A combination of pH-sensitive caplet coatings may be an effective noninvasive strategy to deliver bioactive substances, nutrients, or their precursors to the colon
Bibliographic record
Abstract
We hypothesize that bacterially synthesized nutrients in the large intestine may significantly influence the nutritional status of humans and, specifically, that of the colonocytes. In vivo research with human subjects in this area has been extremely limited because of the absence of a noninvasive means to quantitatively deliver test doses of nutrients, or their precursors, to the colon. The purpose of this study was to design and test the effectiveness of a pH-dependent coating in delivering intact placebo caplets to the large intestine. Barium sulphate caplet cores (19.1 mm x 9.7 mm) were coated with 2 different pH-dependent acrylic copolymer products, Eudragit L100 and S100, in either a 1:0 ratio (100% Eudragit L100) or 3:1 ratio (75% Eudragit L100 and 25% S100). The disintegration profile of each formulation was determined through in vitro testing, then caplets were sequentially administered to 10 healthy volunteers, and monitored in vivo via serial abdominal fluoroscopic images. Test caplets with the 3:1 coating formulation had a 40% higher colon-targeting specificity compared with the 1:0-coated caplets, and tended to begin disintegrating at a later time after administration (p = 0.09). The total time from administration to complete disintegration was also significantly longer for the 3:1-coated caplets (p = 0.003). These results suggest that barium sulphate caplets with a 3:1 acrylic copolymer coating formulation ratio (Eudragit L100 and S100) may be a suitable delivery system for quantifying the biosynthesis of nutrients in the human large intestine and measuring their absorption across the colonic epithelium.
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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) | 0.001 | 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".