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Introduction: To explore the mechanisms of labor by investigating the autophagy of placental and fetal membranes tissue in normal pregnant women. Methods: Placenta and fetal membranes were collected from women with singleton pregnancies without any medical complications and from women who delivered vaginally (labor‐initiated group; L group) or by caesarean section (labor‐noninitiated group; NL group). Autophagosomes were observed by transmission electron microscopy (TEM). Immunofluorescence and western blotting (WB) were used to detect protein levels of the autophagy markers LC3A and LC3B. TEM, immunohistochemistry (IHC), and WB were used to compare autophagy in different parts of the placenta and fetal membranes in the L and NL groups. The expression of LC3B/LC3A, ROCK1, and ROCK2 in the placenta of nonpregnant and pregnant rats was detected by WB and IHC. Results: TEM and IHC results showed an increase in the number of autophagosomes and autophagic lysosomes in the L group, and WB results indicated an increase in the LC3B/A ratio between the placenta and fetal membranes in the L group. Autophagy was significantly increased on the maternal side of the placenta in the L group, and the level of autophagy became higher near rupture in the fetal membranes and near the point where the umbilical cord joins the placenta in the L group. The LC3B/A ratio increased and ROCK1 and ROCK2 levels decreased in postnatal rats. Discussion: Autophagy can occur in the placenta and fetal membranes and its activity is higher at the onset of labor, suggesting a role in labor. [ABSTRACT FROM AUTHOR]

Background: Understanding the interplay between psychopathology of alcohol withdrawal syndrome (AWS) in alcohol use disorder (AUD) patients may improve the effectiveness of relapse interventions for AUD. Network theory of mental disorders assumes that mental disorders persist not of a common functional disorder, but from a sustained feedback loop between symptoms, thereby explaining the persistence of AWS and the high relapse rate of AUD. The current study aims to establish a network of AWS, identify its core symptoms and find the bridges between the symptoms which are intervention target to relieve the AWS and break the self-maintaining cycle of AUD., Methods: Graphical lasso network were constructed using psychological symptoms of 553 AUD patients. Global network structure, centrality indices, cluster coefficient, and bridge symptom were used to identify the core symptoms of the AWS network and the transmission pathways between different symptom clusters., Results: The results revealed that: (1) AWS constitutes a stable symptom network with a stability coefficient (CS) of 0.21-0.75. (2) Anger (Strength = 1.52) and hostility (Strength = 0.84) emerged as the core symptom in the AWS network with the highest centrality and low clustering coefficient. (3) Hostility mediates aggression and anxiety; anger mediates aggression and impulsivity in AWS network respectively., Conclusions: Anger and hostility may be considered the best intervention targets for researching and treating AWS. Hostility and anxiety, anger and impulsiveness are independent but related dimensions, suggesting that different neurobiological bases may be involved in withdrawal symptoms, which play a similar role in withdrawal syndrome., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shen, Chen, Wu, Jiahui, Fang, Jiayi, Yimin, Wang, Liu, Wang and Chen.)

Objective: The aim of this study was to reveal the biological functionalities associated with endoplasmic reticulum stress (ERS)-related genes (ERSGs) in the context of diabetic retinopathy (DR)., Methods: Differentially expressed genes (DEGs) within the DR group and the Control group were identified and then integrated with ERSGs. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) methodologies were used to investigate potential biological mechanisms. A diagnostic model for ERS and a nomogram were formulated based on biomarkers selected through the Least Absolute Shrinkage and Selection Operator method. The diagnostic efficacy of this model was thoroughly evaluated. ERS-associated subtypes were identified, and the Single-Sample GSEA (ssGSEA) and CIBERSORT algorithms were used to assess immune infiltration., Results: We identified 10 ERS-related DEGs (ERSRDEGs) within the DR Group. Subsequently, a diagnostic model was constructed based on 5 ERS genes, namely CCND1, IGFBP2, TLR4, TXNIP, and VIM. The validation analysis demonstrated the commendable diagnostic performance of the model. Analysis of the ssGSEA immune characteristics revealed a positive correlation in the DR group between myeloid-derived suppressor cells (MDSC), regulatory T cells (Tregs), and CCND1 TXNIP. Furthermore, a significant negative correlation was observed between central memory CD4 T cells and CCND1. In the context of CIBERSORT, the results indicated a positive correlation between macrophages and IGFBP2, as well as Tregs and IGFBP2 in the DR group. Notably, a conspicuous negative correlation was identified between resting mast cells and IGFBP2., Conclusion: The present study provides novel diagnostic biomarkers for DR from an ERS perspective., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: Cigarette smoking and low peripheral nitric oxide synthase (NOS) levels are strongly associated with sleep disorders. However, whether cerebrospinal fluid (CSF) NOS relates to sleep disorders and whether CSF NOS mediates the relationship between cigarette smoking and sleep disorders is unclear., Methods: We measured CSF levels of total NOS (tNOS) and its isoforms (inducible NOS [iNOS] and constitutive NOS [cNOS]) in 191 Chinese male subjects. We applied the Pittsburgh Sleep Quality Index (PSQI)., Results: The PSQI scores of active smokers were significantly higher than those of non-smokers, while CSF tNOS, iNOS, and cNOS were significantly lower (all p < 0.001). CSF tNOS, iNOS, and cNOS were negatively associated with PSQI scores in the general population (all p < 0.001). Mediation analysis suggested that CSF tNOS, iNOS, and cNOS mediate the relationship between smoking and PSQI scores, and the indirect effect accounted for 78.93%, 66.29%, and 81.65% of the total effect, respectively., Conclusion: Cigarette smoking is associated with sleep disorders. Active smokers had significantly lower CSF levels of tNOS, iNOS, and cNOS. Furthermore, tNOS, iNOS, and cNOS mediate the relationship between cigarette smoking and sleep quality. This study provides insights into how cigarette smoke affects sleep disorders., Competing Interests: The authors declare that they have no competing interests in this work., (© 2024 Lao et al.)