Increased susceptibility of tumorigenicity and decreased anti-tumor effect of DC vaccination in aged mice are potentially associated with increased number of NK1.1+CD3+ NKT cells.
Bibliographic record
Abstract
AIM: It is established that aging leads to declines in immune function. However, the mechanisms underlying remain poorly understood. METHODS: In this study, we compared the tumoriginecity of MO4 (ovalbumin-transfected) tumor cells, the efficacy of dendritic cell (DC) vaccination and cytotoxic T lymphocytes (CTLs), and the number of NK1.1+CD3+ NKT cells between aged mice and young mice using T cell proliferation and cytotoxicity assays and flow cytometry. RESULTS: We showed that, in comparison to young mice, aged mice are 10-fold more susceptible to tumorigenicity of MO4 tumor cells. Aged mice immunized with bone marrow-derived DCs pulsed with ovalbumin (DCOVA) survived significantly shorter after challenge with MO4 tumor cells as compared to equally treated young mice. Furthermore, CTLs from aged mice immunized with DCOVA displayed 4-fold weaker cytotoxicity as compared to CTLs from immunized young mice. Interestingly, the number of NK1.1+CD3+ NKT cell significantly increase with aging (p < 0.05). Of particular importance, NK1.1+CD3+ NKT cells isolated from aged mice suppress the proliferation of T cells. CONCLUSIONS: Based on these data, we conclude that NK1.1+CD3+ NKT cells from aged mice mediate immunosuppression, and further suggest that increased number of NK1.1+CD3+ NKT cells in aged mice might, among others, diminish their immune function by mediation of immunosuppression.
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 | 0.000 |
| 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) | 0.002 | 0.000 |
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".