Dual active site bridging adsorption mechanism of Ni-WO for boosting urea electrolysis at large current density.

Nickel-based catalysts have recently become promising candidates for urea electrolysis. However, their application is hindered by strong interaction with *COO intermediates. Herein, oxyphilic WO is introduced into Ni to construct dual active sites for regulating reaction intermediate adsorption.

A Dual-Discriminator Generative Adversarial Network for Anomaly Detection.

Multivariate time series anomaly detection has shown potential in various fields, such as finance, aerospace, and security. The fuzzy definition of data anomalies, the complexity of data patterns, and the scarcity of abnormal data samples pose significant challenges to anomaly detection. Researchers have extensively employed autoencoders (AEs) and generative adversarial networks (GANs) in studying time series anomaly detection methods.

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer.

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially.

The Next Layer: Augmenting Foundation Models with Structure-Preserving and Attention-Guided Learning for Local Patches to Global Context Awareness in Computational Pathology.

Foundation models have recently emerged as powerful feature extractors in computational pathology, yet they typically omit mechanisms for leveraging the global spatial structure of tissues and the local contextual relationships among diagnostically relevant regions - key elements for understanding the tumor microenvironment. Multiple instance learning (MIL) remains an essential next step following foundation model, designing a framework to aggregate patch-level features into slide-level predictions. We present EAGLE-Net, a structure-preserving, attention-guided MIL architecture designed to augment prediction and interpretability.

Epidermal Growth Factor Receptor (EGFR)-Targeting Peptides and Their Applications in Tumor Imaging Probe Construction: Current Advances and Future Perspectives.

The epidermal growth factor receptor (EGFR) is a key target for both cancer diagnosis and therapeutic interventions. Assessing EGFR expression before therapy has become routine in clinical practice, yet current methods like biopsy and immunohistochemistry (IHC) have significant limitations, including invasiveness, limited repeatability, and lack of real-time, whole-body data. EGFR-targeted imaging has emerged as a promising alternative.

Dual-signal amplified aptasensor based on PtPdAu mesoporous nanozymes with DNA walker-CHA for ultrasensitive DON detection.

Deoxynivalenol (DON), a prevalent trichothecene mycotoxin in cereals, poses severe threats to human health and agricultural sustainability. Conventional detection methods face limitations in sensitivity and operational complexity for on-site applications. Herein, we develop an electrochemical aptasensor integrating dual-signal amplification strategies: Nb.

First-line MET tyrosine kinase inhibitors versus immunotherapy ± chemotherapy for patients with MET exon 14 skipping mutant metastatic NSCLC.

Purpose: First-line treatment options for MET exon 14 skipping (METex14) mutant metastatic non-small cell lung cancer (NSCLC) vary due to differences in drug approvals and clinical experience. This study investigates factors influencing outcomes with first-line MET tyrosine kinase inhibitors (TKI) versus immune checkpoint inhibitors (ICI)±chemotherapy.

Experimental Design: Clinicopathologic data were collected from patients with metastatic METex14 mutant NSCLC receiving first-line MET TKI or ICI±chemotherapy at five centers.

Design, synthesis, and biological evaluation of Osimertinib-Cy7 (OSA-Cy7) conjugate as potential theranostic agent targeting activating EGFR mutations.

Accurately predicting the therapeutic response of non-small cell lung cancer (NSCLC) patients to tyrosine kinase inhibitors (TKIs) is of significant clinical importance. The use of TKIs in clinical is primarily guided by the detection of EGFR gene mutations. However, the current EGFR mutation assays face challenges such as inconsistent correlation with therapeutic outcomes, inconvenient sample availability and limited sensitivity.

Enhanced digital pathology image recognition via multi-attention mechanisms: the MACC-Net approach.

Digital pathology has revolutionized cancer diagnosis through microscopic analysis, yet manual interpretation remains hindered by inefficiency and subjectivity. Existing deep models for osteosarcoma cell nucleus recognition suffer from the difficulty of capturing hierarchical relationships in single-dimensional attention mechanisms, leading to inaccurate edge recognition. Furthermore, the fixed receptive field of CNNs limits the aggregation of multi-scale information, hindering the differentiation of overlapping cells.

