Ageing, autoimmunity and arthritis: Perturbations of TCR signal transduction pathways with ageing – a biochemical paradigm for the ageing immune system
Bibliographic record
Abstract
It is widely accepted that cell-mediated immune functions decline with age, rendering an individual more susceptible to infection and possibly cancer, as well as to age-associated autoimmune diseases. The exact causes of T-cell functional decline are not known. One possible cause could be the development of defects in the transduction of mitogenic signals following TCR stimulation. This T-cell hyporesponsiveness due to defects of signalling through the TCR either from healthy elderly subjects or from individuals with autoimmune diseases such as rheumatoid arthritis or systemic lupus erythematosus results in an impaired ability to mount efficient immune responses and to maintain responsiveness to foreign antigens. This implies that a high proportion of autoreactive T cells might accumulate either intrathymically or in the periphery. T-cell anergy and differential TCR signalling could thus also be key players in the disruption of tolerance and the onset of autoimmune diseases. The increasing number of the elderly may lead to an increase of clinically important autoimmune diseases. We will review the signal transduction changes through the TCR-CD3 complex in T lymphocytes from healthy elderly subjects, which result in a modification of the activation of transcription factors involved in IL-2 gene expression leading to decreased IL-2 production. The putative contribution of altered T-cell signalling with ageing in the development of autoimmune diseases will be also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".