Human heart under stress: its mysteries revealed by mechano-imaging modeling
Bibliographic record
Abstract
Biological tissues remodeling is largely due to mechanical stresses withstood by the cells, with both anabolic and catabolic responses depending on loading type, magnitude, and duration. Thus in the last 16 years, my research program aimed at providing new imaging and modeling technologies for the robust in vivo mechanical stress evaluation within musculoskeletal or cardiac tissues, a cornerstone in understanding the fundamental mechanobiological processes in humans. It is well established that physical activity is beneficial for human health. Indeed, if more stress is withstood by the cells, more remodeling occurs. However, the thresholds beyond which exercise loading causes regenerative or degenerative remodeling still have to be defined. This issue confirmed my willingness to direct my research towards understanding and quantifying the cardiac behavior under stress conditions through new mechano-imaging modeling technologies. This presentation will introduce the mechanical characterization of human tissues, with an application to the differential impact of chemotherapy on cardiac wall structure. The limits of mechanical testing on biological tissues will be discussed to introduce multiparametric MRI, with an application as an indirect evaluation tool of the mechanical properties of ex-vivo cardiac tissues. Finite element models of the heart will then be summarized, followed by their use for the prediction of the mechanical properties of the myocardium from CMR in acute Lymphoblastic Leukemia survivors and for MRI-based analysis of the blood flow in the left ventricle. The limits of rest data on patients will be discussed to introduce a study on cardiac mechanical performance in childhood ALL survivors assessed by combined CMR and incremental exercise test. The development of a novel inotropic Exercise CMR Protocol for Cardiac Mechanics Characterization will conclude this presentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".