Early signalling changes in T cells with aging
Bibliographic record
Abstract
Abstract Aging is accompanied by many physiological changes and the immune system is not escaping this. This effect of time is affecting most cells of the immune system. Numerous changes have been described but most of them are phenomenological, a typical example is the change of phenotypes in T cells. We are interested since years to uncover the intracellular signaling associated with phenotypic and functional changes of various immune cells. We identified several signaling alterations starting from the early to the most downstream events in several cells like neutrophils, monocytes and T lymphocytes. We not only were interested in the forward signaling driven by kinases but also by the feedback or regulatory signaling mediated by phosphatases. We found profound alterations of regulatory phosphatases activity and phosphorylation (e.g. SHP-1). The question was always whether these changes are occurring also uniformly in T cell or there are differences between the various subpopulations (naive to the continuum of memory T cells). Our studies indicate that there are changes according to the subpopulations and that basal hyper-phosphorylation status we observe may be explained by signaling crosstalks. We also observed that there is a resemblance between naïve and TEMRA T cells, which differ much from CM and RM cells. In conclusion the different T cell subpopulations may have very specific changes distinguishing them from each other. This needs further investigations in relation to their functions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".