scholarly journals Ginsenoside Rb1 does not halt osteoporotic bone loss in ovariectomized rats

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0202885 ◽  
Author(s):  
JiaXin Bei ◽  
XinLe Zhang ◽  
JingKai Wu ◽  
ZhuoQing Hu ◽  
BiLian Xu ◽  
...  
PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0163131 ◽  
Author(s):  
Shuo Huang ◽  
Liangliang Xu ◽  
Yuxin Sun ◽  
Sien Lin ◽  
Weidong Gu ◽  
...  

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0172462
Author(s):  
Shuo Huang ◽  
Liangliang Xu ◽  
Yuxin Sun ◽  
Sien Lin ◽  
Weidong Gu ◽  
...  

Biomaterials ◽  
2021 ◽  
pp. 120819
Author(s):  
Jiaul Hoque ◽  
Yu-Ru V. Shih ◽  
Yuze Zeng ◽  
Hunter Newman ◽  
Nivedita Sangaj ◽  
...  

2011 ◽  
Vol 212 (2) ◽  
pp. 179-186 ◽  
Author(s):  
Rana Samadfam ◽  
Malaika Awori ◽  
Agnes Bénardeau ◽  
Frieder Bauss ◽  
Elena Sebokova ◽  
...  

Peroxisome proliferator-activated receptor (PPAR) γ agonists, such as pioglitazone (Pio), improve glycemia and lipid profile but are associated with bone loss and fracture risk. Data regarding bone effects of PPARα agonists (including fenofibrate (Feno)) are limited, although animal studies suggest that Feno may increase bone mass. This study investigated the effects of a 13-week oral combination treatment with Pio (10 mg/kg per day)+Feno (25 mg/kg per day) on body composition and bone mass parameters compared with Pio or Feno alone in adult ovariectomized (OVX) rats, with a 4-week bone depletion period, followed by a 6-week treatment-free period. Treatment of OVX rats with Pio+Feno resulted in ∼50% lower fat mass gain compared with Pio treatment alone. Combination treatment with Pio+Feno partially prevented Pio-induced loss of bone mineral content (∼45%) and bone mineral density (BMD; ∼60%) at the lumbar spine. Similar effects of treatments were observed at the femur, most notably at sites rich in trabecular bone. At the proximal tibial metaphysis, concomitant treatment with Pio+Feno prevented Pio exacerbation of ovariectomy-induced loss of trabecular bone, resulting in BMD values in the Pio+Feno group comparable to OVX controls. Discontinuation of Pio or Feno treatment of OVX rats was associated with partial reversal of effects on bone loss or bone mass gain, respectively, while values in the Pio+Feno group remained comparable to OVX controls. These data suggest that concurrent/dual agonism of PPARγ and PPARα may reduce the negative effects of PPARγ agonism on bone mass.


Nutrients ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 5853-5870 ◽  
Author(s):  
Zhiguo Zhang ◽  
Lihua Xiang ◽  
Dong Bai ◽  
Wenlai Wang ◽  
Yan Li ◽  
...  

2015 ◽  
Vol 230 (9) ◽  
pp. 2184-2201 ◽  
Author(s):  
Chao Fu ◽  
Dong Xu ◽  
Chang-Yuan Wang ◽  
Yue Jin ◽  
Qi Liu ◽  
...  

2015 ◽  
Vol 18 (12) ◽  
pp. 1349-1356 ◽  
Author(s):  
Jonggun Kim ◽  
Hyung Kwan Kim ◽  
Saehun Kim ◽  
Ji-Young Imm ◽  
Kwang-Youn Whang

2021 ◽  
Author(s):  
Wanyu Li ◽  
Jun Xu ◽  
Shunan Zhang ◽  
Han Guo ◽  
Jianqi Sun ◽  
...  

Abstract Background: As the gold standard for clinical osteoporosis diagnosis, bone mineral density has significant limitations in bone strength assessment and fracture risk prediction. The purpose of this study is to explore a new osteoporotic bone quality evaluation criteria from both diagnosis site selection and bone strength prediction. Methods: Ovariectomized rats with different intensity swimming therapy were investigated in this study. The lumbar vertebrae and femurs of all the rats were scanned by synchrotron radiation computed tomography. Bone microstructure analysis and finite element analysis were combined to obtain bone microstructure parameters and estimated bone strength. And the sensitivity of different skeletal sites to therapy was explored. An elastic network regression model was established to predict bone strength by integrating additional bone microstructure characteristics besides bone mass.Results: Histomorphometry analysis showed that swimming therapy could reduce the risk of osteoporosis of lumbar vertebrae and femur and suggested that the femur might be a more suitable site for osteoporosis diagnosis and efficacy evaluation than the lumbar vertebrae. The average coefficient of determination and average root mean squared error of our predictive model were 0.774 and 0.110. Bland-Altman analysis showed that our model could be a good alternative to the finite element method. Conclusions: The present study developed a machine learning model for prediction of bone strength of osteoporosis model based on synchrotron x-ray imaging and demonstrated that different skeletal sites had different sensitivity to therapy, which is of great significance for the early diagnosis of osteoporosis, the prevention of fractures and the monitoring of therapy.


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