Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.

Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.

Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.

Oral Microalgae-Based Biosystem to Enhance Irreversible Electroporation Immunotherapy in Hepatocellular Carcinoma.

Irreversible electroporation (IRE) is a novel local tumor ablation technique that can potentially stimulate immune responses. However, IRE alone cannot effectively activate the immune system or prevent distant metastases. Therefore, this study utilized the biocompatibility of Chlorella vulgaris (C.

Prevalence, aetiology and antimicrobial resistance profile of diabetic individuals suffering from community-acquired urinary tract infection.

The rise in antimicrobial resistance poses a significant threat to global health, particularly among diabetic patients who are prone to urinary tract infections (UTIs). Pathogens that cause UTI among diabetic patients exhibit significant multidrug resistance (MDR) patterns, necessitating more precise empirical treatment strategies..

The Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.

Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).

Highly specific amyloid and tau PET ligands for ATN classification in suspected Alzheimer's disease patients.

Objective: This study aims to accurately classify ATN profiles using highly specific amyloid and tau PET ligands and MRI in patients with cognitive impairment and suspected Alzheimer's disease (AD). It also aims to explore the relationship between quantified amyloid and tau deposition and cognitive function.

Methods: Twenty-seven patients (15 women and 12 men; age range: 64-81 years) were included in this study.

Functional MRI for stereoscopic vision analysis: an experimental design.

Purpose: The aim was to establish a functional MRI protocol for analyzing human stereoscopic vision in clinical practice. The feasibility was established in a cohort of 9 healthy subjects to determine the functional cortical areas responsible for virtually relief vision.

Methods: Nine healthy right-handed subjects underwent orthoptic examination and functional MRI.

Size-Dependent Separation of Extracellular Vesicle Subtypes with Exodisc Enabling Proteomic Analysis in Prostate Cancer.

Extracellular vesicles (EVs) are emerging as crucial biomarkers in cancer diagnostics and therapeutics with their heterogeneity presenting both challenges and opportunities in prostate cancer research. However, existing methods for isolating and characterizing EV subtypes have been limited by inefficient separation and inadequate proteomic analysis. Here we show an optimized centrifugal microfluidic device, Exodisc, that efficiently isolates large quantities of EV subtypes from particle-enriched medium, enabling comprehensive proteomic analysis of small (EV-S, 20-200 nm) and large (EV-L, >200 nm) EVs.

Trajectories of general and central obesity beyond middle age in relation to late-life cognitive decline and dementia.

Objective: The objective was to evaluate the longitudinal patterns of central and general obesity, identify their genetic and behavioral risk determinants, and investigate the association of distinct obesity trajectories beyond middle age with subsequent cognitive decline and the risk of developing dementia in late life.

Methods: Using a nationally representative, longitudinal, community-based cohort, we examined trajectory patterns of obesity over a 14-year span beyond middle age employing latent mixture modeling. We then evaluated their relationship with subsequent cognitive decline through linear mixed models and with the risk of developing dementia using Cox models, adjusting for confounding variables.

PET-Based Dual-Modal Probes for In Vivo Imaging.

Molecular imaging has significantly advanced the detection and analysis of in vivo metabolic processes, while single-modal techniques remain limited. Dual-modal imaging, particularly positron emission tomography (PET)-based combinations has emerged as a powerful solution, offering enhanced capabilities through integration with magnetic resonance imaging (MRI) or near-infrared fluorescence (NIRF) imaging. This review highlights recent progress in PET-based dual-modal imaging, focusing on the development of various bimodal probes derived from antibodies, nanoparticles, and peptides, and key applications including image-guided surgery and disease assessment.

Analyzing Pathophysiology and Immune Cells and Their Cytokines and Mediators in Precision-Cut Slices of the Murine Lung.

Understanding the dynamic pathophysiology of diseases in the lung, such as asthma and chronic asthma, chronic obstructive pulmonary disease, and lung cancer, is crucial for the treatment, analysis, and outcome of these diseases. Unlike other traditional models, we suggest a protocol that is sustainable and reproducible and offers different analysis methods while maintaining in vivo lung architecture and immune dynamics. This protocol allows one to study the pathophysiological changes, including changes to the immune cells, cytokines, and mediators, in 30 precision-cut lung slices from a single murine lung.

Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging.

Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.

Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.

Background/purpose: Artificial intelligence (AI) can assist in medical diagnosis owing to its high accuracy and efficiency. This study aimed to develop a diagnostic system for automatically determining the degree of tooth wear (TW) using intraoral photographs with deep learning.

Materials And Methods: The study included 388 intraoral photographs.

Advancing diagnostics and disease modeling: current concepts in biofabrication of soft microfluidic systems.

Soft microfluidic systems play a pivotal role in personalized medicine, particularly in in vitro diagnostics tools and disease modeling. These systems offer unprecedented precision and versatility, enabling the creation of intricate three-dimensional (3D) tissue models that can closely emulate both physiological and pathophysiological conditions. By leveraging innovative biomaterials and bioinks, soft microfluidic systems can circumvent the current limitations involving the use of polydimethylsiloxane (PDMS), thus facilitating the development of customizable systems capable of sustaining the functions of encapsulated cells and mimicking complex biological microenvironments.

Recent Updates in the Diagnosis and Management of Kidney Diseases in Multiple Myeloma.

