, this approach is feasible and effective.

Introduction: Acute traumatic intraparenchymal hematoma (tICH) expansion is a major cause of clinical deterioration after brain contusion. Here, an accurate prediction tool for acute tICH expansion is proposed. Methods: A multicenter hospital-based study for multivariable prediction model was conducted among patients (889 patients in a development dataset and 264 individuals in an external validation dataset) with initial and follow-up computed tomography (CT) imaging for tICH volume evaluation. Semi-automated software was employed to assess tICH expansion. Two multivariate predictive models for acute tICH expansion were developed and externally validated. Results: A total of 198 (22.27%) individuals had remarkable acute tICH expansion. The novel Traumatic Parenchymatous Hematoma Expansion Aid (TPHEA) model retained several variables, including age, coagulopathy, baseline tICH volume, time to baseline CT time, subdural hemorrhage, a novel imaging marker of multihematoma fuzzy sign, and an inflammatory index of monocyte-to-lymphocyte ratio. Compared with multihematoma fuzzy sign, monocyte-to-lymphocyte ratio, and the basic model, the TPHEA model exhibited optimal discrimination, calibration, and clinical net benefits for patients with acute tICH expansion. A TPHEA nomogram was subsequently introduced from this model to facilitate clinical Jiangtao Sheng and Weiqiang Chen contributed equally to this work.