<i>In vitro</i> biocompatibility assessment of functionalized magnetite nanoparticles: Biological and cytotoxicological effects
Bibliographic record
Abstract
In the biomedical field, nanomaterials have the potential for use in the targeted delivery of drugs in the human body and in the diagnosis and therapy of certain diseases. In the category of targeted delivery, magnetite (Fe(3)O(4)) nanoparticles have received much attention. As with any similar new therapy, when such nanoparticles are functionalized with chemical groups designed to permit the specific attachment of drugs, cytotoxicological testing is necessary before moving to animal models. Here, we consider several variously functionalized magnetite nanoparticles, including those prepared with (1) a monolayer of oleic acid (Fe(3)O(4)@OA), which is subsequently converted to (2) a shell of amine-containing silane (Fe(3)O(4)@NH(2)), (3) a shell of silica (Fe(3)O(4)@SiO(2)), and (4) a shell of amine-containing silane over a shell of silica (Fe(3)O(4)@SiO(2)@NH(2)). These latter three functionalities were evaluated for biocompatibility, cellular morphology, mitochondrial function (MTT assay), lactate dehydrogenase membrane leakage (LDH assay), and proinflammatory potential through enzyme linked immunosorbent assay (ELISA) for interleukin 6 (IL-6). Controlled tests were performed over a period of 72 h, with results showing LDH leakage and abnormal Il-6 secretion at high concentrations (>50 μg/mL). The tests showed that, in addition to the surface characteristics of the nanoparticles, both the nutrient medium and the time of suspension before exposure to cells also contribute to nanoparticle cytotoxicity.
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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 | 0.001 | 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) | 0.001 | 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".