Assessment of cell death using dynamic light scattering with M-mode optical coherence tomography imaging
Bibliographic record
Abstract
Optical Coherence tomography (OCT) is a rapidly developing light based imaging modality that can provide functional information and visualization of tissue morphology and cell death by quantifying the motion of intracellular structures. In this work, Dynamic light scattering (DLS) with OCT, is used to compute the time-dependent fluctuations in scattered light intensity. Using the DLS-OCT method, we have previously shown a higher rate of intracellular motion detected in apoptotic cells compared to viable cells. In this study, we aim to probe the intracellular motion of cells at much higher scan-rates that we have attempted previously to detect sub-cellular motion with greater sensitivity. To validate the DLS-OCT approach, M-mode OCT images were acquired using Thorlabs SS-OCT system (sampling rate of 100 kHz) to measure the Brownian motion in monodisperse microsphere suspensions. The results demonstrate that ACF decays more rapidly for smaller size microspheres and the experimental decorrelation time values matched with the theoretical values calculated using the Einstein-Stokes equation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".