Multi-scale optical, structural, and mechanical analysis of tumorous tissues (Conference Presentation)
Bibliographic record
Abstract
Living cells sense and respond to their microenvironment through chemical and physical signals. In vivo, their interactions are determined both by the adjacent cells and by the surrounding extracellular matrix (ECM) network. Tumor development is regulated by complex interactions of both normal and cancerous cells with their ECM. Such interactions are not well understood due mainly to the lack of appropriate characterization techniques covering length scales ranging from the molecular to the matrix/cellular level. Our goal is to study the optical/structural and mechanical properties of ECM fibrillar structures in physiological and tumor environments, from the single protein to the cellular/tissue level and to investigate tumor vascularization mechanisms. Combining fluorescence resonance energy transfer and multiple beam interferometry through surface forces apparatus characterization, we measured the molecular conformation, Young’s modulus and viscosity of the ECM deposited by cancer-associated fibroblasts preconditioned with tumor soluble factors derived from an aggressive breast cancer cells line. Our results reveal that tumor factors promote (i) single ECM protein unfolding, (ii) overall ECM stiffening, and (iii) increased ECM viscosity with respect to control. We next quantified the effect of this altered tumor-associated ECM on cell proangiogenic capability. Our findings indicate that the unfolded and stiff tumor-associated ECM significantly enhances the secretion of vascular growth factors by surrounding stromal cells. Collectively, our multi-scale analysis suggests that conformation and mechanics of ECM proteins at both the molecular and the matrix/tissue levels significantly dysregulate the downstream proangiogenic behavior of surrounding cells, which likely contributes to tumor vascularization and development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".