Differentiation of <scp>HaCaT</scp> cell and melanocyte from their malignant counterparts using micro‐<scp>R</scp>aman spectroscopy guided by confocal imaging
Bibliographic record
Abstract
BACKGROUND: Skin cancer is the most common type of cancer in humans. Current techniques for identifying normal and neoplastic tissues are either destructive or not sensitive and specific enough. Raman spectroscopy and confocal imaging may obviate many limitations of existing methods by providing noninvasive, high-resolution, and real-time morphological and biochemical analysis of living tissues and cells. METHODS: We conducted micro-Raman spectroscopy studies on HaCaT cells, melanocytes (MC) and their malignant counterparts squamous cell carcinoma (SCC) and melanoma (MM) cells, respectively. Reflectance confocal imaging is used as guidance for the spectral measurements. RESULTS: Significant differences were found between the spectra of HaCaT cells and SCC cells, MC cells and MM cells, as well as all normal cells (HaCaT and MC) and all tumor cells (SCC and MM). Approximately 90% sensitivity and specificity was achieved for all the separations that we performed. CONCLUSION: Our results demonstrated the robust capability of confocal Raman spectroscopy in separating different cell lines. The acquired Raman spectra of major types of skin cells and their malignant counterparts will be useful for the interpretation of Raman spectra from in vivo skin. We believe it will eventually help diagnosis of skin cancer and other skin disease in clinical dermatology.
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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".