Table_1_Association between serum vitamin D and refractive status in United States adolescents: A cross-sectional study.DOCX

PurposeWe performed this study to determine the relationship between serum vitamin D levels and refractive status in adolescents aged 12–19 years.MethodsCross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database from 2001 to 2006. We used weighted multivariate linear regression models to assess the association between serum vitamin levels and adolescent refractive status and then built a smooth curve fitting to investigate their internal non-linear relationships. Finally, subgroup analysis was performed according to gender, and the threshold effect of serum vitamin D levels on spherical equivalent degree was analyzed using a two-piecewise linear regression model.ResultA total of 5,901 adolescents aged 12 to 19 years were included in this study. After adjusting for all confounding factors, the multiple linear regression model showed no significant correlation between adolescent spherical equivalent degree and serum vitamin D [0.0019 (−0.0018, 0.0046)]. However, smooth curve fitting analysis showed an inverted U-shaped curve relationship between spherical equivalent degree and serum vitamin D levels in adolescents (turning point: 58.1 nmol/L). In analyses by gender subgroup, this inverted U-shaped relationship was found to be more pronounced in female adolescents (turning point: 61.6 nmol/L).ConclusionOur results suggest that the correlation between refractive status and serum vitamin D in adolescents differs by gender. When serum vitamin D concentrations were <61.6 nmol/L in female adolescents and <53.2 nmol/L in male adolescents, the spherical equivalent degree showed a positive correlation with serum vitamin D levels. However, there was no significant correlation when adolescent vitamin levels exceeded this threshold.</p

Data_Sheet_1_Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA.docx

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.</p

Additional file 2 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 2: Table S1

Additional file 6 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 6: Table S5

Additional file 5 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 5: Table S4

Additional file 7 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 7: Table S6

Additional file 4 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 4: Table S3

Additional file 3 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 3: Table S2

Additional file 1 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 1: FigureS1. Cells death determination under different concentrations of ethanoltreatment by methylene blue staining. A,0% ethanol treatment group; B, 3% ethanol treatment group; C, 6% ethanoltreatment group; D, 9% ethanol treatment group; E, 12% ethanol treatment group.Bar=100 μm.Figure S2.  Results of principalcomponent analysis (PCA) of the samples for transcriptome sequencing. FigureS3.  PCA score plots of the samples for metabolomicsanalysis in positive and negative ion modes. A, Positive ion mode; B, Negativeion mode. Table S7. Primersused in this study for real-time quantitative PCR detection

Additional file 1 of Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study

Additional file 1. Patient demographics and baseline characteristics