Trans-illumination intestine projection imaging of intestinal motility in mice
Bibliographic record
Abstract
Functional intestinal imaging holds importance for the diagnosis and evaluation of treatment of gastrointestinal diseases. Currently, preclinical imaging of intestinal motility in animal models is performed either invasively with excised intestines or noninvasively under anesthesia, and cannot reveal intestinal dynamics in the awake condition. Capitalizing on near-infrared optics and a high-absorbing contrast agent, we report the Trans-illumination Intestine Projection (TIP) imaging system for free-moving mice. After a complete system evaluation, we performed in vivo studies, and obtained peristalsis and segmentation motor patterns of free-moving mice. We show the in vivo typical segmentation motor pattern, that was previously shown in ex vivo studies to be controlled by intestinal pacemaker cells. We also show the effects of anesthesia on motor patterns, highlighting the possibility to study the role of the extrinsic nervous system in controlling motor patterns, which requires unanesthetized live animals. Combining with light-field technologies, we further demonstrated 3D imaging of intestine in vivo (3D-TIP). Importantly, the added depth information allows us to extract intestines located away from the abdominal wall, and to quantify intestinal motor patterns along different directions. The TIP system should open up avenues for functional imaging of the GI tract in conscious animals in natural physiological states.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".