Fourier transform infrared (FTIR) spectromicroscopic characterization of stem-like cell populations in human esophageal normal and adenocarcinoma cell lines
Bibliographic record
Abstract
We have tested an approach to identify putative cancer stem cells that involves measurement of the infrared absorption spectrum of individual cells in an aqueous environment, and their subsequent classification using multivariate data analysis techniques. Two primary esophageal cell lines were characterized: the immortalized normal esophageal epithelial cell line, Het-1A, and the esophageal adenocarcinoma cell line, OE33. In addition, we also evaluated spheroids, reflecting stem-like cell populations, which were derived from each parent cell line when grown in serum-free media. As differences in cell size appeared to be a strong discriminating factor, a correction needs to be performed to allow a reliable classification based on infrared absorption spectra. We demonstrated that stem-like cells derived from Het-1A could easily be discriminated on the basis of absorbance differences in the 1000-1200 cm(-1) spectral interval, whereas this was not possible for OE33. Furthermore, we found that changes due to aging of OE33 cells in culture dominated the infrared absorption spectra and somewhat limited the potential of this approach to identify stem-like cell populations using this in vitro model system.
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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 | 0.000 | 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) | 0.001 | 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".