Mucin adsorption on vaterite CaCO3 microcrystals for the prediction of mucoadhesive properties
Bibliographic record
Abstract
Porous vaterite CaCO 3 crystals are widely used as containers for drug loading and as sacrificial templates to assemble polymer-based nano- and micro-particles at mild conditions. Special attention is paid nowadays to mucosal delivery where the glycoprotein mucin plays a crucial role as a main component of a mucous. In this work mucoadhesive properties of vaterite crystals have been tested by investigation of mucin binding to the crystals as a function of (i) time, (ii) glycoprotein concentration, (iii) adsorption conditions and (iv) degree of mucin desialization. Mucin adsorption follows Bangham equation indicating that diffusion into crystal pores is the rate-limiting step. Mucin strongly binds to the crystals ( Δ G = −35 ± 4 kJ mol −1 ) via electrostatic and hydrophobic interactions forming a gel and thus giving the tremendous mucin mass content in the crystals of up to 16%. Despite strong intermolecular mucin-mucin interactions, pure mucin spheres formed after crystal dissolution are unstable. However, introduction of protamine, actively used for mucosal delivery, makes the spheres stable via additional electrostatic bonding. The results of this work indicate that the vaterite crystals are extremely promising carriers for mucosal drug delivery and for development of test-systems for the analysis of the mucoadhesion.
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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 | 0.000 |
| 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 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".