Pea-protein alginate encapsulation adversely affects development of clinical signs of <i>Citrobacter rodentium</i>-induced colitis in mice treated with probiotics
Bibliographic record
Abstract
The efficacy of two strains of Lactobacillus probiotics (Lactobacillus rhamnosus R0011 and Lactobacillus helveticus R0052) immobilized in microcapsules composed of pea protein isolate (PPI) and alginate microcapsules was assessed using a mouse model of Citrobacter rodentium-induced colitis. Accordingly, 4-week-old mice were fed diets supplemented with freeze-dried probiotics (group P), probiotic-containing microcapsules (group PE) (lyophilized PPI-alginate microcapsules containing probiotics), or PPI-alginate microcapsules containing no probiotics (group E). Half of the mice (controls, groups P, PE, and E) received C. rodentium by gavage 2 weeks after initiation of feeding. Daily monitoring of disease symptoms (abnormal behavior, diarrhea, etc.) and body weights was undertaken. Histopathological changes in colonic and cecal tissues, cytokine expression levels, and pathogen and probiotic densities in feces were examined, and the microbial communities of the distal colon mucosa were characterized by 16S rRNA sequencing. Infection with C. rodentium led to marked progression of infectious colitis, as revealed by symptomatic and histopathological data, changes in cytokine expression, and alteration of composition of mucosal communities. Probiotics led to changes in most of the disease markers but did not have a significant impact on cytokine profiles in infected animals. On the basis of cytokine expression analyses and histopathological data, it was evident that encapsulation materials (pea protein and calcium alginate) contributed to inflammation and worsened a set of symptoms in the cecum. These results suggest that even though food ingredients may be generally recognized as safe, they may in fact contribute to the development of an inflammatory response in certain animal disease models.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".