Construction and Characterization of Adenovirus Vectors Encoding Aspartate-<i>β</i>-Hydroxylase to Preliminary Application in Immunotherapy of Hepatocellular Carcinoma
Bibliographic record
Abstract
-hydroxylase (AAH), an overexpressed tumor-associated cell surface protein, is considered as a promising biomarker and therapeutic target for HCC. In this study, we constructed adenovirus vector encoding AAH gene by gateway recombinant cloning technology and preliminarily explored the antitumor effects of DC vaccines harboring AAH. Firstly, the total AAH mRNA was extracted from human HCC tissues; the cDNA was amplified by RT-PCR, verified, and sequenced after TA cloning. Gateway technology was used and the obtained 18T-AAH was used as a substrate, to yield the final expression vector Ad-AAH-IRES2-EGFP. Secondly, bone marrow-derived DCs were infected by Ad-AAH-IRES2-EGFP to yield AAH-DC vaccines. Matured DCs were demonstrated by increased expression of CD11c, CD80, and MHC-II costimulatory molecules. A dramatically cell-killing effect of T lymphocytes coculturing with AAH-DCs on HepG2 HCC cell line was demonstrated by CCK-8 and FCM assays in vitro. More importantly, in an animal experiment, the lysis effect of cytotoxic T lymphocytes (CTLs) on HepG2 cells in the AAH-DC group was stronger than that in the control groups. In conclusion, the gateway recombinant cloning technology is a powerful method of constructing adenovirus vector, and the product Ad-AAH-IRES2-EGFP may present as a potential candidate for DC-based immunotherapy of HCC.
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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 | 0.001 |
| 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".