Investigation on the interactions of lymphoma cells with paclitaxel by Raman spectroscopy
Bibliographic record
Abstract
The single-cell Raman spectra of human Burkitt's lymphoma cells (CA46) including cells treated with different doses of paclitaxel and controls without paclitaxel can be detected by confocal micro-Raman spectroscopy. It shows that the Raman bands at 1094 cm–1assigned to the symmetric stretching vibration mode of O–P–O in the DNA backbone, 1338 cm–1and 1578 cm–1due to adenine and guanine of DNA all decrease in intensity with increasing drug dose. On the contrary, the intensity of peaks at 1257 cm–1due to characteristic vibration ofa-helix of Amide III and 1658 cm–1due to characteristic vibration ofa-helix of Amide I both increases with increasing drug dose. Multivariate statistical methods, such as Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA) were employed to discriminate normal lymphoma cells (CA46) and cells treated with different doses of paclitaxel. It was found that the sensitivity and specificity of differentiating the treated and untreated cell groups increase with drug doses and approach 100% for the high drug dose, consistent with the perception that the cytotoxicity increases with drug dose. These results suggest that Raman spectroscopy combined with multivariate analysis could become a useful tool for assessing the cytotoxicity of drugs such as paclitaxel on human lymphoma cells.
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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".