Stroke-Aware CycleGAN: Improving Low-Field MRI Image Quality for Accurate Stroke Assessment.

Low-field portable magnetic resonance imaging (pMRI) devices address a crucial requirement in the realm of healthcare by offering the capability for on-demand and timely access to MRI, especially in the context of routine stroke emergency. Nevertheless, images acquired by these devices often exhibit poor clarity and low resolution, resulting in their reduced potential to support precise diagnostic evaluations and lesion quantification. In this paper, we propose a 3D deep learning based model, named Stroke-Aware CycleGAN (SA-CycleGAN), to enhance the quality of low-field images for further improving diagnosis of routine stroke.

Correlation between various loads and apoptosis in medial platform chondrocytes in knee varus deformity.

Background: This study aimed to systematically investigate how different degrees of mechanical loading caused by varying varus angles affect stress distribution and chondrocyte apoptosis in the medial tibial plateau of the knee joint. Specifically, it integrates finite-element analysis to simulate biomechanical stress patterns and evaluates the molecular responses (Piezo1, Bax, Bcl-2, and caspase-3 expressions) to elucidate the mechanobiological interplay contributing to cartilage degeneration.

Methods: Four knee models with varus angles (6°, 9°, 12°, and 15°) were constructed from computed tomography images.

Microwave-Enhanced Catalytic Performance of Benzene Oxidation on MOF-Derived Mn/Ce-Co Oxides.

Microwave-assisted processing has shown tremendous promise in accelerating chemical reactions and reducing energy consumption through targeted dielectric heating. This study develops MOF-derived Mn-Co and Ce-Co oxide catalysts for energy-efficient benzene oxidation via microwave catalysis. The MnCo spinel oxides (particularly MnCo11-400) exhibit superior microwave absorption and catalytic activity due to enhanced oxygen mobility and tailored dielectric properties.

Flexible fibre-shaped fuel cells with gel-mediated internal pressure encapsulation.

The development of flexible fuel cells has been hindered by the rigid components and stringent requirements for pressure encapsulation and fuel sealing. Here we report an adaptive internal pressure encapsulation strategy that leverages the dynamic swelling behaviour of woven cotton fibres enclosed in a gel matrix in methanol. This strategy achieves simultaneous interfacial self-reinforcement and pressure modulation, enabling the fabrication of fibre-shaped direct methanol fuel cells.

Ionoelastomer Synapses With Configurable Synaptic Plasticity.

An organic heterostructure, composed of an ionoelastomer that comprises polycationic chains and mobile anions, and a semiconducting polymer, forms a new class of artificial synapses. These ionoelastomer synapses update their synaptic weights by modifying device conductance through the spatial redistribution of anions in response to electrical stimuli. The memory effect of these synapses is highly dependent on the specific anion species present, providing a unique means to modulate synaptic plasticity simply through anion selection.

Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease.

Background: Diabetic kidney disease (DKD) is a severe global complication of diabetes, yet its molecular mechanisms remain incompletely understood. This study aimed to investigate the role of protein glycosylation in DKD pathogenesis and its association with gene expression changes, with the goal of identifying diagnostic biomarkers and personalized therapeutic targets.

Methods: Integrated bioinformatics and machine learning approaches were applied to analyze multiple gene expression datasets.

Influence of antibacterial peptides in drinking water on growth and intestinal health of European meat pigeon.

This study evaluated insect-derived antimicrobial peptides (AMPs) as sustainable alternatives to antibiotics in European meat pigeon production. Using a randomized design, 360 breeding pairs with squabs were allocated to four groups (n = 90/group; 1:1 sex ratio). Controls received untreated water, while treatment groups received 200, 400, or 600 mg/kg AMPs in drinking water for 30 days.

Causal association of metabolic biomarkers and the risk of esophageal cancer: A 2-sample Mendelian randomization study.

Esophageal cancer (EC) is a major malignancy with poor prognosis and a 5-year survival rate below 30%. Metabolites are key biomarkers for cancer diagnosis, prognosis, and treatment. Their changes and metabolic reprogramming are crucial in EC progression.

P-Doped NiCoS/CoZn-LDH Layered Heterostructures: Synergistically Improved Conductivity and Enhanced OH Adsorption for High-Performance Asymmetric Supercapacitors.

Doping induces lattice distortion and modifies the electronic structure of materials, thereby enhancing their electrical conductivity and chemical stability. The built-in electric field at heterojunction interfaces further promotes charge carrier migration and improves surface reaction kinetics. To leverage these synergistic effects, this study employs a combined approach involving sulfidation, phosphidation, and electrodeposition to achieve P-doping and construct a heterojunction layered structure.

