Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

BackgroundSevere mental disorders represent a major public health concern due to the high disability rates and substantial disease burden， which has garnered significant national attention and prompted their inclusion in public health project management systems. However， credible evidence regarding the current status of disease management and factors influencing disease stabilization among patients with severe mental disorders in Luzhou City， Sichuan Province， remains limited. ObjectiveTo investigate the current management status of patients with severe mental disorders in Luzhou City， Sichuan Province， and to analyze influencing factors of disease stabilization among patients under standardized care， so as to provide evidence-based insights for developing targeted management strategies to optimize clinical interventions for this patient population. MethodsIn March 2023， data were extracted from the Sichuan Mental Health Service Comprehensive Management Platform for patients with severe mental disorders in Luzhou City who received management between December 2017 and December 2022. Information on mental health service utilization and clinical status changes was collected. Trend analysis was conducted to evaluate temporal changes in key management indicators for severe mental disorders in Luzhou City. Logistic regression analysis was employed to identify factors influencing the disease stabilization or fluctuation of these patients. ResultsThis study enrolled a total of 20 232 patients. In Luzhou City， the stabilization rate and standardized management rate of severe mental disorders were 94.89% and 79.36% in 2017， respectively， which increased to 95.33% and 96.92% by 2022. The regular medication adherence rate rose from 34.42% in 2018 to 86.81% in 2022. In 2022， the regular medication adherence rate was 71.80% for schizophrenia， 55.26% for paranoid psychosis， and 51.43% for schizoaffective disorder. Multivariate analysis identified the following protective factors for disease stabilization： age of 18~39 years （OR=0.613， 95% CI： 0.409~0.918）， age of 40~65 years （OR=0.615， 95% CI： 0.407~0.931）， urban residence （OR=0.587， 95% CI： 0.478~0.720）， and regular medication adherence （OR=0.826， 95% CI： 0.702~0.973）. Risk factors for disease fluctuation included poor （OR=1.712， 95% CI： 1.436~2.040）， non-inclusion in care-support programs （OR=1.928， 95% CI： 1.694~2.193）， non-participation in community rehabilitation （OR=2.255， 95% CI： 1.930~2.634）， and intermittent medication adherence （OR=3.893， 95% CI： 2.548~5.946）. ConclusionThe stability rate， standardized management rate， and regular medication adherence rate of patients with severe mental disorders in Luzhou City have shown a year-by-year increase. Age， household registration status， economic condition， medication compliance， and community-based rehabilitation were identified as influencing factors for disease fluctuation in these patients. ［Funded by Luzhou Science and Technology Plan Project （number， 2022-ZRK-186）］

BackgroundIn recent years， the prevalence of obstructive sleep apnea （OSA） is escalating in China， leading to a serious disease burden. However， previous studies on the influencing factors of OSA， such as obesity and sleep position， were mostly cross-sectional studies. This approach inherently hinders the identification of dynamic interaction mechanism among multiple variables， consequently obstructing the formulation of individualized intervention strategies. ObjectiveTo investigate the differences in body mass index （BMI）-oxygen saturation-sleep position-heart rate variability （HRV） network models between OSA and non-OSA populations， thereby offering a reference for the early detection and management of OSA. MethodsA total of 384 adult participants undergoing sleep monitoring at the Affiliated Hospital of Southwest Medical University from July 12， 2022 to October 11， 2023 were included. Subjects were categorized into OSA group （n=203） and control group （n=181） based on an apnea-hypopnea index （AHI） threshold of 5 events per hour. Subsequently， BMI-oxygen saturation-sleep position-HRV networks were constructed and compared between two groups. ResultsThere was no significant difference in the overall edge weight （P=0.55） and overall strength （P=0.28） of the network model between control group and OSA group. Notable differences emerged in both the node connection strength （e.g.， minimum oxygen saturation with BMI， sleep in prone position， and mean RR interval） and node centrality indices （mean oxygen saturation， minimum oxygen saturation， AHI in upright position， AHI in right lateral position and mean heart rate） within the two network models （P<0.05）. ConclusionSignificant differences are observed between the non-OSA and OSA populations in specific factors， including sleep position， heart rate and oxygen saturation.

