Label-free detection of neuron–drug interactions using acoustic and Kelvin vibrational fields
Bibliographic record
Abstract
Kelvin and acoustic fields of high-frequency have been employed in the non-invasive investigation of immortalized hypothalamic neurons, in order to assess their response to different concentrations of specific drugs, toxins, a stress-reducing hormone and neurotrophic factors. In an analytical systems biology approach, this work constitutes a first study of living neuron cultures by scanning Kelvin nanoprobe (SKN) and thickness shear mode (TSM) acoustic wave techniques. N-38 hypothalamic mouse neurons were immobilized on the gold electrode of 9 MHz TSM acoustic wave devices and gold-coated slides for study by SKN. The neurons were exposed to the neurochemicals betaseron, forskolin, TCAP, and cerebrolysin. Signals were collected with the TSM in real-time mode, and with the SKN in scanning and real-time modes, as the drugs were applied at biologically significant concentrations. With the TSM, for all drugs, some frequency and resistance shifts were in the same direction, contrary to normal functioning for this type of instrument. Possible mechanisms are presented to explain this behaviour. An oscillatory signal with periodicity of approximately 2 min was observed for some neuron-coated surfaces, where the amplitude of these oscillations was altered upon application of certain neurotrophic factors. These two new techniques present novel and non-invasive electrodeless methods for detecting changes at the cellular level caused by a variety of neuroactive compounds, without killing or destroying the neurons.
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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 | 0.001 | 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".