Application of Magnetic Particles in Medicine and Biology
Bibliographic record
Abstract
Abstract Magnetic particles (MPs) have proved to be valuable tools for manipulation of cells or biomolecules, for transportation of chemical substances or transfer of energy to defined target sites in biological systems, and for clinical diagnostics and therapeutics—bothin vitroas well asin vivo. MPs receive their magnetic properties and responsiveness to magnetic fields most often from the proven biocompatible iron oxides magnetite (Fe3O4) and maghemite (γ‐Fe2O3). The small size of MPs allows them to pass through capillary vessels during blood circulation. In nanoparticulate form or under the influence of strong magnetic fields, some MPs can even extravasate through capillary walls into surrounding tissue and reach many of the cells in the human body. Magnetically assisted delivery of chemo‐ or radiotherapeutics to as well as the generation of heat (hyperthermia) at defined target sites in the body can thus be achieved for treatment purposes, such as tumor therapy. Furthermore, MPs can serve as site‐ and function‐specific contrast agents and thus enhance the diagnostic potential of magnetic resonance imaging (MRI). On the biotechnological side, magnetic labeling of cells and biomolecules with MPs followed by magnetic separation has been utilized for the isolation and analysis of nucleic acids and specific cells, for protein purification, for the detection of pathogenic bacteria and viruses, and for gene transfection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".