NH<sub>4</sub>Cl affects the expression of Wnt/β-catenin pathway in hepatocytes
Bibliographic record
Abstract
We intended to explore whether NH4Cl influences the viability and regulates the expression of Wnt/β-catenin pathway in hepatocytes. The Chang liver cell line was used and cultured with different concentrations of NH4Cl (2.5, 5, 10, 20, 40, and 50 mmol/L) for 12, 24, and 48 h. The viability of hepatocytes was detected by MTT assay. The mRNA and protein expression level was analyzed with qRT–PCR and Western blotting, respectively. NH4Cl concentration significantly affects the viability of hepatocytes. With the increase of NH4Cl concentration, the viability of hepatocytes was decreased, accordingly. The mRNA and protein expression of Wnt1, β-catenin, and cyclin D was significantly increased after treatment with low concentrations of NH4Cl as compared with the control group, whereas their expression levels were decreased after treatment with high concentrations of NH4Cl. The mRNA and protein expression of Wnt1, β-catenin, and cyclin D was also significantly increased after treatment with NH4Cl for a short period as compared with the control group, whereas their expression levels were decreased after treatment with NH4Cl for a long period. In addition, we found NH4Cl treatment significantly reversed the results after RNA silencing of Wnt1 in hepatocytes. NH4Cl influences the viability of hepatocytes and affects the expression of Wnt/β-catenin pathway in hepatocytes.
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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".