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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".