Psyllium supplemented diet restores the development of small intestinal CD4+ CD8aa+ intraepithelial T cells 3849
Bibliographic record
Abstract
Abstract Description Dietary fiber has a role in changing the composition of the gut microbiota, which impacts how the immune system functions. Our lab has previously shown that a low fermentable fiber diet causes impaired development of small intestinal T cell populations such as CD4+ CD8aa+, also called double positive intraepithelial lymphocytes (DP IELs), which have a local regulatory function in the small intestine; and Th17 cells, which help regulate commensal bacteria colonization. This study aims to identify types of fiber that can restore the development of the DP IELs when supplemented in the diet. To do this, we compared the immune cell populations in the intestines of mice fed a standard chow diet and low fiber diet to low fiber diets supplemented with specific fibers, such as b-glucan, pectin, psyllium and inulin. Inulin is known to improve gut health by supporting the colonization of commensal bacteria such as Bifidobacteria and Lactobacilli. Psyllium can decrease gut inflammation and improve gut health by supporting the colonization of bacteria that produce butyrate. Our analyses show that a diet supplemented with psyllium was able to modify the microbiota and restore the development of DP IELs. In further studies we will identify the bacteria metabolizing psyllium and the mechanism by which it restores the development of DP IELs. This knowledge will allow us to identify fibers that are beneficial for the microbiota and support our immune system. Funding Sources Supported by NIDDK/NIH 1R01 DK129950-01; IMSD; NIGMS/NIH 5T32GM148391-02 Topic Categories Mucosal and Regional Immunology (MUC)
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".