Supplementary file 1_Comparative analysis of accuracy and completeness in standardized database generation for complex multilingual lung cancer pathological reports: large language... by Hao Hang (7037264)

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. …”

Significantly Enriched Pathways. by Jiyong Zhang (2498740)

“…Through protein-protein interaction (PPI) network analysis, we identified 41 critical intersecting genes, and ROC curve validation demonstrated that 25 of these genes had an AUC value exceeding 0.85, highlighting their potential as biomarkers for NAFLD in early diagnosis and personalized treatment in the future.This study identifies critical ferroptosis-related genes and immune cell infiltration differences in NAFLD, offering potential biomarkers for early diagnosis and personalized treatment.…”

Table 1_Effects of education level on natural language processing in cardiovascular health communication.xlsx by Stanley Joseph (19282225)

Supplementary file 1_Effects of education level on natural language processing in cardiovascular health communication.xlsx by Stanley Joseph (19282225)

Supplementary file 1_Analysis of the diagnostic value of peripheral blood immune inflammatory indicators of female bladder pain syndrome.zip by Yinglei Wang (3844756)

“…The correlation between these indicators and MBC is secondary outcomes. The optimal cut-off value for the parameters was identified using the receiver operating characteristic (ROC) curve and its area under the curve (AUC) over time.…”

TENS inhibited inflammation of CFL injury rats by regulating the IL-1 by Min Miao (142697)

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

Characteristics of women at admission. by Guiyou Yang (10259597)

“…The PIERS-ML model’s AUC-PRC peaked on day 0 (0.65), and notably decreased thereafter. …”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…The application of artificial intelligence holds promise for delaying renal deterioration and decreasing treatment expenses by facilitating early detection and intervention. …”

Comparative analysis of four distinct microstructures: S1G0, S1G1, S1G2, and S2G0. by Camilo A. S. Afanador (22232423)

“…The porosity of <b>S1G0</b> and <b>S2G0</b> () aligns with <i>μ</i>CT measurements from the literature. Note the increase in porosity in <b>S1G1</b> and <b>S1G2</b> due to the morphological filter. …”

RNA levels of selected CMGC kinases in dHepaRG, PHHs and liver biopsies. by Florentin Pastor (19852287)

Clinical characteristics of IDC patients. by Gangming Zhu (21094310)

“…The combination of ADC value, K^trans, K_ep, V_e, and iAUC had a good ability to discriminate IDC patients (vs. benign controls) with an area under the curve (AUC) [95% confidence interval (CI)] of 0.961 (0.935–0.987). …”

Table 1_Assessing the diagnostic value of qPCR for Trichuris trichiura: sub-analysis of a multi-country clinical trial to determine the efficacy of albendazole compared to an alben... by Pedro E. Fleitas (8704023)

“…Concordance between stool egg counts and Ct-value decreased post-treatment, likely due to reduced KK sensitivity in low-intensity infections. …”

Photosynthesis and respiration rates of (A) <i>Acropora</i> sp. and (B) <i>P. verrucosa</i> for the four groups. by Malte Ostendarp (13217973)

The expression levels of putative VIP1 target genes that were upregulated by brushing in brushed <i>atb’’αβγδ</i> leaves. by Hyuk Sung Yoon (20208672)

Machine Learning Models for High Explosive Crystal Density and Performance by Jack V. Davis (9175514)

“…The inexpensive, yet highly accurate predictions from our models should enable creation of future artificial intelligence (AI) models that are able to screen large numbers (>10⁶) of compounds to find the highest performing compounds in terms of crystal density, detonation velocity and detonation pressure.…”

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”