Investigation into <i>in vitro</i> and <i>in vivo</i> models using intestinal epithelial IPEC-J2 cells and <i>Caenorhabditis elegans</i> for selecting probiotic candidates to control porcine enterotoxigenic <i>Escherichia coli</i>
Bibliographic record
Abstract
AIMS: To identify a fast, economic and reliable method for preselecting lactic acid-producing bacterial (LAB) isolates to control enterotoxigenic Escherichia coli (ETEC). METHODS AND RESULTS: Two assays with porcine intestinal epithelial IPEC-J2 cells or Caenorhabditis elegans for selecting effective probiotic candidates were compared. Both assays were based on measuring death of cells or worms caused by ETEC strain JG280. Six of 13 LAB isolates showed ≥50% protection in each assay, among which only four isolates (≥50% protection) were consistently selected by both assays. Isolate CL9 (Lactobacillus reuteri) was further studied. It reduced gene expression of estA, estB and elt in JG280 in both assays. Furthermore, the isolate protected IPEC-J2 and C. elegans from cell and worm death caused by STa, STb or LT enterotoxin expressed in E. coli DH5α. CL9 also promoted host defensive responses by decreasing IL-8 and increasing IL-10 production in IPEC-J2 cells and expression of antimicrobial peptide genes clec-60, clec-85 in C. elegans. CONCLUSIONS: Caenorhabditis elegans is useful for preselecting probiotic candidates to control ETEC after initial screening with IPEC-J2 cells. SIGNIFICANCE AND IMPACT OF THE STUDY: A combination of IPEC-J2 cell and C. elegans assays can improve the effectiveness in preselecting probiotic candidates.
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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 | 0.000 | 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".