High‐Resolution Characterization of Protein‐Conjugated, mRNA‐Loaded Lipid Nanoparticles by Analytical Ultracentrifugation
Bibliographic record
Abstract
Abstract The study describes a novel use for the Custom Grid (CG) algorithm in UltraScan targeting lipid nanoparticles (LNPs) with cargos ranging from empty LNPs, LNPs loaded with messenger RNA (mRNA), and LNPs conjugated with proteins, or both. The CG method is used to fit sedimentation velocity analytical ultracentrifugation experiments performed in density matching mode to derive partial specific volume, molar mass, and hydrodynamic radius distributions for LNPs. Because LNP cargos often differ in density from the encapsulating lipids, density (or partial specific volume) is a critical quality attribute to quantify LNP composition and cargo loading. It is shown that the CG approach, in combination with D 2 O density matching, faithfully fits even complex cases that exhibit both sedimenting and floating analytes in the same sample without sacrificing generality, and derives density distributions confirming successful cargo loading. In addition, the method provides distributions for hydrodynamic radii, molar mass, and sedimentation coefficients. Analysis of the same samples with the parametrically constrained spectrum analysis provides orthogonal validation in good agreement with the CG analysis. The results show that polydispersity assessment and other metrics alone are unreliable in determining the fraction of empty LNPs present in a formulation, but density profiles obtained here clearly distinguish mRNA‐loaded from empty LNPs.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".