Dynamic Deformation Calculation of Articular Cartilage and Cells Using Resonance-Driven Laser Scanning Microscopy
Bibliographic record
Abstract
The deformation of articular cartilage and its cells at the micro-scale during dynamic activities such as gait has high mechanoregulatory importance. Measuring the cellular geometries during such dynamics has been limited by the rate of microscopic image acquisition. The introduction of resonating mirrors for image rasterization (resonant scanning), rather than the conventional servo control (galvano scanning), has significantly improved the scanning rate by more than 100×. However, the high scanning rate comes at the cost of image quality, thereby posing challenges in image processing. Here, resonance-driven 3-D laser microscopy is used to observe the transient, micro-scale deformation of articular cartilage and its cells under osmotic challenge conditions. Custom image segmentation and deformable registration software were implemented for analysis of the resonance-scanned microscopy data. The software exhibited robust and accurate performance on the osmotic swelling measurements, as well as quantitative validation testing. The resonance-scanning protocol and developed analysis software allow for simultaneous strain calculation of both the local tissue and cells, and are thus a valuable tool for real-time probing of the cell-matrix interactions that are highly relevant in the fields of orthopedic biomechanics, cell mechanobiology, and functional tissue engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".