Longitudinal analyses of CD3ζ-chain expression in the correlation of the disease status in a cohort of head and neck cancer patients (TUM2P.1033)
Bibliographic record
Abstract
Abstract Purpose: Despite advances in multimodality treatment, the 5-year survival rate of Head and neck squamous cancer (HNSCC) patients has unimproved over the last 4 decades. CD3ζ has emerged as a clinically important immunological molecule in HNSCC. Its relevance as a prognostic biomarker of HNSCC, however, has not been formally addressed in a longitudinal study. Methods: Peripheral blood mononuclear cells (PBMC) were collected from 46 patients and 53 controls at the time of diagnosis and post treatments (upto 2 year period). Expression of CD3ζ in the T cells of the PBMC samples were analyzed in flow cytometry. Results: We standardized a method for longitudinal analyses of intracellular CD3z expressions in the PBMC samples. We considered a <10% baseline increase in the normalized MFI of CD3ζ expression as a predictor of disease status in evaluating the follow-up samples of the HNSCC patients. Correlation analysis showed that 27/29 HNSCC patients who showed an increase in the CD3ζ expression relative to their baselines were disease free (negative predictive value. 93.1%). 10/17 HNSCC patients who showed a reduced/no change in the CD3ζ expression died or had recurrent disease (positive predictive value, 58.8 %). Overall accuracy of the assay was 80.43%. The sensitivity and specificity were respectively 83.3% and 79.41%. Conclusion: Our longitudinal analyses supported that a >10% increase in the CD3ζ expression against baseline could be a good prognostic biomarker in HNSCC patients.
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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 | 0.001 | 0.001 |
| 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".