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The Cu‐glutathione (GSH) redox system, essential in biology, is designed here as a supramacromolecular assembly in which the tetrahedral 18e Cu(I) center loses a thiol ligand upon adsorption onto ZIF‐8, as shown by EXAFS and DFT calculation, to generate a very robust 16e planar trigonal single‐atom Cu(I) catalyst. Synergy between Cu(I) and ZIF‐8, revealed by catalytic experiments and DFT calculation, affords CO2 conversion into high‐value‐added chemicals with a wide scope of substrates by reaction with terminal alkynes or propargyl amines in excellent yields under mild conditions and reuse at least 10 times without significant decrease in catalytic efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The role of lower hemoglobin A1c (HbA1c) variability in the effect of sodium glucose cotransporter‐2 inhibitors (SGLT2i) on acute kidney injury (AKI) remains unclear. We compared AKI risk between SGLT2i and dipeptidyl peptidase 4 inhibitors (DPP4i) initiators. Additionally, we aimed to explore the extent to which SGLT2i's influence on AKI risk is mediated by reducing long‐term HbA1c variability. Methods: Using 2018–2022 year data in Yinzhou Regional Health Care Database, we included adult, type 2 diabetes patients who were new users of SGLT2i or DPP4i. The effect of SGLT2i versus DPP4i on AKI, HbA1c variability, and AKI through HbA1c variability was compared using inverse probability of treatment weighted Cox proportional hazards models, median regression models, and causal mediation analysis. Results: With a median follow‐up of 1.76 years, 19 717 adults (for SGLT2i, n = 6008; for DPP4i, n = 13 709) with type 2 diabetes were included. The adjusted hazard ratio for SGLT2i versus DPP4i was 0.79 (95% confidence interval [CI] 0.64–0.98) for AKI. The adjusted differences in median HbA1c variability score (HVS) and HbA1c reduction were −16.67% (95% CI: −27.71% to −5.62%) and −1.98% (95% CI: −14.34% to 10.38%), respectively. Furthermore, lower AKI risk associated with SGLT2i was moderately mediated (22.77%) through HVS. The results remained consistent across various subgroups and sensitivity analyses. Conclusions: Compared to DPP4i, lower AKI risk associated with SGLT2i is moderately mediated through HbA1c variability. These findings enhance our understanding of the effect of SGLT2i on AKI and underscore the importance of considering HbA1c variability in diabetes treatment and management. [ABSTRACT FROM AUTHOR]

Transition metal‐based materials exhibit a broad range of catalytic activities in numerous electrochemical processes. Their displayed catalytic functions rely essentially on their distinctive electronic structures, changes of which play pivotal roles in dictating reaction dynamics within many electrochemical devices. Nevertheless, accurately probing electronic structure changes, especially in real‐time, of active materials remains a formidable challenge. In this work, a viable approach to achieve it within a CoC2O4/Li model system by employing a combined microscopic is demonstrated, spectroscopic analysis, plus a unique operando magnetometry technique. The findings reveal that upon completion of the conventional conversion of CoC2O4, a surface‐dominated capacitance emerges at the Co/Li2C2O4 interface owing to the injection of spin‐polarized electrons. Subsequently, the decomposition of Li2C2O4 proceeds through the releasing of the spin‐polarized electrons from Co, which, therefore serve as a catalyst leading to further discharge products. Such real‐time monitoring of electron transfer is realized by in situ monitoring electronic structure changes of Co, manifested by its intriguing magnetization alterations during the process. This work highlights a novel characterization tool that provides a solid explanation for the commonly observed large capacities in this type of materials and sheds light on design rules and selection guidance of materials for high‐performance electrochemical energy storage systems. [ABSTRACT FROM AUTHOR]

Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, and then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists, we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from sodium-glucose cotransporter-2 inhibitors, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design. [ABSTRACT FROM AUTHOR]

Aim: The transition to the ICD‐10‐CM coding system has reduced the utility of hypoglycaemia algorithms based on ICD‐9‐CM diagnosis codes in real‐world studies of antidiabetic drugs. We mapped a validated ICD‐9‐CM hypoglycaemia algorithm to ICD‐10‐CM codes to create an ICD‐10‐CM hypoglycaemia algorithm and assessed its performance in identifying severe hypoglycaemia. Materials and Methods: We assembled a cohort of Medicare patients with DM and linked electronic health record (EHR) data to the University of North Carolina Health System and identified candidate severe hypoglycaemia events from their Medicare claims using the ICD‐10‐CM hypoglycaemia algorithm. We confirmed severe hypoglycaemia by EHR review and computed a positive predictive value (PPV) of the algorithm to assess its performance. We refined the algorithm by removing poor performing codes (PPV ≤0.5) and computed a Cohen's κ statistic to evaluate the agreement of the EHR reviews. Results: The algorithm identified 642 candidate severe hypoglycaemia events, and we confirmed 455 as true severe hypoglycaemia events, PPV of 0.709 (95% confidence interval: 0.672, 0.744). When we refined the algorithm, the PPV increased to 0.893 (0.862, 0.918) and missed <2.42% (<11) true severe hypoglycaemia events. Agreement between reviewers was high, κ = 0.93 (0.89, 0.97). Conclusions: We translated an ICD‐9‐CM hypoglycaemia algorithm to an ICD‐10‐CM version and found its performance was modest. The performance of the algorithm improved by removing poor performing codes at the trade‐off of missing very few severe hypoglycaemia events. The algorithm has the potential to be used to identify severe hypoglycaemia in real‐world studies of antidiabetic drugs. [ABSTRACT FROM AUTHOR]