Progress and advances in the development of a label-free optofluidic platform based on quantitative phase digital holographic microscopy and microfluidics for the identification of human disease-specific cell phenotypes (Conference Presentation)
Bibliographic record
Abstract
Advances in stem cell technology allow the reprogramming of patient-derived cells, obtained from urine samples or skin biopsies, into induced pluripotent stem cells (iPSCs), which can then be differentiated into any cell type. With this goal in mind, we will present how experimental developments, mostly in the field of microfluidics, can be used to extract several relevant spatiotemporal biophysical properties from the quantitative phase signal provided by digital holographic microscopy, thus moving towards the realization of a truly effective optofluidic platform for non-invasive characterization of cell structure and dynamics. Such label-free characterization is highly conducive to the identification of disease-specific cell phenotypes when comparing iPSC-derived cells from control and diseased patients.
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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".