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Background Despite the importance of medication adherence in treatment effectiveness, little is known about the association between medication non-adherence and self-inflicted violence behaviors. We aimed to assess whether medication non-adherence increased the risk of self-inflicted violence behaviors among schizophrenics in communities (hypothesis 1) and whether the dose–response relationship existed (hypothesis 2). Methods This 12-year cohort study in western China recruited 292,667 community-dwelling schizophrenics. The proportion of regular medication (PRM) was calculated by dividing the time of “regular adherence” by the total time of antipsychotic treatment during follow-up period as an indicator of medication adherence. For hypothesis 1, medication adherence was designated as a binary variable with a threshold of 0.8 (PRM); for hypothesis 2, medication adherence was specified as five-category and continuous variables, respectively. Inverse probability weighting and mixed effects Cox proportional hazards models were conducted for confounders control and survival analyses. Results One hundred eighty-five thousand eight hundred participants were eligible for the final analyses, with a mean age of 47.49 years (SD 14.55 years), of whom 53.6% were female. For hypothesis 1, the medication non-adherence group (PRM < 0.8) had a lower risk of suicide (HR, 0.527, 95% CI, 0.447–0.620), an increased risk of NSSI (HR, 1.229, 95% CI, 1.088–1.388), and non-significant risk of attempted suicide compared with adherence group (PRM ≥ 0.8). For hypothesis 2, the lowest medication adherence (PRM < 0.2) was associated with increased risks of suicide attempt (HR, 1.614, 95% CI, 1.412–1.845), NSSI (HR, 1.873, 95% CI, 1.649–2.126), and a decreased risk of suicide (HR, 0.593, 95% CI, 0.490–0.719). The other non-adherence groups had lower risks for all three self-inflicted violence behaviors. The associations between medication adherence in continuous-variable and three outcomes were consistent with the categorical medication adherence results. Conclusions Almost no medication taken as prescribed was associated with an increased risk of suicide attempt and NSSI. However, medication adherence did not appear to prevent completed suicide. Besides, patients with moderate adherence had a lower incidence of suicide attempt and NSSI. These findings highlight the need for a more detailed portrayal of medication adherence and the need to be vigilant for suicide intent in schizophrenics with good medication adherence who may be overlooked previously.

Inflammasomes are multi-protein complexes that trigger the activation of caspase-1 and the maturation of interleukin-1β （IL-1β）, yet the regulation of these complexes remains poorly characterized. Here we show that nitric oxide （NO） inhibited the NLRP3-mediated ASC pyroptosome formation, caspase-1 activation and IL-1β secretion in myeloid cells from both mice and humans. Meanwhile, endogenous NO derived from iNOS （inducible form of NO synthase） also negatively regulated NLRP3 inflammasome activation. Depletion of iNOS resulted in increased accu- mulation of dysfunctional mitochondria in response to LPS and ATP, which was responsible for the increased IL-1β production and caspase-1 activation, iNOS deficiency or pharmacological inhibition of NO production enhanced NL-RP3-dependent cytokine production in vivo, thus increasing mortality from LPS-induced sepsis in mice, which was prevented by NLRP3 deficiency. Our results thus identify NO as a critical negative regulator of the NLRP3 inflam-masome via the stabilization of mitochondria. This study has important implications for the design of new strategies to control NLRP3-related diseases.

BackgroundThe occurrence rate of dangerous behaviors in patients with severe mental disorders is higher than that of the general population. In China, there is limited research on the prediction of dangerous behaviors in community-dwelling patients with severe mental disorders, particularly in terms of predicting models using data mining techniques other than traditional methods.ObjectiveTo explore the influencing factors of dangerous behaviors in community-dwelling patients with severe mental disorders and testing whether the classification decision tree model is superior to the Logistic regression model. MethodsA total of 11 484 community-dwelling patients with severe mental disorders who had complete follow-up records from 2013 to 2022 were selected on December 2023. The data were divided into a training set (n=9 186) and a testing set (n=2 298) in an 8∶2 ratio. Logistic regression and classification decision trees were separately used to establish predictive models in the training set. Model discriminatio

