Treatment of lactoferrin and antimicrobial peptide N6 on bacterial enteritis caused by <i>Escherichia coli</i> in mice
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in humans and animals. The study aimed to evaluate the efficacy of bovine lactoferrin (bLf) as an adjuvant combined with AMP (N6) in the treatment of E. coli-induced bacterial enteritis. Firstly, 40 female ICR mice were randomly divided into four groups. The ETEC-A, ETEC-B, and ETEC-C groups were gavaged with 0.2 mL of ETEC K88 at 5 × 10 9 , 5 × 10 8 , and 5 × 10 7 CFU/mL for three consecutive days, respectively, the CK control group was given PBS. Based on the clinical symptoms and intestinal changes, the optimal model dose of ETEC K88 was determined to be 5 × 10 8 CFU/mL. Sixty female ICR mice were randomly divided into six groups: CK group (uninfected), NC group (infected and untreated), N6 treatment group (20 mg/kg), bLf treatment group (100 mg/kg), bLf + N6-A treatment group (10 mg/kg N6+100 mg/kg bLf), and bLf + N6-B group (20 mg/kg N6+100 mg/kg bLf). The clinical symptoms, intestinal morphology, inflammatory response and serum metabolites were monitored. The results showed that compared with the NC group, the bLf-N6-A and bLf-N6-B treatment groups had significant reductions in TNF-α and IL-6, significant increases in IL-10, and significant reductions in endotoxin and DAO in plasma ( p < 0.05). Meanwhile, the bLf-N6-A and bLf-N6-B treatment groups significantly increased the expression of ZO-1, claudin-1 and occludin, increased the height of small intestinal mucosal villi and VH/CD after ETEC K88-induced intestinal injury ( p < 0.05). The combination of bLf and N6 relieved enteritis by balancing intestinal mucosal immunity, improving intestinal morphology and barrier function. BLf combined with N6 can be used as an effective therapeutic strategy for the treatment of bacterial enteritis.
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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 | 0.001 |
| 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".