Beta-Amyloid Peptides Enhance the Proliferative Response of Activated CD4+CD28+ Lymphocytes from Alzheimer Disease Patients and from Healthy Elderly
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most frequent form of dementia among elderly. Despite the vast amount of literature on non-specific immune mechanisms in AD there is still little information about the potential antigen-specific immune response in this pathology. It is known that early stages of AD include β-amyloid (Aβ)- reactive antibodies production and inflammatory response. Despite some evidence gathered proving cellular immune response background in AD pathology, the specific reactions of CD4(+) and CD8(+) cells remain unknown as the previous investigations yielded conflicting results. Here we investigated the CD4(+)CD28(+) population of human peripheral blood T cells and showed that soluble β-amyloids alone were unable to stimulate these cells to proliferate significantly, resulting only in minor, probably antigen-specific, proliferative response. On the other hand, the exposure of in vitro pre-stimulated lymphocytes to soluble Aβ peptides significantly enhanced the proliferative response of these cells which had also lead to increased levels of TNF, IL-10 and IL-6. We also proved that Aβ peptide-enhanced proliferative response of CD4(+)CD28(+) cells is autonomous and independent from disease status while being associated with the initial, ex vivo activation status of the CD4(+) cells. In conclusion, we suggest that the effect of Aβ peptides on the immune system of AD patients does not depend on the specific reactivity to Aβ epitope(s), but is rather a consequence of an unspecific modulation of the cell cycle dynamics and cytokine production by T cells, occurring simultaneously in a huge proportion of Aβ peptide-exposed T lymphocytes and affecting the immune system performance.
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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 | 0.000 | 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 | 0.000 |
| 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".