RESUMO
An Exendin-4 analogue that was conjugated with 68Ga exhibited an excellent diagnostic effect on insulinoma in clinical practice. On account of its low molecular weight and short hydration radius, 68Ga-Exendin-4 showed high accumulation in kidney tissues. Nanoparticle-mediated strategies have attracted much attention due to polyvalent properties and the size amplification effect. In this study, Exendin-4 derivatives of radionuclide nanodevices were developed and evaluated. The Exendin-4 derivatives consisting of a ternary block recombinant protein were purified by an inverse transition cycle (ITC) and allowed to self-assemble into a nanodevice under physiological conditions. Our results showed that the nanoassemblies of Exendin-4 derivatives formed homogeneous spherical nanoparticles, exhibited outstanding affinity for insulinoma cells, and could be deposited in insulinoma tissues in vivo. The nanoassembly-mediated Exendin-4 derivatives showed fivefold reduced renal retention and exhibited an outstanding tumor-suppression effect.
RESUMO
OBJECTIVE: Low cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance. METHODS: A total of 2218 patients undergoing valve surgery from June 2019 to Dec 2021 were finally enrolled to construct preoperative and postoperative models. Logistic regression, support vector machine (SVM), random forest classifier, extreme gradient boosting, and deep neural network were executed for model construction, and the performance of models was evaluated by area under the curve (AUC) of the receiver operating characteristic and calibration curves. Our models were interpreted through SHapley Additive exPlanations, and presented as an online tool to improve clinical operability. RESULTS: The SVM algorithm was chosen for modeling due to better AUC and calibration capability. The AUCs of the preoperative and postoperative models were 0.786 (95% CI 0.729-0.843) and 0.863 (95% CI 0.824-0.902), and the Brier scores were 0.123 and 0.107. Our models have higher timeliness and interpretability, and wider coverage than the vasoactive-inotropic score, and the AUC of the postoperative model was significantly higher. Our preoperative and postoperative models are available online at http://njfh-yxb.com.cn:2022/lcos. CONCLUSIONS: The first interpretable ML tool with two prediction periods for online early prediction of LCOS risk after valve surgery was successfully built in this study, in which the SVM model has the best performance, reserving enough time for early precise intervention in critical care.