Circulating Plasma Gelsolin: A Predictor of Favorable Clinical Outcomes in Head and Neck Cancer and Sensitive Biomarker for Early Disease Diagnosis Combined with Soluble Fas Ligand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Head and neck cancer (HNC) accounts for more than 330,000 cancer deaths annually worldwide. Despite late diagnosis being a major factor contributing to HNC mortality, no satisfactory biomarkers exist for early disease detection. Cytoplasmic gelsolin (cGSN) was discovered to predict disease progression in HNC and other malignancies, and circulating plasma gelsolin (pGSN) levels are significantly correlated with infectious and inflammatory disease prognoses. Here, the plasma levels of five candidate biomarkers (circulating pGSN, squamous cell carcinoma antigen, cytokeratin 19 fragment, soluble Fas, and soluble Fas ligand (sFasL)) in 202 patients with HNC and 45 healthy controls were measured using enzyme-linked immunosorbent assay or Millipore cancer multiplex assay. The results demonstrated that circulating pGSN levels were significantly lower in patients with HNC than in healthy controls. Moreover, circulating pGSN outperformed other candidate biomarkers as an independent diagnostic biomarker of HNC in both sensitivity (82.7%) and specificity (95.6%). Receiver operating characteristic curves indicated that combined pGSN and sFasL levels further augmented this sensitivity (90.6%) for early disease detection. Moreover, higher pGSN levels predicted improved prognosis at both 5-year overall survival and progression-free survival. In conclusion, circulating pGSN could be an independent predictor of favorable clinical outcomes and a novel biomarker for the early HNC detection in combination with sFasL.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it