Protective effects of baicalin on LPS-induced injury in intestinal epithelial cells and intercellular tight junctions
Bibliographic record
Abstract
AIMS: To investigate the protective effects and mechanisms of baicalin on lipopolysaccharide (LPS)-induced injury in intestinal epithelial cells and intercellular tight junctions. METHODS: IEC-6 cells were stimulated with LPS (1.0 μg/mL), with or without baicalin, for 24 h. The levels of the inflammatory cytokines interleukin (IL)-6 and tumor necrosis factor (TNF)-α were determined using ELISA. Quantitative real-time PCR was used for determining the mRNA expression level of claudin-3, occludin, and ZO-1; Western blot and immunofluorescence analysis were used for analyzing the expression level and the distribution patterns of ZO-1 protein. RESULTS: Pretreatment with baicalin (10.0 μg/mL) improved LPS-stimulated cell viability and repressed IL-6 and TNF-α levels. In addition, pretreatment with baicalin up-regulated mRNA and protein expression levels of ZO-1 and kept the protein intact in IEC-6 cells injured with LPS. CONCLUSION: Baicalin has the capacity to protect IEC-6 cells and the intercellular tight junctions from LPS-induced injury. The mechanisms may be associated with inhibiting the production of inflammatory cytokines, and up-regulating the mRNA and protein expression of ZO-1.
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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) | 0.001 | 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".