National Clinical Research Center for Digestive Diseases

This retrospective study analyzed patients undergoing inguinal hernia repair between January 2021 and December 2024, using 1:1 nearest-neighbor propensity score matching to compare 280 patients who received perioperative celecoxib plus dexamethasone with 280 who did not, and found an overall one-year recurrence rate of 5.0% (28/560), an independent association between treatment and lower recurrence risk (HR=0.45, 95% CI: 0.24-0.84, P=0.011), a significant interaction with BMI (P=0.032) with a stronger association in obese patients (HR=0.32, 95% CI: 0.14-0.71) than non-obese patients (HR=0.68, 95% CI: 0.31-1.52), along with lower acute pain scores, less postoperative nausea and vomiting (P<0.001), lower chronic pain risk (aOR=0.42, 95% CI: 0.23-0.78, P=0.

This retrospective study analyzed patients undergoing inguinal hernia repair between January 2021 and December 2024, using 1:1 nearest-neighbor propensity score matching to compare 280 patients who received perioperative celecoxib plus dexamethasone with 280 who did not, and found an overall one-year recurrence rate of 5.0% (28/560), an independent association between treatment and lower recurrence risk (HR=0.45, 95% CI: 0.24-0.84, P=0.011), a significant interaction with BMI (P=0.032) with a stronger association in obese patients (HR=0.32, 95% CI: 0.14-0.71) than non-obese patients (HR=0.68, 95% CI: 0.31-1.52), along with lower acute pain scores, less postoperative nausea and vomiting (P<0.001), lower chronic pain risk (aOR=0.42, 95% CI: 0.23-0.78, P=0.

Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.

Using retrospective deidentified clinical data from 1370 inpatients with chronic pancreatitis who underwent extracorporeal shock wave lithotripsy (ESWL) at Changhai Hospital between May 31, 2016 and June 26, 2019, this study retained 55 of 109 variables and compared ten machine-learning and deep-learning algorithms (XGBoost, LightGBM, CatBoost, random forest, support vector machine, artificial neural network, multilayer perceptron, TabNet, Transformer, and Wide&Deep) across three modeling schemes (all preprocessed variables, 21 variables selected by univariate screening, and ADASYN-oversampled training data) using the F1-score as the primary selection metric; TabNet achieved the best held-out test performance in the first two schemes (F1 of 0.

This review systematically evaluates established and emerging non-invasive tests for detecting early-stage liver fibrosis (F1–F2), spanning serum biomarkers, molecular imaging probes, AI-based analysis of conventional imaging, alternative biofluids such as urine, exhaled breath and saliva, gut microbiota-derived biomarkers, and multiomics-integrated models, concluding that these approaches show diagnostic promise but remain constrained by limited validation, absent standardization, and unclear integration into clinical pathways, and proposing a tiered diagnostic framework as a near-term implementation route.

This review systematically evaluates established and emerging non-invasive tests for detecting early-stage liver fibrosis (F1–F2), spanning serum biomarkers, molecular imaging probes, AI-based analysis of conventional imaging, alternative biofluids such as urine, exhaled breath and saliva, gut microbiota-derived biomarkers, and multiomics-integrated models, concluding that these approaches show diagnostic promise but remain constrained by limited validation, absent standardization, and unclear integration into clinical pathways, and proposing a tiered diagnostic framework as a near-term implementation route.