GENE-27. MENINGIOMA RECURRENCE: ROLE OF M1/M2 TUMOUR-ASSOCIATED MACROPHAGE INFILTRATION
Bibliographic record
Abstract
Abstract BACKGROUND Meningioma, a most common brain tumour, has a high rate of recurrence. Tumour-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-tumoural or an immunosuppressive and pro-tumoural microenvironment. TAM subtypes present in meningioma and potential contribution to growth and recurrence is unknown. METHODS Fluorescence immunohistochemistry was used to evaluate the distribution and quantify M1 and M2 TAM populations in 30 meningioma tissues. Association between quantified M1 and M2 cells and M1/M2 ratio to tumour characteristics including WHO grade, tumour recurrence, size, location, peri-tumoural edema and patient demographics such as age and sex was examined. RESULTS TAM cells accounted for ~17% of all cells in tumour tissues. Importantly, greater than 80% of infiltrating TAMs were discovered to be of a polarized pro-tumoural M2 phenotype which positively associated with tumour size. TAM subtype profiles differed significantly between non-recurrent (n=18) and recurrent meningioma (n=12) (P=0.044). Specifically, the M1/M2 cell ratio was decreased by ~250% in recurrent tumours. CONCLUSION This study is the first to confirm existence of pro-tumoural M2 TAMs in the meningioma microenvironment and a potential role in tumour 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 | 0.000 | 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) | 0.002 | 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".