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Nanozymes with multienzyme-like activity have sparked significant interest in anti-tumor therapy via responding to the tumor microenvironment (TME). However, the consequent induction of protective autophagy substantially compromises the therapeutic efficacy. Here, a targeted nanozyme system (Fe-Arg-CDs@ZIF-8/HAD, FZH) is shown, which enhances synergistic anti-tumor ferroptosis/apoptosis therapy by leveraging machine learning (ML). A novel ML model, termed the sequential backward Tree-Classifier for Gaussian Process Regression (TCGPR), is proposed to improve data pattern recognition following the divide-and-conquer principle. Based on this, a Bayesian optimization algorithm is employed to select candidates from the extensive search space. Leveraging this fresh material discovery framework, a novel strategy for enhancing nanozyme-based tumor therapy, has been developed. The results reveal that FZH effectively exerts anti-tumor effects by sequentially responding to the TME, having a cascade reaction to induce ferroptosis. Moreover, the endogenous elevation of high concentration nitric oxide (NO) serves as a direct mechanism for killing tumor cells while concurrently suppressing the protective autophagy induced by oxidative stress (OS), enhancing synergistic ferroptosis/apoptosis therapy. Overall, a novel strategy for improving nanozyme-based tumor therapy has been proposed, underlying the integration of ML, experiments, and biological applications., (© 2024 Wiley‐VCH GmbH.)

Rationale and Objectives: This retrospective casecontrol study evaluated the efficacy and safety of CT-guided preoperative localization via adjacent microcoil implantation for reducing the operative time of laparoscopic partial nephrectomy (LPN) in patients with endophytic renal masses., Methods: Data from patients who underwent LPN for completely endophytic treatment (three points for the ''E'' domain of the R.E.N.A.L. score) renal masses were collected from Beijing Friendship Hospital, Capital Medical University, between January 2020 and May 2023. Microcoils were placed adjacent to the renal masses under CT guidance prior to LPN. The head of the microcoil was pinpointed adjacent to the target endophytic renal mass, and its end tail remained outside the renal surface. Baseline characteristics and clinical, surgical, and postoperative outcomes were compared., Results: Forty patients (microcoil localization, N = 8; standard LPN, N = 32) were included in the analysis. The median clinical tumor size was 15 mm (IQR: 10-22). In all patients in the microcoil localization group, the microcoil was successfully visualized laparoscopically by the surgeons. The microcoil localization technique demonstrated a significantly shorter operative time under general anesthesia (150 vs. 195 min, P = 0.012) and a nonsignificant trend towards a shorter hospital stay (8.6 vs. 11.7 days, P = 0.079). The microcoil localization technique showed a nonsignificant trend toward a reduced total operative time (189 vs. 195 min, P = 0.012). No significant differences were observed in histopathological findings, surgical approach, postoperative eGFR levels, eGFRs, or complication rates were observed between the groups., Conclusion: This study suggested the use of a local microcoil on the surface of the kidney to locate the tumor accurately, which offers a more patient-centered surgical approach and can serve as a standard approach for treating totally endophytic renal masses that require localization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors have nothing to disclose with regard to commercial support., (Copyright © 2024. Published by Elsevier Inc.)

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization., (open access.)

Two-dimensional (2D) transition metal oxide monolayers are currently attracting great interest in materials research due to their versatility and tunable electronic and magnetic properties. In this study, we report the prediction of magnetic phase changes in H x CrO 2 (0 ⩽ x ⩽ 2) monolayer on the basis of first-principles calculations. As the H adsorption concentration x increases from 0 to 0.75, H x CrO 2 monolayer transforms from a ferromagnetic (FM) half-metal to a small-gap FM insulator. When x = 1.00 and 1.25, it behaves as a bipolar antiferromagnetic (AFM) insulator, and eventually becomes an AFM insulator as x increases further up to 2.00. The results suggest that the magnetic properties of CrO 2 monolayer can be effectively controlled by hydrogenation, and that H x CrO 2 monolayers have the potential for realizing tunable 2D magnetic materials. Our results provide a comprehensive understanding of the hydrogenated 2D transition metal CrO 2 and provide a research method that can be used as a reference for the hydrogenation of other similar 2D materials., (© 2023 IOP Publishing Ltd.)

The present study aimed to investigate the association between the content and distribution of fat in the pancreas and liver in patients with type 2 diabetes mellitus (T2DM). A total of 70 patients newly diagnosed with T2DM (T2DM group) and 30 healthy volunteers (normal control group) were enrolled in the present study. Dual-echo magnetic resonance (MR) chemical shift imaging was used to measure the fat content of the liver and the head, body and tail regions of the pancreas. In addition, the distribution of fat in the various regions of the pancreas, as well as the average fat content of the pancreas versus the liver, were compared. The fat content of the pancreatic head, body and tail regions of the T2DM group were 5.59±4.70, 4.80±3.75 and 4.89±3.86%, respectively. The fat content of these regions in the normal control group were 3.89±2.47, 3.30±2.11 and 3.23±2.23%, respectively. The average fat content of the pancreas was 5.19±3.75% for the T2DM group and 3.47±2.00% for the normal control group. The average fat content of the liver was 9.87±3.19% for the T2DM group and 7.24±2.38% for the normal control group. Therefore, the results from MR chemical shift imaging suggested that there were no significant differences in the distribution of fat between the pancreas of patients newly diagnosed with T2DM and that from the healthy population; however, the average fat content in the pancreas of the T2DM group was significantly higher (F=3.597; P<0.05), as compared with the normal control group. In addition, there was no correlation between the fat contents in the pancreas and liver in patients newly diagnosed with T2DM and the healthy population.