Representing Tumor-Associated Macrophages as the Angiogenesis and Tumor Microenvironment Regulator
Bibliographic record
Abstract
Over the recent years, studies in the area of cancer microenvironment and the cellular groups existing in this environment have indicated the significant role of them in progression of cancer studies.Among the mentioned cellular groups, as the main inflammatory components of stroma, Tumor associate macrophage (TAM) cells have the capacity of affecting the cancer tissue in different aspects.With their plasticity capacity, macrophages can change into M1 (classic) or M2 (alternative) macrophage reacting to different signals.In the tumor environment, they usually change into the M2 phenotype, and this phenotype can create a precancerous role in the macrophage and facilitate the invasion of tumor cells and metastasis, angiogenesis, remodeling of the extracellular matrix, and suppression of the immune system.The various roles of these cells and their reversibility have made the TAMs a potential target of the cancer treatment.This process takes place by different mechanisms such as Interference with TAMs survival, Inhibition of macrophage recruitment, repolarization of M2-like TAMs towards an M1-like phenotype, nano particle and liposome-based drug delivery system.This review study investigates the markers and the function of M1, M2, and tumor-associated macrophages, and finally, it proposes the latest clinical and laboratory approach for targeting the TAMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".