Immune Checkpoint inhibitor Therapy in Various Cancers
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are a new way of immunotherapy, not simply refers to the improvement of immunity to the body, but by improving the immune microenvironment around the tumor, thereby activating immune cell activity in vivo to achieve anti-tumor purposes. Now, CTLA‐4 and PD‐1 or PD‐L1 monoclonal antibody are mainly developed relatively successfully for immune checkpoints, in addition to other new immune checkpoints that have been discovered and clinically tested. However, while immune checkpoint inhibitors have been developed successively, some vague problems still need to be solved, such as the large gap between the immunotherapy effects of different patients. These issues are critical to the selection of immune checkpoint inhibitors. In this review, based on the study of the immunosuppressive mechanism of CTLA-4 and PD-1/PD-L1, the application of related immune checkpoint inhibitors in cancer treatment is discussed starting from three representative types of cancer. At the same time, according to the existing problems, some common immune-related adverse events and newly discovered immune checkpoints are summarized, and the future research direction of ICIs is further explored.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".