Objective To investigate the impact of scenario class teaching on language expression,communication skills,and final exam performance of non-clinical majors students in the course of Medical Parasitology.Method Undergraduate students of non-clinical medical programs from Kunming Medical University in 2022 were selected as the subjects and randomly divided into a scenario class group and a non-scenario class group.Questionnaires were administered to compare the two groups regarding their interest in the laboratory classes,enjoyment levels,and knowledge retention.Additionally,the final exam scores of the two groups were compared.Results Students in the scenario class group showed significantly higher interest(82.6%)and enjoyment levels(88.3%)for laboratory classes compared to the non-scenario class group(73.0%and 60.1%,respectively,P<0.05).Students in the scenario class group believed that situational teaching enhanced their self-learning ability(82.06%),interest in learning(83.2%),willingness to express themselves(83.2%),confidence in expression(81.8%),and communication skills(87.9%).Additionally,It effectively facilitated their understanding of the occurrence and development of parasitic diseases(85.9%)and familiarity with the diagnosis and treatment process(86.8%),thereby cultivating clinical thinking.In terms of final exam scores,the scenario class group had a higher average score(22.80±0.18)than the non-situational classroom group(21.47±0.17,P<0.05).Conclusion Sc-enario class teaching in Medical Parasitology can effectively improve students'self-learning ability,language expression,and communication skills,cultivate clinical thinking,and enhance academic performance,demonstrating significant teaching advantages.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective:To identify additional loci associated with depression and the hippocampus (HIP) through genome-wide association study.Methods:The depression-related genome-wide association study (GWAS) meta summary data was downloaded from the official website of the Psychiatric Genomics Consortium, which had involved 170 756 cases and 329 443 controls. The left and right hippocampal volume GWAS data sets were downloaded from the UK Biobank, which involved 33 224 participants. The conditional false discovery rate (condFDR) was used to identify novel genetic loci for depression and left and right hippocampal volumes, and a conjunctional false discovery rate (conjFDR) was used to evaluate the enrichment of pleiotropic loci between depression and left and right hippocampal volumes.Results:Respectively, 7, 13, and 12 new loci have been associated with depression, left hippocampal volume and right hippocampal volume, with a significant threshold of condFDR < 0.01. A site of rs1267073 locus was found to be shared by the depression and right hippocampal volume with a threshold of conjFDR < 0.01.Conclusion:Above findings have provided more insights into the genetic mechanisms underlying the volume of hippocampus and the risk for depression. The results may also provide evidence for future clinical trials for treating depression.

Objective: Previous research has explored a variety of mental disorders associated with Internet Gaming Disoder (IGD) and Social Media Addiction (SMA). To date, few studies focused on the network characteristics and investigated mood and sleep symptoms across SMA and IGD of adolescence at a group-specific level. This study aims to identify different characteristics of IGD and SMA and further determine the group-specific psychopathology process among adolescents. Methods: We conducted a cross-sectional study to recruit a cohort of 7,246 adolescents who were scored passing the cutoff point of Internet Gaming Disorder Scale-Short Form and Bergen Social Media Addiction Scale, as grouped in IGD and SMA, or otherwise into the control group. Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-item, and Pittsburgh Sleep Quality Index were assessed for the current study, and all assessed items were investigated using network analysis. Results: Based on the analytical procedure, the participants were divided into three groups, the IGD group (n=789), SMA group (n=713) and control group (n=5,744). The edge weight bootstrapping analysis shows that different groups of networks reach certain accuracy, and the network structures of the three groups are statistically different (pcontrol-IGD=0.004, pcontrol-SMA<0.001, pIGD-SMA<0.001). The core symptom of SMA is “feeling down, depressed, or hopeless”, while IGD is “feeling tired or having little energy”. Conclusion Although IGD and SMA are both subtypes of internet addiction, the psychopathology processes of IGD and SMA are different. When dealing with IGD and SMA, different symptoms should be addressed.