Journal: Diabetes, Metabolic Syndrome and Obesity

Article Title: Association of Uric Acid to Creatinine Ratio with Metabolic Dysfunction-Associated Fatty Liver in Non-Obese Individuals Without Type 2 Diabetes Mellitus

doi: 10.2147/DMSO.S445916

Figure Lengend Snippet: Receiver operating characteristics analysis of serum uric acid to creatinine ratio in predicting the risk of MAFLD in different groups. ( A ) ROC of overall MAFLD. ( B ) ROC of obesity without T 2 DM. ( C ) ROC of non-obesity without T 2 DM. ( D ) ROC of obesity with T 2 DM. ( E ) ROC of non-obesity with T 2 DM.

Article Snippet: To assess the predictive value of sUA/Cr, we examined the receiver operating characteristic (ROC) and calculated the areas under the curves (AUCs) using MedCalc 20.022 statistical software.

Techniques:

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Identifying T cell exhaustion-associated genes in osteosarcoma and constructing a risk prognostic model. (A) The heatmap of DEGs between TARGET-OS and GTEx databases, with elevated expression depicted in red and diminished expression in blue. (B) The intersection of DEGs and TEXRGs yielded osteosarcoma-associated differentially expressed TEXRGs. (C) The heatmap of osteosarcoma-associated differentially expressed TEXRGs, with heightened expression shown in red and reduced expression in blue. (D) The chord diagram presents the functional enrichment analysis of osteosarcoma-associated differentially expressed TEXRGs. (E) The functional enrichment network and table of osteosarcoma-associated differentially expressed TEXRGs. (F) Univariate Cox regression analysis identified 37 potential prognostic TEXRLs for osteosarcoma, comprising 12 high-risk TEXRLs and 25 low-risk TEXRLs. (G) LASSO regression analysis and determining the optimal penalty parameter for LASSO regression. (H) The survival status map and risk heatmap of risk model TEXRLs in the total sample group. (I) The Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (J) The survival analysis of the complete sample cohort ( p < 0.001), as well as the training cohort ( p < 0.001) and test cohort ( p = 0.005), demonstrated significant disparities in survival outcomes between patients categorized as red high-risk and blue low-risk. (K) Time-dependent ROC curves, 1 year (AUC = 0.821), 3 years (AUC = 0.861), and 5 years (AUC = 0.814). (L) Clinical ROC curves, Risk score (AUC = 0.821), Age (AUC = 0.453), Gender (AUC = 0.464), and Met (AUC = 0.905). (M, N) Univariate and multivariate COX regression analyses in the total sample group.

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Expressing, Functional Assay

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Survival prediction validation of risk models in training and testing groups. (A, B) The survival status map and risk heatmap of risk model TEXRLs in the training group. (C) In the training group, the Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (D) Time-dependent ROC curves in the training group, 1 year (AUC = 0.966), 3 years (AUC = 0.993), and 5 years (AUC = 0.994). (E) Clinical ROC curves in the training group, Risk score (AUC = 0.966), Age (AUC = 0.325), Gender (AUC = 0.359), and Met (AUC = 0.856). (F, G) Univariate and multivariate COX regression analyses in the training group. (H, I) The survival status map and risk heatmap of risk model TEXRLs in the test group. (J) In the test group, the Kaplan-Meier survival curve effectively demonstrates that patients in the red high-risk group exhibited a substantially lower overall survival rate compared to those in the blue low-risk group. (K) Time-dependent ROC curves in the test group, 1 year (AUC = 0.667), 3 years (AUC = 0.741), and 5 years (AUC = 0.694). (L) Clinical ROC curves in the test group, Risk score (AUC = 0.667), Age (AUC = 0.603), Gender (AUC = 0.570), and Met (AUC = 0.956). (M, N) Univariate and multivariate COX regression analyses in the test group.

