Hsien-Yi Wang

Hsien-Yi Wang


Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2016 in Journal of Clinical Lipidology
Verified
Journal Article
Closed access
KWKuan-Ta Wu
Kuan-Ta Wu
PKPo-Lin Kuo
Po-Lin Kuo
SSShih-Bin Su
Shih-Bin Su
YCYi-Yu Chen
Yi-Yu Chen
MYMing-Lum Yeh
Ming-Lum Yeh
HWHsien-Yi Wang
Hsien-Yi Wang

Verified
Journal Article
Open access
PDF file
TYTzu-Hsuan Yeh
Tzu-Hsuan Yeh
KTKuan-Chieh Tu
Kuan-Chieh Tu
HWHsien-Yi Wang
Hsien-Yi Wang
Jui-Yi ChenJui-Yi Chen
Jui-Yi Chen
This article provides a thorough overview of the biomarkers, pathophysiology, and molecular pathways involved in the transition from acute kidney injury (AKI) and acute kidney disease (AKD) to chronic kidney disease (CKD). It categorizes the biomarkers of AKI into stress, damage, and functional markers, highlighting their importance in early detection, prognosis, and clinical applications. This review also highlights the links between renal injury and the pathophysiological mechanisms underlying AKI and AKD, including renal hypoperfusion, sepsis, nephrotoxicity, and immune responses. In addition, various molecules play pivotal roles in inflammation and hypoxia, triggering maladaptive repair, mitochondrial dysfunction, immune system reactions, and the cellular senescence of renal cells. Key signaling pathways, such as Wnt/β-catenin, TGF-β/SMAD, and Hippo/YAP/TAZ, promote fibrosis and impact renal function. The renin–angiotensin–aldosterone system (RAAS) triggers a cascade leading to renal fibrosis, with aldosterone exacerbating the oxidative stress and cellular changes that promote fibrosis. The clinical evidence suggests that RAS inhibitors may protect against CKD progression, especially post-AKI, though more extensive trials are needed to confirm their full impact.

1 February 2018 in Medicine
Verified
Journal Article
Open access
TCTsair-Wei Chien
Tsair-Wei Chien
YCYu Chang
Yu Chang
HWHsien-Yi Wang
Hsien-Yi Wang
Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear. By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms. Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40). SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

12 March 2021 in Medicine
Verified
Journal Article
Open access
KLKeng-Wei Lee
Keng-Wei Lee
TCTsair-Wei Chien
Tsair-Wei Chien
YYYu-Tsen Yeh
Yu-Tsen Yeh
WCWilly Chou
Willy Chou
HWHsien-Yi Wang
Hsien-Yi Wang
Background: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most. Methods: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country. Results: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61–0.86), 0.58 (0.31–0.84), and 0.54 (0.44–0.64), respectively. An online time–event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents. Conclusion: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

1 May 2013 in Clinica Chimica Acta
Verified
Journal Article
Closed access
Yu-Chang TyanYu-Chang Tyan
Yu-Chang Tyan
Shih-Bin SuShih-Bin Su
Shih-Bin Su
STSing-Sing Ting
Sing-Sing Ting
HWHsien-Yi Wang
Hsien-Yi Wang
PLPao-Chi Liao
Pao-Chi Liao

8 January 2024 in Preprints.org
Verified
Preprint
Open access
PDF file
TYTzu-Hsuan Yeh
Tzu-Hsuan Yeh
KTKuan-Chieh Tu
Kuan-Chieh Tu
HWHsien-Yi Wang
Hsien-Yi Wang
Jui-Yi ChenJui-Yi Chen
Jui-Yi Chen
This article provides a thorough overview of biomarkers, pathophysiology, and molecular pathways involved in the transition from acute kidney injury (AKI), acute kidney disease (AKD) to chronic kidney disease (CKD). It categorizes the biomarkers of AKI into stress, damage, and functional markers, highlighting their importance in early detection, prognosis, and clinical application. The pathophysiological mechanisms underlying AKI, AKD, including renal hypoperfusion, sepsis, nephrotoxicity, and immune responses, with the impact on renal injury. In addition, various molecules play pivotal roles in inflammation and hypoxia, triggering maladaptive repair, mitochondrial dysfunction, immune system reactions, and cellular senescence of renal cell. Key signaling pathways, such as Wnt/β-Catenin, TGF-β/SMAD, and Hippo/YAP/TAZ, promote fibrosis and impact renal function. The Renin-Angiotensin-Aldosterone System (RAAS) triggers a cascade leading to renal fibrosis, with aldosterone exacerbating oxidative stress and cellular changes that promote fibrosis. Clinical evidence suggests RAS inhibitors may protect against CKD progression, especially post-AKI, though more extensive trials are needed to confirm their full impact.