Genetic Engineering of Dendritic Cells by Adenovirus-Mediated TNF-α Gene Transfer
Bibliographic record
Abstract
Dendritic cells (DCs) are one of the most potent antigen-presenting cells (APCs). They migrate as precursors from the bone marrow into various organs, where they usually reside in an inactive state ( 1 ). However, during this regional residency, these cells can efficiently endocytose and process antigens ( 2 ). Upon activation, they undergo a differentiation process that results in decreased antigen-processing capacity and enhanced expression of major histocompatibility complex (MHC) and costimulatory molecules, after which they migrate to the lymphoid organs to interact with or activate naive T cells ( 3 , 4 ). Because of the critical roles DCs have in the generation of primary immune responses, an important avenue of investigation is their potential for modulating immunologic functions, such as the induction of immune tolerance or tumor immunity. Recently, it has been shown that DCs pulsed with tumor-derived MHC class I-restricted peptides or tumor lysates are able to induce significant cytotoxic T-lymphocyte (CTL)-dependent antitumor immune responses in vitro as well as in vivo ( 5 - 7 ). However, the therapeutic efficiency of these DC vaccine strategies has been quite limited, because they have protected against rechallenge with only small numbers of parental tumor cells or inhibited very earlystage-established tumors. Thus, a strategic goal of current cancer vaccine research has become the induction of stronger tumor-specific CTL responses. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".