Self-Assembled Bioactive Glass/Starch/GelMA Microspheres for Hemostasis and Bone Regeneration in Bone Trauma Emergency Treatment.

The high incidence of bone trauma has necessitated the development of multifunctional biomaterial implants with both hemostatic and osteogenic properties to address clinical challenges such as bone nonunion and immune rejection associated with nondegradable bone hemostatic materials. In this study, starch/bioactive glass/GelMA-based self-assembling composite microspheres were developed as a novel bone trauma filler exhibiting dual functions of efficient hemostasis and bone regeneration. These micron-scale composite microspheres can be applied to bone trauma sites during surgical procedures or emergency situations.

Enhancing navigation control accuracy of guidance line drawing robot by dual antenna GNSS and MEMS IMU.

At present, there are some problems in the construction of road marking, such as low efficiency and poor precision, so using the guidance line drawing robot is an effective solution. However, satellite signals are easily disturbed by the environment, navigation accuracy and stability cannot meet the needs of robot construction positioning, and the high cost of optical fiber inertial navigation is not conducive to the popularity of the robot. Therefore, a navigation control algorithm based on low-cost GNSS/INS for guidance line drawing robots is proposed in this paper.

Gestational Exposure to Particulate Matter and Preeclampsia Risk: Evidence from a Meta-Analysis and Mendelian Randomization Study.

Preeclampsia (PE) is a severe hypertensive disorder that occurs during pregnancy. Exposure to particulate matter (PM), particularly fine particles such as PM2.5/PM10, has been investigated as a potential environmental risk factor for its development.

TdT/Cas12a-based biosensor for sensitive detection of DNA breakpoints in sperm cryopreservation.

With the advancement of assisted reproductive technology (ART), sperm cryopreservation has become an essential technique in both clinical and research applications. However, maintaining DNA integrity during the freezing and thawing process remains a significant challenge. This study introduces an innovative TdT/Cas12a-based biosensor to enable precise detection and quantification of sperm DNA breakages.

A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.

Accurate localization and identification of protein complexes in cryo-electron tomography (cryo-ET) volumes are essential for understanding cellular functions and disease mechanisms. However, automated annotation of these macromolecular assemblies remains challenging due to low signal-to-noise ratios, missing wedge artifacts, heterogeneous backgrounds, and structural diversity. In this study, we present a hybrid framework integrating You Only Look Once (YOLO) object detection with UNet3D volumetric segmentation, enhanced by density-based spatial clustering of applications with noise (DBSCAN) post-processing for automated protein particle annotation in cryo-ET volumes.

Dose-Dependent Effects of Remimazolam on Early Perioperative Neurocognitive Disorders in Elderly Colorectal Cancer Patients Undergoing Laparoscopic Surgery.

Objective: To study the effect of different doses of remimazolam on the maintenance of anesthesia and its impact on postoperative cognitive function in elderly patients undergoing laparoscopic colorectal surgery.

Methods: A total of 120 elderly patients scheduled for laparoscopic colorectal surgery under general anesthesia (both sexes included, ASA grade (II-III) were randomly assigned to experimental groups (R1-R3) and control group (P), with 30 cases per group. The experimental groups received different remimazolam maintenance doses: R1 (0.

Identification of novel macrophages and bone morphogenetic protein signals causing ectopic calcification and impairing muscle regeneration.

Ectopic calcification is an abnormal regenerative response occurring in various tissues following injury, surgery, or genetic mutations. However, its underlying mechanisms remain unclear. By comparing three macrophage depletion methods using clodronate liposome, Csf1r neutralizing antibody, and macrophage-specific gene deletion (), we found that F4/80(+)Csf1r(-) macrophages were specifically increased in calcified muscles in notexin-injected mice and BaCl-injected mice.

RCSAN residual enhanced channel spatial attention network for stock price forecasting.

This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. The R-CSAN adopts an encoder-decoder architecture, where the encoder extracts feature correlations from historical data through multiple layers of channel-spatial attention modules, and the decoder incorporates a masking mechanism to prevent future information leakage and introduces a cross-attention mechanism to model inter-market correlations. Experiments conducted on four cross-market stock datasets, including Amazon, Maotai, Ping An, and Vanke, demonstrate that R-CSAN significantly outperforms not only traditional baseline models such as ARIMA, LSTM, and CNN-LSTM, but also recent Transformer-based approaches like Informer, Autoformer, and iTransformer on metrics including RMSE, MAE, MAPE, [Formula: see text], and return on investment.

Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.

Purpose: To investigate the feasibility and accuracy of using deep learning and dosiomics features, as well as their combination with dose-volume histogram (DVH) parameters and clinical factors to predict grade 4 radiation-induced lymphopenia (G4RIL) for patients with esophageal cancer (EC) who undergoing radiotherapy (RT).

Methods: This retrospective study enrolled 545 patients with EC who underwent RT between January 2015 and December 2023 from five medical centers, and divided them into a training set, an internal validation set, an external test set 1, and an external test set 2, respectively. Dosiomics (D) and deep learning dosiomics (DLD) models were built to predict the probability of G4RIL based on radiation dose distributions using five-fold cross-validation.

Regulation of mA RNA reader protein OsECT3 activity by lysine acetylation in the cold stress response in rice.

N-Methyladenosine (mA) reader proteins, which recognize mA to regulate RNA metabolism, are important for plant adaptation to the changing environment. It remains unknown how the activities of plant mA reader proteins are regulated in plant responses to stress. Here we show that the rice mA reader protein EVOLUTIONARILY CONSERVED C-TERMINAL REGION 3 (OsECT3), required for rice tolerance to cold, is post-translationally modified by lysine acetylation, which reduces its mA-binding activity.

Assessing the efficacy of the natural disaccharide trehalose in ameliorating diet-induced obesity and metabolic dysfunction.

Trehalose is a naturally occurring disaccharide with versatile commercial applications and health benefits, including promise as a therapeutic for obesity and diabetes. Although numerous previous reports purport the therapeutic uses of orally ingested trehalose, the abundance of glycosidases in the gastrointestinal tract suggest the potential for significant limitations of oral trehalose that have not been addressed. We first fed mice a high-fat diet (HFD) while providing trehalose by both oral and intraperitoneal routes.

Ratiometric fluorescence sensor based on MOF construction to detect enrofloxacin in animal-derived foods.

In this study, gold nanoclusters (AuNCs) were encapsulated in a metal-organic frame ZIF-8 based on the aggregation-induced luminescence effect (AIE), ratio fluorescent probes with strong orange fluorescence (AuNCs@ZIF-8) were successfully synthesized. The structural features of ZIF-8 were used to constrain the spin motion of GSH@AuNCs to trigger the AIE effect, thus improving the fluorescence performance of GSH@AuNCs. The fluorescence of AuNCs@ZIF-8 was significantly enhanced by 6.

How Collectivism and Virtual Idol Characteristics Influence Purchase Intentions: A Dual-Mediation Model of Parasocial Interaction and Flow Experience.

With the rise of virtual idols in marketing, especially in collectivist cultures, their impact on consumer behavior warrants further exploration. This study applies social identity theory, flow theory, and the SOR model to examine how collectivism and virtual idol characteristics (external characteristics, content features, and homophily) influence Chinese consumers' purchase intentions through parasocial interaction (PSI) and flow experience. A survey of 496 respondents, analyzed via structural equation modeling (SEM), shows that collectivism and virtual idol characteristics positively affect PSI, with homophily having the strongest impact.

Developing a generalized nonlinear mixed-effects biomass model at stand-level under different age conditions for Chinese fir based on LiDAR and ground survey data in southern China.

Introduction: Chinese fir is a crucial afforestation and timber species in southern China. Accurate estimation of its stand biomass is vital for forest resource assessment, ecological industry development, and ecosystem management. However, traditional biomass prediction methods often face limitations in terms of accuracy and efficiency, highlighting the need for more robust modeling approaches.

Tumor Pro-Senescence Strategy to Enhance Mild Photothermal Therapy of Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) presents a substantial clinical challenge due to its aggressive nature and resistance to conventional therapies, thereby developing novel treatment strategies is critical. In this work, we take advantage of the insensitivity of heat shock protein (HSP) expression in senescent tumor cells to combine mild photothermal therapy (MPTT) for efficient DLBCL treatment. Insensitivity to HSPs implies that cells are less capable of activating their usual stress protection mechanisms during MPTT, which reduces their tolerance to heat damage.

A Combined-Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke.

Acute minor ischemic stroke patients often experience recurrence shortly after symptom onset, highlighting the importance of predicting stroke recurrence for guiding treatment decisions. This study evaluated the effectiveness of machine learning models in predicting in-hospital recurrence. The study cohort comprised 322,135 patients with acute minor ischemic stroke from 1439 centers, as established by Chinese Stroke Center Alliance.