Macrophages, Inflammation, and Lung Cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer mortality worldwide, and at only 18%, it has one of the lowest 5-year survival rates of all malignancies. With its highly complex mutational landscape, treatment strategies against lung cancer have proved largely ineffective. However with the recent success of immunotherapy trials in lung cancer, there is renewed enthusiasm in targeting the immune component of tumors. Macrophages make up the majority of the immune infiltrate in tumors and are a key cell type linking inflammation and cancer. Although the mechanisms through which inflammation promotes cancer are not fully understood, two connected hypotheses have emerged: an intrinsic pathway, driven by genetic alterations that lead to neoplasia and inflammation, and an extrinsic pathway, driven by inflammatory conditions that increase cancer risk. Here, we discuss the contribution of macrophages to these pathways and subsequently their roles in established tumors. We highlight studies investigating the association of macrophages with lung cancer prognosis and discuss emerging therapeutic strategies for targeting macrophages in the tumor microenvironment.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".