Antiferroelectric SnO Network with Amorphous Surface for Electrochemical N Fixation.

Electrochemical nitrogen fixation-a sustainable pathway for converting abundant N into NH using renewable energy-holds transformative potential for revolutionizing artificial nitrogen cycles. Nevertheless, even the state-of-the-art catalytic systems also suffer from inadequate N adsorption capacity, which critically limits ammonia production rates and Faradaic efficiency (FE). To overcome this bottleneck, we strategically leveraged the antiferroelectric properties of SnO to establish dipole-dipole interactions with N molecules, synergistically enhancing both N adsorption and activation kinetics.

Synergy effect of oxyphilic cobalt‑tungsten oxide and copper‑cobalt alloy on water dissociation and hydrogen adsorption for pH-universal hydrogen evolution.

Developing pH-universal hydrogen evolution reaction (HER) electrocatalysts demands the simultaneous optimization of water dissociation kinetics and hydrogen adsorption. Herein, a CuCo/CoWO heterostructure with an area of 600 cm was fabricated via a facile one-step electrodeposition strategy. It only needs 193.

Effect of the reflection at the soft tissue-bone interface on propagating characteristics of radial extracorporeal shock wave in soft tissue.

This study aims to investigate the effect of reflection at the soft tissue-bone interface on shock wave propagation within soft tissue using finite element methods. Results showed that reflection caused obvious differences in the propagation process and attenuation characteristics of shock waves. The energy flux density (EFD) at the same target was proportional to the impact pressure.

Who determines nasopharyngeal carcinoma inpatients' medical expenditure? Importance factor ranking using the random forest model.

Background: Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor most commonly in China. In 2013, NPC incidence and mortality in China were also at high levels worldwide, which poses a great health burden in China. This study analyzes the medical expenditure and influencing factors of inpatients with NPC and aims to provide reference suggestions for reducing medical expenditure for NPC.

Blood pressure in the first 6 hours for older adults with stroke after endovascular therapy: a pooled analysis of the DEVT and RESCUE BT randomized clinical trials.

Optimal systolic blood pressure (SBP) targets after endovascular therapy (EVT) for stroke in older adults (≥ 65 years) remain undefined. This study assessed age-stratified associations between early post-EVT SBP (first 6 h) and outcomes. Post hoc analysis of two trials.

The FN1-targeted delivery of low-molecular-weight heparin to venous clots revolutionizes thrombosis treatment in CVT.

The systemic administration of low-molecular-weight heparin (LMWH) as the standard initial treatment for venous thromboembolism is intended to prevent thrombus growth during the acute phase and prevent thrombus recurrence during the chronic phase. However, precisely controlling the specificity and localization of LMWH presents significant challenges, which has necessitated research into targeted delivery methods. Furthermore, due to different thrombosis mechanisms, tailored pharmacologic interventions are needed, particularly for arterial and venous thrombosis.

In Situ Alloying CsFAPbl Perovskite Quantum Dots with Suppressed Composition Confinement for Solar Cells with 18.34% Efficiency.

Cesium-formamidinium lead triiodide perovskite quantum dots (CsFAPbI PQDs) exhibit high potential for efficient photovoltaics due to their ideal bandgap and good phase stability. However, synthesizing the CsFAPbI PQDs with tunable composition and high optoelectronic properties remains a significant challenge due to the large difference in the crystallization temperature and chemical environment between the mono-cation Cs- and FA-based PQDs. Herein, a low-temperature sequential injection (LTSI) strategy is introduced to in situ alloying CsFAPbI PQDs for efficient solar cells.

Fatal Umbilical Cord Hemorrhage Triggering Neonatal Demise: Clinicopathological Insights from the Case Analysis.

Background: Umbilical cord hemorrhage (UCH) is a rare but catastrophic obstetric emergency associated with nearly 50% fetal mortality, and its precise pathogenic mechanisms remain elusive in clinical practice. The pathophysiological cascade involves hemorrhagic expansion from ruptured umbilical vessels predominantly the umbilical vein which generates compressive forces on adjacent umbilical arteries within the constrained Wharton's jelly. This acute vascular compromise precipitates the sudden cessation of fetoplacental circulation, culminating in irreversible hypoxic-ischemic injury.

A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks.

