Upregulation of interleukin-12 receptor on peripheral blood mononuclear cells and HLA class I, HLA class II or ICAM-1 on melanoma cells by B7.1 and interleukin-12: a mechanism for immunostimulatory impact of melanoma cells adenovirally transfected with B7.1 and IL12?
Bibliographic record
Abstract
Melanoma is an immunogenic tumour and may express both HLA class I and class II molecules. These can be recognized by cytotoxic T-cells. Melanoma cells can evade immunosurveillance due to the lack of co-stimulatory molecules such as B7.1 or B7.2. Interleukin-12 (IL12) exerts antitumour effects, and B7.1 and IL12 synergistically induce effective antitumour immunity. We investigated the immunostimulatory potential of melanoma cells adenovirally transduced with B7.1, IL12 or B7.1 plus IL12. We observed that: (i) melanoma cells transduced with B7.1 plus IL12 can elicit a strong proliferative response from peripheral blood mononuclear cells (PBMCs); (ii) a high level of TH1 cytokine production from PBMCs was induced by melanoma cells transduced with Adv-B7.1 plus Adv-IL12; (iii) the expression of HLA class I antigens, HLA class II antigens or ICAM-1 antigens was higher on melanoma cells transduced with Adv-lL12 or Adv-B7.1 plus IL12 than those transduced with Adv-LacZ or wild-type melanoma cells; and (iv) the expression of IL12 receptors on PBMCs was upregulated by melanoma cells transfected with Adv-IL12 or Adv-B7.1 plus IL12. Thus, melanoma cells transduced with both Adv-lL12 and B7.1 may represent another clinical approach for antimelanoma gene therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".