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Biomarker Discovery

Journal: Frontiers in Immunology

Article Title: Identification and functional characterization of T-cell exhaustion-associated lncRNA AL031775.1 in osteosarcoma: a novel therapeutic target

doi: 10.3389/fimmu.2025.1517971

Figure Lengend Snippet: Analysis of the prognostic prediction ability of single genes from the risk model. (A) The effect of high AC090559.1 expression on the prognosis of osteosarcoma overall survival is statistically significant. (B) The effect of high AC135178.4 expression on the prognosis of osteosarcoma overall survival is statistically significant. (C) Kaplan-Meier survival curve analysis indicates that the expression level of AL031775.1cannot be used to predict the survival prognosis of osteosarcoma patients. (D) The effect of low LINC01060 expression on the prognosis of osteosarcoma overall survival is statistically significant. (E) The effect of high LINC02777 expression on the prognosis of osteosarcoma overall survival is statistically significant. (F) Kaplan-Meier survival curve analysis indicates that the expression level of PSMB8-AS1 cannot be used to predict the survival prognosis of osteosarcoma patients. (G) Time-dependent ROC curves of AC090559.1, 1 year (AUC = 0.802), 3 years (AUC = 0.693), and 5 years (AUC = 0.607). (H) Time-dependent ROC curves of AC135178.4, 1 year (AUC = 0.680), 3 years (AUC = 0.593), and 5 years (AUC = 0.579). (I) Time-dependent ROC curves of AL031775.1, 1 year (AUC = 0.671), 3 years (AUC = 0.735), and 5 years (AUC = 0.712). (J) Time-dependent ROC curves of LINC01060, 1 year (AUC = 0.522), 3 years (AUC = 0.681), and 5 years (AUC = 0.678). (K) Time-dependent ROC curves of LINC02777, 1 year (AUC = 0.676), 3 years (AUC = 0.709), and 5 years (AUC = 0.663). (L) Time-dependent ROC curves of PSMB8-AS1, 1 year (AUC = 0.698), 3 years (AUC = 0.655), and 5 years (AUC = 0.521).

Article Snippet: ROC curves and area under the ROC curve (AUC) were calculated using MedCalc for Windows version 19.3.0 (MedCalc Software, Ostend, Belgium).

Techniques: Expressing

Journal: Scientific Reports

Article Title: Neural networks versus Logistic regression for 30 days all-cause readmission prediction

doi: 10.1038/s41598-019-45685-z

Figure Lengend Snippet: Performance analysis of the tested models. Panels A–E report the average ROC curve of the best models. The optimal cutoff is based on the average Youden-Index of each model for all 5-folds. Standard deviation of the optimal cutoff position is reported on the graph. Panel F reports the cumulative average AUC performance as a function of patients’ timeline length.

Article Snippet: We used the area under the ROC curve (AUC) as our performance measure with confidence intervals computed using the approach reported in LeDell et al . .

Techniques: Standard Deviation

Journal: Scientific Reports

Article Title: Neural networks versus Logistic regression for 30 days all-cause readmission prediction

doi: 10.1038/s41598-019-45685-z

Figure Lengend Snippet: Trained models’ performance based on the area under the ROC curve (AUC). CI: confidence interval.

Article Snippet: We used the area under the ROC curve (AUC) as our performance measure with confidence intervals computed using the approach reported in LeDell et al . .

Techniques:

Journal: PLoS ONE

Article Title: Plasma SerpinA5 in conjunction with uterine artery pulsatility index and clinical risk factor for the early prediction of preeclampsia

doi: 10.1371/journal.pone.0258541

Figure Lengend Snippet: a. Single Prediction analysis. Receiver operating characteristic (ROC) curve analysis. As a single predictor SerpinA5, the AUC = 0.881 (95% CI 0.805–0.956)], with 89.58% sensitivity and 81.25% specificity. b. Combinated Prediction analysis. A combination of plasma SerpinA5, maternal factors and abnormal UtA-PI (above the 95th percentile) enhanced the predictive value for preeclampsia (United AUC was 0.946 (95% CI 0.905–0.988). (Pre-BMI: Pre-pregnancy BMI).

Article Snippet: Receiver operating characteristic (ROC) curves, the sensitivity, specificity, and area under the ROC curve (AUC) were calculated by Medcalc v19.6.1 (MedCalc Software bvba, Ostend, Belgium).

Techniques: Clinical Proteomics