Browse Articles

Long-term survival outcomes of neoadjuvant chemotherapy in stage II-III HR+/HER2- breast cancer

Sex and age differences in the association between routine suicide newspaper reporting and change in admissions of suicidal patients: An investigation at an emergency and critical care center in Tokyo

Background The influence of media reporting on suicide and suicide attempts (SA) has been rigorously studied. Previous studies focused on characteristics like celebrity status, sex and age of suicide decedents, which may facilitate suicide and SA occurrence. These studies have informed guidelines for responsible media reporting. However, the nuanced effects on different sex and age groups of suicidal individuals remain less understood. Methods This study examined the association between the characteristics of initial suicide reports in four major newspapers and the differences in admission numbers (Δs) of SA patients across sex and age groups during pre- and post-article release one-week periods. Data from an Emergency and Critical Care Center from 2012 to 2019 were obtained through a review of medical records. Nonparametric MANOVAs were employed to investigate Δs for sex and age groups of SA patients in relation to sex and age of suicide decedents, incident types, and methods. Significant differences were evaluated using Wilcoxon tests. Results A total of 1,205 articles on 676 suicide incidents and 1,081 SA admissions were analyzed. MANOVAs revealed a significant association between the Δs and the reported suicide methods. Δ for females was positively associated with reports of gas poisoning and other infrequently reported methods, whereas Δ for males was negatively associated with gas poisoning. Δ for younger patients was positively associated with infrequently reported methods and negatively with firearm discharge. No significant associations were found between Δ and sex and age of decedents or incident types. Discussion This study demonstrates a differential impact of suicide news articles reporting gas poisoning, infrequently reported suicide methods, and firearm discharge, on SA patient admissions across sex and age groups. These differences may arise from variations in emotional responses to the suicide methods, which can be understood in both psychological and socio-cultural contexts. Further research is needed to clarify the determinants of differential influences to more effectively address and mitigate the risks of suicide and SAs.

Spectrum of Acute Kidney Injury and its Outcome in Chronic Liver Disease Patients in the Emergency Department

Genetic isolation and metabolic complexity of an Antarctic subglacial microbiome

Abstract Microbes inhabiting and evolving in aquatic ecosystems beneath polar ice sheets subsist under energy-limited conditions while in relative isolation from surface gene pools and their common ancestral populations of origin. Samples obtained from beneath West Antarctic Ice Sheet (WAIS) allowed us to examine evolutionary relationships of and identify metabolic pathways in microbial genomes recovered from the Mercer Subglacial Lake (SLM) ecosystem. We obtained 1,374 single-cell amplified genomes (SAGs) from individual bacterial and archaeal cells that were isolated from samples of SLM’s water column and sediments. These genomes reveal that a diversity of microorganisms including Patescibacteria exists in SLM. Comparative analyses show that most genomes correspond to new species and taxonomic groups, with phylogenomic and functional evidence supporting their genetic isolation from marine and surface biomes. Genomic data reveal diverse metabolisms in SLM that are capable of oxidizing organic and inorganic compounds via aerobic or anaerobic respiration. Distinct metabolic guild structures are observed for the subglacial populations, where trophic shifts from organotrophy to chemolithotrophy may depend on oxygen availability. Our SAG data suggest versatile metabolic capabilities in the characterized microbial assemblage, reveal key energy-generating strategies in the subglacial aquatic ecosystem, and provide a framework to assess microbial evolution beneath WAIS.

Origin of Anion‐Rich Solvation Structures in Siloxane Electrolytes

Abstract High‐voltage lithium (Li) metal batteries (LMBs) are promising next‐generation high‐energy‐density rechargeable batteries. Siloxane electrolytes exhibit excellent performance in high‐voltage LMBs. Herein, the mechanisms responsible for the Li metal compatibility and high‐voltage resistance of siloxane electrolytes were probed by classical molecular dynamics (MD) simulations, first‐principles calculations, and experimental characterizations. Siloxane electrolytes have been demonstrated to deliver anion‐rich solvation structures, which are induced by weak Li ion (Li + )–solvent interactions and strong Li + –anion interactions. The silicon (Si)─oxygen (O) bond energy of siloxane is larger than that of carbon (C)─O of C‐siloxane (replacing Si atoms in siloxane with C atoms) because the atomic radius of Si is larger than that of C, and the Pauli exclusion of Si is smaller than that of C. Additionally, ab initio molecular dynamics (AIMD) simulations revealed that the decomposition of siloxane produces substances containing Si─O fragments on Li metal surfaces, which is beneficial for interfacial stability. This work reveals the mechanism of interfacial stability and intrinsic stability of siloxane electrolytes, providing a theoretical basis for the practical application of siloxane electrolytes in high‐voltage LMBs.

