Tumor-associated macrophage infiltration in meningioma
Bibliographic record
Abstract
Abstract Background Meningioma, a most common brain tumor, has a high rate of recurrence. Tumor-associated macrophages (TAMs) are the most abundant immune cell type in meningioma. TAMs display functional phenotypic diversity and may establish either an inflammatory and anti-tumoral or an immunosuppressive and pro-tumoral microenvironment. TAM subtypes present in meningioma and potential contribution to growth and recurrence is unknown. Methods Immunofluorescence staining was used to quantify M1 and M2 TAM populations in tissues obtained from 30 meningioma patients. Associations between M1 and M2 cells, M1:M2 cell ratio to tumor characteristics, WHO grade, recurrence, size, location, peri-tumoral edema, and patient demographics such as age and sex were examined. Results TAM cells accounted for ~18% of all cells in meningioma tissues. More than 80% of infiltrating TAMs were found to be of pro-tumoral M2 phenotype and correlated to tumor size (P = .0409). M1:M2 cell ratio was significantly decreased in WHO grade II, compared to grade I tumors (P = .009). Furthermore, a 2.3-fold difference in M1:M2 ratio between primary (0.14) and recurrent (0.06) tumors was observed (n = 18 and 12 respectively, P = .044). Conclusion This study is the first to confirm existence of pro-tumoral M2 TAMs in the meningioma microenvironment, emphasizing its potential role in tumor growth and recurrence.
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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 | 0.001 | 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".