Simultaneous delivery of several antimicrobial drugs from multi‐compartment glycerol‐silicone membranes
Bibliographic record
Abstract
Abstract Combining drugs is an efficient tool in fighting bacterial infections. It has been proven to enable an increased antimicrobial activity compared to mono‐drug therapies. Current commercial dressings for treatment of infected wounds provide delivery of one drug only. Therefore, there is an obvious need to develop a technology enabling incorporation and release of two and more actives. Ideally, such technology should prevent potential cross‐reactions between the drugs during long‐term storage. Here, a concept of drug compartmentalization within distinct glycerol domains of glycerol‐silicone elastomer membranes is presented. Multiple drugs are encapsulated within different types of glycerol domains hampering any cross‐reactions. The drugs are simultaneously released upon contact with aqueous media and the release kinetics can be precisely adjusted by tuning various material parameters, such as glycerol content and membrane thickness. Ultimately, the drug release capabilities and the antimicrobial potential are evaluated against a variety of bacteria in an agar diffusion assay. The membranes are proven effective against different bacterial species, which confirms the efficiency of release even at minimum moisture levels. The glycerol‐silicone platform technology enables delivery of virtually unlimited combinations of drugs from different drug families (e.g., antimicrobial, anti‐inflammatory, and pain relief agents) facilitating new ways of treating wound disorders.
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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 | 0.000 | 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 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".