Serum Neurofilament Light, Glial Fibrillary Acidic Protein and Tau Are Possible Serum Biomarkers for Activity of Brain Metastases and Gliomas
Bibliographic record
Abstract
BACKGROUND: Primary central nervous system (CNS) tumors and brain metastases (BMs) are major causes of morbidity and mortality, accompanied by low survival rates. Efforts to early discovery of CNS malignancies are critical. However, to date, there are no biomarkers approved for detection of cancer activity in the brain. Blood levels of neurofilament light (NfL) and tau, as well as glial fibrillary acidic protein (GFAp), show promise as biomarkers for brain injury in previous studies. Therefore, we performed a cross-sectional study to investigate correlations of those biomarkers with CNS activity of gliomas and BMs. METHODS: Serum samples of 36 participants of a single centered institution were tested for NfL, GFAp and tau with Simoa immunoassay, and correlated with clinical and radiological data. RESULTS: = 0.03). Although statistically significant (P = 0.04), differences in concentrations were not clinically meaningful for tau levels. Serum NfL (sNfL) and GFAp concentrations were higher in the group of patients with CNS tumors with disease in progression versus CNS with stable disease (P = 0.03 and P = 0.01, respectively). In addition, sNfL were higher in patients with metastatic solid tumors with known BMs than in those with metastatic tumors with no BM (P = 0.0004). CONCLUSION: sNfL and GFAp both apparently vary closely with presence and activity of gliomas and BMs. Further studies in larger populations are needed to expand these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".