3D printed pH-responsive colonic capsules for the delivery of live aqueous bacterial suspensions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it