Electrochemical fecal pellet sensor for simultaneous real-time ex vivo detection of colonic serotonin signalling and motility
Bibliographic record
Abstract
Various investigations have focused on understanding the relationship between mucosal serotonin (5-HT) and colonic motility, however contradictory studies have questioned the importance of this intestinal transmitter. Here we described the fabrication and use of a fecal pellet electrochemical sensor that can be used to simultaneously detect the release of luminal 5-HT and colonic motility. Fecal pellet sensor devices were fabricated using carbon nanotube composite electrodes that were housed in 3D printed components in order to generate a device that had shape and size that mimicked a natural fecal pellet. Devices were fabricated where varying regions of the pellet contained the electrode. Devices showed that they were stable and sensitive for ex vivo detection of 5-HT, and no differences in the fecal pellet velocity was observed when compared to natural fecal pellets. The onset of mucosal 5-HT was observed prior to the movement of the fecal pellet. The release of mucosal 5-HT occurred oral to the fecal pellet and was linked to the contraction of the bowel wall that drove pellet propulsion. Taken, together these findings provide new insights into the role of mucosal 5-HT and suggest that the transmitter acts as a key initiator of fecal pellet propulsion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".