Electronic Stool (e-Stool): A Novel Self-Stabilizing Video Capsule Endoscope for Reliable Non-Invasive Colonic Imaging
Bibliographic record
Abstract
Video capsule endoscopy (VCE) has become a popular non-invasive technique to study the small intestine. However, colonic VCE has been problematic due to capsule tumbling in the larger lumen of this organ. Self-stabilizing VCE is a novel method to visualize the colon without tumbling utilizing a biomimetic approach. The proposed design uses the free energy of the body’s natural processes employed to move chyme, and imitates the formation and propagation of stool. In its final stage, it physically and mechanically mimics natural feces. The process starts by administering the capsule orally. The capsule size, shape, and material were chosen to provide a smooth transit throughout the gastrointestinal (GI) tract. Once it reaches the colon, its special outer casing enzymatically dissolves. A stabilizing component that is attached to the back end of the capsule starts quickly expanding in the cecum by osmosis. This increase of the volumetric size of the expandable component (stabilizing component) invokes natural peristalsis by colonic mass reflex. Since the expansion process takes place very quickly, the capsule gets stabilized before the expansion-provoked peristalsis starts. At the final stage, the artificially created expanded component (behaving like an artificial stool) centralizes the capsule during its voyage in the colon, allowing a very smooth transit due to its viscosity. The aim of the present study is to present the design of the capsule from a biomimetic perspective and to comparatively quantify the mechanical properties of the design with those of actual human stool.
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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 | 0.000 |
| 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 | 0.001 |
| 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".