Ionizable Lipid Containing Nanocarriers for Antimicrobial Agent Delivery
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
Antimicrobial resistance (AMR) poses a global health crisis demanding innovative solutions. Traditional antibiotics, though pivotal over the past century in combating bacterial infections, face diminished efficacy against evolving bacterial defense mechanisms, especially in Gram-negative strains. This study explores self-assembled ionizable lipid nanoparticles (LNPs) with the incorporation of two ionizable lipid components (one cationic, one anionic) in nanocarriers for advanced antimicrobial drug delivery of the broad-spectrum antibiotic Piperacillin (Pip). Incorporating cationic ionizable lipid ALC-0315, recognized as a functional lipid in the Pfizer-BioNTech mRNA-based SARS-CoV-2 vaccine, into LNPs allowed mesophase transition, pH responsiveness, and ionization behavior in acidic environments found in sites of bacterial infections, to be studied using synchrotron small angle X-ray scattering, dynamic light scattering, and a 2-(p-toluidino)-6-naphthalene sulfonic acid assay. Incorporating another anionic ionizable lipid, oleic acid not only modulates the LNPs' physicochemical properties, such as size, internal phase nanostructure, and surface charge but also synergistically enhances the antimicrobial potency together with ALC-0315 with a benefit enhancing permeability and fusion with bacterial membranes. This study introduces a strategy for tailoring ionizable lipid compositions in LNPs, providing a new approach to antimicrobial treatment contributing to the fight against AMR.
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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