Checkpoint Inhibitors and Hepatotoxicity
Bibliographic record
Abstract
Uncontrolled immune response to a pathogen or any protein can lead to tissue damage and autoimmune diseases, that represent aberrant immune responses of the individual to its own cells and/or proteins. The immune checkpoint system is the regulatory mechanism that controls immune responses. Tumor cells escape the immune surveillance mechanism, avoiding immune detection and elimination by activating these checkpoints and suppressing the anti-tumor response, thus allowing formation of tumors. Antigenic modulation facilitates masking and contributes to the escape of tumor cells. In addition, there are growing cell promoters, like transforming growth factor β (TGF-β), contributing to escape mechanisms. Targeting the immunological escape of malignant cells is the basis of immune oncology. Checkpoint inhibitors, cytokines and their antibodies may enhance the immune system's response to tumors. Currently, immunomodulatory agents have been designed, evaluated in clinical trials and have been approved by both European and United States Drug Agencies. The present review is a reflection of the increasingly important role of the checkpoint inhibitors. Our aim is to review the side effects with the emphasis on hepatic adverse reactions of these novel biological drug interventions.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".