Mimicking the Impact of Infant Tongue Peristalsis on Behavior of Solid Oral Dosage Forms Administered During Breastfeeding
Bibliographic record
Abstract
An in vitro simulation system was developed to study the effect of an infant's peristaltic tongue motion during breastfeeding on oral rapidly disintegrating tablets in the mouth, for use in rapid product candidate screening. These tablets are being designed for use inside a modified nipple shield worn by a mother during breastfeeding, a proposed novel platform technology to administer drugs and nutrients to breastfeeding infants. In this study, the release of a model compound, sulforhodamine B, from tablet formulations was studied under physiologically relevant forces induced by compression and rotation of a tongue mimic. The release profiles of the sulforhodamine B in flowing deionized water were found to be statistically different using 2-way ANOVA with matching, when tongue mimic rotation was introduced for 2 compression levels representing 2 tongue strengths (p = 0.0013 and p < 0.0001 for the lower and higher compression settings, respectively). Compression level was found to be a significant factor for increasing model compound release at rotational rates representing nonnutritive breastfeeding (p = 0.0162). This novel apparatus is the first to simulate the motion and pressures applied by the tongue and could be used in future infant oral product development.
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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".