Theoretical Quantification of the Polyvalent Binding of Nanoparticles Coated with Peptide-MHC to TCR-Nanoclusters
Bibliographic record
Abstract
Abstract Nanoparticles (NPs) coated with pMHCs can reprogram a specific type of CD4+ T cells into diseasesuppressing T regulatory type 1 cells by binding to their TCRs expressed as TCR-nanoclusters (TCR nc ). NP size and number of pMHCs coated on them (called valence) can be adjusted to increase their efficacy. Here we explore how this polyvalent interaction is manifested and examine if it can facilitate T cell activation. This is done by developing a multiscale biophysical model that takes into account the complexity of this interaction. Using the model, we quantify pMHC insertion probabilities, dwell time of NP binding, TCR nc carrying capacity, the distribution of covered and bound TCRs by NPs, and cooperativity in the binding of pMHCs within the contact area. Model fitting and parameter sweeping further reveal that moderate jumps between IFN γ dose-response curves at low valences can occur, suggesting that the geometry of NP binding can prime T cells for activation.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".