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We pioneered a dual-targeted trinity of antibody–peptide–drug delivery consortium to combat HER2+ tumors. This innovative approach leverages the self-assembly of peptides with high affinity to antibodies to create nanofibers for antibody encapsulation, offering a novel strategy in antibody drug delivery. [ABSTRACT FROM AUTHOR]

Aims/Introduction: To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods: This is a 3‐year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10‐fold cross‐validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi‐layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results: A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10‐fold cross‐validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823–0.886) in the training set and 0.835 (95% CI 0.779–0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814–0.866) in the training set and 0.834 (95% CI 0.785–0.884) in the test set. Conclusions: In the real‐world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value. [ABSTRACT FROM AUTHOR]

Objective: Distant metastasis often indicates a poor prognosis, so early screening and diagnosis play a significant role. Our study aims to construct and verify a predictive model based on machine learning (ML) algorithms that can estimate the risk of distant metastasis of newly diagnosed follicular thyroid carcinoma (FTC). Design: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Patients: A total of 5809 FTC patients were included in the data analysis. Among them, there were 214 (3.68%) cases with distant metastasis. Method: Univariate and multivariate logistic regression (LR) analyses were used to determine independent risk factors. Seven commonly used ML algorithms were applied for predictive model construction. We used the area under the receiver‐operating characteristic (AUROC) curve to select the best ML algorithm. The optimal model was trained through 10‐fold cross‐validation and visualized by SHapley Additive exPlanations (SHAP). Finally, we compared it with the traditional LR method. Results: In terms of predicting distant metastasis, the AUROCs of the seven ML algorithms were 0.746–0.836 in the test set. Among them, the Extreme Gradient Boosting (XGBoost) had the best prediction performance, with an AUROC of 0.836 (95% confidence interval [CI]: 0.775–0.897). After 10‐fold cross‐validation, its predictive power could reach the best [AUROC: 0.855 (95% CI: 0.803–0.906)], which was slightly higher than the classic binary LR model [AUROC: 0.845 (95% CI: 0.818–0.873)]. Conclusions: The XGBoost approach was comparable to the conventional LR method for predicting the risk of distant metastasis for FTC. [ABSTRACT FROM AUTHOR]