Detection of stiff nanoparticles within cellular structures by contact resonance atomic force microscopy subsurface nanomechanical imaging
Bibliographic record
Abstract
Detecting stiff nanoparticles buried in soft biological matrices by atomic force microscopy (AFM) based techniques represents a new frontier in the field of scanning probe microscopies, originally developed as surface characterization methods. Here we report the detection of stiff (magnetic) nanoparticles (NPs) internalized in cells by using contact resonance AFM (CR-AFM) employed as a potentially non-destructive subsurface characterization tool. Magnetite (Fe3O4) NPs were internalized in microglial cells from cerebral cortices of mouse embryos of 18 days by phagocytosis. Nanomechanical imaging of cells was performed by detecting the contact resonance frequencies (CRFs) of an AFM cantilever held in contact with the sample. Agglomerates of NPs internalized in cells were visualized on the basis of the local increase in the contact stiffness with respect to the surrounding biological matrix. A second AFM-based technique for nanomechanical imaging, i.e., HarmoniX™, as well as magnetic force microscopy and light microscopy were used to confirm the CR-AFM results. Thus, CR-AFM was demonstrated as a promising technique for subsurface imaging of nanomaterials in biological samples.
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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) | 0.000 | 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".