Associations between multiple sleep dimensions and suicide and non-suicidal self-injury: a cross-sectional study of 3828 Chinese young people.

PURPOSE: Suicide and non-suicidal self-injury (NSSI) are preventable concerns in young people. Suicidal ideation (SI), suicidal plans (SP) and suicidal attempt (SA) are closely related to death. Sleep problems are known risk factors for suicide and NSSI. This study aimed to explore the relationship between sleep, suicidality and NSSI. METHODS: Participants were 3,828 middle school and college students aged 11-23 years from urban and rural areas of Henan Province. Sleep, suicidal phenomena and NSSI were assessed by applying self-reported questionnaires. Chi-squared tests were utilized to demonstrate the demographic data and sleep variables. The correlation between sleep, suicidality and NSSI were explored by using binary logistic regression, while adjusting socio-demographic characteristics with multivariate models. RESULTS: Sleep variables except mid-sleep time were related to suicidal phenomena (P < 0.05). Greater social jet lag (SJL) [≥ 2 h (h)] was associated with increased risk of SI [Odds ratios (OR) = 1.72, 95% confidence intervals (CI):1.40-2.11], SP (OR = 2.10, 95%CI:1.59-2.79) and SA (OR = 1.50, 95%CI:1.00-2.26). Non-only child participants with SJL (≥ 2 h) had significantly increased odds of SI (OR = 1.75, 95%CI: 1.41-2.18) and SP (OR = 2.25, 95%CI: 1.66-3.05). Eveningness chronotype had the strongest correlation with SI (OR = 3.87, 95%CI:2.78-5.38), SP (OR = 4.72, 95%CI:2.97-7.50), SA (OR = 6.69, 95%CI:3.08-14.52) and NSSI (OR = 1.39, 95%CI:1.02-1.90). CONCLUSION: Overlong or short sleep duration, SJL, eveningness chronotype and other sleep abnormalities (e.g., daytime dysfunction, low sleep efficiency) were associated with a higher prevalence of SI, SP and SA. Additionally, eveningness was significantly correlated with NSSI among young people. These findings suggested the importance of assessing and intervening in sleep habits to prevent suicide and NSSI in young people.

Construction and validation of a joint diagnosis model based on random forest and artificial intelligence network for hepatitis B-related hepatocellular carcinoma.

Background: Hepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC. Methods: Gene Expression Omnibus (GEO) Series (GSE)19665, GSE55092, and GSE121248 were used to identify significant differentially expressed genes (DEGs). The enrichment analysis was performed on Metascape online tool. The RF algorithm and ANN were used to select the potential predictive gene panels and construct an HBV-related HCC diagnostic model. Subsequently, GSE17548, GSE104310, GSE44074, and GSE136247 were used to test the accuracy of the ANN model. Finally, the CIBERSORT algorithm was used to assess the abundance of immune infiltrates in all samples. Results: First, 116 genes were identified as DEGs, and the DEGs were particularly enriched in cellular hormone metabolic process, monocarboxylic acid metabolic process, NABA extracellular matrix (ECM) AFFILIATED steroid metabolic process and metabolism of bile acid and bile salt. DNA topoisomerase II alpha (TOP2A), C-type lectin domain family 1 member B (CLEC1B), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), ficolin 2 (FCN2), C-X-C motif chemokine ligand 14 (CXCL14), cyclase associated actin cytoskeleton regulatory protein 2 (CAP2), ficolin 3 (FCN3), kynurenine 3-monooxygenase (KMO) and cadherin related family member 2 (CDHR2) were available to develop an HBV-related HCC diagnostic model. After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833). Finally, the percentage of infiltrating immune cell types [B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), T cells gamma delta, natural killer (NK) cells resting, NK cells activated, Macrophages M0, Dendritic cells activated, Mast cells activated] for hepatitis B-related HCC were significantly different from that of non-cancerous liver tissue with HBV. Conclusions: A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.

Sleep-Body Composition Relationship: Roles of Sleep Behaviors in General and Abdominal Obesity in Chinese Adolescents Aged 17-22 Years.

This study aimed to investigate the association between sleep behaviors and body composition, which was measured by bioelectrical impedance analysis (BIA) among Chinese adolescents. Overall, 444 students (65.3% females, 19.12 ± 1.177 years) completed questionnaires describing sleep characteristics. Sleep characteristics were derived from subjective means. Body composition was obtained from BIA by InBody 720 (Biospace Co. Ltd., Seoul, Republic of Korea). Regression models tested relationships between sleep and body composition after adjustment for covariates. Students with weekday nap duration (>30 min/d) exerted higher waist-height ratio (WHtR) (B = 0.013, FDR-corrected p = 0.080). Average sleep duration (≤7 h/d) was linked to more WHtR (B = 0.016, FDR-corrected p = 0.080). People with high social jetlag showed gained visceral fat area (B = 7.475), WHtR (B = 0.015), waist to hip ratio (B = 0.012), fat mass index (B = 0.663) and body fat percentage (B = 1.703) (all FDR-corrected p < 0.1). Individuals with screen time before sleep (>0.5 h) exhibited higher visceral fat area (B = 7.934, FDR-corrected p = 0.064), WHtR (B = 0.017, FDR-corrected p = 0.080), waist to hip ratio (B = 0.016, FDR-corrected p = 0.090), fat mass index (B = 0.902, FDR-corrected p = 0.069) and body fat percentage (B = 2.892, FDR-corrected p = 0.018). We found poor sleep characteristics were closely related to general and abdominal obesity.

Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China.

BACKGROUND: Unhealthy lifestyles are risk factors for non-communicable diseases (NCDs) and tend to be clustered, with a trajectory that extends from adolescence to adulthood. This study investigated the association of diets, tobacco, alcohol, physical activity (PA), screen time (ST) and sleep duration (SD) in a total of six lifestyles, separately and as cumulative lifestyle scores, with sociodemographic characteristics among school-aged adolescents in the Chinese city of Zhengzhou. METHODS: In the aggregate, 3,637 adolescents aged 11-23 years were included in the study. The questionnaire collected data on socio-demographic characteristics and lifestyles. Healthy and unhealthy lifestyles were identified and scored, depending on the individual score (0 and 1 for healthy and unhealthy lifestyles respectively), with a total score between 0 and 6. Based on the sum of the dichotomous scores, the number of unhealthy lifestyles was calculated and divided into three clusters (0-1, 2-3, 4-6). Chi-square test was used to analyze the group difference of lifestyles and demographic characteristics, and multivariate logistic regression was used to explore the associations between demographic characteristics and the clustering status of unhealthy lifestyles. RESULTS: Among all participants, the prevalence of unhealthy lifestyles was: 86.4% for diet, 14.5% for alcohol, 6.0% for tobacco, 72.2% for PA, 42.3% for ST and 63.9% for SD. Students who were in university, female, lived in country (OR = 1.725, 95% CI: 1.241-2.398), had low number of close friends (1-2: OR = 2.110, 95% CI: 1.428-3.117; 3-5: OR = 1.601, 95% CI: 1.168-2.195), and had moderate family income (OR = 1.771, 95% CI: 1.208-2.596) were more likely to develop unhealthy lifestyles. In total, unhealthy lifestyles remain highly prevalent among Chinese adolescents. CONCLUSION: In the future, the establishment of an effective public health policy may improve the lifestyle profile of adolescents. Based on the lifestyle characteristics of different populations reported in our findings, lifestyle optimization can be more efficiently integrated into the daily lives of adolescents. Moreover, it is essential to conduct well-designed prospective studies on adolescents.

A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study.

OBJECTIVE: To build a clinical-radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). MATERIALS AND METHODS: A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical-radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC). RESULTS: Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873-0.921) in the internal validation cohort, and 0.911 (95% CI 0.891-0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896-0.941) and 0.883 (95% CI 0.851-0.902), while the AUC of clinical-radiomics model was 0.950 (95% CI 0.925-0.967) and 0.942 (95% CI 0.927-0.958) respectively. CONCLUSION: The proposed clinical-radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.

Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

Activation of GPR39 with TC-G 1008 attenuates neuroinflammation via SIRT1/PGC-1α/Nrf2 pathway post-neonatal hypoxic-ischemic injury in rats.

BACKGROUND: Hypoxic-ischemic encephalopathy (HIE) is a severe anoxic brain injury that leads to premature mortality or long-term disabilities in infants. Neuroinflammation is a vital contributor to the pathogenic cascade post-HIE and a mediator to secondary neuronal death. As a plasma membrane G-protein-coupled receptor, GPR39, exhibits anti-inflammatory activity in several diseases. This study aimed to explore the neuroprotective function of GPR39 through inhibition of inflammation post-hypoxic-ischemic (HI) injury and to elaborate the contribution of sirtuin 1(SIRT1)/peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α)/nuclear factor, erythroid 2 like 2(Nrf2) in G-protein-coupled receptor 39 (GPR39)-mediated protection. METHODS: A total of 206 10-day-old Sprague Dawley rat pups were subjected to HIE or sham surgery. TC-G 1008 was administered intranasally at 1 h, 25 h, 49 h, and 73 h post-HIE induction. SIRT1 inhibitor EX527, GPR39 CRISPR, and PGC-1α CRISPR were administered to elucidate the underlying mechanisms. Brain infarct area, short-term and long-term neurobehavioral tests, Nissl staining, western blot, and immunofluorescence staining were performed post-HIE. RESULTS: The expression of GPR39 and pathway-related proteins, SIRT1, PGC-1α and Nrf2 were increased in a time-dependent manner, peaking at 24 h or 48-h post-HIE. Intranasal administration of TC-G 1008 reduced the percent infarcted area and improved short-term and long-term neurological deficits. Moreover, TC-G 1008 treatment significantly increased the expression of SIRT1, PGC-1α and Nrf2, but downregulated the expressions of IL-6, IL-1ß, and TNF-α. GPR39 CRISPR EX527 and PGC-1α CRISPR abolished GPR39's neuroprotective effects post-HIE. CONCLUSIONS: TC-G 1008 attenuated neuroinflammation in part via the SIRT1/PGC-1α/Nrf2 pathway in a neonatal rat model of HIE. TC-G 1008 may be a novel therapeutic target for treatment post-neonatal HIE injury.

