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Cited by in CrossRef
For: Song JJ, Han XF, Chen JF, Liu KM. Correlation between glycated hemoglobin A1c, urinary microalbumin, urinary creatinine, β2 microglobulin, retinol binding protein and diabetic retinopathy. World J Diabetes 2023; 14(7): 1103-1111 [PMID: 37547593 DOI: 10.4239/wjd.v14.i7.1103]
URL: https://www.wjgnet.com/1948-9358/full/v14/i7/1103.htm
Number Citing Articles
1
Yujia Chen, Xinan Liu, Meniga Shengbu, Qian Shi, Suolang Jiaqiu, Xianrong Lai. Biomarkers: New Advances in Diabetic NephropathyNatural Product Communications 2025; 20(2) doi: 10.1177/1934578X251321758
2
So Hee Lee, Gyubeom Hwang, Dong Yun Lee, Ja Young Jeon, Seung-Jin Kwag, Seo Young Sohn, Sang Joon Park, Dughyun Choi, Sang Youl Rhee, Rae Woong Park. Prediction of diabetic retinopathy using machine learning and its association with dementia risk in older adults with type 2 diabetes mellitusDiabetes Research and Clinical Practice 2025; : 112378 doi: 10.1016/j.diabres.2025.112378
3
Penglu Yang, Bin Yang, Sameena Naaz. Development and validation of predictive models for diabetic retinopathy using machine learningPLOS ONE 2025; 20(2): e0318226 doi: 10.1371/journal.pone.0318226
4
Yazi Zhang, Menglong Shi, Dehui Peng, Weijie Chen, Yucong Ma, Wenting Song, Yuetong Wang, Haiyin Hu, Zhaochen Ji, Fengwen Yang. QiMing granules for diabetic retinopathy: a systematic review and meta-analysis of randomized controlled trialsFrontiers in Pharmacology 2024; 15 doi: 10.3389/fphar.2024.1429071