Spatiotemporal maps reveal regional differences in the effects on gut motility for <i>Lactobacillus reuteri</i> and <i>rhamnosus</i> strains
Bibliographic record
Abstract
BACKGROUND: Commensal bacteria such as probiotics that are neuroactive acutely affect the amplitudes of intestinal migrating motor complexes (MMCs). What is lacking for an improved understanding of these motility effects are region specific measurements of velocity and frequency. We have combined intraluminal pressure recordings with spatiotemporal diameter maps to analyze more completely effects of different strains of beneficial bacteria on motility. METHODS: Intraluminal peak pressure (PPr) was measured and video recordings made of mouse ex vivo jejunum and colon segments before and after intraluminal applications of Lactobacillus rhamnosus (JB-1) or Lactobacillus reuteri (DSM 17938). Migrating motor complex frequency and velocity were calculated. KEY RESULTS: JB-1 decreased jejunal frequencies by 56% and 34% in colon. Jejunal velocities increased 171%, but decreased 31% in colon. Jejunal PPr decreased by 55% and in colon by 21%. DSM 17938 increased jejunal frequencies 63% and in colon 75%; jejunal velocity decreased 57%, but increased in colon 146%; jejunal PPr was reduced 26% and 12% in colon. TRAM-34 decreased frequency by 71% and increased velocity 200% for jejunum, but increased frequency 46% and velocity 50% for colon; PPr was decreased 59% for jejunum and 39% for colon. CONCLUSIONS & INFERENCES: The results show that probiotics and other beneficial bacteria have strain and region-specific actions on gut motility that can be successfully discriminated using spatiotemporal mapping of diameter changes. Effects are not necessarily the same in colon and jejunum. Further research is needed on the detailed effects of the strains on enteric neuron currents for each gut region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".