Raman micro‐spectroscopy applied to treatment resistant and sensitive human ovarian cancer cells
Bibliographic record
Abstract
Despite the many advances intended to enhance the response to treatment, the survival rate of patients with ovarian cancer has only marginally improved in the past few decades. One major cause for this, is the lack of diagnostics for platinum-resistant disease. The goal of this study was to determine whether Raman micro-spectroscopy in conjunction with multivariate statistical analysis could discriminate between chemically fixed cisplatin-resistant (A2780cp) and cisplatin-sensitive (A2780s) human ovarian carcinoma cells. Raman spectra collected from individual cells were pre-processed and subsequently analyzed with Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA). Statistically significant differences (P < 0.0001) were observed between the Raman spectra of A2780s and A2780cp cells. A diagnostic accuracy of 82% was obtained using the PCA-LDA classifier model for the discrimination between the A2780s and A2780cp cells. The loading plot analysis suggests that relative increases in proteins and glutathione in the cisplatin-resistant cells compared to the cisplatin-sensitive cells are most likely the major source of discrimination between the two types of cells. These results support the potential application of Raman spectroscopy in the identification of chemo-resistant tumors prior to treatment.
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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".