Liposomal Nanovesicles for Efficient Encapsulation of Staphylococcal Antibiotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Liposomes are attractive vehicles for localized delivery of antibiotics. There exists, however, a gap in knowledge when it comes to achieving high liposomal loading efficiencies for antibiotics. To address this issue, we investigated three antibiotics of clinical relevance against staphylococcal infections with different hydrophilicity and chemical structure, namely, vancomycin hydrochloride, teicoplanin, and rifampin. We categorized the suitability of different encapsulation techniques on the basis of encapsulation efficiency, lipid requirement (important for avoiding lipid toxicity), and mass yield (percentage of mass retained during the preparation process). The moderately hydrophobic (teicoplanin) and highly hydrophobic (rifampin) antibiotics varied significantly in their encapsulation load (max 23.4 and 15.5%, respectively) and mass yield (max 74.1 and 71.8%, respectively), favoring techniques that maximized partition between the aqueous core and the lipid bilayer or those that produce oligolamellar vesicles, whereas vancomycin hydrochloride, a highly hydrophilic molecule, showed little preference to any of the protocols. In addition, we report significant bias introduced by the choice of analytical method adopted to quantify the encapsulation efficiency (underestimation of up to 24% or overestimation by up to 57.9% for vancomycin and underestimation of up to 61.1% for rifampin) and further propose ultrafiltration and bursting by methanol as the method with minimal bias for quantification of encapsulation efficiency in liposomes. The knowledge generated in this work provides critical insight into the more practical, albeit less investigated, aspects of designing vesicles for localized antibiotic delivery and can be extended to other nanovehicles that may suffer from the same biases in analytical protocols.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it