Noise Spectroscopy Analysis for Estimation of a Specific Biomolecule in a Complex Mixture Using Solid-State Nanopore
Bibliographic record
Abstract
This paper presents a noise spectroscopy analysis of the current traces recorded in a functionalized silicon oxide nanopore in presence of specific antigen (Hep-B), nonspecific antigen (BSA), and their complex mixture for the first time. It is observed that though the on and off dwell times can differentiate nonspecific antigen from specific antigen in pure buffer, an approximate quantification of the specific antigen with low-dissociation constant of the receptor-ligand pair, becomes almost impossible in complex mixture. This has been ascribed to the significant overlap in the current blockade sensitivity values between the different concentration ranges of the specific antigen. On the contrary, a noise spectroscopy analysis shows a Lorentzian spectrum in presence of specific antigen with a distinct shift in the roll-off frequency, such that upto 1-nM BSA concentration; it has been possible to estimate the concentration of the specific antigen even for 1-pM Hep-B. However, for BSA concentration greater than 1 nM, the roll-off frequency for a particular concentration of specific antigen starts deviating from its value in pure buffer and overlaps with other concentration range. This problem has been addressed by processing the fractional change in current blockade and roll-off frequency by a partial least square-discriminant analysis based multivariate statistical model. It has been observed that the learning model yields 91.5% correct classification with the solutions, and has been able to predict the concentration of Hep-B quite closely even for a low value of 1 pM in presence of 100 nM concentration of BSA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 teacher head, 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".