Ovalbumin immune tolerance factors induce OVA specific peripheral immune tolerance in naive BALB/c mice
Bibliographic record
Abstract
Objective:To investigate the immunoregulation effect of ovalbumin immune tolerance factors (OVA ITFs).Methods:Components that were smaller than 3 kD were isolated from the splenic lymphocytes lysates of OVA tolerance mice or naive mice,respectively,named as OVA ITFs or OVA ITFs conttrol.Naive BALB/c mice were divided into 5 groups,group A,B,C,D were injected i.v.by OVA ITFs,OVA ITFs control,splenic lymphocytes from OVA tolerant mice or PBS,respectively,group E as the blank control.The percentages of CD4+CD25+ T cell subpopulation from spleens before and after adoptive transfer were measured with flow cytometer;OVA specific lymphocyte responses were assessed by MTT assay.The levels of IL-10 and TGF-β1 in the culture supernatants were tested by ELISA kits.Results:For OVA ITFs or splenic lymphocytes from OVA tolerant mice,the percentages of CD4+CD25+ T cell subpopulation from spleens after adoptive transfer were raised significantly compared with that before adoptive transfer (15.32%±1.03% and 15.35%±0.62% vs 9.97%±1.38%,P0.05).The stimulation index of OVA-specific lymphocytes responses were 0.699±0.05 and 0.704±0.03 respectively,lymphocytes proliferation were suppressed significantly when compared with that of PBS control group(1.356±0.07,P0.05).The production of active TGF-β1 was showed of profound increases compared with that of PBS control group (129.15±6.14 and 106.71±2.89 pg/ml vs 52.82±3.68 pg/ml,P0.01),but IL-10 levels were not detectable.For OVA ITFs control,no significant differences were observed before and after adoptive transfer on either percentage of CD4+CD25+ T cell subpopulation,responses of OVA specific lymphocytes or IL-10 and TGF-β1 levels.Conclusion:OVA ITFs can transfer OVA specific immune tolerance to naive recipient mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".