Purpose: 3D U-Net deep neural networks are widely used for predicting radiotherapy dose distributions. However, dose prediction for lung cancer IMRT is limited to conventional radiotherapy, with significant errors in predicting the intermediate and low-dose regions.

Methods: We included a mixed dataset of conventional radiotherapy and simultaneous integrated boost (SIB) radiotherapy with various prescription schemes.

Focus-tunable real-time imaging system based on ultrathin perovskite curved image sensor with hierarchical mesh design.

The human visual system's real-time focus-tunable imaging capability has inspired curved imaging system development. However, curved image sensors mimicking the human retina typically lack tunable curvature to match the curved Petzval surface throughout the focus-tunable range. Here, we propose a focus-tunable real-time curved imaging system based on a tunable-curvature perovskite curved image sensor.

Explainable mortality prediction models incorporating social health determinants and physical frailty for heart failure patients.

There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable machine learning (ML) models to assess the prognostic value of SDOH and PF at multiple time points. We analyzed data from adult patients admitted to the intensive care unit (ICU) for the first time with a diagnosis of HF.

Diminishing returns of task-oriented interaction in digitally-mediated dynamic teams: evidence from amateur sports organizing.

Although extant research has emphasized task-oriented processes in teams, its focus on dynamic teams in digital environments remains limited, particularly regarding non-linear effects. Integrating attention capacity theory and activation theory, this study proposes a curvilinear (inverted-U) relationship between task-oriented interaction and team organizing efficiency in digitally-mediated teams. Analyzing 455 spontaneous sports teams from an open-boundary organizational platform revealed support for the proposed curvilinear relationship, with team identification moderating the effect.

Laser-Induced 3D Graphene Enabled Polymer Composites with Improved Mechanical and Electrical Properties Toward Multifunctional Performance.

Conductive graphene-based composites are attracting substantial interest due to their excellent mechanical and electrical properties for potential applications in electronics. Typically, such composites are fabricated by infiltrating the 3D graphene framework with the polymer matrix. However, the production of 3D graphene foams is limited by the challenges in preparing graphene dispersions, while 3D printing presents a significant breakthrough in the fabrication of desired 3D graphene-based structures.

[Clinical and histological evaluation of three-dimensional printing individualized titanium mesh for alveolar bone defect repair].

Objectives: To evaluate the osteogenic efficacy of three-dimensional printing individualized titanium mesh (3D-PITM) as a scaffold material in guided bone regeneration (GBR).

Methods: 1) Patients undergoing GBR for alveolar bone defects were enrolled as study subjects, and postoperative healing complications were recorded. 2) Postoperative cone beam computed tomography (CBCT) scans acquired at least 6 months post-surgery were used to calculate the percentage of actual bone formation volume.

Multisystem fatal metastasis of large cell lung carcinoma (LCLC) masquerading as acute cerebral infarction: A case report.

Rationale: Large cell lung carcinoma (LCLC) is a rare undifferentiated malignant epithelial tumor of the lung. The diagnostic complexity of LCLC stems from its pronounced histological heterogeneity and diverse clinical presentation, particularly when extrapulmonary manifestations constitute the initial disease presentation, complicating early detection.

Patient Concerns: A 58-year-old smoker presented with acute-onset dizziness, lethargy, and communication difficulties lasting 1 day.

Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects.

Mg-based thermoelectric materials are becoming ideal candidates for thermoelectric applications, owing to their eco-friendliness and abundant availability. To overcome the limitations of conventional experimental methods and accelerate the development of high-performance thermoelectric materials, this study leverages high-throughput computing and machine learning to perform a comprehensive and systematic evaluation of a vast array of Mg-based thermoelectric materials. Our findings highlight the pivotal role of thermal expansion in modulating the thermoelectric figure of merit (ZT) in Mg-based systems.

Privacy-preserving federated transfer learning for enhanced liver lesion segmentation in PET-CT imaging.

Positron Emission Tomography-Computed Tomography (PET-CT) evolution is critical for liver lesion diagnosis. However, data scarcity, privacy concerns, and cross-institutional imaging heterogeneity impede accurate deep learning model deployment. We propose a Federated Transfer Learning (FTL) framework that integrates federated learning's privacy-preserving collaboration with transfer learning's pre-trained model adaptation, enhancing liver lesion segmentation in PET-CT imaging.

