The application of Ussing chambers for determining the impact of microbes and probiotics on intestinal ion transport
Bibliographic record
Abstract
Host-microbe interactions have gained considerable attention in recent years with regards to their role in various organic disorders and diseases. In particular, research efforts have focused on the intestinal microbiota, where the largest and most diverse populations not only co-exist with the host, but also directly influence the state and function of the gastrointestinal (GI) tract. Moreover, both human and animal studies alike are now beginning to show a positive influence of probiotic bacteria on GI disorders associated with diarrhoea or constipation. Diarrheagenic GI diseases, such as those caused by Vibreo cholera or enterpathogenic Eschericia coli, have well-characterised interactions with the host that explain much of the observed symptoms, in particular severe diarrhoea. However, the mechanisms of action of nonpathogenic bacteria or probiotics on host physiology are less clearly understood. In the context of defining the mechanisms of action of probiotics in vitro, the Ussing chamber has proven to be a particularly useful tool. Here, we will present data from several studies that have defined molecular targets for microbes and putative probiotics in the regulation of intestinal secretory and absorptive function, and we will discuss these in the context of their application in pathogen- or inflammation-induced alterations in intestinal ion transport.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".