The immune effects of multiple antigen peptides containing the mimic epitopes of the adhesion protein of<i>Mycoplasma genitalium</i>
Bibliographic record
Abstract
The purpose of this study was to investigate the humoral and cellular immune responses stimulated by multiple antigen peptides (MAPs) containing the mimic epitopes of Mycoplasma genitalium adhesion protein (MgPa). Three MAPs containing the mimic epitopes of MgPa were synthesized on a branched polylysine matrix. After purification and characterization, these MAPs were used to immunize BALB/c mice. The immunoglobulin G (IgG) antibody and the subtype of IgG antibody in the serum of the immunized mice were detected by indirect ELISA (enzyme-linked immunosorbent assay). The proliferation of the spleen lymphocyte was detected using the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) assay. The gamma interferon (IFN-γ) and interleukin-4 (IL-4) levels in the cultured supernatant of spleen lymphocytes were measured by ELISA. The 3 different MAPs were prepared with high purity. Levels of IgG, IgG1, and IgG2a antibodies were elevated in the mice serum immunized by all 3 MAPs. The major antibody isotype was IgG2a. Importantly, mice immunized with a mixture of the 3 MAPs produced significantly more antibodies than those immunized with a single MAP (p < 0.05). Moreover, these MAPs could stimulate the proliferation of spleen lymphocytes of immunized mice and induce the production of IFN-γ and IL-4. The IFN-γ and IL-4 levels stimulated by the mixed MAPs were significantly higher than those stimulated by a single MAP (p < 0.01). The 3 different MAPs could induce strong cellular and humoral immune responses. The immunoreactivity of the mixed MAPs was stronger than that of the single MAP.
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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) | 0.000 | 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".