Bibliographic record
Abstract
Interleukins serve as communicating molecules between cells, mediating key interactions in the tumor microenvironment (TME) between immune cells and non-immune cells. Interleukin-10 (IL-10), a multifunctional cytokine with multiple properties, has been extensively studied in various aspects of immunology and cancer biology. IL-10 is pleiotropic, promotes cytotoxicity, yet inhibits antitumor-responses. In recent years, the role of IL-10 in ovarian cancer (OC) progression and treatment has gained significant scientific attention, elucidating the signaling pathways triggered by IL-10 action. OC, the leading cause of gynecologic cancer-related deaths, is characterized by ascites, which hosts an intricate TME that is not responsive to treatment by immune checkpoint inhibition. IL-10, known for its immunosuppressive and anti-inflammatory properties, plays a complex role in OC progression, immune modulation, and therapeutic response and has a potential therapeutic property as a target and as an effector. As the literature of basic science research studying the role of IL-10 in the TME of OC scopes a few decades and some data is contrasting, it is important to review the literature and provide concise input derived from it. This review aims to provide a comprehensive overview of the current understanding of IL-10 in OC, highlighting its influence on tumor growth, immune evasion, and potential as a therapeutic target.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".