Biodegradable magnetic microspheres for drug targeting, temperature controlled drug release, and hyperthermia
Bibliographic record
Abstract
Abstract Magnetic microspheres (MMS) used for magnetic drug targeting consist of magnetic nanoparticles (MNP) and a pharmaceutical agent embedded in a polymeric matrix material. The application of MNP for drug targeting enables guiding the MMS to a target area, imaging the position of the MMS with magnetic particle imaging, and finally inducing drug release. As latter takes place by degradation of the MMS or diffusion through the matrix, an increase in temperature, e.g. through magnetic hyperthermia, leads to an accelerated drug release. Here, MMS consisting of poly(lactic-coglycolic) acid (PLGA) with different monomer ratios were prepared by an oil-in-water emulsion evaporation method. The model drug Camptothecin (CPT) and magnetic multicore nanoparticles (MCNP) with a high specific heating rate were embedded into the microspheres. We obtained MMS in the preferred size range of 1 to 2 μm with a concentration of MCNP of 16wt%, a drug load of about 0.5wt% and an excellent heating performance of 161 W/gMMS. Investigations of the drug release behaviour showed an accelerated drug release when increasing the temperature from 20 °C to 37 °C or 43 °C by using a water bath. In addition, an increase in drug release of about 50% through magnetic heating of the MMS up to 44 °C compared to 37 °C was observed. By this, a magnetic hyperthermia induced CPT release from PLGA MMS is demonstrated for the very first time.
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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".