Study on the properties of modified octadecyltrichlorosilane (OTS) anti-relaxation coatings for cesium atomic cell: a molecular dynamics simulation.

To investigate the anti-relaxation performance of FOTS-modified OTS coatings on the inner walls of cesium (Cs) atomic cell, this study employs molecular dynamics (MD) simulations to explore the self-assembly process of FOTS-modified OTS molecular chains on the SiO (001) surface and evaluates the effects of FOTS chain amounts, water molecule content, and temperature on the diffusion behavior of Cs atoms. Results show that the optimized interface model of the FOTS-modified OTS coating and SiO substrate achieves thermodynamic and energetic equilibrium under the conditions of 25 °C and 2000 ps. The film formation process of FOTS-modified OTS chains on SiO surfaces involves three distinct stages: initial anchoring, conformational rearrangement, and structural relaxation and equilibrium configuration.

Effect of toe box size on basketball-specific movement performance.

Purpose: While an expanded toe box (TB) design offers notable benefits for foot health in basketball players, its impact on movement performance remains insufficiently elucidated. This study aimed to explore this relationship and inform the development of advanced shoe designs.

Methods: We conducted a controlled laboratory study with a within-subject crossover design.

Prognostic value of left atrial strain analysis using cardiac magnetic resonance feature tracking technology for patients with acute myocarditis.

Background: Acute myocarditis (AM) is induced by multiple factors, and its clinical symptoms and prognosis vary significantly. Existing diagnostic and prognostic assessment methods have certain limitations. Myocardial strain analysis based on cardiac magnetic resonance feature tracking (CMR-FT) can detect myocardial damage in the subclinical stage, but studies on the correlation between left atrial (LA) strain and the prognosis of AM are relatively limited.

Early-stage diagnosis of HIV-associated neurocognitive disorders via multiple learning models based on resting-state functional magnetic resonance imaging.

Background: People living with human immunodeficiency virus (PLWH) are at risk of human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND). The mildest disease stage of HAND is asymptomatic neurocognitive impairment (ANI), and the accurate diagnosis of this stage can facilitate timely clinical interventions. The aim of this study was to mine features related to the diagnosis of ANI based on resting-state functional magnetic resonance imaging (rs-fMRI) and to establish classification models.

Supramolecular Microspheres Fabricated from Macrocycle-Incorporated Polymers and Their Biomedical Applications Based on Host-Guest Recognition.

Microspheres have emerged as a pivotal platform for micron-scale drug delivery, yet their utility has been greatly hindered by limitations in biodegradability, drug loading efficiency, and release kinetics, underscoring the urgent need for a next-generation microsphere platform that integrates high performance, scalability, and multifunctionality. Leveraging host-guest recognition, a series of macrocycle-incorporated polymers is synthesized and engineered a new class of supramolecular microspheres, which feature precisely tunable components, including host molecules, guest cargoes, and polymer components, as well as customizable morphologies, while enabling cost-effective, large-scale production. Following systematic validation of the host-guest recognition between β-cyclodextrin (β-CD) and lanreotide, we developed supramolecular microspheres (LAN@S-CPMs) that achieve a drug loading capacity and release duration approximately twice that of conventional microspheres, effectively curbing the disease progression of acromegaly.

Predicting Prognosis of Light-Chain Cardiac Amyloidosis by Magnetic Resonance Imaging and Deep Learning.

Aims: Light-chain cardiac amyloidosis (AL-CA) is a progressive heart disease with high mortality rate and variable prognosis. Presently used Mayo staging method can only stratify patients into four stages, highlighting the necessity for a more individualized prognosis prediction method. We aim to develop a novel deep learning (DL) model for whole-heart analysis of cardiovascular magnetic resonance-derived late gadolinium enhancement (LGE) images to predict individualized prognosis in AL-CA.

Assessment and correction of the respiratory phase misalignment using different signals in 4-dimensional imaging and free-breathing gated radiotherapy.

Objective: To propose and validate a correction method for the respiratory phase shift of using different motion management systems in the 4-dimensional (4D) imaging and free-breathing gated radiotherapy.

Materials And Methods: Synchronized cone-beam CT (CBCT) and optical surface images (OSI) of 30 patients at two institutions were included. Reference diaphragm-signals were extracted from CBCT projections using Amsterdam-Shroud (AS) method.