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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Effects of heavy metal exposure on hypertension: A machine learning modeling approach
Wenxiang Li, Guangyi Huang, Ningning Tang, et al.
Chemosphere (2023) Vol. 337, pp. 139435-139435
Closed Access | Times Cited: 18

Showing 18 citing articles:

Heavy Metal Exposure and Cardiovascular Disease
Ziwei Pan, Tingyu Gong, Ping Liang
Circulation Research (2024) Vol. 134, Iss. 9, pp. 1160-1178
Closed Access | Times Cited: 35
A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures
Zhu Guiming, Yanchao Wen, Kexin Cao, et al.
Frontiers in Public Health (2024) Vol. 12
Open Access | Times Cited: 8
A new approach for predicting oil mobilities and unveiling their controlling factors in a lacustrine shale system: Insights from interpretable machine learning model
Enze Wang, Yingxiao Fu, Tonglou Guo, et al.
Fuel (2024) Vol. 379, pp. 132958-132958
Closed Access | Times Cited: 7
Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study
Jiexin Chen, Qiongbing Zheng, Youmian Lan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Inflammatory markers link triglyceride-glucose index and obesity indicators with adverse cardiovascular events in patients with hypertension: insights from three cohorts
Yanqiu Huang, Yi Zhou, Yinchuan Xu, et al.
Cardiovascular Diabetology (2025) Vol. 24, Iss. 1
Open Access
Association of glyphosate exposure with multiple adverse outcomes and potential mediators
Wenxiang Li, Daizai Lei, Guangyi Huang, et al.
Chemosphere (2023) Vol. 345, pp. 140477-140477
Closed Access | Times Cited: 9
Serum albumin and liver dysfunction mediate the associations between organophosphorus pesticide exposure and hypertension among US adults
Yinqiao Dong, Wei Xu, Shiping Liu, et al.
The Science of The Total Environment (2024) Vol. 948, pp. 174748-174748
Closed Access | Times Cited: 2
Integration of interpretable machine learning and environmental magnetism elucidates reduction mechanism of bioavailable potentially toxic elements in lakes after monsoon
Ligang Deng, Yifan Fan, Mingjia Li, et al.
The Science of The Total Environment (2024) Vol. 954, pp. 176418-176418
Closed Access | Times Cited: 1
Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective
Jun Liu, Xingyu Li, Peng Zhu
Biological Trace Element Research (2024) Vol. 202, Iss. 12, pp. 5438-5452
Closed Access | Times Cited: 1
Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models
Zhen Feng, Ying’ao Chen, Yuxin Guo, et al.
American Journal of Clinical Nutrition (2024) Vol. 120, Iss. 2, pp. 407-418
Closed Access | Times Cited: 1
Toxicity study of mineral medicine haematitum
Min Lu, Jia-Li Rao, Jing Ming, et al.
Journal of Ethnopharmacology (2024) Vol. 333, pp. 118406-118406
Closed Access | Times Cited: 1
Development and validation of a new nomogram for OA based on machine learning
Qiongbing Zheng, Jiexin Chen, Youmian Lan, et al.
Research Square (Research Square) (2024)
Open Access
Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach
Yanchao Gui, Si-Yu Gui, Xinchen Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
A machine learning model predicts stroke associated with blood cadmium level
Wenwei Zuo, Xuelian Yang
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Environmental chemical exposures and a machine learning-based model for predicting hypertension in NHANES 2003–2016
Kun Guo, Weicheng Ni, Leilei Du, et al.
BMC Cardiovascular Disorders (2024) Vol. 24, Iss. 1
Open Access
Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology
Qingan Fu, Yanze Wu, Min Zhu, et al.
Ecotoxicology and Environmental Safety (2024) Vol. 286, pp. 117210-117210
Open Access
Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning
Hewei Xiao, Xue‐Yan Liang, Huijuan Li, et al.
Ecotoxicology and Environmental Safety (2024) Vol. 286, pp. 117238-117238
Open Access
Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning
Min Shen, Zhang Yin-e, Runqing Zhan, et al.
Ecotoxicology and Environmental Safety (2024) Vol. 290, pp. 117570-117570
Closed Access
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