LncRNA CRNDE facilitates epigenetic suppression of CELF2 and LATS2 to promote proliferation, migration and chemoresistance in hepatocellular carcinoma.

Our study aimed to investigate the expression, functional significance, and related mechanism of long noncoding RNA CRNDE (colorectal neoplasia differentially expressed) in hepatocellular carcinoma (HCC) pathogenesis. The resulted revealed that CRNDE was significantly overexpressed in HCC tissues and cell lines, and was statistically correlated with poor clinical outcome. CRNDE knockdown markedly decreased HCC cell proliferation, migration, and chemoresistance. In addition, in vivo experiments confirmed the suppressive effect of CRNDE knockdown on HCC progression. Mechanically, CRNDE directly bound to EZH2 (enhancer of zeste homolog), SUZ12 (suppressor of zeste 12), SUV39H1, and mediated their inhibition of tumor suppressor genes, including CUGBP Elav-like family member 2 (CELF2) and large tumor suppressor 2 (LATS2). CELF2 exerted tumor suppressive effect in HCC and was involved in CRNDE-mediated oncogenic effect. In addition, the oncogenic effects of CRNDE on HCC proliferation, migration and tumorigenesis, as well as its inhibition of Hippo pathway were abolished by LATS2 overexpression. Together, our work demonstrated the importance of CRNDE in HCC progression and elucidated the underlying molecular mechanisms. These findings provided new insights into HCC pathogenesis and chemoresistance mediated by CRNDE.

Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. METHODS: In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. RESULTS: First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. CONCLUSIONS: The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

Low-intensity pulsed ultrasound promotes the proliferation of human bone mesenchymal stem cells by activating PI3K/AKt signaling pathways.

Low-intensity pulsed ultrasound (LIPUS) is a promising therapy that is widely used in clinical applications and fundamental research. Previous research has shown that LIPUS exposure has a positive effect on stem cell proliferation. However, the impact of LIPUS exposure on human bone marrow mesenchymal stem cells (hBMSCs) remains unknown. In our study, the effect and mechanism of LIPUS exposure on the proliferation of hBMSCs were investigated, and the optimal parameters of LIPUS were determined. hBMSCs were obtained and identified by flow cytometry, and the proliferation of hBMSCs was measured using the Cell Counting Kit-8 assay to determine cell cycle and cell count. Expression levels of the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKt) pathway proteins and cyclin D1 were determined by western blot analysis. Next, hBMSCs were successfully cultured and identified as multipotent mesenchymal stem cells. We found that LIPUS could promote the proliferation of hBMSCs when the exposure time was 5 or 10 minutes per day. Furthermore, 50 or 60 mW/cm2 LIPUS had a more significant effect on cell proliferation, but if cells were irradiated by LIPUS for 20 minutes once a day, an intensity of at least 50 mW/cm2 could markedly inhibit cell growth. Cell cycle analysis demonstrated that LIPUS treatment drives cells to enter S and G2/M phases from the G0/G1 phase. LIPUS exposure increased phosphorylation of PI3K/AKt and significantly upregulated expression of cyclin D1. However, these effects were inhibited when cells were treated with PI3K inhibitor (LY294002), which in turn reduced LIPUS-mediated proliferation of hBMSCs. These results suggest that LIPUS exposure may be involved in the proliferation of hBMSCs via activation of the PI3K/AKt signaling pathway and high expression of cyclin D1, and the intensity of 50 or 60 mW/cm2 and exposure time of 5 minutes were determined to be the optimal parameters for LIPUS exposure.

Clinical and molecular analysis of hereditary non-polyposis colorectal cancer in Chinese colorectal cancer patients.

AIM: To analyze the frequency of hereditary non-polyposis colorectal cancer (HNPCC) in Chinese colorectal cancer (CRC) patients, and to discuss the value of microsatellite instability (MSI) and/or immunohistochemistry (IHC) for MSH2/MLH1 protein analysis as pre-screening tests in China. METHODS: The Amsterdam criteria I and II (clinical diagnosis) and/or germline hMLH1/hMSH2 mutations (genetic diagnosis) were used to classify HNPCC families. Genetic tests, including microsatellite instability, immunohistochemistry for MSH2/MLH1 proteins and hMSH2/hMLH1 genes, were performed in each proband. RESULTS: From July 2000 to June 2004, 1988 patients with colorectal cancer were analysed and 114 CRC patients (5.7%) from 48 families were categorized as having HNPCC, including 76 from 26 families diagnosed clinically and 38 from the other 22 families diagnosed genetically. The sensitivity and specificity of high MSI and IHC for predicting mutations were 100% and 54%, and 79% and 77%, respectively. CONCLUSION: The frequency of HNPCC is approximately 10% among all Chinese CRC cases. The MSI and IHC detections for hMSH2/hMLH1 proteins are reliable pre-screening tests for hMLH1/hMSH2 germline mutations in families suspected of having HNPCC.