Luminescence modulation via solvent-triggered single-crystal-to-single-crystal transformation in a non-porous molecular crystal.

Stimuli-responsive crystalline materials have demonstrated significant potential for developing multifunctional systems. Achieving precise structural modulation in non-porous crystalline phases remains a critical challenge, particularly in correlating molecular-level conformational changes with macroscopic properties. Here, we report a solvatomorphic crystalline system, (1-azabicyclo[2.

Fast Heterogeneous Graph Neural Network Generation via Meta Contrastive Learning.

Heterogeneous Graph Neural Networks (HGNNs) are extensively utilized for information extraction from intricate heterogeneous graphs. Despite their prevalence, crafting an efficient HGNN architecture is a formidable challenge, necessitating both extensive domain expertise and considerable expenditure of time to fine-tune network hyperparameters. Heterogeneous Graph Neural Architecture Search (HGNAS) algorithms offer a viable solution, automatically discovering optimal network architectures for predefined tasks within a given search space, and frequently surpassing the performance of manually designed models.

Reversal of amino acid metabolism patterns between circulating blood and tumors as a new biomarker for the Zhengxu Xieshi syndrome in patients with esophageal squamous cell carcinoma.

Objective: To uncover the biological foundation of the prevailing TCM syndrome in individuals with Esophageal squamous cell carcinoma (ESCC), Zhengxu Xieshi (ZXXS), which is characterized by a deficiency in vital and an excess in evil .

Methods: We investigated shifts in vital by quantifying systemic metabolic changes in the peripheral blood. Serum metabolomic profiling was conducted on the ESCC cohort 1 along with a matched healthy control cohort.

On-chip mode multiplexing communication based on pixelated superstructure duty cycle optimization.

Mode multiplexing, which transmits parallel data information via orthogonal light field modes, has attracted extensive research attention in on-chip high-capacity optical communication. To reduce the device footprint, a mode multiplexer with sub-wavelength pixelated superstructures is proposed. However, due to the accumulation of device fabrication deviation, the communication performance of mode multiplexer will be deteriorated.

Rb-He excimer involved in four-wave mixing process to produce redshifted collimated blue light.

The four-wave mixing (FWM) process is an important method for generating collimated blue light from alkali vapor. In this paper, we introduced high-pressure He gas to broaden the absorption linewidth of Rb atoms for the pump laser. By using an optical parametric oscillator (OPO) that outputs tunable wavelength pump laser, we performed two-photon excitation of the Rb-He mixture and achieved redshifted and asymmetric broadened collimated light (CL) around 420 nm.

High-contrast dual-modal imaging of absorption and scattering using low-rank and sparse matrix decomposition of light fields.

Optical absorption and scattering are critical properties of biological tissues, but strong background light often obscures this information, limiting imaging contrast and the visualization of tissue microstructures. Current methods for enhancing imaging contrast rely on image processing and noise suppression, but often lose critical details under strong background light. To address this issue, we propose a dual-modal imaging technique based on a low-rank and sparse matrix decomposition (LRSD) of light fields, enabling simultaneous high-contrast imaging of absorption and scattering and significantly improving imaging performance.

Gene signatures characterizing driver mutations in lung squamous carcinoma are predictive of the progression of pre-cancer lesions.

Unlabelled: Patients with lung squamous cell carcinoma (LUSC) are often diagnosed at advanced stages, limiting opportunities for early intervention. LUSC develops through a multistep progression from low-grade lesions to high-grade lesions, including carcinoma in situ (CIS), of which approximately half progress to invasive cancer while the other half regress. Although frequent mutations and copy number alterations have been documented in LUSC and observed in precursor lesions, their prognostic significance in precancerous stages remains largely unexplored.

Brønsted bases promote interfacial proton transfer for enhanced biomass electrocatalysis.

Efficient electrooxidation of 5-hydroxymethylfurfural (HMF) is essential for the production of valuable chemicals from biomass. However, the sluggish interfacial proton transfer kinetics during the reaction significantly hinder its progress. This study proposes that the introduction of Brønsted bases on cobalt-based catalysts can enhance HMF oxidation by modulating interfacial proton transfer kinetics.

