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.

Schizophrenia and magnetic resonance imaging research: A scientometric analysis during 2014 to 2023.

Background: Recently, magnetic resonance imaging (MRI) has emerged as a leading technique for investigating schizophrenia (SZ) pathological mechanisms, prompting an increase in related studies. This study aims to examine the field's research status and trends via bibliometric analysis.

Method: The publications on SZ and MRI over the past decade were retrieved from the Web of Science Core Collection (WOSCC) On October 15, 2023.

Predictive machine learning models based on VASARI features for WHO grading, isocitrate dehydrogenase mutation, and 1p19q co-deletion status: a multicenter study.

The objective of our study was to develop predictive models using Visually Accessible Rembrandt Images (VASARI) magnetic resonance imaging (MRI) features combined with machine learning techniques to predict the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation status, and 1p19q co-deletion status of high-grade gliomas. To achieve this, we retrospectively included 485 patients with high-grade glioma from the First Affiliated Hospital of Xinjiang Medical University, of which 312 patients were randomly divided into a training set (n=218) and a test set (n=94) in a 7:3 ratio. Twenty-five VASARI MRI features were selected from an initial set of 30, and three machine learning models - Multilayer Perceptron (MP), Bernoulli Naive Bayes (BNB), and Logistic Regression (LR) - were trained using the training set.

A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis.

Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis.

Tumor suppressor FRMD3 controls mammary epithelial cell fate determination via notch signaling pathway.

The luminal-to-basal transition in mammary epithelial cells (MECs) is accompanied by changes in epithelial cell lineage plasticity; however, the underlying mechanism remains elusive. Here, we report that deficiency of inhibits mammary gland lineage development and induces stemness of MECs, subsequently leading to the occurrence of triple-negative breast cancer. Loss of in mice results in a luminal-to-basal transition phenotype.

Calibrating ultrasonic sensor measurements of crop canopy heights: a case study of maize and wheat.

Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy.

Role of long non-coding RNA TCONS_02443383 in regulating cell adhesion and peroxisome proliferator-activated receptor (PPAR) signaling genes in atherosclerosis: A New Zealand white rabbit model study.

Objective: In this study, we performed RNA sequencing (RNA-seq) on the abdominal aorta tissue of New Zealand rabbits and investigated the potential association of lncRNA TCONS_02443383 with the development of AS through bioinformatics analysis of the sequencing data. The obtained results were further validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Method: We induced an AS model in New Zealand rabbits by causing balloon injury to the abdominal aorta vascular wall and administering a high-fat diet.

Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort.

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.

Objectives: Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence.

Methods: A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted.

A novel score for early prediction of urinary tract infection risk in patients with acute ischemic stroke: a nomogram-based retrospective cohort study.

This study aimed to construct and externally validate a user-friendly nomogram-based scoring model for predicting the risk of urinary tract infections (UTIs) in patients with acute ischemic stroke (AIS). A retrospective real-world cohort study was conducted on 1748 consecutive hospitalized patients with AIS. Out of these patients, a total of 1132 participants were ultimately included in the final analysis, with 817 used for model construction and 315 utilized for external validation.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

Rationale And Objectives: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.

Background: Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.

MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images.

Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model.

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images.

The Value of the Cardiac Magnetic Resonance Intravoxel Incoherent Motion Technique in Evaluating Microcirculatory Dysfunction in Hypertrophic Cardiomyopathy.

Objective: To evaluate the value of the cardiac magnetic resonance intravoxel incoherent motion (IVIM) technique in microcirculatory dysfunction in patients with hypertrophic cardiomyopathy (HCM).

Methods: The medical records of 19 patients with HCM in our hospital from January 2020 to May 2021 were collected retrospectively, and 23 healthy people with a similar age and gender distribution to the patients with HCM were included as controls. All the included subjects underwent clinical assessment and cardiac magnetic resonance imaging.

A region-specific modulation of sleep slow waves on interictal epilepsy markers in focal epilepsy.

Objective: Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy.

Methods: Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording.

Mechanism of extracellular space changes in cryptococcal brain granuloma revealed by MRI tracer.

Purpose: This study aimed to investigate the changes in extracellular space (ECS) in cryptococcal brain granuloma and its pathological mechanism.

Materials And Methods: The animal model of cryptococcal brain granuloma was established by injecting 1 × 10 CFU/ml of type A suspension into the caudate nucleus of Sprague-Dawley rats with stereotactic technology. The infection in the brain was observed by conventional MRI scanning on days 14, 21, and 28 of modeling.

Preparation and properties of fucoxanthin-loaded liposomes stabilized by sea cucumber derived cholesterol sulfate instead of cholesterol.

The preparation of steady-state phospholipid liposomes requires cholesterol as a stabilizer, but excessive intake of cholesterol may increase the risk of cardiovascular disease. The sulfated sterols extracted from sea cucumber, mainly including sulfated 24-methylene cholesterol and cholesterol sulfate, have been reported to have a variety of physiological activities. Sulfated sterols are similar to cholesterol in structure and have the potential to replace cholesterol to prepare novel stable multifunctional liposomes, allowing the liposomes to act as carriers for the delivery of less bioavailable nutrients while allowing sulfated sterols in the lipid bilayer to exert physiologically active effects.

A rare case report of primary Sjögren's syndrome with clinical characteristics similar to those of CLIPPERS.

Background: Primary Sjögren's syndrome (pSS) is an autoimmune inflammatory disease characterized by dryness of the eyes, mouth and other mucous membranes. Patients with pSS can also present with extraglandular manifestations, such as pulmonary, kidney and nervous system involvement. Central nervous system (CNS) manifestations have rarely been described in pSS.