Whether material deprivation-related childhood socio-economic disadvantages (CSD) and care-related adverse childhood experiences (ACE) have different impacts on depressive symptoms in middle-aged and older people is unclear.

In the Guangzhou Biobank Cohort Study, CSD and ACE were assessed by 7 and 5 culturally sensitive questions, respectively, on 8,716 participants aged 50+. Depressive symptoms were measured by 15-item Geriatric Depression Scale (GDS). Multivariable linear regression, stratification analyses, and mediation analyses were done.

Higher CSD and ACE scores were associated with higher GDS score in dose-response manner (P for trend <0.001). Participants with one point increment in CSD and ACE had higher GDS score by 0.11 (95% confidence interval [CI], 0.09–0.14) and 0.41 (95% CI, 0.35–0.47), respectively. The association of CSD with GDS score was significant in women only (P for sex interaction <0.001; women: β (95% CI)=0.14 (0.11–0.17), men: 0.04 (−0.01 to 0.08)). The association between ACE and GDS score was stronger in participants with high social deprivation index (SDI) (P for interaction = 0.01; low SDI: β (95% CI)=0.36 (0.29–0.43), high SDI: 0.64 (0.48–0.80)). The proportion of association of CSD and ACE scores with GDS score mediated via education was 20.11% and 2.28%.

CSD and ACE were associated with late-life depressive symptoms with dose-response patterns, especially in women and those with low adulthood socio-economic status. Education was a major mediator for CSD but not ACE. Eliminating ACE should be a top priority.

Hippocampal disruptions represent potential neuropathological biomarkers in depressed adolescents with cognitive dysfunctions. Given heterogeneous outcomes of whole-hippocampus analyses, we investigated subregional abnormalities in depressed adolescents and their associations with symptom severity and cognitive dysfunctions.

MethodsSeventy-nine first-episode depressive patients (ag = 15.54 ± 1.83) and 71 healthy controls (age = 16.18 ± 2.85) were included. All participants underwent T1 and T2 imaging, completed depressive severity assessments, and performed cognitive assessments on memory, emotional recognition, cognitive control, and attention. Freesurfer was used to segment each hippocampus into 12 subfields. Multivariable analyses of variance were performed to identify overall and disease severity-related abnormalities in patients. LASSO regression was also conducted to explore the associations between hippocampal subfields and patients’ cognitive abilities.

Depressed adolescents showed decreases in dentate gyrus, CA1, CA2/3, CA4, fimbria, tail, and molecular layer. Analyses of overall symptom severity, duration, self-harm behavior, and suicidality suggested that severity-related decreases mainly manifested in CA regions and involved surrounding subfields with disease severity increases. LASSO regression indicated that hippocampal subfield abnormalities had the strongest associations with memory impairments, with CA regions and dentate gyrus showing the highest weights.

Hippocampal abnormalities are widespread in depressed adolescents and such abnormalities may spread from CA regions to surrounding areas as the disease progresses. Abnormalities in CA regions and dentate gyrus among these subfields primarily link with memory impairments in patients. These results demonstrate that hippocampal subsections may serve as useful biomarkers of depression progression in adolescents, offering new directions for early clinical intervention.

Oil palm has been criticized for being an environmentally unfriendly oil crop. In recent decades, oil palm plantations have extended into conservation landscapes, causing severe environmental damage and harming biodiversity. Nevertheless, oil palm remains a highly productive oil crop from which most of the world's vegetable oil is produced. Therefore, measuring the environmental impact of oil palm plantations and identifying suitable land to support its sustainable development is crucial.

To meet the rising global palm oil demand sustainably, we tracked annual land cover changes in oil palm plantation and mapped areas worldwide suitable for sustainable oil palm cultivation. From 1982 to 2019, 3.6 Mha of forests were converted to oil palm plantations. Despite a recent decline in overall conversion, the shift from forest to oil palm plantations has become increasingly more common over the last decade, rising from 14.1 to 34.5% between 2009 and 2019. During 1982–2019, 2.23 Mha of peatland and 0.1 Mha of protected areas were converted for oil palm plantations. The potential sustainable land amounts to 103.5–317.9 Mha (Asia: 44.6–105.1 Mha, Africa: 34.7–96.4 Mha, and Latin America: 35.2–116.5 Mha). Future oil palm expansion is anticipated to take place in countries like Brazil, Nigeria, Colombia, Indonesia, Ivory Coast, the Democratic Republic of the Congo, and Ghana, where more sustainable land is available for cultivation. Malaysia, on the other hand, is about to exceed the area of sustainable cultivation, and further expansion is not recommended. These findings can advance our understanding of the environmentally damaging impacts of oil palm and enhance the feasibility of sustainable oil palm development.

