Antibiotic-Loaded Calcium Crosslinked Alginate Wound Dressings Fabricated via Pressurized Gas eXpanded Liquids Technology in Combination with Supercritical Adsorptive Precipitation for Treating Methicillin-Resistant <i>Staphylococcus</i> Infections
Bibliographic record
Abstract
Abstract Antibiotic resistance is a major healthcare challenge globally, and the development of antimicrobial therapies and modes for their targeted delivery is not keeping pace. Many promising antimicrobial candidates are abandoned early in discovery because of their high hydrophobicity and low bioavailability, limiting their evaluation in preclinical models. Therefore, developing drug delivery technologies compatible with potent yet hydrophobic antimicrobial candidates could revitalize a stagnant antibiotic pipeline. Herein, we combined Pressurized Gas eXpanded liquid technology (PGXTEC) with supercritical adsorptive precipitation to load and subsequently deliver poorly water-soluble antimicrobial compounds directly to an infected wound. PGXTEC-processed cross-linked sodium alginate compressed into disks exhibits extremely high specific surface area (∼160 m2/g) to enable drug impregnation and effective exudate absorption. As proof of concept, PGXTEC alginate disks loaded with fusidic acid (FA) suppressed bacterial growth in full thickness wounds infected with methicillin-resistant Staphylococcus aureus (MRSA); furthermore, PGXTEC disks loaded with tigecycline (TIG), typically considered a bacteriostatic antibiotic when used conventionally against MRSA, sterilized wounds with bactericidal activity even at low overall drug doses relative to the conventionally used therapeutic doses for intravenous TIG. PGXTEC combined with adsorptive precipitation is thus a flexible platform technology to deliver hydrophobic antibiotics in a bioavailable format, offering the potential to revive classes of antimicrobial compounds that are excluded early in the discovery process due to low water solubility and incompatible modes of delivery.
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 | 0.000 | 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".