PSVII-16 Galactosylated chitosan-oligosaccharides have anti-adhesive effect against enterotoxigenic Escherichia coli in piglets
Bibliographic record
Abstract
Abstract Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in piglets. In vitro, high molecular weight β-galactosylated chitosan-oligosaccharides (Gal-COS) had strong anti-adhesive activity against ETEC-expressing K88 fimbriae (ETEC K88) binding to porcine erythrocytes. This study assessed the effects of Gal-COS differing in structure on anti-adhesive properties against ETEC in a small intestinal segment perfusion (SISP) model in 8 piglets (BW 10 kg; 5-wk old). With 10 jejunal segments in each pig, 5 segments were infected with ETEC K88, and the other 5 segments were infused with saline (non-ETEC). Every 2 paired segments (ETEC or non-ETEC) from the same pig were treated for 8 h with 64 ml of 10 g L-1 of one of the following test products: 1) α-Gal-COS; 2) β-Gal-COS; 3) exopolysaccharides produced by Lactobacillus reuteri; and 4) raffinose in a double 4 × 4 Latin square with a saline control. Infection by ETEC K88 was verified by quantitative PCR. Net fluid loss was calculated as difference of fluid loss between ETEC segment and its paired non-ETEC segment. Data were analyzed using the mixed model with segment and test product as fixed effects, and pig as random effect. Number of eubacterial rRNA genes was 10-fold greater (P < 0.001) in ETEC segments than non-ETEC segments, indicating that ETEC K88 accounted for > 90% of bacterial gene counts. Test product did not affect (P > 0.10) the number of ETEC bacteria in the outflow fluid. Furthermore, net fluid loss caused by ETEC tended (P = 0.08) to be decreased by β-Gal-COS compared to all other treatments. In conclusion, the in vivo SISP model confirmed that Gal-COS had anti-diarrheal effects, indicating that β-Gal-COS is a potential feed additive to reduce the ETEC-induced diarrhea in piglets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".