Lateral Assessment of Mucomimetic Hydrogels to Evaluate Correlation between Microscopic and Macroscopic Properties
Bibliographic record
Abstract
A major limitation in the development of mucosal drug delivery systems is the design of in vitro models that accurately reflect in vivo conditions. Traditionally, models seek to mimic characteristics of physiological mucus, often focusing on property-specific trial metrics such as rheological behavior or diffusion of a nanoparticle of interest. Despite the success of these models, translation from in vitro results to in vivo trials is limited. As a result, several authors have called for work to develop standardized testing methodologies and characterize the influence of model properties on drug delivery performance. To this end, a series of trials is performed on 12 mucomimetic hydrogels reproduced from literature. Experiments show that there is no consistent correlation between barrier performance and rheological or microstructural properties of the tested mucomimetic hydrogels. In addition, the permeability of both mucopenetrating and mucoadhesive nanoparticles is assessed, revealing non-obvious variations in barrier properties such as the relative contributions of electrostatic and hydrophobic interactions in different models. These results demonstrate the limitations of predicting mucomimetic behavior with common characterization techniques and highlight the importance of testing barrier performance with multiple nanoparticle 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".