Intervention of ZhiXiao TongMaiNing on Proliferation of Human Renal Tubular Epithelial Cell HK-2 Induced by TGF-β_1
Bibliographic record
Abstract
Objective: To explore the mechanism of ZhiXiao TongMaiNing in the prevention and treatment of renal interstitial fibrosis by observing the influence of ZhiXiao TongMaiNing on proliferation of human renal tubular epithelial cells HK-2 induced by transforming growth factor-β1(TGF-β1).Method: HK-2 cells were cultured with DMEM/F12(1:1) containing 10% fetal bovine serum and divided into control group,TGF-β1 group(TGF-β110 ng/mL),control group of animal serum(TGF-β110 ng/mL +10% animal serum),first intervention group(TGF-β110 ng/mL + 10 % low dose of ZhiXiao TongMaiNing),second intervention group(TGF-β110 ng/mL + 10% middle dose of ZhiXiao TongMaiNing) and third intervention group(TGF-β110 ng/mL + 10 % high dose of ZhiXiao TongMaiNing).HK-2 cell morphology and cell proliferation were observed respectively through the microscope and MTT assay.Result: 10 ng/mL TGF-β1could induce remarkably proliferation of human renal tubular epithelial cells HK-2,comparison indicated notable difference with blank control group(P0.05),it can enhance with the growth of time,but cell proliferation was inhibited to a certion extent after combination with ZhiXiao TongMaiNing(P0.05).Conclusion: ZhiXiao TongMaiNing can inhibit the proliferation of HK-2 and prevent renal fibrosis to some 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".