[Synergic effect of adenoviral vector-encoding human alpha-fetoprotein and interferon-gamma on immunity against hepatocellular carcinoma in mice].
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Primary hepatocellular carcinoma (HCC) is a common malignant tumor. Up to date, no effective treatment for HCC is available. This study was to investigate the synergic effect of adenoviral vector-encoding xenogeneic alpha-fetoprotein (AFP) and interferon-gamma (IFN-gamma) on the immunity against HCC in mice. METHODS: IFN-gamma gene was cloned using reverse transcription-polymerase chain reaction (RT-PCR). Replication-defective adenovirus encoding both human AFP and murine IFN-gamma was constructed. The cytotoxic activity of antigen-specific cytotoxic T lymphocytes (CTLs) was detected 7 days after the intradermal immunization of C57BL/6 mice with Ad-hAFP, Ad-IFN-gamma or Ad-hAFP/IFN-gamma using standard (51)Cr release assay. The survival of mice after the subcutaneous inoculation of 5x10(6) hepatoma Hepa 1-6 cells was checked. RESULTS: The cytotoxic activity of CTLs elicited by Ad-hAFP/IFN-gamma was much stronger than that by Ad-hAFP or Ad-IFN-gamma alone. At an effector:target (E:T) ratio of 10:1, the cytotoxic activities of CTLs in Ad-hAFP/IFN-gamma, Ad-hAFP, and Ad-IFN-gamma groups were (43.8+/-5.5)%, (28.2+/-3.2)%, and (12.8+/-1.9)%, at a ratio of 30:1, were (79.6+/-6.4)%, (51.9+/-4.3)%, and (15.6+/-2.3)%, and at a ratio of 90:1, were (88.2+/-6.3)%, (62.5+/-4.8)%, and (26.5+/-2.4)%, respectively. The tumor formation rates were 80% in Ad-hAFP-immunized mice and 100% in Ad-IFN-gamma-immunized mice at 2 months after inoculation with Hepa1-6 cells; no tumor formed in Ad-hAFP/IFN-gamma-immunized mice, and all mice in this group survived during two-month observation. CONCLUSION: Ad-hAFP can efficiently induce immunity against HCC in mice, and IFN-gamma enhances such an effect.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".