J-Score: Joint Distribution Learning with Score-based Diffusion for Accelerating T1rho Mapping.

The T1rho mapping technique necessitates acquiring multiple T1rho-weighted images at various spin-lock times (TSL), which results in a lengthy scan time and significantly limits its widespread clinical use. Undersampling is a significant strategy to accelerate T1rho imaging, where it is crucial to model and utilize the joint spatiotemporal correlations priors among different TSL multi-contrast images for high-quality reconstruction. However, current methods that use simplified physical relaxation correlations or black-box deep neural networks to define joint correlations often yield inaccurate results.

Taste impairment in patients with Parkinsonism.

Background: Taste impairment is a prevalent issue among individuals with idiopathic Parkinson's disease (iPD). However, understanding taste disorders among different Parkinsonism remains incomplete. Our objective was to assess the incidence and severity of taste responses to sweet, salty, sour, bitter, and umami substances in patients with iPD, progressive supranuclear palsy (PSP), and multiple system atrophy (MSA).

Assessment of Piezo1 Expression in Urinary Exfoliated Cells as a Diagnostic Indicator for Multiple System Atrophy.

Background: Multiple system atrophy (MSA) shares clinical features with idiopathic Parkinson' s disease (iPD) and progressive supranuclear palsy (PSP), yet reliable biomarkers for differential diagnosis remain elusive.

Objectives: This study aimed to evaluate Piezo1/2 expression in urinary exfoliated cells as a potential biomarker for MSA differentiation.

Methods: Piezo1/2 expression levels were quantified in urinary exfoliated cells from 76 MSA patients, 103 iPD patients, 59 PSP patients, and 126 healthy controls (HCs) across three independent cohorts using multiple analytical techniques.

Effect of fecal microbiota transplantation on patients with sporadic amyotrophic lateral sclerosis: a randomized, double-blind, placebo-controlled trial.

Background: Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder marked by the progressive loss of motor neurons. Recent insights into ALS pathogenesis underscore the pivotal role of the gut microbiome, prompting an investigation into the potential therapeutic impact of fecal microbiota transplantation (FMT) on sporadic ALS patients.

Methods: Conducted as a double-blind, placebo-controlled, parallel-group, randomized clinical trial, the study enrolled 27 participants from October 2022 to April 2023.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data.

Current status and trend of mitochondrial research in lung cancer: A bibliometric and visualization analysis.

This study summarizes and analyzes the relationship between mitochondria and the pathogenesis of lung cancer. The related articles in the Web of Science core literature database are searched and collected, and the data are processed by R software, Citespace, VOSviewer, and Excel. A total of 4476 related papers were retrieved, 4476 articles from 20162 co-authors of 3968 institutions in 84 countries and published in 951 journals.

Induction of tau pathology and motor dysfunction in mice by urinary exosomes from progressive supranuclear palsy patients.

Background: Progressive supranuclear palsy (PSP) is characterized by the presence of hyperphosphorylated and misfolded tau aggregates in neurons and glia. Recent studies have illuminated the prion-like cell-to-cell propagation of tau via exosomes. Recognizing the potential significance of excretion through urine as a crucial pathway for eliminating pathological tau from the central nervous system, this study aimed to investigate whether exosomes derived from the urine of PSP-Richardson's syndrome (PSP-RS) patients can elicit tau pathology and PSP-like symptoms in mice.

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events.

Background: The cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters.

Objectives: This study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM.

Enlarged perivascular spaces in alcohol-related brain damage induced by dyslipidemia.

Perivascular spaces (PVSs) as the anatomical basis of the glymphatic system, are increasingly recognized as potential imaging biomarkers of neurological conditions. However, it is not clear whether enlarged PVSs are associated with alcohol-related brain damage (ARBD). We aimed to investigate the effect of long-term alcohol exposure on dyslipidemia and the glymphatic system in ARBD.

Integrating data distribution prior via Langevin dynamics for end-to-end MR reconstruction.

Purpose: To develop a novel deep learning-based method inheriting the advantages of data distribution prior and end-to-end training for accelerating MRI.

Methods: Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end-to-end adversarial training to mitigate the hyper-parameter selection and shorten the testing time compared to traditional probabilistic reconstruction.

PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.

Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, eliminating the explicit training procedure but often struggling to remove noise and artifacts under significant degradation. This work introduces a novel self-supervised accelerated parallel MRI approach called PEARL, leveraging a multiple-stream joint deep decoder with two cross-fusion schemes to accurately reconstruct one or more target images from compressively sampled k-space.

Cognitive Impairment in Chronic Kidney Disease Is Associated with Glymphatic System Dysfunction.

Introduction: This study was designed to explore the associations between impaired cognition in chronic kidney disease (CKD) patients and the dysfunction of the glymphatic system.

Method: Data were obtained from 77 CKD patients and 50 age-matched healthy control individuals from the First Affiliated Hospital of Zhengzhou University. CKD patients were stratified into with and without impaired cognitive function.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results.

Annihilation-Net: Learned annihilation relation for dynamic MR imaging.

Background: Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability.

Purpose: This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation-Net and use it for accelerating dynamic MRI.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module.

K-UNN: k-space interpolation with untrained neural network.

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g.

Accelerating Magnetic Resonance T Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART).

Quantitative magnetic resonance (MR) [Formula: see text] mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor models have been employed and demonstrated exemplary performance in accelerating MR [Formula: see text] mapping.

Efficacy of faecal microbiota transplantation in patients with progressive supranuclear palsy-Richardson's syndrome: a phase 2, single centre, randomised clinical trial.

Background: Faecal microbiota transplantation (FMT) has demonstrated efficacy in treating gastrointestinal (GI) diseases, such as infection (CDI) and inflammatory bowel disease (IBD). GI dysfunction is a frequent and occasionally dominating symptom of progressive supranuclear palsy-Richardson's syndrome (PSP-RS). However, it is not known whether FMT has clinical efficacy for PSP-RS.

Microbiota-ear-brain interaction is associated with generalized anxiety disorder through activation of inflammatory cytokine responses.

Introduction: Generalized anxiety disorder (GAD) is one of the most enduring anxiety disorders, being associated with increased systemic inflammation. However, the trigger and mechanisms underlying the activation of inflammatory cytokine responses in GAD remain poorly understood.

Materials And Methods: We characterized the ear canal microbiome in GAD patients through 16S rRNA gene sequencing and metagenomic sequencing and identified the serum inflammatory markers in GAD patients.

Mutations in ARHGEF15 cause autosomal dominant hereditary cerebral small vessel disease and osteoporotic fracture.

Cerebral small vessel disease (CSVD) is a prominent cause of ischemic and hemorrhagic stroke and a leading cause of vascular dementia, affecting small penetrating vessels of the brain. Despite current advances in genetic susceptibility studies, challenges remain in defining the causative genes and the underlying pathophysiological mechanisms. Here, we reported that the ARHGEF15 gene was a causal gene linked to autosomal dominant inherited CSVD.

Impaired glymphatic drainage underlying obstructive sleep apnea is associated with cognitive dysfunction.

Obstructive sleep apnea (OSA) is highly prevalent but easily undiagnosed and is an independent risk factor for cognitive impairment. However, it remains unclear how OSA is linked to cognitive impairment. In the present study, we found the correlation between morphological changes of perivascular spaces (PVSs) and cognitive impairment in OSA patients.

Accelerated cardiac cine MRI using spatiotemporal correlation-based hybrid plug-and-play priors (SEABUS).

. The plug-and-play prior (P) can be flexibly coupled with multiple iterative optimizations, which has been successfully applied to the inverse problems of medical imaging. In this work, for accelerated cardiac cine magnetic resonance imaging (CC-MRI), the Spatiotemporal corrElAtion-based hyBrid plUg-and-play priorS (SEABUS) integrating a local Pand a nonlocal Pare introduced.

Characteristic-constrained accelerating MR T1rho mapping with blockwise infimal convolution of matrix elastic-net regularization.

Background: Magnetic resonance parameter mapping (MRPM) plays an important role in clinical applications and biomedical researches. However, the acceleration of MRPM remains a major challenge for achieving further improvements.

Purpose: In this work, a new undersampled k-space based joint multi-contrast image reconstruction approach named CC-IC-LMEN is proposed for accelerating MR T1rho mapping.