Adjuvant effects of CpG oligodeoxynucleotides on responses against T‐independent type 2 antigens
Bibliographic record
Abstract
Oligodeoxynucleotides containing CpG motifs (CpG-ODN) are potent in vitro B-cell activators and they have been successfully used to increase in vivo antibody responses to T-dependent peptide and protein antigens. In contrast, the use of CpG-ODN to enhance in vivo antibody responses to various T-independent type 2 (TI-2) antigens has recently generated contradictory results. In this study, we compared the CpG-ODN stimulatory effect on antibody responses of adult and young BALB/c mice to trinitrophenylaminoethyl-carboxymethyl (TNP) -Ficoll and to polysaccharides (PS) from several distinct serotypes of Streptococcus pneumoniae (SPn). CpG-ODN co-administration significantly enhanced antigen-specific immunoglobulin M (IgM), IgG, IgG1 and IgG2a titres to TNP-Ficoll. The depletion of CD4+ cells by monoclonal antibodies (GK1.5) identified their essential role in CpG-ODN-mediated enhancement of antibody responses. In contrast to TNP-Ficoll, CpG-ODN failed to enhance IgM and IgG responses to any of the 18 SPnPS serotypes tested. Providing T-cell epitopes by the conjugation of SPnPS to the carrier protein tetanus toxoid again allowed CpG-ODN to mediate enhancement of IgG, IgG2a and IgG3 responses to most SPnPS serotypes. Thus, antigen-presenting cell/T-cell interaction appears to largely mediate the in vivo influence of CpG-ODN on antibody responses to TI-2 antigens. In early life, additional factors limit CpG-ODN modulation of antibody responses to TI-2 antigens.
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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) | 0.001 | 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".