Tumor-neutrophil crosstalk promotes in vitro and in vivo glioblastoma progression
Bibliographic record
Abstract
Introduction: The tumor microenvironment (TME) of glioblastoma (GB) is characterized by an increased infiltration of immunosuppressive cells that attenuate the antitumor immune response. The participation of neutrophils in tumor progression is still controversial and a dual role in the TME has been proposed. In this study, we show that neutrophils are reprogrammed by the tumor to ultimately promote GB progression. Methods: assays, we demonstrate the existence of bidirectional GB and neutrophil communication, directly promoting an immunosuppressive TME. Results and discussion: Neutrophils have shown to play an important role in tumor malignancy especially in advanced 3D tumor model and Balb/c nude mice experiments, implying a time- and neutrophil concentration-dependent modulation. Studying the tumor energetic metabolism indicated a mitochondria mismatch shaping the TME secretome. The given data suggests a cytokine milieu in patients with GB that favors the recruitment of neutrophils, sustaining an anti-inflammatory profile which is associated with poor prognosis. Besides, glioma-neutrophil crosstalk has sustained a tumor prolonged activation via NETs formation, indicating the role of NFκB signaling in tumor progression. Moreover, clinical samples have indicated that neutrophil-lymphocyte ratio (NLR), IL-1β, and IL-10 are associated with poor outcomes in patients with GB. Conclusion: These results are relevant for understanding how tumor progression occurs and how immune cells can help in this process.
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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 | 0.001 |
| 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".