GENERATION OF AN EFFECTIVE IN VIVO CTL RESPONSE USINGVACCINE APPROACHES IN A MODEL OF AUTOIMMUNITY
Bibliographic record
Abstract
Background: Various interactions between tumours and their environment make it difficult to determine the factors that are essential to the induction of a robust CTL response by immunotherapeutic strategies against tissue antigens. We therefore sought to develop a model of autoimmunity, in which vaccine strategies could be evaluated for their ability to elicit CTL activity towards a model antigen. Methods: Transgenic mice expressing lymphocytic choriomeningitis virus glycoprotein (LCMV-gp) specifically in the insulin-producing beta-islets of the pancreas allowed anti-beta islet CTL responses to be measured by blood glucose levels, tetramer and intracellular cytokine staining. Peptide vaccines consisted of i.v. administration of the known immunodominant epitopesof LCMV-gp alongside dendritic cell (DC) maturation stimuli. DC vaccines consisted of i.v.administration of 2x10^6 mature LCMV-gp peptide-pulsed bone-marrow-derived DC. Results: While administration of antigenic peptides with adjuvants and/or costimulatory molecules were ineffective, DC vaccination induced hyperglycemia in approximately 70% of mice. Interestingly, while a class II epitope was not required for induction of autoimmunity, pulsing with a single class I epitope was insufficient. Furthermore, while tetramer-positive populations could be seen in the blood of peptide-vaccinated mice, no such population is seen in DC-treated mice. Conclusions: The lack of correlation between presence of tetramer-positive populations in the blood and effective beta-islet cell cytolysis underlines the requirement for better markers of CTL activity inclinical trials. These results demonstrate the effectiveness of DC vaccination in this model and allow for further investigation of the factors that direct a strong CD8+ T-cell response towards tissue antigens.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".