3D printed pH-responsive colonic capsules for the delivery of live aqueous bacterial suspensions
Bibliographic record
Abstract
Delivering live bacterial therapeutics orally to the colon is challenging due to the harsh gastrointestinal (GI) conditions and/or the manufacturing processes involved in the production of dry formulations, which can drastically decrease cell viability. In a previous work, we evaluated the performance of a 3D printed pH-responsive capsule capable of encapsulating aqueous cargos. We herein evaluate its ability to encapsulate live bacterial suspensions with limited processing steps. The capsules maintained their integrity in conditions simulating the upper GI tract (stomach and proximal intestine) and only released their contents in the environment of the lower intestine, i.e. , ileum and colon. The mean viability of individual or mixed selected strains remained above 75% during a full simulated GI transit to the colon. In beagle dogs, genomic DNA of 2 out of the 3 delivered strains was detected in the feces, and DNA copy levels did not differ between the capsules and the control suspension of non-encapsulated bacteria. These results could be attributed to the differing physiological conditions of fasted beagle dogs vs. the simulated environments, or possibly to a non-optimal assessment of bacterial colonization. A follow-up study after capsule treatment, incorporating sampling from various colonic tissues and fluids, could provide some insights into bacterial colonization process. • Aqueous live bacterial suspensions die under simulated GI conditions without protection. • 3D printed capsules release bacteria in simulated colonic conditions (>75% viability). • TaqMan assays detect strains in feces; microbiome screened prior to delivery. • 3D printed capsules open in vivo in beagle dogs. • DNA levels tracked post delivery of liquid or encapsulated bacteria in beagle dogs.
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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 | 0.001 | 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".