Development of a Coflowing Device for the Size-Controlled Preparation of Magnetic-Polymeric Microspheres as Embolization Agents in Magnetic Resonance Navigation Technology
Bibliographic record
Abstract
Droplet microfluidics technology has recently been introduced to generate particles for many biomedical applications that include therapeutic embolizing agents in hepatic, uterine or bronchial arteries. Embolic agents are available in a variety of shapes and sizes that are adjusted according to the target vessel characteristics. Magnetic embolic agents can additionally be navigated to the target location (e.g., a tumor) through the blood system by applying an external magnetic field. This technology is termed Magnetic Resonance Navigation (MRN). Here we introduce a high throughput method to produce homogeneously sized magnetic microspheres (MMS) as blood vessel embolic agents for use in combination with MRN. The system for MMS production consists of a simple 3D printed micro coflowing device that is able to produce biocompatible, degradation rate controllable poly(lactic- co -glycolic acid) (PLGA) microspheres encasing magnetic nanoparticles. Axisymmetric flow is obtained with a central needle injecting the dispersed phase surrounded by a continuous phase and leads to the formation of size-controlled droplets that turn into homogeneously sized MMS linearly dependent on the inner needle diameter. MMS morphology, mean particle size and size distribution were quantified from SEM images. Magnetic performance of MMS was investigated using a vibrating sample magnetometer. MMS were nontoxic toward HUVEC (human umbilical vein endothelial cells) and HEK293 (human embryonic kidney) cells. The presented micro coflowing method allows for the reliable production of large MMS sized 130–700 μm with narrow size distribution (CV < 7%) and magnetic properties useful for MRN.
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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 | 0.001 | 0.001 |
| 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".