A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis.

Background/aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD.

Development and Evaluation of Five-in-One Vaccine Microneedle Array Patch for Diphtheria, Tetanus, Pertussis, Hepatitis B, and Type b: Immunological Efficacy and Long-Term Stability.

: The development of a five-in-one vaccine microneedle patch (five-in-one MN patch) aims to address challenges in administering vaccines against Diphtheria (DT), Tetanus (TT), Pertussis (wP), Hepatitis B (HBsAg), and type b (Hib). Combining multiple vaccines into a single patch offers a novel solution to improve vaccine accessibility, stability, and delivery efficiency, particularly in resource-limited settings. : The five-in-one MN patch consists of four distinct microneedle arrays: DT and TT vaccines are coated together on one array, while wP, HepB, and Hib vaccines are coated separately on individual arrays.

Application of Mesenchymal Stem Cell-Derived Schwann Cell-like Cells Spared Neuromuscular Junctions and Enhanced Functional Recovery After Peripheral Nerve Injury.

In general, the nerve cells of the peripheral nervous system regenerate normally within a certain period after the physical damage of their axon. However, when peripheral nerves are transected by trauma or tissue extraction for cancer treatment, spontaneous nerve regeneration cannot occur. Therefore, it is necessary to perform microsurgery to connect the transected nerve directly or insert a nerve conduit to connect it.

Risk of Contrast-Induced Acute Kidney Injury in Computed Tomography: A 16 Institutional Retrospective Cohort Study.

Objectives: Concern about contrast-induced acute kidney injury (CI-AKI) may delay the timely administration of contrast media for computed tomography (CT). The precise causative effect of iodinated contrast media on CI-AKI and its relevant risk factors remains an area of ongoing investigation. Therefore, this study aimed to determine the risk of CI-AKI following contrast-enhanced CT and its predisposing risk factors.

PGC-1α drives small cell neuroendocrine cancer progression toward an ASCL1-expressing subtype with increased mitochondrial capacity.

Adenocarcinomas from multiple tissues can converge to treatment-resistant small cell neuroendocrine (SCN) cancers composed of ASCL1, POU2F3, NEUROD1, and YAP1 subtypes. We investigated how mitochondrial metabolism influences SCN cancer (SCNC) progression. Extensive bioinformatics analyses encompassing thousands of patient tumors and human cancer cell lines uncovered enhanced expression of proliferator-activatedreceptor gamma coactivator 1-alpha (PGC-1α), a potent regulator of mitochondrial oxidative phosphorylation (OXPHOS), across several SCNCs.

Long-Term Outcome of Rescue Stenting for Acute Intracranial Atherosclerotic Stenosis Related Large Vessel Occlusion in Anterior Circulation.

Background And Purpose: Rescue stent (RS) is an accepted rescue option after failed mechanical thrombectomy (MT) for acute ischemic stroke due to intracranial atherosclerotic stenosis (ICAS)-related large vessel occlusion (LVO). However, the long-term outcomes (≥ 12 months) of RS have not yet been elucidated.

Materials And Methods: We retrospectively analyzed the data of 154 patients with RS for ICAS-related LVO, which were identified from prospectively maintained multicenter database of RS after MT failure, to assess good outcome (mRS 0-2), mortality, stroke recurrence, symptomatic intracranial hemorrhage (SICH) and stent patency.

Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response.

Aims: Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR).

Methods And Results: This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture.

Multiplexed expansion revealing for imaging multiprotein nanostructures in healthy and diseased brain.

Proteins work together in nanostructures in many physiological contexts and disease states. We recently developed expansion revealing (ExR), which expands proteins away from each other, in order to support better labeling with antibody tags and nanoscale imaging on conventional microscopes. Here, we report multiplexed expansion revealing (multiExR), which enables high-fidelity antibody visualization of >20 proteins in the same specimen, over serial rounds of staining and imaging.

