Early Diagnosis of Small Fiber Neuropathies By Electrochemical Means: Optimization of Sensing Materials
Bibliographic record
Abstract
Small fiber neuropathy is a dysfunction caused by diseases such as type II diabetes and cystic fibrosis. Nowadays, it is possible to analyze neuropathy with SudoscanTM technology (Impeto Medical Inc.) which provides a rapid and non-invasive early diagnosis. This technology is based on measurements of the electrochemical conductance of the skin viathe imposition of low amplitude voltages between electrodes applied to the skin and measuring the low current generated. The output measurement is related to the sweat composition associated to glands innervations and their permeability to chloride and proton ions [1]. The results obtained with this in vivo technology show a typical anode current response (curve J-E) with a significant offset and a linear part. The characteristic shape of the J-E graph easily indicates the type of disease and its progress [2]. To have a deeper understanding of the mechanisms occurring at electrodes and optimize the sensitivity of the instrument, in vitro experiments were performed in mimetic electrolytic solutions of sweat. Preliminary in vitro experiments led to same shapes than in vivo but with larger current densities (≈ 50 times greater than those obtained in vivo). This phenomenon is probably due to the high resistivity of a human body and the difference of the electrodes system between in vitro and in vivo measurements. For these reasons, it was necessary to modify in vitro conditions to get similar current densities and mimic realistically in vivo conditions. To this end, major modifications in the in vitro system were realized: (i) Concerning the in vitro configuration: the electrodes arrangement was changed and 3 identical electrodes were used leading to a decrease in the current densities by a factor of 10 (ii) To simulate the body resistance and, consequently, to decrease the current densities in vitro, the electrolyte composition was optimized by increasing its viscosity to reduce the flux of chloride ions to the electrode surface. The new composition allowed to reach current densities close to those obtained in vivo [3]. The aim of this work was to find the optimized electrolyte, mimicking sweat in terms of composition and the resistance of the human body under in vitroconditions. Then, the goal was to go deeper in the knowledge on the mechanism governing the electrode response to sweat. For this, linear voltammetry and electrochemical impedance spectroscopy were implemented to study the influence of sweat components such as pH, chloride concentration, buffer and carbonate concentration on a nickel model electrode [4]. Finally, different stainless steel electrodes were tested. Such electrodes are required because they are not allergenic and less expensive than nickel, but they need to be sensible to ions detection in sweat, with reproducible results allowing the early detection of diseases. References: [1] A. Calmet, H. Ayoub, V. Lair, and S. Griveau, Actual. Chim., vol. 390, pp. 48–49, 2014. [2] P. Brunswick and N. Bocquet, “Système d’analyse électrophysiologique,” 0753461. [3] A. Calmet, K. Khalfallah, H. Ayoub, V. Lair, S. Griveau, P. Brunswick, F. Bedioui, and M. Cassir, Electrochim. Acta, vol. 140, pp. 37–41, Sep. 2014. [4] H. Ayoub , S. Griveau, V. Lair, P. Brunswick, M. Cassir, and F. Bedioui, Electroanalysis, vol. 22, no. 21, pp. 2483–2490, Nov. 2010.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".