Enzymatically active probiotic bacteria for topical and oral therapy
Bibliographic record
Abstract
A novel approach whereby one can use probiotic bacteria for the purpose of topical and oral therapy is presented.More specifically, a treatment modality using enzymatically active probiotic production of gaseous nitric oxide (gNO) for wound healing, antimicrobial, cosmetic, and dermatologic therapy is presented.In another aspect, a probiotic treatment modality for metabolic disease and metabolic syndrome using nitrate reductase (NiR) active probiotic bacteria is explored.In concurrence to these requirements, several in-vitro methods are designed and discussed in this report.For some of these studies the use of alginate microcapsules is explored.Results show that probiotic patches can be used for the production of gNO above therapeutic levels and for therapeutic durations and that probiotic gNO-producing patches are highly bacteriostatic, bactericidal, and fungicidal.Results show that the novel gNO-producing probiotic patch can be used to improve wound healing and increase the likelihood of wound closure in ischemic and infected full-thickness dermal wounds in a New Zealand White Rabbit model and that daily application of the patch is safe with respect to body weight, blood morphology, haematology, blood biochemistry, and methemoglobin levels.Also, results show that novel NiR-active probiotic bacteria can be selected for nitrate reductase (NiR) activity in-vitro, and can be microencapsulated or delivered free under simulated GI conditions, and in the presence of various food matrices while maintaining NiR activity, confirming the lab scale feasibility of the approach in delivering the probiotic orally for treating hypertension, inflammatory bowel disease, gastric ulcers, diabetes and II thrombosis.These findings may provide effective, safe, and less costly alternatives for delivering gNO topical and oral for therapy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".