Bibliographic record
Abstract
Magnetic microspheres (MMS) used for magnetic drug targeting consist of a polymeric matrix material, which contains magnetic nanoparticles (MNP) and a pharmaceutical agent.The application of MNP for drug targeting offers the opportunity to guide the MMS to a target area, image the position of the MMS with MRI or MPI and finally trigger drug release.As drug release takes place by degradration of the matrix or diffusion through the matrix, an increase in temperature, e.g. through magnetic hyperthermia, leads to an accelerated drug release.Additionally, increasing the target area's temperature by magnetic hyperthermia can result in a higher efficiency of the applied drug.Here, MMS consisting of three types of poly(lactic-co-glycolic)acid (PLGA) with different monomer ratios were prepared by an oil-in-water emulsion evaporation method.The model drug Camptothecin and magnetic multicore nanoparticles (MCNP), developed in previous studies by our group with a high specific heating rate, have also been incorporated into the microspheres.The resulting microspheres have been characterized concerning their size and morphology, magnetic properties, and heating performance.The drug release behaviour was investigated in dependence of PLGA-type and temperature.We obtained uniformly shaped microspheres in the preferred size range of 1 to 2 µm with a concentration of MCNP of 16% by mass, a drug load of about 0.5% by mass, and a heating performance of 170 W/g(MMS), which is very promising for hyperthermia applications.Investigations on the drug release behaviour showed an accelerated drug release when increasing the temperature from 20°C to 37°C (body temperature) or 43°C (typical hyperthermia value) 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, the magnetic hyperthermia induced drug release is demonstrated for the very first time.S89
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.045 |
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".