Yusuke Kashiwagi
Overview
Explore the profile of Yusuke Kashiwagi including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
51
Citations
311
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Funaki R, Ogawa K, Mashitani Y, Oh T, Kashiwagi Y, Tanaka T, et al.
Physiol Rep
. 2024 Nov;
12(22):e70128.
PMID: 39567192
Recent discoveries have identified intrapulmonary bronchopulmonary anastomoses (IBAs) as a relatively common phenomenon forming intrapulmonary right-to-left shunts. This study hypothesizes that IBAs play a significant role in the pathophysiology of...
2.
Yasutake R, Nagoshi T, Yoshii A, Takahashi H, Oi Y, Kimura H, et al.
Peptides
. 2024 Nov;
182:171316.
PMID: 39490746
Several cell biology studies have focused on the effects of hypoxic environments on cardiomyocytes. However, the effect of anoxic conditions on cardiomyocytes remains largely unexplored. In the present study, we...
3.
Okuyama T, Nagoshi T, Hiraki N, Tanaka T, Oi Y, Kimura H, et al.
Int J Cardiol Heart Vasc
. 2024 Sep;
54:101508.
PMID: 39314921
Background: Unexpectedly low natriuretic peptide (NP) levels in proportion to heart failure severity are often observed in obese individuals. However, the magnitude of NP elevation in response to acute cardiac...
4.
Ito S, Inoue Y, Nagoshi T, Aizawa T, Kashiwagi Y, Morimoto S, et al.
Heart Vessels
. 2024 Sep;
40(3):191-202.
PMID: 39269471
The Geriatric Nutritional Risk Index (GNRI) is a straightforward nutritional risk assessment tool with an established relationship with poor prognosis in patients with heart failure. However, the utility of the...
5.
Kashiwagi Y, Nagoshi T, Tanaka Y, Oi Y, Kimura H, Ogawa K, et al.
Sci Rep
. 2024 Jul;
14(1):16493.
PMID: 39020009
Recently, a mild elevation of the blood ketone levels was found to exert multifaceted cardioprotective effects. To investigate the effect of angiotensin receptor neprilysin inhibitors (ARNIs) on the blood ketone...
6.
Ishibashi R, Koshizaka M, Takatsuna Y, Tatsumi T, Maezawa Y, Shiko Y, et al.
J Diabetes Investig
. 2024 Jun;
15(9):1231-1238.
PMID: 38874094
Aims/introduction: Severe diabetic macular edema (DME) is often resistant to anti-vascular endothelial growth factor therapy. Steroids are particularly effective at reducing edema by suppressing inflammation; they are also used as...
7.
Ishibashi R, Inaba Y, Koshizaka M, Takatsuna Y, Tatsumi T, Shiko Y, et al.
Diabetes Obes Metab
. 2024 Jan;
26(4):1510-1518.
PMID: 38240052
Aim: We assessed the effectiveness of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in reducing the administration frequency of anti-vascular endothelial growth factor (VEGF) agents in patients with diabetic macular oedema (DMO)...
8.
Tanaka Y, Nagoshi T, Takahashi H, Oi Y, Yasutake R, Yoshii A, et al.
iScience
. 2023 Sep;
26(9):107730.
PMID: 37694143
We recently reported that the selective inhibition of urate transporter-1 (URAT1), which is primarily expressed in the kidneys, ameliorates insulin resistance by attenuating hepatic steatosis and improving brown adipose tissue...
9.
Hiraki N, Nagoshi T, Okuyama T, Tanaka T, Oi Y, Kashiwagi Y, et al.
Am J Physiol Heart Circ Physiol
. 2023 Aug;
325(4):H856-H865.
PMID: 37594489
In addition to the classical actions of hemodynamic regulation, natriuretic peptides (NPs) interact with various neurohumoral factors that are deeply involved in the pathophysiology of cardiovascular diseases. However, their effects...
10.
Inoue H, Oya M, Aizawa M, Wagatsuma K, Kamimae M, Kashiwagi Y, et al.
BMC Nephrol
. 2023 Jun;
24(1):196.
PMID: 37386392
Background: Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy...