Preparation of Heat-Denatured Macroaggregated Albumin for Biomedical Applications Using a Microfluidics Platform
Bibliographic record
Abstract
Albumin is widely used in pharmaceutical applications to alter the pharmacokinetic profile, improve efficacy, or decrease the toxicity of active compounds. Various drug delivery systems using albumin have been reported, including microparticles. Macroaggregated albumin (MAA) is one of the more common forms of albumin microparticles, which is predominately used for lung perfusion imaging when labeled with radionuclide technetium-99m ( 99m Tc). These microparticles are formed by heat-denaturing albumin in a bulk solution, making it very challenging to control the size and dispersity of the preparations (coefficient of variation, CV, ∼50%). In this work, we developed an integrated microfluidics platform to create more tunable and precise MAA particles, the so-called microfluidic-MAA (M2A2). The microfluidic chips, prepared using off-stoichiometry thiol-ene chemistry, consist of a flow-focusing region followed by an extended and water-heated curing channel (85 °C). M2A2 particles with diameters between 70 and 300 μm with CVs between 10 and 20% were reliably prepared by adjusting the flow rates of the dispersed and continuous phases. To demonstrate the pharmaceutical utility of M2A2, particles were labeled with indium-111 ( 111 In) and their distribution was assessed in healthy mice using nuclear imaging. 111 In-M2A2 behaved similarly to 99m Tc-MAA, with lung uptake predominately observed early on followed by clearance over time by the reticuloendothelial and renal systems. Our microfluidic chip represents an elegant and controllable method to prepare albumin microparticles for biomedical applications.
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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) | 0.001 | 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 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".