The QuickSwitch Quant HLA-A*02:01 Tetramer Kit can be used for determining the biological activity of a cancer vaccine.
Bibliographic record
Abstract
Abstract We have devised a fast and user-friendly assay (QuickSwitch™ Quant) that can both help determine binding of novel peptides to MHC class I molecules and generate new specificity MHC class I tetramers for peptide specific T cell detection. This study aimed to determine whether the QuickSwitch™ Quant HLA-A*02:01 Tetramer Kit can also be used for evaluating the biological activity of a vaccine. The tested vaccine was DPX-Survivac, an ovarian cancer vaccine candidate which consists of several survivin peptide antigens that are each restricted to a different human class I allele. We sought to evaluate the specificity and sensitivity of the QuickSwitch™ Quant HLA-A* 02:01 Tetramer Kit-PE for assessing the biological activity of SurA2.M, an HLA-A2-restricted peptide, in DPX-Survivac. The complete vaccine also contains non-HLA-A2 restricted peptides, lipids and a polynucleotide adjuvant. We tested the detection of the peptide prepared individually in a buffered solution or in the DPX-Survivac vaccine prepared in an aqueous formulation. Results indicate that peptide exchange rate of SurA2.M is similar whether it is dissolved individually in a buffered solution or mixed with other components of the vaccine. Results also may be dependent on the affinity of the peptide for HLA-A2. Thus, by optimizing a concentration curve using individual peptides, the QuickSwitch™ Quant HLA-A*02:01 Tetramer Kit can be used to quantify the concentration of HLA-A2 restricted peptides in simple solutions or more complex formulations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".