To promote or inhibit glioma progression, that is the question for IL-33
Bibliographic record
Abstract
IL-33, a member of the IL-1 cytokine family has been shown to play a dual role within the body. First IL-33, similar to other IL-1 family members, is a secreted cytokine that binds to the cell surface receptor ST2 to induce a number of cell signaling pathways. Second, IL-33 enters the nucleus where it binds chromatin and directs transcriptional control of an array of growth factors and cytokines. Consistent with its complex cellular regulation, IL-33 mediates an array of biological functions by acting on a wide range of innate and adaptive immune cells. Recently, we found that IL-33 is expressed in a large number of human glioma patient specimens where its expression within the tumor correlates with the increased presence of Iba+ cells that include both resident microglia and recruited monocyte and macrophages. Strikingly, glioma derived expression of IL-33 correlates with a dramatic decrease in overall survival of tumor-bearing animals and thus supports its role as an influential factor in gliomagenesis. Notably however, when the nuclear localization function of IL-33 is crippled, the tumor microenvironment is programmed to be anti-tumorigenic and results in prolonged overall survival suggesting that when educated appropriately this could represent a novel therapeutic strategy for glioma (De Boeck et al. (2020), Nat Commun, doi: 10.1038/s41467-020-18569-4).
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".