Mingyi Shao

Surface water is a major source of domestic water supply in China, yet its quality remains uneven despite recent improvements. Chronic exposure to surface-

This study aimed to develop and validate a nomogram prediction model based on the clinical characteristics, optic nerve status, and visual function to predict treatment outcomes after primary glaucoma surgery, providing a tool for clinical decision-making. Clinical data from patients who underwent primary glaucoma surgery and received timolol maleate eye drops between January 2021 and March 2024 were retrospectively analyzed. Univariate and multivariate Logistic regression analyses were used to identify independent predictors. A nomogram was constructed based on regression coefficients. Model performance was assessed using the concordance index (C-index) and calibration curves. Among 220 enrolled patients, 154 were assigned to the training set and 66 to the validation set. Poor treatment outcomes occurred in 27.27% and 25.76% of each group, respectively. Multivariate analysis identified cup-to-disc ratio, vascular resistance index, distant visual acuity, mean visual field level, central visual field sensitivity, and frequency contrast sensitivity (low, medium and high) as independent predictors of poor outcomes (all P < 0.05). The nomogram demonstrated high predictive accuracy, with area under the curve values of 0.928 (95% CI 0.879-0.978) in the training set and 0.823 (95% CI 0.640-1.000) in the validation set. Sensitivity and specificity were 0.879 and 0.827 in the training set, and 0.778 and 0.919 in the validation set, respectively. Calibration curves indicated good model fit. The nomogram model accurately predicts treatment outcomes after primary glaucoma surgery, showing strong discriminative ability and calibration. It may assist clinicians in personalizing postoperative treatment strategies. Further large-sample, multicenter studies are warranted for validation and broader application.

Helicobacter pylori is one of the main causes of gastritis and gastric ulcer. Early detection of H. pylori is of great significance for the prevention of gastric cancer. Herein, a sensitive electrochemical aptasensor using AgNPs-GO as redox probes was established for the specific detection of H. pylori in blood serum and stool samples. Firstly, AgNPs-GO nanocomposites were obtained by in-situ reduction of AgNPs on graphene oxide (GO) surface with glucose as reducing agent, in which AgNPs showed good biocompatibility and chemical stability, as well as redox property, and GO provided a large surface area to assemble a large number of AgNPs. Subsequently, the electrodeposition of AuNPs further improved the conductivity of the aptasensor. Additionally, the streptavidin was introduced into the aptasensor to effectively bind the biotin-modified aptamers. In this way, aptamers could be tethered to the surface through SA-biotin linkage, and H. pylori was selectivly binded by the aptamers subsequently. Under optimal conditions, the aptasensor could detect H. pylori in a wide concentration range (10¹ CFU mL⁻¹–10⁸ CFU mL⁻¹) with a low detection limit of 3 CFU mL⁻¹. What’s more, the developed method showed excellent performance in practical application, which provided a promising possibility for the detection of other pathogens in clinical diagnosis.

Aims Lower extremity atherosclerotic disease (LEAD) is the primary cause of ulcers, gangrene, and amputations in patients with type 2 diabetes mellitus (T2DM), stage I is the crucial time for prevention and intervention to improve the prognosis of T2DM-LEAD. The purpose of this study was to develop and validate a personalized predictive model to determine the risk of outcomes in stage I patients with T2DM-LEAD. Methods There were 1603 stage I patients with T2DM-LEAD at baseline in this retrospective study. Least absolute shrinkage and selection operator regression was applied to filter predictive variables. Cox regression was used to construct a nomogram prediction model. The model’s 3-year and 5-year predictive performance was evaluated in terms of its discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve, calibration curve, decision curve analysis, respectively. Results Patients were randomly divided into a development cohort (n = 1122) and a validation cohort (n = 481). Age, cerebrovascular diseases, diabetic kidney disease, diabetic retinopathy, low-density lipoprotein cholesterol, fibrinogen, D-dimer and anti-platelet drugs were selected as predictive factors. The model presented moderate discrimination in development and validation sets with AUCs of 70.3 (95% CI: 65.2-75.3) and 70.1 (95% CI: 64.5-75.7) for the 3-year prediction. Andthe AUC values for the 5-year prediction in development and validation sets were 72.8 (95% CI: 67.6-78.1) and 75.9 (95% CI: 69.0-82.8), respectively. The calibration curve for the 3-year and 5-year predictions demonstrated good agreement between the predicted and actual probabilities, and decision curve analysis showed a wide range of beneficial clinical utility. Conclusion The prediction model can identify the risk of stage I patients with T2DM-LEAD who are likely to develop outcomes events within 3 years and 5 years. It is valuable for clinical decisions and helps healthcare providers and policy makers to develop more personalized clinical treatment strategies, which has significant public health implications. Graphical Abstract

Introduction: The diagnostic performance of liquid biopsy-based biomarkers for HCC was comprehensively compared in this network meta-analysis (NMA).Methods: A thorough literature search was conducted to identify all comparative studies from January 1, 2000, to January 11, 2024. The QUADAS-2 tool was utilized to appraise the quality of studies involving diagnostic performance. R (v4.3.3) and an ANOVA model-based NMA were used to assess the diagnostic accuracy of each biomarker.Results: This study included 82 studies comprising a total of 15,024 patients.CircRNA demonstrated significantly superior performance in distinguishing HCC from healthy populations (superiority index: 3.550 (95% CI [0.143-3])) compared to other diagnostic biomarkers for HCC. “mRNA exhibited significantly superior performance in distinguishing HCC from liver disease patients (superiority index:10.621 (95% CI [7-11])) compared to other diagnostic biomarkers for HCC. Further subgroup analysis of the top-ranking liquid biopsy-based diagnostic biomarkers revealed that hsa_circ_000224 (superiority index: 3.091 (95% CI[0.143-9]) ranked remarkably higher in distinguishing HCC from both healthy populations and liver disease patients. Subgroup analysis of mRNA demonstrated that KIAA0101 mRNA (superiority index: 2.434 (95% CI [0.2-5]) ranked remarkably higher in distinguishing HCC from healthy populations and liver disease patients, respectively.Discussion: The results of this meta-analysis show that circRNA and mRNA are the first choice for HCC diagnosis. Subsequent analysis of circRNA and mRNA highlighted hsa_circ_000224, hsa_circ_0003998, KIAA0101 mRNA and GPC-3mRNA as the optimal diagnostic biomarkers for distinguishing HCC from healthy populations and liver disease patients, respectively. Well-structured prospective studies are crucial to comprehensively validate these findings.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/,identifier CRD42024521299.

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Background This study aims to develop and validate a predictive model for Hepatocellular Carcinoma (HCC) patients using deep learning algorithms and to explore its clinical applicability. Methods HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and 5-fold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. Results The study included 2,197 HCC patients, randomly divided into a training cohort (70%, n = 1,537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model provided more accurate and better-calibrated survival estimates for predicting 1-year, 3-year, and 5-year survival rates (AUC: 0.803–0.824). We deployed the NMTLR model as a web application for clinical practice. Conclusion The predictive model developed using the deep learning algorithm NMTLR demonstrated excellent performance in prognostication for Primary Hepatocellular Carcinoma.