Silver and Copper Nanoparticles Hosted by Carboxymethyl Cellulose Reduce the Infective Effects of Enterotoxigenic Escherichia coli:F4 on Porcine Intestinal Enterocyte IPEC-J2
Bibliographic record
Abstract
Zero-valent copper and silver metals (Ms) nanoparticles (NPs) supported on carboxymethylcellulose (CMC) were synthesized for treating Enterotoxigenic Escherichia coli fimbriae 4 (ETEC:F4), a major cause of diarrhea in post-weaned pigs. The antibacterial properties of Cu0/CMC and Ag0/CMC were assessed on infected porcine intestinal enterocyte IPEC-J2, an in vitro model mimicking the small intestine. The lower average particle size (218 nm) and polydispersity index [PDI]: 0.25) for Ag0/CMC, when compared with those of Cu0/CMC (367 nm and PDI 0.96), were explained by stronger Ag0/CMC interactions. The minimal inhibitory concentration (MIC) and half inhibitory concentration (IC50) of Ag0/CMC were lower in both bacteria and IPEC-J2 cells than those of Cu0/CMC, confirming that silver nanoparticles are more bactericidal than copper counterparts. IPEC-J2, less sensitive in MNP/CMC treatment, was used to further investigate the infective process by ETEC:F4. The IC50 of MNP/CMC increased significantly when infected IPEC-J2 cells and ETEC were co-treated, showing an inhibition of the cytotoxicity effect of ETEC:F4 infection and protection of treated IPEC-J2. Thus, it appears that metal insertion in CMC induces an inhibiting effect on ETEC:F4 growth and that MNP/CMC dispersion governs the enhancement of this effect. These results open promising prospects for metal-loaded biopolymers for preventing and treating swine diarrhea.
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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 | 0.000 |
| 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 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".