Bibliographic record
Abstract
Many countries are facing the aging of their population, and many more will face a similar obstacle in the near future, which could be a burden to many healthcare systems. Increased susceptibility to infections, cardiovascular and neurodegenerative disease, cancer as well as reduced efficacy of vaccination are important matters for researchers in the field of aging. As older adults show higher prevalence for a variety of diseases, this also implies higher risk of complications, including nosocomial infections, slower recovery and sequels that may reduce the autonomy and overall quality of life of older adults. The age-related effects on the immune system termed as "immunosenescence" can be exemplified by the reported hypo-responsiveness to influenza vaccination of the elderly. T cells, which belong to the adaptive arm of the immune system, have been extensively studied and the knowledge gathered enables a better understanding of how the immune system may be affected after acute/chronic infections and how this matters in the long run. In this review, we will focus on T cells and discuss the surface and molecular markers that are associated with T cell senescence. We will also look at the implications that senescent T cells could have on human health and diseases. Finally, we will discuss the benefits of having these markers for investigators and the future work that is needed to advance the field of T cell senescence markers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".