Hyaluronic acid-based extracellular matrix triggers spontaneous M2-like polarity of monocyte/macrophage
Bibliographic record
Abstract
Hyaluronic acid (HA) is found in various tumor tissues, and is considered tumor-associated extracellular matrix (ECM). Within this tumor-associated ECM, stromal cells, especially immune cells, are involved in tumor progression. However, the effects of tumor-associated ECM on the characteristics of immune cells remain unexplored. Therefore, we studied the triggering effect of HA on spontaneous M2-like polarity of monocytes/macrophages using HA-mixed collagen (HA-COL) matrix. In the presence of HA, expression of the HA receptor (CD44) and M2 polarity-related genes was upregulated in human monocytes (THP-1 cells). We confirmed the CD44-mediated activation of STAT3 in THP-1 cells cultured in an HA-rich environment. Furthermore, when we induced the THP-1 cells to differentiate into cells with M1 or M2 polarity within an HA-rich environment, the HA-rich environment influenced the direction of induction. Our findings might improve understanding of the crosstalk between immune cells and tumor-associated ECM, and facilitate development of tumor immunotherapy strategies.
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 | 0.000 | 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) | 0.001 | 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".