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Objective: The goal was to examine (a) how parental involvement affects mental health in left‐behind children (LBC), and (b) how the interaction effect of parental involvement and grandparent involvement affects mental health in LBC. Background: Given the rapid industrialization and urbanization in China, a substantial number of rural laborers have migrated to cities to earn a living. However, it is difficult for many children to stay with their parents and they have to be left behind. Methods: The sample consisted of 309 left‐behind children who had grandparents to care for them. The participants completed the Inventory of Father Involvement, Inventory of Mother Involvement, Inventory of Closest Grandparent Involvement, Needs Satisfaction Questionnaire, and Emotional Experience of Well‐being Questionnaire. Results: Needs satisfaction mediated the effect of father involvement on emotional well‐being (EWB) and the effect of mother involvement on EWB in LBC. The closest grandparent involvement of LBC moderated the effect of mother involvement on needs satisfaction, but it did not moderate the effect of father involvement on needs satisfaction. The closest grandparent involvement of LBC moderated the indirect effect of mother involvement on EWB through needs satisfaction. Conclusions: Needs satisfaction is an essential mediator between parental involvement and EWB. Grandparental involvement of LBC plays a complementary role in mother involvement. Their interaction effect facilitates the fulfilment of needs, and improves their EWB in LBC. Implications: The mental health of LBC can be improved by helping their mothers and grandparents to meet various needs of LBC. [ABSTRACT FROM AUTHOR]

Rationale: The aim of this study was to analyze the metabolomics of lung with different host inflammation of acute respiratory distress syndrome (ARDS) for the identification of biomarkers for predicting severity under different inflammatory conditions. Methods: Cecal ligation and puncture (CLP) and lipopolysaccharide (LPS)‐intratracheal injection induced acute lung injury (ALI) were used. A mouse model was used to explore lung metabolomic biomarkers in ALI/ARDS. The splenectomy model was used as an auxiliary method to distinguish between hyper‐ and hypo‐inflammatory subtypes. Plasma, lung tissue and bronchoalveolar lavage fluid (BALF) samples were collected from mice after CLP/LPS. The severity of lung injury was evaluated. Expression of tumor necrosis factor‐α (TNF‐α) in mice serum and lung was tested by enzyme‐linked immunosorbent assay (ELISA) and polymer chain reaction (PCR). Polymorphonuclear cells in BALF were counted. The lung metabolites were detected by gas chromatography/mass spectrometry (GC/MS), and the metabolic pathways predicted using the KEGG database. Results: The LPS/CLP‐Splen group had more severe lung injury than the corresponding ALI group; that in the CLP‐Splen group was more serious than in the LPS‐Splen group. TNF‐α expression was significantly elevated in the serum and lung tissue after LPS or CLP, and higher in the LPS/CLP‐Splen group than in the corresponding ALI group. The level of TNF‐α in the CLP‐Splen group was elevated significantly over that in the LPS‐Splen group. Both these groups also showed significant neutrophil exudation within the lungs. During differential inflammation, more differential metabolites were detected in the lungs of the CLP group ALI mice than in the LPS group. A total of 41 compounds were detected in the lungs of the CLP and CLP‐Splen groups. Contrastingly, eight compounds were detected in the lungs of the LPS and LPS‐Splen groups. The LPS‐Splen and CLP‐Splen groups had significant neutrophil exudation in the lung. Random forest analysis of lung‐targeted metabolomics data indicated 4‐hydroxyphenylacetic acid, 1‐aminocyclopentanecarboxylic acid (ACPC), cis‐aconitic acid, and hydroxybenzoic acid as strong predictors of the hyper‐inflammatory subgroup in the CLP group. Furthermore, with splenectomy, 13 differential metabolic pathways between the CLP and LPS groups were revealed. Conclusions: Hyper‐inflammatory subgroups of ARDS have a greater inflammatory response and a more active lung metabolism. Combined with the host inflammation background, biomarkers from metabolomics could help evaluate the response severity of ARDS. [ABSTRACT FROM AUTHOR]