Immunosenescence and Cancer
Bibliographic record
Abstract
The immune system, interacting intimately with the cancer cells in a tumor, may combat or favor cancer development and progression, or both. One of the most important risk factors for solid cancers is age. With increasing age, numerous alterations at multiple levels including molecular, cellular, organ, and systemic are occurring. With age many physiological systems are changing, including the immune system. Several alterations occur in both arms of the immune system with aging. Although the innate immune system shows changes with aging, the most important alterations occur in the adaptive immune system, especially involving T cells. Many changes in the immune system may decrease its capacity to combat the emerging or progressing tumor. Further, the age-related immune changes may favor the cancer development. The most important changes which may decrease the immune response efficiency are the changes in T cell functions and phenotypes, concomitant with the presence of a low-grade inflammation. A consensus is now emerging in oncology that not only the cancer cells themselves should be studied but also their macro- and microenvironments. In this context, the study of the interrelation of the immune response and the tumor at various stages is essential to enhance our capacity for intervention in elderly cancer patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".