How should suitable land be chosen for the establishment of oil palm plantations to support the sustainable development of the oil palm plantation industry?

COVID-19 lockdowns increased the risk of mental health problems, especially for children with autism spectrum disorder (ASD). However, despite its importance, little is known about the protective factors for ASD children during the lockdowns.

Based on the Shanghai Autism Early Developmental Cohort, 188 ASD children with two visits before and after the strict Omicron lockdown were included; 85 children were lockdown-free, while 52 and 51 children were under the longer and the shorter durations of strict lockdown, respectively. We tested the association of the lockdown group with the clinical improvement and also the modulation effects of parent/family-related factors on this association by linear regression/mixed-effect models. Within the social brain structures, we examined the voxel-wise interaction between the grey matter volume and the identified modulation effects.

Compared with the lockdown-free group, the ASD children experienced the longer duration of strict lockdown had less clinical improvement (β = 0.49, 95% confidence interval (CI) [0.19–0.79], p = 0.001) and this difference was greatest for social cognition (2.62 [0.94–4.30], p = 0.002). We found that this association was modulated by parental agreeableness in a protective way (−0.11 [−0.17 to −0.05], p = 0.002). This protective effect was enhanced in the ASD children with larger grey matter volumes in the brain's mentalizing network, including the temporal pole, the medial superior frontal gyrus, and the superior temporal gyrus.

This longitudinal neuroimaging cohort study identified that the parental agreeableness interacting with the ASD children's social brain development reduced the negative impact on clinical symptoms during the strict lockdown.

The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).

The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.

We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.

This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.

This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.

This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.

To investigate the safety and feasibility of midazolam for conscious sedation in transcatheter device closure of atrial septal defects guided solely by transthoracic echocardiography.

A retrospective analysis was performed on 55 patients who underwent transcatheter device closure of atrial septal defects from October, 2019 to May, 2020. All patients received intravenous midazolam and local anesthesia with lidocaine to maintain sedation. A group of previous patients with unpublished data who underwent the same procedure with general anesthesia was set as the control group. The relevant clinical parameters, the Ramsay sedation scores, the numerical rating scale, and the post-operative satisfaction questionnaire were recorded and analyzed.

In the midazolam group, the success rate of atrial septal defect closure was 98.2%. Hemodynamic stability was observed during the procedure. None of the patients needed additional endotracheal intubation for general anesthesia. Compared with the control group, the midazolam group had no statistically significant differences in the Ramsay sedation score and numerical rating scale scores. Patients in the midazolam group experienced more post-operative satisfaction than those in the control group.

Conscious sedation using midazolam is a safe and effective anesthetic technique for transcatheter device closure of atrial septal defects guided solely by transthoracic echocardiography.

To establish optimal gestational weight gain (GWG) in Chinese pregnant women by Chinese-specific BMI categories and compare the new recommendations with the Institute of Medicine (IOM) 2009 guidelines.

Multicentre, prospective cohort study. Unconditional logistic regression analysis was used to evaluate the OR, 95 % CI and the predicted probabilities of adverse pregnancy outcomes. The optimal GWG range was defined as the range that did not exceed a 1 % increase from the lowest predicted probability in each pre-pregnancy BMI group.

From nine cities in mainland China.

A total of 3731 women with singleton pregnancy were recruited from April 2013 to December 2014.

The optimal GWG (ranges) by Chinese-specific BMI was 15·0 (12·8–17·1), 14·2 (12·1–16·4) and 12·6 (10·4–14·9) kg for underweight, normal weight and overweight pregnant women, respectively. Inappropriate GWG was associated with several adverse pregnancy outcomes. Compared with women gaining weight within our proposed recommendations, women with excessive GWG had higher risk for macrosomia, large for gestational age and caesarean section, whereas those with inadequate GWG had higher risk for low birth weight, small for gestational age and preterm delivery. The comparison between our proposed recommendations and IOM 2009 guidelines showed that our recommendations were comparable with the IOM 2009 guidelines and could well predict the risk of several adverse pregnancy outcomes.

Inappropriate GWG was associated with higher risk of several adverse pregnancy outcomes. Optimal GWG recommendations proposed in the present study could be applied to Chinese pregnant women.