Biohybrid Living Material with Antibacterial and Regenerating Properties Based on Probiotic Bacteria Stress Metabolism Modulation
Bibliographic record
Abstract
Wound healing is an intricate process that involves various biochemical pathways at each stage of tissue regeneration. Wound therapy is a series of distinct treatment stages that has a limited efficacy if wounds are of complex etiologies. A modern approach to this problem may be the development of bifunctional adaptive biohybrid systems that can concurrently affect pathogens' growth, inflammation, and tissue regeneration. We have developed biohybrid living material with antibacterial and regenerating properties based on induced hormesis by oxidative stress onto probiotic bacteria with prolonged synthesis of hydrogen peroxide, increased antibacterial action, and regeneration of the burn wound. Material demonstrates almost complete wound healing with a wound area difference 3-4 times with natural healing in vivo burn wound model for 21 days, antibacterial activity against wound antibiotic-resistance pathogens Escherichia coli K12 and Staphylococcus aureus ATCC 29213 in 4 and 5-fold, respectively in co-cultivation model, and has no toxicity to human skin fibroblasts and β-hemolysis in the in vitro model. Our findings promise the improving tissue regeneration of burn wounds, therapy against antibiotic-resistance pathogens by eliminating antibiotics, and other classical bactericides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".