Characterisation of zinc delivery from a nipple shield delivery system using a breastfeeding simulation apparatus
Bibliographic record
Abstract
Zinc delivery from a nipple shield delivery system (NSDS), a novel platform for administering medicines to infants during breastfeeding, was characterised using a breastfeeding simulation apparatus. In this study, human milk at flow rates and pressures physiologically representative of breastfeeding passed through the NSDS loaded with zinc-containing rapidly disintegrating tablets, resulting in release of zinc into the milk. Inductively coupled plasma optical emission spectrometry was used to detect the zinc released, using a method that does not require prior digestion of the samples and that could be applied in other zinc analysis studies in breast milk. Four different types of zinc-containing tablets with equal zinc load but varying excipient compositions were tested in the NSDS in vitro. Zinc release measured over 20 minutes ranged from 32-51% of the loaded dose. Total zinc release for sets tablets of the same composition but differing hardness were not significantly different from one another with P = 0.3598 and P = 0.1270 for two tested pairs using unpaired t tests with Welch's correction. By the same test total zinc release from two sets of tablets having similar hardness but differing composition were also not significantly significant with P = 0.2634. Future zinc tablet composition and formulation optimisation could lead to zinc supplements and therapeutics with faster drug release, which could be administered with the NSDS during breastfeeding. The use of the NSDS to deliver zinc could then lead to treatment and prevention of some of the leading causes of child mortality, including diarrheal disease and pneumonia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".