R749; 背景 严重精神障碍患者危险行为发生率较一般人群更高,我国对社区严重精神障碍患者危险行为发生风险的预测研究尚不多见,尤其缺乏除传统预测方法之外的数据挖掘技术预测模型的研究和比较.目的 采用Logistic回归分析及分类决策树构建社区严重精神障碍患者危险行为发生风险的预测模型,检验分类决策树模型是否优于Logistic回归模型.方法 于2023年12月,选取2013年—2022年随访记录完整的11 484名社区严重精神障碍在管患者,按8∶2随机分为训练集(n=9 186)与测试集(n=2 298).在训练集中,分别使用Logistic回归分析和分类决策树建立预测模型,在测试集评价模型的区分度和校准度.结果 1 115例(9.71%)严重精神障碍患者在随访期间出现危险行为.Logistic回归分析结果显示,城市户籍、贫困、有监护人、精神残疾、危险行为史阳性、自知力不全、自知力缺失、有阳性症状是患者发生危险行为的危险因素(OR=1.778、1.459、2.719、1.483、3.890、1.423、2.528、2.124,P均<0.01);年龄≥60岁、受过教育、医嘱无需用药以及社会功能一般是患者发生危险行为的保护因素(OR=0.594、0.824、0.422、0.719,P<0.05或0.01).基于测试集的ROC曲线下面积(AUC)=0.729(95%CI:0.692～0.766),准确率为70.97%,灵敏度为59.71%,特异度为72.05%;分类决策树结果显示,危险行为史、阳性症状、社会功能总评分、经济状况、自知力、户籍、残疾情况以及年龄是患者发生危险行为的影响因素,基于测试集的AUC=0.721(95%CI:0.705～0.737),准确率为68.28%,灵敏度为64.46%,特异度为68.60%.结论 分类决策树模型较Logistic回归模型对社区严重精神障碍患者危险行为发生风险的预测效果不具有更大优势.

The BCL6 (B-Cell Lymphoma 6) gene is a proto-oncogene that is often expressed in diffuse large B-cell lymphomas (DLBCLs). BCL6 loss of function can kill DLBCL cells, demonstrating that BCL6 is necessary for the survival of DLBCL cells and could be a therapeutic target. In this study, we found that BCL6 protein levels were consistently upregulated in DLBCL tissues, whereas its mRNA levels varied randomly in tissues, suggesting that a post-transcriptional mechanism was involved in BCL6 regulation. We used bioinformatics analysis to search for miRNAs, which potentially target BCL6, and identified specific targeting sites for miR-10a in the 3′-untranslated region (3′-UTR) of BCL6. We further identified an inverse correlation between miR-10a levels and BCL6 protein levels, but not mRNA levels, in DLBCL tumor tissue samples. By overexpressing or knocking down miR-10a in DLBCL cells, we experimentally validated that miR-10a directly recognizes the 3′-UTR of the BCL6 transcript and regulated BCL6 expression. Furthermore, we demonstrated that negatively regulating BCL6 by miR-10a suppressed the proliferation and promoted apoptosis of DLBCL cells.

The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

Cytokine storm and multi-organ failure are the main causes of SARS-CoV-2-related death. However, the origin of excessive damages caused by SARS-CoV-2 remains largely unknown. Here we show that the SARS-CoV-2 envelope (2-E) protein alone is able to cause acute respiratory distress syndrome (ARDS)-like damages in vitro and in vivo. 2-E proteins were found to form a type of pH-sensitive cation channels in bilayer lipid membranes. As observed in SARS-CoV-2-infected cells, heterologous expression of 2-E channels induced rapid cell death in various susceptible cell types and robust secretion of cytokines and chemokines in macrophages. Intravenous administration of purified 2-E protein into mice caused ARDS-like pathological damages in lung and spleen. A dominant negative mutation lowering 2-E channel activity attenuated cell death and SARS-CoV-2 production. Newly identified channel inhibitors exhibited potent anti-SARS-CoV-2 activity and excellent cell protective activity in vitro and these activities were positively correlated with inhibition of 2-E channel. Importantly, prophylactic and therapeutic administration of the channel inhibitor effectively reduced both the viral load and secretion of inflammation cytokines in lungs of SARS-CoV-2-infected transgenic mice expressing human angiotensin-converting enzyme 2 (hACE-2). Our study supports that 2-E is a promising drug target against SARS-CoV-2.

Inflammatory bowel disease (IBD) is a chronic and nonspecific intestinal inflammatory condition with high relapse rate. Its pathogenesis has been linked to dysbacteriosis, genetic and environmental factors. In recent years, a new type of lymphocytes, termed innate lymphoid cells, has been described and classified into three subtypes of innate lymphoid cells—group 1, group 2 and group 3. An imbalance among these subsets’ interaction with gut microbiome, and other immune cells affects intestinal mucosal homeostasis. Understanding the role of innate lymphoid cells may provide ideas for developing novel and targeted approaches for treatment of IBD.

Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.

Myasthenia gravis (MG) is an autoimmune di sease characterized by muscle weakness. Experimental autoimmune myasthenia gravis (EAMG) serves as an animal model for MG research. Despite advancements in EAMG modeling, limited drug absorption and variability in disease manifestation among animals resulted in a low success rate of model induction. This study aimed to optimize and standardize the modeling process by exploring different induction conditions to improve success rates. We employed female Lewis rats and C57BL/6 mice to compare their sensitivity to model induction and applied a controlled variable approach to acetylcholine receptor (AChR) and H37Ra dosage, mixing time, and injection sites. Results showed that Lewis rats were more suitable than C57BL/6 mice, and 75 µg AChR peptides were more effective than 50 µg. The immune-boosting effect of 1 mg H37Ra Mycobacterium tuberculosis was weaker than 2 mg. While drug mixing time had little impact, increasing injection sites on backs and including foot pads injection, significantly improved drug absorption.