ADAM17 Inhibition Protects Cognition in Intermittent Hypoxia: The Role of TREM2.

Purpose: The triggering receptor expressed on myeloid cells 2 (TREM2) is a new therapeutic target in Alzheimer's disease. However, its role in obstructive sleep apnea (OSA)-related cognitive impairment is still unclear. This study aimed to investigate the effect and regulatory mechanism of TREM2 on cognitive impairment related to OSA.

Associations between trajectories of plasma biomarkers for Alzheimer's disease, brain structures, and cognitive function: a prospective cohort study in the UK Biobank.

Plasma amyloid-β(Aβ)42/40 ratio, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and phosphorylated tau181(p-tau181) are promising biomarkers for Alzheimer's disease (AD)-related pathology. We aimed to explore the longitudinal trajectories of these biomarkers in association with changes in structural brain markers and cognition, and the impact of cognitive reserve on their associations with cognitive function. This cohort study included 1270 individuals (mean age 59.

Flexible, self-adhesive and eco-stable bioelectronics with dual-network phytic acid-based ionic hydrogel for biomechanical and physiological signal monitoring.

Seamless integration with biological tissues and environmental adaptability are essential for continuous health monitoring, yet conventional bioelectronics often suffer from mechanical mismatch, poor adhesion, and limited stability. Here, we develop a self-adhesive and environmentally resilient ionic hydrogel (PHS-PA) with exceptional conductivity, flexibility, and durability. Constructed from a dual network of polyvinyl alcohol (PVA) and zwitterionic poly(SBMA-co-HEMA), physically cross-linked by phytic acid (PA), PHS-PA exhibits strong tissue adhesion, high ionic conductivity (10.

Nonreciprocal surface plasmonic neural network for decoupled bidirectional analogue computing.

To address the burgeoning demand for computing capacity in artificial intelligence, researchers have explored optical neural networks that show advantages of ultrafast speed, low power consumption, ultra-high bandwidth, and high parallelism. However, most existing optical networks are reciprocal, where forward and backward propagation are intrinsically coupled. This results in the backward pathway remaining largely unexplored, hindering the realization of integrated perception-response systems.

Malignancy in Idiopathic Inflammatory Myopathies: Recent Insights.

Idiopathic inflammatory myopathies (IIMs) are a group of rare autoimmune diseases in which several subtypes, primarily dermatomyositis (DM), are associated with a high risk of malignancy. Although the relationship between the pathogenesis of IIMs and co-occurring malignancy is poorly understood, there is a growing evidence base for specific risk factors which are associated with cancer-associated myositis (CAM), including myositis-specific or myositis-associated autoantibodies (MSAs/MAAs). In addition, given that patients with different subtypes of IIMs, as classified according to clinical manifestations or serum autoantibody measurement, exhibit heterogeneity in tumorigenesis, further research is urgently needed to explore the pathogenesis of CAM, in order to develop novel biomarkers and therapeutic alternatives for patients.

Risk Factor Analysis of Transient Ischaemic Attack for Moyamoya Disease after a First Combined Revascularization Surgery.

Objective: This study aims to elucidate the characteristics and risk factors for transient ischaemic attack (TIA) after the combined revascularization surgical procedure for first diagnosed Moyamoya disease (MMD) patients.

Methods: This retrospective study involved 129 patients with MMD who underwent their first combined revascularization with the FLOW 800 system at multiple centers from April 2019 to July 2023. Patients were divided into TIA and non-TIA groups according to whether TIA occurred within 6 months post-revascularization.

Distinct features of humoral and cellular immunity against Omicron breakthrough infection among chronic liver disease patients: An experience from a follow-up cohort.

Immunity response following vaccination and infection toward the SARS-CoV-2 Omicron variant remains limited in chronic liver disease (CLD) population. This study aims to investigate humoral and cell-mediate immunity to Omicron BA.4/5 among CLD patients with/without breakthrough infections (BI).

Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer.

Background: Ulcerative colitis (UC) is a chronic inflammatory condition that predisposes patients to colorectal cancer (CRC) through mechanisms that remain largely undefined. Given the pivotal role of cellular senescence in both chronic inflammation and tumorigenesis, we integrated machine learning and bioinformatics approaches to identify senescence-related biomarkers and potential therapeutic targets involved in the progression from UC to CRC.