Computational discovery of three two-dimensional arsenides with high stability and exceptional solar-to-hydrogen conversion efficiency.

Photocatalytic water splitting (PWS) for hydrogen production is a crucial strategy to address the energy crisis and environmental pollution. In this study, we investigated the stability, electronic structure, and PWS performance of three 2D materials, SiAs, GeAs, and SnAs, using first-principles calculations. Our results reveal that all three monolayers exhibit high stability, with heat resistance up to 1000 K.

The sustained effect of 5-week EmotionCore mindfulness training on emotion regulation and emotional intelligence: heterogeneous benefits for depression and anxiety across subgroups.

Background: The present study investigated the sustained effects of a 5-week EmotionCore mindfulness training program on emotion regulation, emotional intelligence (EI), trait mindfulness, depression, and anxiety among 120 undergraduates, while exploring the mediating mechanisms of training effect and heterogeneous effects across subgroups.

Methods: Participants were randomly assigned to a mindfulness group (n=60) or a waitlist control group (n=60), with assessments conducted at baseline (T1), post-training (T2), and one-month follow-up (T3).

Results: Results demonstrated that mindfulness training significantly enhanced adaptive cognitive emotion regulation strategies (ACERSs), EI, and trait mindfulness at both T2 and T3.

Exploring the correlation between nocturnal awakenings and occupational burnout in internal medicine physicians via online survey.

This study aims to screen for occupational burnout among internal medicine physicians through an online survey, and to explore the relationship between burnout and anxiety, depression, stress, and sleep. The findings are expected to help improve the mental and physical health of physicians, thereby enhancing the medical quality in hospitals. Maslach burnout inventory-general survey (MBI-GS) was used to assess physicians' burnout status, Minnesota satisfaction questionnaire (MSQ) was used to evaluate the job satisfaction, Generalized anxiety disorder 7 (GAD-7), Patient health questionnaire-9 (PHQ-9), and Perceived stress scale (PSS) were used to assess the psychological status, and Pittsburgh sleep quality index (PSQI) was used to evaluate sleep conditions.

Distinct Clinicogenomic Features and Immunotherapy Associations in Pulmonary Sarcomatoid Carcinoma: A Multicenter Retrospective Study.

Introduction: Pulmonary sarcomatoid carcinoma (PSC) is a rare NSCLC subtype with poor prognosis. Outcomes to immune checkpoint inhibitors (ICIs) and genomic features in PSC remain underexplored compared with other NSCLC subtypes.

Methods: Patients from three institutions and the National Cancer Database (NCDB) with metastatic NSCLC treated with ICI alone or with chemotherapy were identified.

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remains limited, and single-feature biomarkers like PD-L1 prove inadequate. We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy.

Identification Of ANGPT2, FLT3, IGF1, and SPP1 associated with glycolysis and PI3K/Akt signaling pathway in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a malignant hepatic neoplasm characterized by rapid cellular proliferation facilitated by aerobic glycolysis. Additionally, the PI3K/Akt pathway enhances angiogenesis, thereby promoting the growth of HCC cells. This study aimed to identify biomarkers associated with glycolysis and the PI3K/Akt signaling pathway in HCC.

Correction: Bronchoalveolar lavage cell percentages as diagnostic markers of immune checkpoint inhibitor pneumonitis.

[This corrects the article DOI: 10.3389/fmed.2025.

Enhancing Graph Neural Networks for Out-of-Distribution Graph Detection.

Graph neural networks (GNNs) have shown promise in graph classification tasks, but they struggle to identify out-of-distribution (OOD) graphs often encountered in real-world scenarios, posing a significant obstacle for their open-world deployment. Due to the unpredictable nature of the various distributions to which OOD graphs adhere, the challenge of OOD graph detection lies in enabling models to capture distribution differences between in-distribution (ID) and OOD graphs. Current methods often introduce a subset of OOD patterns, such as synthetic OOD graphs, to facilitate learning the discrimination between ID and OOD graphs.