Early_Cardiovascular-肾脏-代谢综合征 by Zhaoqi Yan (21707837)

“…., rsID).CHR: Chromosome number where the SNP is located.BP: Base pair position of the SNP on the chromosome.MAF: Minor Allele Frequency (frequency of the less common allele, 0–0.5).A1: Other allele (reference/non-effect allele).A2: Effect allele (allele for which the effect size is estimated).i: Index/iteration number (e.g., model or subgroup identifier).lhs: Left-hand side of the SEM equation (dependent variable, e.g., trait/factor).op: SEM operator (e.g., ~ = regression, =~ = latent measurement).rhs: Right-hand side of the SEM equation (independent variable, e.g., SNP).est: Regression coefficient estimate (effect size of SNP on lhs).se_c: Standard error of the coefficient estimate (est).Z_Estimate: Z-score for the estimate (est / se_c).Pval_Estimate: P-value for the estimate (significance of est).Q: Cochran’s Q statistic (measures heterogeneity of effects across subgroups/studies).Q_df: Degrees of freedom for the Q statistic.Q_pval: P-value for Cochran’s Q (significance of heterogeneity; <0.05 indicates significant heterogeneity).N_hat: Estimated effective sample size (total or weighted sample size used in analysis).…”

Table 1_Exploring plasticisers-osteoporosis links and mechanisms: a cohort and network toxicology study.docx by Xingyao Yang (22173817)

“…</p>Results<p>All logistic regression models confirmed a significant positive correlation between MEHP levels and OP. …”

Table 6_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Table 2_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Table 3_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Data Sheet 1_Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function.docx by Chenyang Xu (723996)

“…Stepwise logistic regression analysis was conducted to identify risk factors associated with these subgroups.…”

Table 1_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Table 5_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Table 4_Shapley additive explanations based feature selection reveals CXCL14 as a key immune-related gene in predicting idiopathic pulmonary fibrosis.xlsx by Bin Chen (63682)

“…Using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, we constructed a logistic regression model with robust predictive performance, achieving an AUC of 0.92 in the training cohort and 0.89 in the validation cohort, with sensitivity of 88% and specificity of 85%. …”

Dorsal and chinmo mutually influence each other’s expression, driving tumor growth while inhibiting differentiation. by Caroline Dillard (15381444)

“…Insets show a decrease of elav+ (magenta) cells within the tumor upon chinmo^OE. …”

Data Sheet 1_The impact of physical exercise on death anxiety in older adults: the chain-mediating role of self-efficacy and psychological resilience.zip by Feixuan Li (8774858)

“…</p>Results<p>Physical exercise was significantly negatively correlated with death anxiety (r = -0.740, p < 0.01) and was a significant negative correlate in the regression model (β = -0.196, p < 0.001). …”

Image 2_Temporal trends and future projections of cysticercosis-induced epilepsy: insights from the global burden of disease study 2021- a cross-sectional study.tif by Siyuan Yang (2659174)

“…Age-period-cohort mode, the Auto Regressive Integrated Moving Average (ARIMA) model, and joinpoint regression analysis were also carried out.…”

Data_Sheet_2_Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies.docx by Ying Gao (145030)

“…Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models.…”

Table_1_Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies.docx by Ying Gao (145030)

“…Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models.…”

Table 1_Endometriosis-related infertility in China: analysis of the global burden of disease study 2021.docx by Jianting Lao (22382329)

“…</p>Methods<p>Using age-standardized prevalence rates (ASPR) of endometriosis-related infertility (1990–2021) from the GBD 2021 database, we analyzed Chinese females aged 15–49 years. Joinpoint regression identified significant trend changes in ASPR, while age-period-cohort (APC) modeling decomposed effects into age, period, and birth cohort dimensions using 5-year intervals.…”

Data Sheet 1_Exploring relationships between dating app use and sexual activity among young adult college students.pdf by Jaquetta M. Reeves (20222985)

“…Future research should explore the long-term effects of dating app use on sexual health and evaluate the effectiveness of app-based interventions in promoting safer sexual practices.…”

Image 1_Temporal trends and future projections of cysticercosis-induced epilepsy: insights from the global burden of disease study 2021- a cross-sectional study.tif by Siyuan Yang (2659174)

“…Age-period-cohort mode, the Auto Regressive Integrated Moving Average (ARIMA) model, and joinpoint regression analysis were also carried out.…”

Image 4_Temporal trends and future projections of cysticercosis-induced epilepsy: insights from the global burden of disease study 2021- a cross-sectional study.tif by Siyuan Yang (2659174)

“…Age-period-cohort mode, the Auto Regressive Integrated Moving Average (ARIMA) model, and joinpoint regression analysis were also carried out.…”

Table_2_Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies.docx by Ying Gao (145030)

“…Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models.…”

Table_3_Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies.docx by Ying Gao (145030)

“…Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models.…”