Comparison of Drying Techniques to Produce Stable and Bioavailable Encapsulated ACE-2 Nanoparticles
Bibliographic record
Abstract
Background/Objectives: COVID-19 infection continues globally, with frequent emergence of unfamiliar SARS-CoV-2 variants acting to impair immunity. The competitive binding of SARS-CoV-2 spike proteins and angiotensin-converting enzyme 2 (ACE-2) can decrease the binding of the virus on native ACE-2 receptors on healthy human cells. It remains a practical approach to lessen viral spread. In this study, a method to encapsulate ACE-2 in the form of chitosan/tripolyphosphate cross-linked nanoparticles (NPs) was developed with emphasis placed on the best dehydration method to secure functional ACE-2 nanoparticles. Methods: Methods: Preparation conditions were assessed by varying pH (4.0–6.5) and the ratio between chitosan and ACE-2 mixing ratios (1:1, 1.5:1, 2:1, 2.5:1, and 3:1). The formulated NPs were then dehydrated using different approaches that included spray-drying (SD), freeze-drying (FD), and spray-freeze drying (SFD) and used varying mannitol concentrations (0, 1:1, and 5:1 of total weight). The mannitol was served as a cryoprotectant in this study. Results: The best formulation achieved used a pH 5.5 with a mixing chitosan–ACE-2 ratio of 2:1, where ACE-2-loaded NPs had an average particle size of 303.7 nm, polydispersity index (PDI) of 0.21, encapsulation efficiency (EE) of 98.4%, and ACE-2 loading content (LC) of 28.4%. After reconstitution, all SD samples had a relatively low yield rate, but the ACE-2 NPs dehydrated specifically using SFD required a lower amount of added mannitol (1:1 of its total weight) and produced a higher yield rate (p < 0.05) and similar PDI and EE values, along with relatively good particle size and LC. This formulation also produced a high ACE-2 release and uptake in differentiated Caco-2 cells, thus representing an effective ACE-2 encapsulation procedure for use with dry powders. Conclusions: This work showed that spray-freeze drying was the best method to dehydrate ACE-2 NPs, using less cryoprotectant to create a significant advantage in terms of greater loading capacity with lower additive requirements.
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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 | 0.000 |
| 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) | 0.001 | 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".