Detection of ionic concentration fluctuations using tapered microscopic optical waveguides
Bibliographic record
Abstract
The various ions present in the extra- and intracellular mediums, such as potassium, occupy an important role for many biological phenomena. So far, biologists and electrophysiologists have been working hard to understand the role of several ions in cellular behaviors. Our objective is to measure ionic concentration fluctuations using a tapered optical (fiber) guide and an ionic indicator. The optical cylindrical waveguides are tapered to a final diameter of 10 micrometers. The resulting probes are first used to transmit excitation light (349 nm wavelength) into the solution, and then, they are used to collect a potassium indicator (PBFI) fluorescence. The indicator emission depends on the potassium concentration and, by monitoring this fluorescence, a correlation can be made with the potassium current. Two types of optical waveguides have been studied: a multimode fiber optic (Thorlabs FG-200-UCR) and a borosilicate capillary (generally used as an electrophysiological electrode). Results show that concentration fluctuations in the order of 10 mM can be monitored using tapered optical guides. However the signal to noise ratio and the sensing repeatability are requiring further improvements. Thus, tapered optical waveguides can be used as ionic sensors. It has been demonstrated that sensors as small as 10 micrometers are sensitive to concentration fluctuations. Optical indicators are widely used in microscopy and they offer many possibilities in terms of their specificity (for ions as well as for other particles). Thus optical fibers, by guiding the light into deep regions, allow for the use of optical indicators in vivo.
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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 | 0.001 |
| 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 | 0.001 | 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".