Lipid Fatty Acid Chain Length Influence over Liposome Physicochemical Characteristics Produced in a Periodic Disturbance Mixer
Bibliographic record
Abstract
Liposomes nanoparticles (LNPs) are versatile delivery systems that transport from drugs to genes. Liposome physicochemical properties determine how lipid nanoparticles will interact with biological systems. Thus, tuning these characteristics is of paramount importance for developing innovative formulations. Micromixers have shown to control average liposome size in a range relevant for drug delivery applications (50-200 nm) by changing the flow conditions. However, other factors might dramatically change liposome properties, such as the lipid fatty acid chain length. In this work, liposomes were produced using a periodic disturbance mixer (PDM) utilizing two different types of lipid mixtures, which differ solely in the number of carbons in the lipid fatty acid chain of the primary lipid as well as in the lipid mixture concentration. A clear relationship between these properties and liposome size and size distribution was found. The studied factors did not influence zeta potential results. The bending elasticity modulus of the studied lipids and their terminal head groups might play a role in the observed liposome properties.
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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".