Binding with HSP90β, cimifugin ameliorates fibrotic cataracts in vitro and in vivo by inhibiting TGFβ signaling pathways
Bibliographic record
Abstract
Fibrotic cataracts, the most frequent complications after phacoemulsification, cannot be cured by drugs in clinic. The primary mechanism underlying the disease is the epithelial-mesenchymal transition (EMT). Cimifugin is a natural monomer component of traditional Chinese medicines. Previous researches have demonstrated the effect of cimifugin inhibiting EMT in the lung. The purpose of this work is to evaluate the impact of cimifugin on EMT in the lens and elucidate its precise mechanism. The pathogenesis of fibrotic cataracts was simulated using TGFβ2-induced cell model of EMT and the injury-induced anterior subcapsular cataract animal model. Through H&E staining and immunofluorescence of mice eyeballs, we discovered that cimifugin can inhibit the expansion of fibrotic lesions in vivo. Furthermore, at mRNA and protein levels, we confirmed that cimifugin can allay EMT of lens epithelial cells (LECs) in vitro and in vivo. Additionally, the inhibition of cimifugin on the activation of TGFβ-related signaling pathways was certified by immunoblot. HSP90β, the target of cimifugin, was predicted by network pharmacology and verified by drug affinity responsive target stability, the cellular thermal shift assay, and microscale thermophoresis. Moreover, co-immunoprecipitation revealed the interaction between HSP90β and TGFβ receptor (TGFβR) II. Together, our findings showed that by weakening the binding of HSP90β and TGFβRII, cimifugin suppressed the TGFβ signaling pathways to alleviate fibrotic cataracts. Cimifugin is a promising medication for the treatment of fibrotic cataracts.
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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 | 0.001 |
| 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".