Nanoparticle size and production efficiency are affected by the presence of fatty acids during albumin nanoparticle fabrication
Bibliographic record
Abstract
We have previously identified extensive glycation, bound fatty acids and increased quantities of protein aggregates in commercially available recombinant HSA (rHSA) expressed in Oryza sativa (Asian rice) (OsrHSA) when compared to rHSA from other expression systems. We propose these differences may alter some attributes of nanoparticles fabricated with OsrHSA, as studies have associated greater quantities of aggregates with increased nanoparticle diameters. To determine if this is the case, nanoparticles were fabricated with OsrHSA from various suppliers using ethanol desolvation and subsequent glutaraldehyde cross-linking. All nanoparticles fabricated with OsrHSA showed larger diameters of approximately 20 to 90nm than particles fabricated with either defatted bovine serum albumin (DF-BSA) (100.9 ± 2.8nm) or human plasma albumin (pHSA) (112.0 ± 4.0nm). It was hypothesized that the larger nanoparticle diameters were due to the presence of bound fatty acids and this was confirmed through defatting OsrHSA prior to particle fabrication which yielded particles with diameters similar to those fabricated with pHSA. For additional conformation, DF-BSA was incubated with dodecanoic acid prior to desolvation yielding particles with significantly larger diameters. Further studies showed the increased nanoparticle diameters were due to the bound fatty acids modulating electrostatic interactions between albumin nanoparticles during the desolvation and not changes in protein structure, stability or generation of additional albumin oligomers. Finally the presence of dodecanoic acid was shown to improve doxorubicin loading efficiency onto preformed albumin nanoparticles.
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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 | 0.001 | 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 | 0.001 | 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 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".