Treatment of Open Wound Infections Using Drug-Impregnated Polymer Hydrogels: A Dual Approach
Bibliographic record
Abstract
The skin is the body’s largest organ and serves a variety of essential functional and aesthetic purposes. Wounds, burns, and other abrasions to the skin can have consequential effects on the rest of the body if not properly managed. Open wounds are often a breeding ground for bacterial infections and can pose a severe threat to individuals with compromised immune systems and other at-risk groups. Antibiotic- resistant bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), thrive in these polymicrobial environments and are often difficult to treat. In tandem, bottlenecks in the drug discovery pipeline lead to slow development of novel compounds and routes of administration. One such challenge is the inherent issue of solubility of antibiotic compounds. Otherwise promising drug candidates face challenges in administration in critical cases, such as open wounds, due to their poor aqueous solubility. These barriers highlight the need for unconventional approaches to drug discovery and the delivery of therapeutics. In collaboration with an Edmonton-based biotechnology company and other research groups at McMaster University, we performed a comparative analysis of two novel hydrogels loaded with antibiotics of interest to address the aforementioned challenges in treating infected wounds. Utilizing patented technology and an optimized excisional murine wound model, the two proposed routes of antibiotic administration show promise in delivering drugs with inherently low water solubility and offer several other advantages in the development of efficient drug delivery vehicles.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".