The studies revealed that evaluation of actual health disorders in people exposed to vibration requires up-to-date methodology, elaboration of new criteria that justify prophylactic measures. Taking into account a concept of occupational risk, scientists should have new approaches to hygienic regulation of general vibration in connection with load, levels, direction, quota of low-frequency components in total spectrum of mechanical oscillations.
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A practical distribution pattern of α-SMA-positive carcinoma associated fibroblasts indicates poor prognosis of patients with pancreatic ductal adenocarcinoma.
Transl Oncol
January 2025
Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Naval Medical University (Second Military Medical University), 168 Changhai Road, Shanghai 200433, China. Electronic address:
Purpose The present study aimed to clarify the distribution pattern of carcinoma associated fibroblasts (CAFs) across pancreatic ductal adenocarcinoma (PDAC) and its prognostic prediction value. Methods Data of two cohorts were retrospectively collected from consecutive patients who underwent primary pancreatic resection from January 2015 to December 2017. We used tumor specimens to screen out the most suitable markers for the spatial distribution analysis for CAFs subpopulations.
View Article and Find Full Text PDFIdentification and validation of mitochondria-related LncRNA signatures as a novel prognostic model for glioma.
Anticancer Drugs
January 2025
Department of Neurosurgery, Binzhou Medical University Hospital, Binzhou.
A predictive model for long-term survival is needed, and mitochondrial dysfunction is a key feature of cancer metabolism, though its link to glioma is not well understood. The aim of this study was to identify the molecular characteristics associated with glioma prognosis and explore its potential function. We analyzed RNA-seq data from The Cancer Genome Atlas and identified differentially expressed mitochondrial long noncoding RNAs (lncRNAs) using R's 'limma' package.
View Article and Find Full Text PDFHighly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer.
JCI Insight
January 2025
Department of Biomedical Engineering, Oregon Health and Science University, Portland, United States of America.
Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients.
View Article and Find Full Text PDFAdvanced Markers for Hemodynamic Monitoring in Cardiogenic Shock and End-Stage Heart Failure: A Mini Review.
Heart Fail Rev
January 2025
Division of Cardiovascular Medicine, University of Utah Health & School of Medicine, 30 N Mario Capecchi Drive, HELIX Building 3rd Floor, Salt Lake City, UT, 84112, USA.
Right heart catheterization (RHC) provides critical hemodynamic insights by measuring atrial, ventricular, and pulmonary artery pressures, as well as cardiac output (CO). Although the use of RHC has decreased, its application has been linked to improved outcomes. Advanced hemodynamic markers such as cardiac power output (CPO), aortic pulsatility index (API), pulmonary artery pulsatility index (PAPi), right atrial pressure to pulmonary capillary wedge pressure ratio (RAP/PCWP) and right ventricular stroke work index (RVSWI) have been introduced to enhance risk stratification in cardiogenic shock (CS) and end-stage heart failure (HF) patients.
View Article and Find Full Text PDFA Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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