Physicochemical Changes of Apoferritin Protein during Biodegradation of Magnetic Metal Oxide Nanoparticles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The biodegradation of therapeutic magnetic-oxide nanoparticles (MONPs) in the human body raises concerns about their lifespan, functionality, and health risks. Interactions between apoferritin proteins and MONPs in the spleen, liver, and inflammatory macrophages significantly accelerate nanoparticle degradation, releasing metal ions taken up by apoferritin. This can alter the protein’s biological structure and properties, potentially causing health hazards. This study examines changes in apoferritin’s shape, electrical surface potential (ESP), and protein-core composition after incubation with cobalt-ferrite (CoFe 2 O 4 ) oxide nanoparticles. Using atomic force microscopy (AFM) and scanning Kelvin probe force microscopy (SKPFM), we observed changes in the topography and ESP distribution in apoferritin nanofilms over time. After 48 h, the characteristic apoferritin hole (∼1.35 nm) vanished, and the protein’s height increased from ∼3.5 to ∼7.5 nm due to hole filling. This resulted in a significant ESP increase on the filled-apoferritin surface, attributed to the formation of a heterogeneous chemical composition and crystal structure (γ-Fe 2 O 3, Fe 3 O 4, CoO, CoOOH, FeOOH, and Co 3 O 4 ). These changes enhance electrostatic interactions and surface charge between the protein and the AFM tip. This approach aids in predicting and improving the MONP lifespan while reducing their toxicity and preventing apoferritin deformation and dysfunction.
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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".