The DOI matches its Crossref record; 364 of the 364 references it cites were found in a registry or an index.
Kidney fibrosis: from mechanisms to therapeutic medicines
Open accessAbstract
Chronic kidney disease (CKD) is estimated to affect 10–14% of global population. Kidney fibrosis, characterized by excessive extracellular matrix deposition leading to scarring, is a hallmark manifestation in different progressive CKD; However, at present no antifibrotic therapies against CKD exist. Kidney fibrosis is identified by tubule atrophy, interstitial chronic inflammation and fibrogenesis, glomerulosclerosis, and vascular rarefaction. Fibrotic niche, where organ fibrosis initiates, is a complex interplay between injured parenchyma (like tubular cells) and multiple non-parenchymal cell lineages (immune and mesenchymal cells) located spatially within scarring areas. Although the mechanisms of kidney fibrosis are complicated due to the kinds of cells involved, with the help of single-cell technology, many key questions have been explored, such as what kind of renal tubules are profibrotic, where myofibroblasts originate, which immune cells are involved, and how cells communicate with each other. In addition, genetics and epigenetics are deeper mechanisms that regulate kidney fibrosis. And the reversible nature of epigenetic changes including DNA methylation, RNA interference, and chromatin remodeling, gives an opportunity to stop or reverse kidney fibrosis by therapeutic strategies. More marketed (e.g., RAS blockage, SGLT2 inhibitors) have been developed to delay CKD progression in recent years. Furthermore, a better understanding of renal fibrosis is also favored to discover biomarkers of fibrotic injury. In the review, we update recent advances in the mechanism of renal fibrosis and summarize novel biomarkers and antifibrotic treatment for CKD.
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Chen, Y., Wang, L., Kang, Q., Zhang, X., Yu, G., Wan, X., Wang, J., & Zhu, K. (2017) · Cellular Physiology and Biochemistry · doi:10.1159/000477308 · Cited by 10,679
Regulation of chromatin by histone modifications
Bannister, A. J., & Kouzarides, T. (2011) · Cell Research · doi:10.1038/cr.2011.22 · Cited by 6,012
Dapagliflozin in Patients with Chronic Kidney Disease
Heerspink, H. J. L., Stefánsson, B. V., Correa-Rotter, R., Chertow, G. M., Greene, T., Hou, F.-F., Mann, J. F. E., McMurray, J. J. V., Lindberg, M., Rossing, P., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2024816 · Cited by 5,618
A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis.
King, T. E., Bradford, W. Z., Castro-Bernardini, S., Fagan, E. A., Glaspole, I., Glassberg, M. K., Gorina, E., Hopkins, P. M., Kardatzke, D., Lancaster, L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1402582 · Cited by 3,975
Emerging roles of caspase-3 in apoptosis
Porter, A. G., & Jänicke, R. U. (1999) · Cell Death & Differentiation · doi:10.1038/sj.cdd.4400476 · Cited by 3,855
Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes
Bakris, G. L., Agarwal, R., Anker, S. D., Pitt, B., Ruilope, L. M., Rossing, P., Kolkhof, P., Nowack, C., Schloemer, P., Joseph, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2025845 · Cited by 2,935
Naturally occurring p16Ink4a-positive cells shorten healthy lifespan
Baker, D. J., Childs, B. G., Durik, M., Wijers, M. E., Sieben, C. J., Zhong, J., A. Saltness, R., Jeganathan, K. B., Verzosa, G. C., Pezeshki, A., et al. (2016) · Nature · doi:10.1038/nature16932 · Cited by 2,876
VEGF in signaling and disease: beyond discovery and development
Apte, R. S., Chen, D. S., & Ferrara, N. (2019) · Cell · doi:10.1016/j.cell.2019.01.021 · Cited by 2,721
Haberland, M., Montgomery, R. L., & Olson, E. N. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2485 · Cited by 2,526
Senolytics improve physical function and increase lifespan in old age
Xu, M., Pirtskhalava, T., Farr, J. N., Weigand, B. M., Palmer, A. K., Weivoda, M. M., Inman, C. L., Ogrodnik, M. B., Hachfeld, C. M., Fraser, D. G., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0092-9 · Cited by 2,287
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Clinical applications of stem cell-derived exosomes
Tan, F., Li, X., Wang, Z., Li, J., Shahzad, K., & Zheng, J. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01704-0 · Cited by 708
Jha, R., Lopez-Trevino, S., Kankanamalage, H. R., & Jha, J. C. (2024) · Biomedicines · doi:10.3390/biomedicines12051098 · Cited by 144
Emerging epigenetic modifications in renal fibrosis: From mechanisms to treatments
Suo, X., Ge, Q., Peng, L., Zhu, Q., Zhang, M., Cheng, X., Wang, F., Jin, J., Wang, J., & Meng, X. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.012 · Cited by 8
Cheung, C. K., & Mariani, L. H. (2026) · Kidney International Supplements · doi:10.1016/j.kisu.2026.01.003 · Cited by 2
Metadata
- Authors
- Huang, R., Fu, P., & Ma, L.
- Venue
- Signal Transduction and Targeted Therapy
- Published
- 2023-03-17
- Volume
- 8
- Issue
- 1
- Pages
- 129-129
- ISSN
- 2059-3635 (ISSN-L)
- OpenAlex
- W4327811820
- Cited by
- 781
- References
- 364
- FWCI
- 129.26
- Type
- journal article
- Language
- en
- Open access
- gold
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