Optimization of Whole Tumor Cell Vaccines by Interaction with Phagocytic Receptors
Bibliographic record
Abstract
The principal event in the function of whole-cell cancer vaccines is the ingestion of vaccine-delivered tumor antigen-containing material, which is performed by the patient's antigen-presenting cells (APCs) through the employment of their phagocytic receptors. The goal of the present study was to identify the phagocytic receptors critical for the therapeutic efficacy of whole-cell cancer vaccines. The model of photodynamic therapy (PDT)-generated vaccines based on mouse SCCVII tumors was utilized, with in vitro expanded SCCVII cells treated by PDT serving as the vaccine material used for treating mice bearing established SCCVII tumors. The therapeutic impact, monitored as delayed progression of vaccinated tumors, was almost completely eliminated when antibodies specifically blocking the activity of LOX-1 scavenger receptor were administered to mice 30 min before vaccination. Similar, but much less pronounced, impacts were found with antibodies neutralizing the activity of CR3/CR4 receptors recognizing complement-opsonized vaccine cells, and with those blocking activating Fcγ receptors that recognized IgG antibody-based opsonins. A strikingly contrary action, a greatly enhanced tumor control by the vaccine, was found by blocking immune inhibitory receptor, FcγRIIB. The reported findings establish, therefore, an attractive strategy that can be effectively exploited for potent therapeutic enhancement of PDT-generated (and probably other) whole-cell tumor vaccines.
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 | 0.000 |
| 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".