Publications by authors named "C Sengenes"
Article Synopsis
- Dedifferentiated and Well-differentiated liposarcoma tumors show an increase in the MDM2 oncogene, which drives tumor growth through unique metabolic functions.
- The study finds that liposarcoma cells rely on serine produced by distant muscle tissues for survival, although the source of serine is not fully understood.
- Treatment with an FDA-approved anti-interleukine-6 monoclonal antibody can hinder serine synthesis and lead to decreased tumor growth and increased cancer cell death, suggesting a potential therapy for liposarcoma patients.*
[What if the origin of FAPs was contributing to their heterogeneity in muscle?].
Med Sci (Paris)
November 2023
Fibro-adipogenic progenitors (FAPs) are resident mesenchymal stromal cells (MSCs) of skeletal muscle. They play a crucial role in muscle homeostasis and regeneration through their paracrine activity. Recent technological advances in single-cell RNA sequencing have allowed the characterization of the heterogeneity within this cell population.
View Article and Find Full Text PDFFibro-adipogenic progenitors (FAPs) play a crucial role in skeletal muscle regeneration, as they generate a favorable niche that allows satellite cells to perform efficient muscle regeneration. After muscle injury, FAP content increases rapidly within the injured muscle, the origin of which has been attributed to their proliferation within the muscle itself. However, recent single-cell RNAseq approaches have revealed phenotype and functional heterogeneity in FAPs, raising the question of how this differentiation of regenerative subtypes occurs.
View Article and Find Full Text PDFThe epicardial adipose tissue (EAT) is the visceral fat depot of the heart which is highly plastic and in direct contact with myocardium and coronary arteries. Because of its singular proximity with the myocardium, the adipokines and pro-inflammatory molecules secreted by this tissue may directly affect the metabolism of the heart and coronary arteries. Its accumulation, measured by recent new non-invasive imaging modalities, has been prospectively associated with the onset and progression of coronary artery disease (CAD) and atrial fibrillation in humans.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to see if a deep learning tool could tell the difference between two muscle diseases, FSHD1 and myositis, using whole-body MRI scans without needing doctors to check the images manually.
- They tested 40 patients: 19 with FSHD1 and 21 with myositis, and the deep learning tool performed well, getting about 69% to 77% of the diagnoses right depending on the body parts scanned.
- In comparison, two expert doctors also did well, but the deep learning tool was able to correctly identify more patients that the doctors got wrong, showing it’s a useful tool for medical diagnosis.