Applications of scanning probe microscopy in neuroscience research
Bibliographic record
Abstract
Abstract Scanning probe microscopy techniques allow for label-free high-resolution imaging of cells, tissues, and biomolecules in physiologically relevant conditions. These techniques include atomic force microscopy (AFM), atomic force spectroscopy, and Kelvin probe force microscopy, which enable high resolution imaging, nanomanipulation and measurement of the mechanoelastic properties of neuronal cells, as well as scanning ion conductance microscopy, which combines electrophysiology and imaging in living cells. The combination of scanning probe techniques with optical spectroscopy, such as with AFM-IR and tip-enhanced Raman spectroscopy, allows for the measurement of topographical maps along with chemical identity, enabled by spectroscopy. In this work, we review applications of these techniques to neuroscience research, where they have been used to study the morphology and mechanoelastic properties of neuronal cells and brain tissues, and to study changes in these as a result of chemical or physical stimuli. Cellular membrane models are widely used to investigate the interaction of the neuronal cell membrane with proteins associated with various neurological disorders, where scanning probe microscopy and associated techniques provide significant improvement in the understanding of these processes on a cellular and molecular level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".