An impedance-based chemiresistor for the real-time detection of gut microbiota-generated short-chain fatty acids
Bibliographic record
Abstract
Abstract Short-chain fatty acids (SCFAs) are key molecules produced by gut bacteria in the intestine, that are absorbed into the bloodstream and strongly influence human health. SCFA disruption and imbalances have been linked to many diseases; however, they are seldom used diagnostically as their detection requires extensive sample preparation and expensive equipment. In this work, an electrochemical sensor was developed to enable real-time, quantitative measurement of SCFAs from complex samples in liquid phase without the need for extraction, evaporation, or destruction. An impedance-based sensor for in vitro detection of acetic acid, propionic acid, and butyric acid (accounting for more than 95% of SCFAs in the intestine) was fabricated by the deposition of a ZnO and polyvinyl alcohol (PVA) on the surface of a microfabricated interdigitated gold electrode. The sensor was first exposed to a broad, physiologically relevant range of concentrations of SCFAs in isolation (0.5–20 mg/ml) and unlike previously published SCFA sensors that could detect only in gas form with the aid of evaporation, it was able to detect them directly in the liquid phase at room temperature. Electrochemical impedance spectroscopy analysis was then applied to the mixture of SCFAs prepared at different ratios and in complex media at concentrations ranging from 0.5 to 10 mg/ml, which showed the capability of the sensor to measure SCFAs in experimentally relevant mixture. The recorded faradaic responses were then used to train a fit-to-data model to utilize the sensor to screen human bacterial isolates and detect which species secrete SCFAs in vitro . This work will allow for the rapid and non-destructive determination of the levels of SCFAs in complex biological samples, providing a miniaturized, highly stable, and highly sensitive sensor for real-time monitoring applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".