Multiple myeloma (MM) represents a difficult-to-treat plasma cell malignancy and the second most common hematologic malignancy in adults, significantly impacting kidney function. The spectrum of kidney involvement in MM is broad, encompassing electrolyte imbalances, tubular injury, and even rare glomerular diseases. The evolution of MM treatment modalities has led to notable improvements in the long-term survival of patients experiencing kidney-related complications.

A deep learning framework for screening of anticancer drugs at the single-cell level.

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells.

Symptom Network and Subgroup Analysis in Patients with Exacerbation of Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study.

Purpose: This study aims to construct a contemporaneous symptom network of inpatients with Exacerbation of Chronic Obstructive Pulmonary Disease (ECOPD) based on the symptom cluster, identify core and bridge symptoms, and patient subgroups with different symptom clusters based on individual differences in the intensity of patient symptom experiences.

Patients And Methods: This study used convenience sampling to collect demographic, symptom, auxiliary examination, and prognosis information of 208 inpatients with ECOPD from April 2022 to October 2023. The data underwent exploratory factor analysis (EFA), symptom network analysis, latent class analysis (LCA), Spearman correlation analysis, Wilcoxon signed-rank test, single-factor regression and multiple-factor stepwise regression.

Use of Magnetic Resonance Neurography for Sensory Nerve Injuries of the Head and Neck.

Background: Identification of peripheral nerve injuries of the head and neck can be challenging due to a broad spectrum of symptoms from neuropathic pain to headaches and migraine. This article aimed to present the clinical features and diagnostic workup of patients with acute and chronic peripheral nerve injuries of the head and neck using magnetic resonance neurography (MRN), to demonstrate potential advantages compared with conventional magnetic resonance imaging (MRI).

Methods: Patients who presented with suspected peripheral nerve injury were either referred for a conventional MRI or MRN.

Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer.

In this review, we explore the application of next-generation sequencing in liver cancer research, highlighting its potential in modern oncology. Liver cancer, particularly hepatocellular carcinoma, is driven by a complex interplay of genetic, epigenetic, and environmental factors. Key genetic alterations, such as mutations in , , and , alongside epigenetic modifications such as DNA methylation and histone remodeling, disrupt regulatory pathways and promote tumorigenesis.

Metal-Phenolic Nanomedicines Targeting Fatty Acid Metabolic Reprogramming to Overcome Immunosuppression in Radiometabolic Cancer Therapy.

Radiation therapy (RT) is a prevalent cancer treatment; however, its therapeutic outcomes are frequently impeded by tumor radioresistance, largely attributed to metabolic reprogramming characterized by increased fatty acid uptake and oxidation. To overcome this limitation, we developed polyphenol-metal coordination polymer (PPWQ), a novel nanoradiotherapy sensitizer specifically designed to regulate fatty acid metabolism and improve RT efficacy. These nanoparticles (NPs) utilize a metal-phenolic network (MPN) to integrate tungsten ions (W), quercetin (QR), and a PD-L1-blocking peptide within a PEG-polyphenol scaffold.

Unlocking the Potential of miRNAs in Sepsis Diagnosis and Prognosis: From Pathophysiology to Precision Medicine.

The clinical syndrome appears as a dysregulated host response to infection that results in life-threatening organ dysfunction known as Sepsis. Sepsis is a serious public health concern where for every five deaths in ICU there is one patient who dies with sepsis worldwide. Sepsis is featured as unbalanced inflammation and immunosuppression which is sustained and profound, increasing patient susceptibility to secondary infections and mortality.

Macrophage Membrane-Encapsulated Carbon Dots for Precise Targeting Diagnosis and Treatment of Bacterial Infections.

How to accurately diagnose and treat bacterial infections in vivo remains a huge challenge. Therefore, we have developed a targeted delivery nanosystem by coextruding the pretreated macrophage membrane of with carbon dots (M@CD). The M@CD nanosystem demonstrates potent antibacterial effects both in vivo and in vitro through the generation of reactive oxygen species (ROS).

CARDIAC-STAR: prevalence of cardiovascular comorbidities in patients with HR + /HER2 - metastatic breast cancer.

Background: Cardiovascular (CV) comorbidities and concurrent medications with risk of heart rate-corrected QT interval (QTc) prolongation can impact treatment decisions and safety discussions for patients with breast cancer. However, limited data are available regarding their prevalence in patients with HR + /HER2- metastatic breast cancer (mBC). We evaluated the prevalence of CV comorbidities, the use of concurrent medications with risk of QTc prolongation, and treatment patterns in patients with newly diagnosed HR + /HER2 - mBC.

A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission.

Background: Mixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis short-read WGS data.

Lipidome disruption in Alzheimer's disease brain: detection, pathological mechanisms, and therapeutic implications.

Alzheimer's disease (AD) is among the most devastating neurodegenerative disorders with limited treatment options. Emerging evidence points to the involvement of lipid dysregulation in the development of AD. Nevertheless, the precise lipidomic landscape and the mechanistic roles of lipids in disease pathology remain poorly understood.

Decoding airway granulogenesis in children: unveiling risk factors for tracheobronchial foreign body aspiration and complications.

Background: Exogenous foreign body aspiration is a common high-risk condition in children. In a few cases, foreign body aspiration can lead to airway granulomas that interfere with tracheoscopic foreign body removal and threaten the life of the child.

Methods: This study was a retrospective analysis of the clinical data of 184 pediatric patients who were admitted to Quanzhou Children's Hospital from 2018 to 2021 with exogenous tracheobronchial foreign bodies.