Proteogenomic analysis dissects early-onset breast cancer patients with prognostic relevance.

Early-onset breast cancer is known for its aggressive clinical characteristics and high prevalence in East Asian countries, but a comprehensive understanding of its molecular features is still lacking. In this study, we conducted a proteogenomic analysis of 126 treatment-naïve primary tumor tissues obtained from Korean patients with young breast cancer (YBC) aged ≤40 years. By integrating genomic, transcriptomic, and proteomic data, we identified five distinct functional subgroups that accurately represented the clinical characteristics and biological behaviors of patients with YBC.

Predicting Recurrence after Sublobar Resection in Patients with Lung Adenocarcinoma Using Preoperative Chest CT Scans.

Background Sublobar resection for lung cancer is usually guided by cutoff values for consolidation size (maximal diameter of the solid tumor component) and consolidation-to-tumor ratio (CTR). The effects of these factors as continuous variables and the reason for established cutoffs are, to the knowledge of the authors, unexplored. Purpose To quantitatively assess the predictive value of CTR and consolidation size for cancer recurrence risk after sublobar resection in clinical stage IA lung adenocarcinoma.

Automated quantification of cerebral microbleeds in susceptibility-weighted MRI: association with vascular risk factors, white matter hyperintensity burden, and cognitive function.

Background And Purpose: To train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMB) on susceptibility-weighted MRI; and to find associations between CMB, cognitive impairment, and vascular risk factors.

Materials And Methods: Participants in this single-institution retrospective study underwent brain MRI to evaluate cognitive impairment between January-September 2023. For training the DL model, the nnU-Net framework was used without modifications.

Prognostic Value of Pulmonary Artery Systolic Pressure in Severe Rheumatic Mitral Stenosis.

Background: Current guidelines recommend intervention for asymptomatic rheumatic mitral stenosis (MS) with mitral valve area ≤1.5 cm based on indicators including pulmonary arterial systolic pressure (PASP) >50 mm Hg and new-onset atrial fibrillation; however, evidence supporting this is lacking.

Methods: This single-center retrospective study included patients with rheumatic MS between 2006 and 2022.

Real-Time Imaging Assessment of Stress-Induced Vascular Inflammation Using Heartbeat-Synchronized Motion Compensation.

Background: Chronic mental stress accelerates atherosclerosis through complicated neuroimmune pathways, needing for advanced imaging techniques to delineate underlying cellular mechanisms. While histopathology, ex vivo imaging, and snapshots of in vivo images offer promising evidence, they lack the ability to capture real-time visualization of blood cell dynamics within pulsatile arteries in longitudinal studies.

Methods: An electrically tunable lens was implemented in intravital optical microscopy, synchronizing the focal plane with heartbeats to follow artery movements.

Context-restricted PD-(L)1 checkpoint agonism by CTLA4-Ig therapies inhibits T cell activity.

T cell surface CTLA4 sequesters the costimulatory ligands CD80 and CD86 on antigen-presenting cells (APCs) to prevent autoimmunity. Therapeutic immunosuppression by recombinant CTLA4-immunoglobulin (Ig) fusion proteins, including abatacept, is also attributed to CD80/CD86 blockade. Recent studies show that CTLA4-Ig binding to APC surface cis-CD80:PD-L1 complexes can release the inhibitory ligand PD-L1, but whether this contributes to T cell inhibition remains unclear.

The reovirus variant RP116 is oncolytic in immunocompetent models and generates reduced neutralizing antibodies to Type 3 Dearing.

The mammalian reovirus Type 3 Dearing (T3D) is a naturally occurring oncolytic virus. We previously identified a T3D variant isolated from persistently infected cancer cells that has a premature stop codon mutation in the gene, generating a truncated σ1-attachment protein that lacks the globular head. We now report on the molecular characterization of this variant, named RP116, and assess its antitumor potential in human cancer cells and syngeneic mouse models.

Advance computational tools for multiomics data learning.

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data.