Factors influencing referral among women attending post abortion care at tertiary hospitals in Northern Uganda

How fast-and-frugal trees can inform diagnostic and intervention decisions for enhancing elite athlete performance

The key to fostering the individual potential of an elite athlete lies in deciding what to prioritize in training. Heuristic decision tools such as fast-and-frugal trees (FFTrees) have proven to be effective and suitable for identifying promising determinants in this context. FFTrees are binary decision trees that can make decisions based on only one reason. The objective of this study was to examine the applicability of FFTrees to inform intervention decisions in elite athletes. We aimed to create FFTrees and evaluate their ability to determine individually beneficial interventions. We collected cognitive, psychosocial, and motor-performance diagnostic data from 466 German elite athletes in different sports disciplines. First, we used principal component analysis to identify components representing types of interventions across sports. These served as cues for the FFTrees. As a result, the PCA identified six cues. Two sport-specific FFTrees were created using these six cues. One FFTree was created for trampoline with four cues (relative grip strength, motor cost, motor inhibition, visual selective attention) and 90% correct predictions. The other FFTree was created for volleyball with four cues (motor inhibition, motor cost, countermovement jump, Y-Balance Test) and 75% correct predictions. To conclude, the high accuracy confirms that FFTrees enable data-based decisions for interventions based on sport-specific demands and the preferences of coaches. We argue that FFTrees are beneficial in projects collecting multidisciplinary variables for personalizing interventions in elite athletes. Coaching practice benefits from using FFTrees by providing reference values when an intervention could enhance performance. In the future, we advocate that team sports develop position-specific FFTrees. In conclusion, FFTrees empower decision-makers by efficiently identifying athletes’ adaptation potentials.

Scoring the Future: Rethinking Prognostication in Acute-on-Chronic Liver Failure

Restoring mucosal barrier homeostasis by in situ formation of a living-synthetic therapeutic coating

Outside Back Cover: Intracellular Delivery of Native Proteins by BioReversible Arginine Modification (BioRAM) on Amino Groups (Angew. Chem. Int. Ed. 34/2025)

Redox‐ and NIR‐Active Iron(III) Triradicals as Catalysts for Radical Polymerization of Acrylamides and Methacrylates

Abstract Two unprecedented redox‐active, low‐spin Fe(III)‐triradical complexes, [Fe(III)(SS‐NHC═S •− ) 3 ]·NHC═S ( 1 ·NHC═S; E═S) and [Fe(III)(SS‐NHC═Se •− ) 3 ] ( 2 ; E═Se) have been synthesized and structurally characterized by SCXRD. They were further characterized spectroscopically using IR, Raman, EPR, and UV–vis‐NIR spectroscopy. The low‐spin electronic configuration of the central Fe(III) ion ( 1 ) and the nature of the magnetic interaction between the Fe(III) center and the three unpaired electrons in 1 have been investigated by magnetic measurements. In addition, the bonding stability and electron density distribution in 1 were studied by quantum chemical calculations and correlated with experimental results. Finally, a series of well‐defined functional homopolymers were synthesized via catalytic polymerization reactions using [Fe(III)(SS‐NHC═S •− ) 3 ] ( 1 ) as a catalyst at ambient temperature. These reactions yielded poly( N , N ‐dimethylacrylamide) (PDMA), poly( N ‐isopropyl acrylamide) (PNIPAM), poly(dimethyl amino ethyl methacrylate) (PDMAEMA), and poly(benzyl methacrylate) (PBzMA) with low dispersities ranging from 1.2 to 1.22. The successful synthesis of various diblock copolymers confirmed excellent chain‐end fidelity of the synthesized homopolymers. These homopolymers and diblock copolymers highlight the versatile catalytic polymerization reactions of these Fe‐radical complexes. Herein, we present a report on the polymerization of various acrylamides and methacrylates using a redox‐active Fe‐dithiolene complex for the first time.

Morphology of the glymphatic and meningeal lymphatic structures of the bottlenose dolphin

Declines and pronounced state-level variation in clozapine use among Medicare patients

Schizophrenia-spectrum disorders are debilitating and contribute to a substantial economic burden. Clinicians have historically underutilized clozapine, an atypical antipsychotic traditionally reserved for treatment-resistant schizophrenia, due to the medication’s adverse effect profile and associated management requirements, concerns of complications from poor treatment adherence, and inadequate training/exposure to its use. In addition to alleviating schizophrenia symptoms when multiple other medications have failed, clozapine has other areas of demonstrated effectiveness, such as reduced suicide ideation and action, aggression, substance use, and all-cause mortality benefits that compel its use. This study aimed to characterize clozapine utilization by United States (US) Medicare patients from 2015 to 2020. Additionally, we identified the states that prescribed significantly different amounts than the national average. We observed a steady decrease in clozapine use adjusted for population (−18.0%) and spending (−24.9%) over time. For all years, there was pronounced geographic heterogeneity (average: nine-fold) in population-corrected clozapine use. Massachusetts (2015−20: 95.4, 82.7, 76.8, 72.2, 71.2, 63.7 prescriptions per thousand enrollees) and South Dakota (2015−20: 78.0, 77.4, 78.4, 75.6, 72.0, 71.6) were the only states that prescribed significantly more than average, and none prescribed significantly less. Clozapine use by US Medicare patients is low, decreasing, and concerning for underutilization—patterns previously identified for US Medicaid recipients. Further study of the reasons for the pronounced state variation is needed. Education interventions, training reform, and devices that ease required routine blood monitoring are all practical solutions to optimize clozapine use.

