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http://dx.doi.org/10.1378/chest.101.3_supplement.10s | DOI Listing |
Sci Rep
December 2024
Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Plasma secretory proteins are associated with various diseases, including aortic dissection (AD). However, current research on the correlation between AD and plasma protein levels is scarce or lacks specificity. This study aimed to explore plasma secretory proteins as potential biomarkers for AD.
View Article and Find Full Text PDFImmun Ageing
November 2024
Department of Cell and Developmental Biology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, 69978, Israel.
Background: Mast cells (MCs) are tissue resident cells of the immune system, mainly known for their role in allergy. However, mounting evidence indicates their involvement in the pathology of age-related diseases, such as Alzheimer's disease, Parkinson's disease, and cancer. MC numbers increase in aged tissues, but how ageing affects MCs is poorly understood.
View Article and Find Full Text PDFAllergol Select
October 2024
Center for Child and Adolescent Health, Helios Hospital Krefeld, Academic Hospital of RWTH Aachen, Krefeld.
J Sport Health Sci
September 2024
Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC 3052, Australia. Electronic address:
Purpose: Regular exercise can reduce incidence and progression of breast cancer, but the mechanisms for such effects are not fully understood. The purpose of this study was to examine the mechanisms behind the protective effects of exercise.
Methods: We used a variety of rodent and human experimental model systems to determine whether exercise training can reduce tumor burden in breast cancer and to identify mechanism associated with any exercise training effects on tumor burden.
J Proteomics
October 2024
Department of Bioinformatics, Pondicherry University, Puducherry 605014, India. Electronic address:
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases.
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