Contribution au développement de capteurs à ondes Love pour l’évaluation des propriétés mécaniques des monocouches cellulaires
Bibliographic record
Abstract
Cell monolayers are one of the main tools in research and development in biotechnology and medicine. It has been found that alterations in the mechanical properties of cells, such as changes in stiffness and viscosity, can reflect different states of the cells, such as the case of cells that are infected by malaria, or the case of some cancer cells. Atomic force microscopy is one of the most commonly used techniques for testing mechan- ical properties in cells. However, this technique probes very focused regions of cells at a time. To overcome this limitation, it is needed to develop new techniques that allow the evaluation of regions of cell monolayers as a whole instead of focused regions. Surface acoustic wave sensors have potential features for monitoring mechanical properties in cell monolayers because they can transduce mechanical changes in their environment directly and in real time; are compact, and are compatible with biological applications. This Ph.D. work explores the use of Love surface acoustic wave sensors designed to work with cell monolayers. It also aims to solve inverse problems to estimate the changes in the mechanical properties of materials deposited on the sensor. The results showed that it is possible to design and fabricate Love wave sensors with high enough penetration depth to work with structures of interest in cell monolayers (such as its cytoskeleton). We also found that the sensors sensitivity can be high enough to detect viscosity changes between vsicosity ranges of 0.9 cP and 3 cP. The determination of material properties with Love wave sensors and inverse problems is suitable for applications such as polymer thin films. However, for the estimation of mechanical properties of the cell monolayers, it was identified the need to add more information to the problem or to explore different techniques to perform the estimations in cell monolayers. Love wave sensors, unlike other optical devices and electrical impedance sensors, provide direct measurements of mechanical changes occurring at the sensor surface, so the role of Love wave sensors can be advantageous in the development of devices for measuring and monitoring mechanical changes in cell monolayers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".