Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

Air pollution and associated health impact and economic loss embodied in inter-provincial electricity transfer in China.

As the largest producer and consumer of coal in the world, China heavily relies on coal resources for thermal power generation. Owing to the unbalanced distribution of energy resources, electricity transfer among regions in China plays a key role in promoting economic growth and ensuring energy safety. However, little is known about air pollution and the related health impacts resulting from electricity transfer. This study assessed PM2.5 pollution and related health and economic losses attributable to the inter-provincial electricity transfer in mainland China in 2016. The results show that a large amount of virtual air pollutant emissions were transferred from energy-abundant northern, western and central China to well-developed and populated eastern coastal regions. Correspondingly, the inter-provincial electricity transfer dramatically reduced the atmospheric levels of PM2.5 and related health and economic losses in eastern and southern China, while increasing those in northern, western and central China. The health benefits attributable to inter-provincial electricity transfer were mainly found in Guangdong, Liaoning, Jiangsu and Shandong, whereas the extra health loss is concentrated in Hebei, Shanxi, Inner Mongolia, and Heilongjiang. Overall, the inter-provincial electricity transfer led to an extra increase of 3600 (95 % CI: 3200-4100) PM2.5-related deaths and 345 (95 % CI: 294-389) million USD of economic loss in China in 2016. The results could assist air pollution mitigation strategies for the thermal power sector in China by strengthening the cooperation between suppliers and consumers of electricity.

Development of novel in silico model for developmental toxicity assessment by using naïve Bayes classifier method.

Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naïve Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization.