Missing value percentage for variables. by Xin Liu (43569)

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Sensitivity analysis of LMR thresholds based on ROC curves for associated variables. by Daysi Zulema Diaz-Obregón (22537744)

Exclusion and enrollment summary from NHANES. by Lu Han (151620)

“…<div><p>Background</p><p>Blood-cell-based inflammatory biomarkers are increasingly recognized for their diagnostic value in infections due to their clinical accessibility. …”

Feature engineering. by Youli Jiang (15249070)

Trend coefficients and AUC statistics across four control populations (“Pop’n 1” to “Pop’n 4”) and four warming populations (“Pop’n 5” to “Pop’n 8”), as calculated from empirical time series during the approach to criticality. Each statistical metric was calculated within fifteen-day sliding windows, throughout the pre-critical interval. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60.... by Madeline Jarvis-Cross (22394247)

“…Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. …”

Table 1_Association of serum chemokine (C–C motif) 21 and receptor (C–C motif) 7 with Hashimoto’s thyroiditis——a preliminary clinical investigation.docx by Xiaoting Gui (22516922)

“…The results of receiver operating characteristic curve analysis indicated that CCL21 has value for the diagnosis of Hashimoto’s thyroiditis [AUC (95% CI)=0.998 (0.996-1.000), p<0.001]. …”

Inflammation-free survival by predicted risk group. by Sanghee Kim (75767)

Table 1_Development of a clinical prediction model for poor treatment outcomes in the intensive phase in patients with initial treatment of pulmonary tuberculosis.docx by Bin Lu (65603)

“…Logistic regression analysis identified several independent risk factors for poor treatment outcomes, including diabetes, cavities in the lungs, tracheobronchial TB, increased C-reactive protein, and decreased hemoglobin. …”

Supplementary file 1_Association between the hemoglobin-to-red cell distribution width ratio and three-month unfavorable outcome in older acute ischemic stroke patients: a prospect... by Huang Luwen (20861381)

“…ROC analysis revealed that HRR had the highest AUC (0.64, 95% CI: 0.61–0.67), followed by hs-CRP (0.60, 95% CI: 0.57–0.63), FPG/HbA1c (0.59, 95% CI: 0.55–0.63), and WBC (0.55, 95% CI: 0.51–0.58).…”

Impact of pathogen shedding characteristics on estimation accuracy. by Yun Lin (77976)

“…(B) estimation accuracies of Ct-based Rt over testing periods for pathogen 1-4 corresponding to examples shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013527#pcbi.1013527.g001" target="_blank">Fig 1C</a>. Dots and horizontal lines represent median and interval estimates of the AUC over 100 bootstrapped samples(see Methods), with the interval estimates taken as the 2.5 and 97.5 percentiles of all 100 estimated AUC values during bootstrapping. …”

Table_1_Correlation between ratio of fasting blood glucose to high density lipoprotein cholesterol in serum and non-alcoholic fatty liver disease in American adults: a population b... by Xianjing Jin (19535434)

“…Background<p>Based on previous research, elevated fasting blood glucose (FBG) and decreased high-density lipoprotein cholesterol (HDL-C) levels are associated with non-alcoholic fatty liver disease (NAFLD). …”

Data Sheet 1_Impact of the lipid–inflammation axis on endometriosis risk: a multicenter case–control study using mediation analysis.pdf by Yanan Duan (113608)

“…</p>Results<p>For each 1 mmol/L decrease in high-density lipoprotein cholesterol (HDL-C), EM risk increased by 55%. …”

Attention reduces decision uncertainty under high cognitive demand. by Rahul Garg (3064578)

“…(L) Fraction of glomeruli with significant changes with cued-odor. Yellow: increased, dark grey: decreased, light grey: unchanged. …”

Table 5_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”

Table 1_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”

Table 4_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”

Table 3_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”

Table 6_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”

Table 2_Immune-related diagnostic indicators and targeted therapies for COPD combined with NASH were identified and verified via WGCNA and LASSO.xlsx by Jianwei Hong (6918776)

“…A nomogram diagnostic model was constructed based on these two core genes. The AUC value for S100A9 was 0.887, for MYH2 was 0.877, and for the nomogram was 0.889, demonstrating excellent diagnostic efficacy. …”