The effect of aminoglycoside antibiotics on the thermodynamic properties of liposomal vesicles
Bibliographic record
Abstract
Liposomes are ideal drug-delivery systems because they can alter the pharmacokinetic characteristics and biodistribution profile of the incorporated bioactive molecule. The effect of the aminoglycoside antibiotics, gentamicin (GN), tobramycin (TOB), and amikacin (AMI), on the thermodynamic properties of multilamellar vesicles composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) was studied by using differential scanning calorimetry (DSC), electron paramagnetic resonance (EPR), and (31)P nuclear magnetic resonance (NMR) spectroscopy. The relationship between the structure of aminoglycoside antibiotics and their effect on the physical properties of the liposomal bilayers was investigated. The incorporation of the drugs was achieved and an osmotic gradient created by controlling the mole ratio of the drug inside to that outside of the DPPC vesicles so that [drug(inside DPPC)]/[drug(outside DPPC)] was 1:0, 1:0.2, 1:1, or 1:2.5. Incorporation of the drugs into liposomes caused the T(m) to shift to a higher temperature and the delta H(m) and delta T(1/2) values to decrease. The 2A(max) and the order parameter (S), obtained from the EPR spectra, indicated that the fluidity of the liposomal membrane was affected by the type of drug and by the concentration used; GN and TOB decreased the fluidity and disturbed chain packing at mole ratios of [drug(inside DPPC)]/[drug(outside DPPC)] ranging from 1:0 to 1:0.2, while AMI increased the fluidity and disrupted chain packing at an osmotic gradient of 1:2.5. In conclusion, the molecular organization and thermotropic properties of the multilamellar DPPC vesicles were dependent on the osmotic gradient and structure of the aminoglycoside.
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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 | 0.001 |
| 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) | 0.001 | 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".