S100A2: A potential biomarker to differentiate malignant from tuberculous pleural effusion
Bibliographic record
Abstract
BACKGROUND: S100 calcium binding protein A2 (S100A2)-which has been testified to have an abnormal expression in non-small cell lung cancer (NSCLC)-is considered as an effective biomarker in the diagnosis and prognosis of this malignancy. In this study, we detected the S100A2 levels in pleural effusion, aiming to evaluate its potential value in differentiating malignant pleural effusion (MPE) from tuberculous pleural effusion (TPE). METHODS: We collected pleural effusion from 104 NSCLC patients with MPE and 96 tubercular pleurisy cases. Enzyme-linked immunosorbent assay (ELISA) was performed to measure the levels of S100A2 in these samples. Meanwhile, the serum S100A2 levels were also examined in same subjects. The data concerning the expression of those commonly-used markers, including CEA, CYFRA211 and NSE, were obtained from medical records. RESULTS: Like other classified biomarkers, S100A2 had an over-expression in both pleural effusion and sera of the NSCLC patients compared with controls (P = 0.000), though having a lower P value. Receiver operating characteristic (ROC) analysis showed that the levels of S100A2 in pleural effusion (PE) could distinguish MPE from tuberculous pleurisy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.887), and its diagnostic value in hydrothorax was obviously higher than in serum (AUC = 0.709). CONCLUSION: Our results indicate that levels of S100A2 are significantly elevated in MPE, and that S100A2 may serve as a diagnostic biomarker for NSCLC patients with MPE. In further studies, we will validate our findings with a larger sample population.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".