Biosensing with AFM
Bibliographic record
Abstract
Atomic force microscope (AFM) was used as a nanomechanical transducer for biosensing in different ways -this will be demonstrated on experiments with proteins, nucleic acids, their affinity complexes and cells. The cantilever tip modified with biorecognition element served for affinity sensing. The interaction of ssDNA binding protein with oligonucleotides was imaged using bare tips, the binding forces in the affinity complex were studied using the ligand-modified tip and the ForceRobot for automated recording of force-distance curves. Similar experiments characterized immunoreactions between antibody and antigen (human serum albumin, microbial cells), hybridization of nucleic acids; interactions were confirmed using surface plasmon resonance and electrochemical measurements. Properties of mast cells related to biotransformation events triggered by antigens were imaged with AFM and correlated to real-time measurements of model allergens and antiallergic substances with piezoelectric sensors. Furthermore, periodic beating of cardiomyocytes was followed with oscillations of the contacting cantilever. Recording of contractions and electric activity of cardiomyocytes was obtained using the AFM cantilever with conductive tip functioning as transducer for cellular biosensor suitable for evaluation of physiologically active compounds in real time. Advanced tools of nanobiotechnology allow to realize biosensing experiments at the level of a single cell and few individual molecules.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".