Prominent Targets for Cancer Care: Immunotherapy Perspective
Bibliographic record
Abstract
Objective: Recent scientific advances have expanded insight into the immune system and its response to malignant cells. In the past few years, immunotherapy has attained a hallmark for cancer treatment, especially for patients suffering from the advanced-stage disease. Modulating the immune system by blocking various immune checkpoint receptor proteins through monoclonal antibodies has improved cancer patients' survival rates. Methods: The scope of this review spans from 1985 to the present day. Many journals, books, and theses have been used to gather data, as well as Internet-based information such as Wiley, PubMed, Google Scholar, ScienceDirect, EBSCO, SpringerLink, and Online electronic journals. Key Findings: Current review elaborates on the potential inhibitory and stimulatory checkpoint pathways which are emerged and have been tested in various preclinical models, clinical trials, and practices. Twenty-odd such significant checkpoints are identified and discussed in the present work. Conclusion: A large number of ongoing studies reveal that combination therapies that target more than one signaling pathway may become effective in order to maximize efficacy and minimize toxicity. Moreover, these immunotherapy targets can be a part of integrated therapeutic strategies in addition to classical approaches. It may become a paradigm shift as a promising strategy for cancer treatment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".