Adoptive transfer of splenocytes to study cell-mediated immune responses in hepatitis C infection using HCV transgenic mice
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) is a major cause of chronic hepatitis and a health problem affecting over 170 million people around the world. We previously studied transgenic mice that express HCV Core, Envelope 1 and Envelope 2 proteins predominantly in the liver, resulting in steatosis, liver and lymphoid tumors, and hepatocellular carcinoma. Herein, the immune-mediated cell response to hepatitis C antigens was evaluated by adoptive transfers of carboxyfluorescein succinimidyl ester (CFSE) labelled splenocytes from HCV immunized mice into HCV transgenic mice. RESULTS: In comparison to non-transgenic mice, there was a significant decrease in the percentage of CFSE-labeled CD4+ and CD8+ T cells in transgenic mouse peripheral blood receiving adoptive transfers from immunized donors. Moreover, the percentage of CFSE-labeled CD4+ and CD8+ T cells were significantly higher in the spleen of transgenic and non-transgenic mice when they received splenocytes from non-immunized than from immunized mice. On the other hand, the percentages of CD4+ and CD8+ T cells in the non-transgenic recipient mouse lymph nodes were significantly higher than the transgenic mice when they received the adoptive transfer from immunized donors. Interestingly, livers of transgenic mice that received transfers from immunized mice had a significantly higher percentage of CFSE labeled T cells than livers of non-transgenic mice receiving non-immunized transfers. CONCLUSIONS: These results suggest that the T cells from HCV immunized mice recognize the HCV proteins in the liver of the transgenic mouse model and homed to the HCV antigen expression sites. We propose using this model system to study active T cell responses in HCV infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".