The effect of CpG ODN on the immune responses and immune-contraception induced by ZP~(121-140) synthetic peptide
Bibliographic record
Abstract
Objective:To investigate the effect of CpG ODN on the immune responses and immune-contraception induced by ZP121-140 synthetic peptide.Methods:BALB/c mice were given an injection into the left tibialis anterior muscle of ZP121-140 synthetic peptide with 20 μg CpG ODN or CFA,then the mice were given other injections at 2,4,6 weeks using the same formulation.The mice's blood was collected before each vaccination and after the last vaccination every 2 weeks.The specific IgG and IgA in sera and non-specific cytokines IFN-γ,TNF-α,IL-10 in vaginal mucosa were measured by ELISA.The ovarial pathological changes were undertaken using hematoxylin and eosin-stained paraffin sections.Results:The specific IgG in sera and IgA in vaginal mucosa induced by ZP121-140 synthetic peptide combined with CpG ODN were no more than those of ZP121-140 synthetic peptide combined with CFA.There were significant increases in IFN-γ and TNF-α when CpG ODN was mixed with ZP121-140 synthetic peptide and the increase of CpG ODN was more significant than that of CFA.Otherwise there was a significant decrease in IL-10 when CpG ODN was mixed with ZP121-140 synthetic peptide and the decrease of CpG ODN was more significant than that of CFA.There was no significant difference in the rate of pregnancy between CpG ODN group and CFA group,but the average number of birth mice in CpG ODN group was less than that in CFA group.No pathological changes were found in the ovaries of experimental mice.Conclusion:The adjuvant effect of CpG ODN is more advantageous than that of CFA in contraception vaccine research.
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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".