Unique depot formed by an oil based vaccine facilitates active antigen uptake and provides effective tumour control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Oil emulsions are commonly used as vaccine delivery platforms to facilitate slow release of antigen by forming a depot at the injection site. Antigen is trapped in the aqueous phase and as the emulsion degrades in vivo the antigen is passively released. DepoVax™ is a unique oil based delivery system that directly suspends the vaccine components in the oil diluent that forces immune cells to actively take up components from the formulation in the absence of passive release. The aim of this study was to use magnetic resonance imaging (MRI) with additional biological markers to evaluate and understand differences in clearance between several different delivery systems used in peptide-based cancer vaccines. METHODS: C57BL/6 mice were implanted with a cervical cancer model and vaccinated 5 days post-implant with either DepoVax (DPX), a water-in-oil emulsion (w/o), a squalene oil-in-water emulsion (squal o/w) or a saponin/liposome emulsion (sap/lip) containing iron oxide-labeled targeted antigen. MRI was then used to monitor antigen clearance, the site of injection, tumour and inguinal lymph node volumes and other gross anatomical changes. HLA-A2 transgenic mice were also vaccinated to evaluate immune responses of human directed peptides. RESULTS: We demonstrated differences in antigen clearance between DPX and w/o both in regard to how quickly the antigen was cleared and the pattern in which it was cleared. We also found differences in lymph node responses between DPX and both squal o/w and sap/lip. CONCLUSIONS: These studies underline the unique mechanism of action of this clinical stage vaccine delivery system.
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.
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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it