The Effects of ZhiXiao TongMaiNing on Smad 2, Smad 3 and Smad 7 mRNA Expressions of Human Renal Tubular Epithelial Cells HK-2 Induced by TGF-β_1
Bibliographic record
Abstract
Objective: To explore the effects of ZhiXiao TongMaiNing on Smad 2, Smad 3 and Smad 7 of human renal tubular epithelial cells(HK-2) which were induced by transforming growth factor(TGF-β1). Method:HK-2 cells were cultured in the media of DMEM/F12(1:1) containing 10% fetal bovine serum; the cells were divided into six groups: blank control group, TGF-β1group(TGF-β110 ng/mL), blank serum control group(TGF-β110ng/mL+10% blank serum), low dose group of TCM medicated serum(TGF-β110 ng/mL+10% low dose medicated serum of ZhiXiao TongMaiNing), moderate dose group of TCM medicated serum(TGF-β110 ng/mL+10% moderate dose medicated serum of ZhiXiao TongMaiNing) and high dose group of TCM medicated serum(TGF-β110ng/mL+10% high dose medicated serum of ZhiXiao TongMaiNing). After intervened for 24 hours, Smad 2, Smad 3and Smad 7 mRNA expressions were detected with fluorescence quantitative PCR. Result: Smad2 and Smad3 mRNA expressions were raised significantly, Smad 7 mRNA expressions were decreased when HK-2 cells were induced with TGF-β1, it had significant difference compared with blank control group(P0.05), but after intervened with medicated serum of ZhiXiao TongMaiNing, Smad 2 and Smad 3 mRNA expressions were lowered gradually,Smad 7 mRNA expressions were improved, it had statistical meaning compared with TGF-β1group(P0.05). Blank serum presented no effects. Conclusion: ZhiXiao TongMaiNing could regulate mRNA expressions of trans-differentiated Smad signal channel of HK-2 cells induced with TGF-β1and inhibit the fibrosis of renal tubular epithelial cells to a certain extent.
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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 | 0.001 |
| 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 | 0.000 | 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".