Methods: Gene expression profiles from six GEO datasets were analyzed to identify differentially expressed genes (DEGs) using the limma package in R.

Immune infiltration and stromal heterogeneity in pancreatic cancer: A prognostic model guiding immunotherapy response.

Pancreatic adenocarcinoma (PAAD) is among the most common malignant tumors of the gastrointestinal tract and has a poor prognosis. Research on its pathogenesis and treatment remains insufficient; therefore, the present study aimed to develop a novel model for predicting PAAD prognosis. Transcriptome data for pancreatic cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases (GSE28735), with TCGA data serving as the training set and GSE28735 data as the validation set.

Enhancing Domain Generalization in Medical Image Segmentation With Global and Local Prompts.

Enhancing domain generalization (DG) is a crucial and compelling research pursuit within the field of medical image segmentation, owing to the inherent heterogeneity observed in medical images. The recent success with large-scale pre-trained vision models (PVMs), such as Vision Transformer (ViT), inspires us to explore their application in this specific area. While a straightforward strategy involves fine-tuning the PVM using supervised signals from the source domains, this approach overlooks the domain shift issue and neglects the rich knowledge inherent in the instances themselves.

Anti-tuberculosis therapy combined with ventriculoperitoneal shunt for tuberculous meningitis combined with hydrocephalus: A case report.

Tuberculous meningitis is a severe and potentially life-threatening form of central nervous system tuberculosis. Hydrocephalus is a critical complication and risk factor for the high mortality of tuberculous meningitis, exacerbating neurological damage and intensifying treatment. We reported a critical case of an elderly male patient with altered consciousness of consciousness who was diagnosed as tuberculous meningitis with hydrocephalus.

Multiparametric MRI-Based Intratumoral and Peritumoral Radiomics for Distinguishing Solitary Intrahepatic Mass-Forming Cholangiocarcinoma From Colorectal Liver Metastases.

Objective: To establish a model based on intratumoral and peritumoral radiomics for preoperatively differentiating solitary intrahepatic mass-forming cholangiocarcinoma (IMCC) lesions from colorectal cancer liver metastases (CRLM).

Methods: Preoperative MRI scans from IMCC patients were retrospectively obtained from three academic medical centers. Radiomics features were extracted from the intratumoral and multiple peritumoral regions.

A new dataset and versatile multi-task surgical workflow analysis framework for thoracoscopic mitral valvuloplasty.

Surgical Workflow Analysis (SWA) on videos is critical for AI-assisted intelligent surgery. Existing SWA methods primarily focus on laparoscopic surgeries, while research on complex thoracoscopy-assisted cardiac surgery remains largely unexplored. In this paper, we introduce TMVP-SurgVideo, the first SWA video dataset for thoracoscopic cardiac mitral valvuloplasty (TMVP).

Sustainable and Orthogonally Closed-Loop Recyclable Acetal-Based Long-Chain Polyesters.

It is an emerging trend to develop biobased polymers to mimic commodity plastics. New physical and chemical functions are critically needed. We report a strategy for synthesizing long-chain aliphatic poly(ester)acetals (PEAc) with orthogonal degradability.

ATGL modulates lipid-protein interactions to restore foaming capacity in yolk-residue egg white systems.

Residual egg yolk in egg white, a common issue in industrial processing, impairs foaming properties vital for aerated bakery products. This study reveals that yolk residue ≥0.7 % (w/w) destabilizes foams via lipid bridge formation and interfacial protein network disruption.

DLP 3D printing of hyperelastic photocurable perivascular scaffolds enabling patient-specific vascular remodeling.

Photopolymerization-based 3D printing, enabled the fabrication of complex, patient-specific scaffolds with high resolution and spatial precision. However, most photocurable biomaterials exhibited mechanical mismatch with soft tissues such as vascular, cartilage, and tendon tissues. To address this limitation, we developed a biodegradable and elastomeric resin (A-PLCL/4SH), composed of methacrylate-functionalized poly(L-lactide-co-ε-caprolactone) (A-PLCL) and pentaerythritol tetra(3-mercaptopropionate) (PETA-4SH), which enabled high-fidelity digital light processing (DLP) 3D printing.