Authors Response: Potential Confounding Factors in the Use of the C-reactive Protein/Procalcitonin Ratio as a Prognostic Tool in Intensive Care Unit Patients with Sepsis

Bioengineered hybrid dual-targeting nanoparticles reprogram the tumour microenvironment for deep glioblastoma photodynamic therapy

Outside Front Cover: Exploring Mesoionic Imine‐Carbodiimide (MII‐CDI) Adducts: 1,3 H‐Shift, N(I) Compounds and Guanidinate‐Type Ligands (Angew. Chem. Int. Ed. 34/2025)

Potential ecological risk and accumulation of heavy metal(loid)s in soils of Macao in China as a non-industrial and tourist City

Detecting infrared UAVs on edge devices through lightweight instance segmentation

Motivation Infrared unmanned aerial vehicle (UAV) detection for surveillance applications faces three conflicting requirements: accurate detection of pixel-level thermal signatures, real-time processing capabilities, and deployment feasibility on resource-constrained edge devices. Current deep learning approaches typically optimize for one or two of these objectives while compromising the third. Method This paper presents YOLO11-AU-IR, a lightweight instance segmentation framework that addresses these challenges through three architectural innovations. First, Efficient Adaptive Downsampling (EADown) employs dual-branch processing with grouped convolutions to preserve small-target spatial features during multi-scale fusion. Second, HeteroScale Attention Network (HSAN) implements grouped multi-scale convolutions with joint channel-spatial attention mechanisms for enhanced cross-scale feature representation. These architectural optimizations collectively reduce computational requirements while maintaining detection accuracy. Third, Adaptive Threshold Focal Loss (ATFL) introduces epoch-adaptive parameter tuning to address the extreme foreground-background imbalance inherent in infrared UAV imagery. Results YOLO11-AU-IR is evaluated on the AUVD-Seg300 dataset, achieving 97.7% mAP@0.50 and 75.2% mAP@0.50:0.95, surpassing the YOLO11n-seg baseline by 1.7% and 4.4%, respectively. The model reduces parameters by 24.5% and GFLOPs by 11.8% compared to YOLO11n-seg, while maintaining real-time inference at 59.8 FPS on an NVIDIA RTX 3090 with low variance. On the NVIDIA Jetson TX2, under INT8 CPU-only deployment, YOLO11-AU-IR retains 95% mAP@0.50 with minimal memory footprint and stable performance, demonstrating its practical edge compatibility. Ablation studies further confirm the complementary contributions of EADown, HSAN, and ATFL in enhancing accuracy, robustness, and efficiency. Code and dataset are publicly available at https://github.com/chen-yuzhi/YOLO11-AU-IR.

Nature variations of OsNLP4 responsible for nitrogen use efficiency divergence in the two rice subspecies

Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning

Abstract Accurate and timely prediction of high-flow nasal cannula (HFNC) treatment failure in patients with acute hypoxemic respiratory failure (AHRF) can lower patient mortality. Previous studies have highlighted inconsistencies in the predictive performance of existing indices, such as ROX and mROX, which are limited by their reliance on oxygenation parameters alone. To address this, we developed a machine learning-based predictive model using temporal data from AHRF patients, aimed at facilitating quicker development of individualized treatment plans and intervention strategies for healthcare professionals. We extracted 15 non-invasive and 15 laboratory features, including patient demographic characteristics, Glasgow Coma Scale, blood gas analysis, chemical assay, and complete blood cell count features. In addition to five machine learning models and an ensemble classifier, an long short-term memory (LSTM) network was included to assess deep learning performance on time-series data. Our study enrolled 427 patients with 498 treatment records. The soft-voting ensemble algorithm achieved an optimal predictive performance with an AUC of 0.839 (95% CI 0.786–0.889) for the all-features model, while logistic regression using common features achieved an AUC of 0.767 (95% CI 0.704–0.825), outperforming ROX and mROX indices. Incorporating blood gas analysis features improved the non-invasive model’s performance by 0.104. This study introduces a machine learning model integrated with a dynamic real-time alert system for predicting HFNC treatment failure in AHRF patients, demonstrating improved performance over traditional indices in internal validation and showing potential for decision support in select healthcare settings.