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Distinct peripheral pro-inflammatory profile associated with tuberous sclerosis complex and epilepsy.
Epilepsia
January 2025
Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
Objective: Tuberous sclerosis complex (TSC) is a monogenetic disorder associated with sustained mechanistic target of rapamycin (mTOR) activation, leading to heterogeneous clinical manifestations. Epilepsy and renal angiomyolipoma are the most important causes of morbidity in adult people with TSC (pwTSC). mTOR is a key player in inflammation, which in turn could influence TSC-related clinical manifestations.
View Article and Find Full Text PDFBody Composition at Diagnosis and Early Response in Pediatric Hodgkin Lymphoma.
Cancer Epidemiol Biomarkers Prev
January 2025
University of Alabama at Birmingham, Birmingham, AL, United States.
Background: The association between skeletal muscle and adipose tissue (body composition) and early response using positron emission tomography (PET) in pediatric Hodgkin lymphoma (HL) remains unstudied.
Methods: Patients enrolled on Children's Oncology Group studies AHOD0031 (intermediate-risk HL) and AHOD0831 (high-risk HL) with digital abdominal computed tomography (CT) scans at diagnosis and PET scans after 2 cycles (PET2) were included. Two consecutive slices at the third lumbar vertebra were identified and skeletal muscle index (SMI, in cm2/m2) and total adipose tissue index (TATI, in cm2/m2) were calculated using sliceOmatic (Magog, Canada) and height at diagnosis.
Multiplexed Molecular Endophenotypes Help Identify Hub Genes in Non-Small Cell Lung Cancer: Unlocking Next-Generation Cancer Phenomics.
OMICS
January 2025
Department of Biotechnology, Brainware University, Barasat, West Bengal, India.
Next-generation cancer phenomics by deployment of multiple molecular endophenotypes coupled with high-throughput analyses of gene expression offer veritable opportunities for triangulation of discovery findings in non-small cell lung cancer (NSCLC) research. This study reports differentially expressed genes in NSCLC using publicly available datasets (GSE18842 and GSE229253), uncovering 130 common genes that may potentially represent crucial molecular signatures of NSCLC. Additionally, network analyses by GeneMANIA and STRING revealed significant coexpression and interaction patterns among these genes, with four notable hub genes-, , and -identified as pivotal in NSCLC progression.
View Article and Find Full Text PDFAge-Trajectories of Higher-Order Diffusion Properties of Major Brain Metabolites in Cerebral and Cerebellar Gray Matter Using In Vivo Diffusion-Weighted MR Spectroscopy at 3T.
Aging Cell
January 2025
Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
Healthy brain aging involves changes in both brain structure and function, including alterations in cellular composition and microstructure across brain regions. Unlike diffusion-weighted MRI (dMRI), diffusion-weighted MR spectroscopy (dMRS) can assess cell-type specific microstructural changes, providing indirect information on both cell composition and microstructure through the quantification and interpretation of metabolites' diffusion properties. This work investigates age-related changes in the higher-order diffusion properties of total N-Acetyl-aspartate (neuronal biomarker), total choline (glial biomarker), and total creatine (both neuronal and glial biomarker) beyond the classical apparent diffusion coefficient in cerebral and cerebellar gray matter of healthy human brain.
View Article and Find Full Text PDFAutomatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.
Diagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
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