Developing a mechanism to measure the absorption of bacterially‐synthesized folate across the colon: Testing of caplets with a pH‐sensitive coating designed for colon targeting
Bibliographic record
Abstract
We hypothesize that bacterially synthesized folate in the large intestine may significantly influence the folate status of humans and, specifically, colonocytes. Research in this area has been limited due to the absence of a non‐invasive means to quantitatively deliver test doses of folate to the colon. The purpose of this study was to design and test the effectiveness of two pH‐dependent coatings in delivering intact placebo caplets to the large intestine. Barium sulphate caplet cores (19.1 x 9.7mm) were coated with Eudragit ® L100 and S100, in either a 1:0 or 3:1 ratio. Each formulation was administered to ten healthy volunteers, and monitored in‐vivo via serial abdominal fluoroscopic images. Test caplets with 3:1 coating formulations had 40% higher colon‐targeting specificity compared to 1:0 caplets, and tended to begin dissolving at a later time after administration (p=0.09). The total time from administration to complete dissolution was also significantly longer for 3:1 coated caplets (p=0.003). These results suggest that barium sulphate caplets with a 3:1 (Eudragit L100:S100) coating formulation ratio would be a suitable delivery system for investigating the absorption of folate across the large intestine. Sponsored by NSERC and the CIHR Training Program in Clinical Research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".