OS-RRG: Observation State-Aware Radiology Report Generation With Balanced Diagnosis and Attention Intervention.

Radiology report generation (RRG) aims to automatically generate detailed textual descriptions and diagnoses for clinical radiography, alleviating radiologists' workloads, aiding inexperienced radiologists, and minimizing errors. RRG is challenging due to the need to generate coherent and clinically accurate multisentence reports that describe various medical conditions. Although previous diagnosis-guided methods achieve impressive diagnostic accuracy by explicitly converting the identified observation states (OSs) (e.

DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation.

Radiology Report Generation (RRG) automates the creation of radiology reports from medical imaging, enhancing the efficiency of the reporting process. Longitudinal Radiology Report Generation (LRRG) extends RRG by incorporating the ability to compare current and prior exams, facilitating the tracking of temporal changes in clinical findings. Existing LRRG approaches only extract features from prior and current images using a visual pre-trained encoder, which are then concatenated to generate the final report.

Tensor wheel completion for visual data with sparsity and smoothness on latent space.

Tensor wheel decomposition has recently drawn lots of attentions in tensor completion, due to its advantages of wheel topology in exploring the intrinsic relationships. However, since the rank of tensor wheel is defined as a vector, it is very hard to select one rather-good rank for tensor completion when the model is rank-sensitive, i.e.

Detection of pathogens and vaccine design strategies for Streptococcus pneumoniae.

Streptococcus pneumoniae, often referred to as S. pneumoniae, plays a critical role in numerous infectious conditions, notably targeting the respiratory system with diseases such as lobar pneumonia, bronchitis, and pleurisy. This bacterium is the predominant cause of bacterial pneumonia in children and a major agent of bacterial meningitis in those over three months of age.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting.

Source-free domain adaptation (SFDA) for segmentation aims at adapting a model trained in the source domain to perform well in the target domain with only the source model and unlabeled target data. Inspired by the recent success of Segment Anything Model (SAM) which exhibits the generality of segmenting images of various modalities and in different domains given human-annotated prompts like bounding boxes or points, we for the first time explore the potentials of Segment Anything Model for SFDA via automatedly finding an accurate bounding box prompt. We find that the bounding boxes directly generated with existing SFDA approaches are defective due to the domain gap.

Single-cell RNA sequencing reveals anterograde trans-synaptic degeneration and exacerbated synaptic remodeling in myopia.

Myopia is a serious public health issue worldwide. Damage to retinal ganglion cells (RGCs) in the retina induces degeneration of the visual cortex, which is known as anterograde trans-synaptic degeneration (TSD). However, the role of TSD in myopia is still unknown.

Human-Centric explanations for users in automated Vehicles: A systematic review.

Background: The decision-making processes of automated vehicles (AVs) can confuse users and reduce trust, highlighting the need for clear and human-centric explanations. Such explanations can help users understand AV actions, facilitate smooth control transitions and enhance transparency, acceptance, and trust. Critically, such explanations could improve situational awareness and support timely, appropriate human responses, thereby reducing the risk of misuse, unexpected automated decisions, and delayed reactions in safety-critical scenarios.

The sex-specific effects of sodium alginate with varying viscosities on lipid metabolism and gut microbiota in rats.

Background: Sodium alginate (SA), a dietary fiber, is known to influence lipid metabolism and gut microbiota. However, its long-term effects of viscosity/dose and sex-specific differences remain underexplored. This study investigates the impact of SA with different viscosities and doses on lipid metabolism and gut microbiota in 120 male and female Sprague-Dawley rats over 90 days.

Progress and challenges of artificial intelligence in lung cancer clinical translation.

Artificial intelligence (AI) algorithms, such as convolutional neural networks and transformers, have significantly impacted cancer care. For lung cancer, AI holds great potential in addressing smoking cessation, personalized screening, and imaging genomics. And these data could be incorporated to optimize treatment selection.