A distinct role of CD4+ Th17 and Th17-stimulated CD8+ CTL in induction of antitumor immunity and experimental autoimmune encephalomyelitis (101.5)
Bibliographic record
Abstract
Abstract Th17 and CD8+ cytotoxic T lymphocytes(CTL) cells eradicate established tumors and induce experimental autoimmune encephalomyelitis(EAE). To assess their potential relationship, we generated ovalbumin(OVA) or myelin oligodendrocyte glycoprotein(MOG) specific Th17 cells by in vitro cultivation of OVA-pulsed dendritic cells(DCova) with CD4 T cells derived from T cell receptor transgenic OTII mice or MOG peptide-pulsed splenocytes with CD4 T cells purified from MOG immunization induced EAE C57BL/6 mice. We found these Th17 cells expressed transcriptional factor RORγt and secreted IL17. We also found that OVA-specific Th17 cells which acquired major histocompatibility complex/peptide(pMHC) I and costimulatory molecules by DCova activation stimulated OVA-specific CTL response and antitumor immunity against OVA-expressing BL6-10ova tumor. The stimulatory effect is mediated by acquired CD80 costimulation and targeted to CD8 T cells in vivo via acquired pMHC I complexes. Th17-stimulated CTL, but not the Th17 cells shown to have no in vitro killing activity play a major therapeutic role in eradication of early stage of BL6-10ova tumors(3mm dia). We also found that MOG-specific Th17 cells stimulated MOG-specific CTL responses, and both Th17 cells and Th17-stimulated CTL are involved in induction of EAE with a major pathogenic role played by the former. In total our data elucidate a distinct role of Th17 cells and Th17-stimulated CTL in induction of antitumor immunity and EAE.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".