Mesothelin expression prediction in pancreatic cancer based on multimodal stochastic configuration networks.

Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed.

Neuroimaging markers and disability scales in multiple sclerosis: A systematic review and meta-analysis.

Background: Multiple sclerosis (MS) is a central nervous system disorder marked by progressive neurological impairments. Magnetic resonance imaging (MRI) parameters are key paraclinical measures that play a crucial role in the diagnosis, prognosis, and monitoring of MS-related disability. This study aims to analyze and summarize the existing literature on the correlation between MRI parameters and disability in people with MS (pwMS).

Dysphagia is a risk factor of malnutrition in X-linked Dystonia-Parkinsonism.

Introduction: Malnutrition is a leading cause of death for persons living with X-linked dystonia-parkinsonism (XDP), a degenerative disease endemic to the Philippines. Difficulty swallowing has been linked to malnutrition in other populations; however, knowledge of this relationship is limited in XDP. As such, the purpose of this study was to determine the association between dysphagia and malnutrition in this population.

Effects of cholestasis and hyperammonemia on dendritic spine density and turnover in rat hippocampal neurons.

Adults and children with cholestatic liver disease are at risk for type C hepatic encephalopathy (HE) and may present lifelong neurocognitive impairment. While the underlying cellular and molecular mechanisms are still incompletely understood, ammonium and bile acids (BAs) seem to play a key role in this pathology, by crossing the blood-brain-barrier and modifying neuronal homeostasis and synaptic plasticity. This experimental study aimed to investigate the effects of ammonium and BAs on dendritic spines of rat hippocampal CA1 neurons.

Intracranial aneurysm instability prediction model based on 4D-Flow MRI and HR-MRI.

This study aims to develop a reliable predictive model for assessing intracranial aneurysm (IA) instability by utilizing four-dimensional flow magnetic resonance imaging (4D-Flow MRI) and high-resolution MRI (HR-MRI). Initially, we curated a prospective dataset, dubbed the primary cohort, by aggregating patient data that was consecutively enrolled across two centers from November 2018 to November 2021. Unstable aneurysms were defined as those with symptoms, morphological change or ruptured during follow-up periods.

Transcranial focused ultrasound stimulation alleviates NLRP3-related neuroinflammation induced by ischemic stroke via regulation of the Nespas/miR-383-3p/SHP2 pathway.

Transcranial focused ultrasound stimulation (tFUS) has emerged as a promising therapeutic strategy for mitigating brain injury in animal models. In this study, the effects and mechanisms of tFUS on ischemic stroke were explored in a transient middle cerebral artery occlusion (MCAO) rat model. Low-intensity tFUS was administered to the ischemic hemisphere 24 h post-MCAO for seven consecutive days.

Onset timing of letter processing in auditory and visual sensory cortices.

Here, we report onset latencies for multisensory processing of letters in the primary auditory and visual sensory cortices. Healthy adults were presented with 300-ms visual and/or auditory letters (uppercase Roman alphabet and the corresponding auditory letter names in English). Magnetoencephalography (MEG) evoked response generators were extracted from the auditory and visual sensory cortices for both within-modality and cross-sensory activations; these locations were mainly consistent with functional magnetic resonance imaging (fMRI) results in the same subjects.

Susceptibility-induced internal gradients reveal axon morphology and cause anisotropic effects in the diffusion-weighted MRI signal.

Diffusion-weighted MRI is our most promising method for estimating microscopic tissue morphology in vivo. The signal acquisition is based on scanner-generated external magnetic gradients. However, it will also be affected by susceptibility-induced internal magnetic gradients caused by interactions between the tissue and the static magnetic field of the scanner.

Claustrum volumes are lower in schizophrenia and mediate patients' attentional deficits.

Background: While the last decade of extensive research revealed the prominent role of the claustrum for mammalian forebrain organization, i.e., widely distributed claustral-cortical circuits coordinate basic cognitive functions such as attention, it is poorly understood whether the claustrum is relevant for schizophrenia and related cognitive symptoms.

Manganese-based type I collagen-targeting MRI probe for in vivo imaging of liver fibrosis.

Liver fibrosis is a common pathway shared by all forms of progressive chronic liver disease. There is an unmet clinical need for noninvasive imaging tools to diagnose and stage fibrosis, which presently relies heavily on percutaneous liver biopsy. Here we explored the feasibility of using a novel type I collagen-targeted manganese (Mn)-based MRI probe, Mn-CBP20, for liver fibrosis imaging.

Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.

Accurate Alzheimer's Disease (AD) progression prediction is essential for early intervention. The TADPOLE challenge, involving 92 algorithms, used multimodal biomarkers to predict future clinical diagnosis, cognition, and ventricular volume. The winning algorithm, FROG, utilized a Longitudinal-to-Cross-sectional (L2C) transformation to convert variable longitudinal histories into fixed-length feature vectors, which contrasted with most existing approaches that fitted models to entire longitudinal histories, e.

Evaluation of Brain Age as a Specific Marker of Brain Health.

Brain age is a powerful marker of brain health. Furthermore, brain age models are trained on large datasets, thus giving them a potential advantage in predicting outcomes - much like the success of finetuning large language models for specific applications. However, it is also well-accepted in machine learning that models trained to directly predict specific outcomes (i.

Radiomic features add incremental benefit to conventional radiological feature-based differential diagnosis of lung nodules.

Purpose: To investigate the incremental benefit of adding radiomic features to conventional semantic radiological feature-based differential diagnosis between benign and malignant lung nodules.

Methods: From May 2017 to March 2021, 393 patients with 465 pathologically confirmed lung nodules were enrolled with 54 patients with 54 lung nodules as external testing. Based on manually segmented lung nodules, 1409 radiomics features were extracted.