A volume‐exclusion normalization procedure for quantitative Raman confocal microspectroscopy of immersed samples applied to human embryonic stem cells
Bibliographic record
Abstract
Raman microspectroscopy is a quantitative instrumental method with considerable promise for the nondestructive analysis of living biological samples. Amongst samples of particular interest are human embryonic stem cells because of their therapeutic potential and because examination using Raman microspectroscopy does not appear to adversely affect this potential. However, it can be difficult to compare different spectra obtained with this technique and to quantify the native cellular constituents of such samples because their characteristic dimensions are difficult to establish or may vary from point to point. We present here a method to normalize spectra and estimate sample thicknesses based on a reference component present in the basal cell culture medium when we perform spectroscopy on colonies of living cells. Because more basal medium is displaced from the sampling volume as the cell layer increases in thickness, and because this component is present in the medium but excluded from cells, a concomitant decline therefore occurs in the intensity of the Raman scattering from the reference component. This permits comparisons between samples because their spectra can be scaled in inverse relation to their excluded volumes. Furthermore, estimations of sample thicknesses can also be obtained based on the same concept. Thus, the absolute quantification of cellular components becomes possible because cell sample volumes can be determined. Although applied to human embryonic stem cells, the approach is sufficiently general to be adapted for use with other samples. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".