Development Of Dynamic Light Scattering Optical Coherence Tomography Methods For Detecting Cell Death
Bibliographic record
Abstract
Dynamic light scattering (DLS) techniques can provide information about the quantity, size, and motion of light scatterers within a volume based on temporal fluctuations in its light scattering profile. In DLS, autocorrelation functions (ACFs) are computed from light intensity vs time signals acquired from optical imaging setups. A parameter known as the decorrelation time is computed from each ACF and is inversely related to the average motion speed of scatterers within the imaging volume. Optical coherence tomography is an imaging modality that generates 2D cross-sectional images based on light backscattered from a sample, and the combination of DLS with OCT is known as dynamic light scattering optical coherence tomography (DLS-OCT). Previously, DLS-OCT has been used to detect apoptosis, a form of programmed cell death, in non-adherent leukemia cells. Cells undergoing apoptosis experience predictable morphological changes that results in an increase in intracellular motion, and therefore a decrease in decorrelation time. We applied DLS-OCT methods to quantify the decorrelation times in adherent breast cancer cell pellets that were either untreated, treated with 20 ng/mL paclitaxel for 24 or 48 hours, or deprived of media for 24 or 48 hours. The mean decorrelation times in the paclitaxel-treated and nutrient deprived groups were significantly lower than in the untreated cells (p<0.05), suggestive of increased intracellular motion due to morphological cellular changes associated with cell death. We also investigated a new model to fit to ACFs generated by DLS-OCT of cell pellets. Typically, ACFs are fit to single exponential decay curves. We developed a model that expresses the ACFs from in vitro experiments as a sum of multiple exponential decay curves using an algorithm known as CONTIN. The curves produced by CONTIN fitted the experimental data much better than the single exponential decay fits. We speculate that the CONTIN fits, each of which resembled a superposition of three exponential decay functions, may result from light scattered from three different types of scatterers within cells, such as lysosomes, mitochondria, and nuclei.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".