1.Stimulatory effect of puerarin on bone formation through co-activation of nitric oxide and bone morphogenetic protein-2/mitogen-activated protein kinases pathways in mice.
Shiow-Yunn SHEU ; Chia-Chung TSAI ; Jui-Sheng SUN ; Ming-Hong CHEN ; Man-Hai LIU ; Man-Ger SUN
Chinese Medical Journal 2012;125(20):3646-3653
BACKGROUNDEstrogen deficiency results in loss of bone mass. Phytoestrogens are plant-derived non-steroidal compounds with estrogen-like activity that bind to estrogen receptors. The main aim of this study was to investigate the effect of the phytoestrogen puerarin on adult mouse osteoblasts.
METHODSOsteoblast cells were harvested from 8-month old female imprinting control region (ICR) mice. The effects of puerarin stimulation on the proliferation, differentiation and maturation of osteoblasts were examined. The production of nitric oxide (NO) and the expression of bone morphogenetic protein-2 (BMP-2), SMAD4, mitogen-activated protein kinases (MAPK), core binding factor α1/ runt-related transcription factor 2 (Cbfa1/Runx2), osteoprotegerin (OPG), and receptor activator of NF-κB ligand (RANKL) genes were analyzed. The activation of signal pathways was further confirmed by specific pathway inhibitors.
RESULTSThe osteoblast viability reached its maximum at 10(-8) mol/L puerarin. At this concentration, puerarin increases the proliferation and matrix mineralization of osteoblasts and promotes NO synthesis. With 10(-8) mol/L puerarin treatment, BMP-2, SMAD4, Cbfa1/Runx2, and OPG gene expression were up-regulated, while the RANKL gene expression is down-regulated. Concurrent treatment involving the (bone morphogenetic protein) BMP antagonist Noggin or the NOS inhibitor L-NAME diminishes puerarin induced cell proliferation, Alkaline phosphatase (ALP) activity, NO production, as well as the BMP-2, SMAD4, Cbfa1/Runx2, OPG, and RANKL gene expression.
CONCLUSIONSIn this in vitro study, we demonstrate that puerarin is a bone anabolic agent that exerts its osteogenic effects through the induction of BMP-2 and NO synthesis, subsequently regulating Cbfa1/Runx2, OPG, and RANKL gene expression. This effect may contribute to its induction of osteoblast proliferation and differentiation, resulting in bone formation.
Animals ; Bone Morphogenetic Protein 2 ; genetics ; physiology ; Cell Differentiation ; drug effects ; Cell Survival ; drug effects ; Cells, Cultured ; Female ; Isoflavones ; pharmacology ; MAP Kinase Signaling System ; physiology ; Mice ; Mice, Inbred ICR ; Nitric Oxide ; physiology ; Osteoblasts ; drug effects ; metabolism ; Osteogenesis ; drug effects ; Phytoestrogens ; pharmacology ; RANK Ligand ; genetics
2.Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program
Ming-Ying LU ; Chung-Feng HUANG ; Chao-Hung HUNG ; Chi‐Ming TAI ; Lein-Ray MO ; Hsing-Tao KUO ; Kuo-Chih TSENG ; Ching-Chu LO ; Ming-Jong BAIR ; Szu-Jen WANG ; Jee-Fu HUANG ; Ming-Lun YEH ; Chun-Ting CHEN ; Ming-Chang TSAI ; Chien-Wei HUANG ; Pei-Lun LEE ; Tzeng-Hue YANG ; Yi-Hsiang HUANG ; Lee-Won CHONG ; Chien-Lin CHEN ; Chi-Chieh YANG ; Sheng‐Shun YANG ; Pin-Nan CHENG ; Tsai-Yuan HSIEH ; Jui-Ting HU ; Wen-Chih WU ; Chien-Yu CHENG ; Guei-Ying CHEN ; Guo-Xiong ZHOU ; Wei-Lun TSAI ; Chien-Neng KAO ; Chih-Lang LIN ; Chia-Chi WANG ; Ta-Ya LIN ; Chih‐Lin LIN ; Wei-Wen SU ; Tzong-Hsi LEE ; Te-Sheng CHANG ; Chun-Jen LIU ; Chia-Yen DAI ; Jia-Horng KAO ; Han-Chieh LIN ; Wan-Long CHUANG ; Cheng-Yuan PENG ; Chun-Wei- TSAI ; Chi-Yi CHEN ; Ming-Lung YU ;
Clinical and Molecular Hepatology 2024;30(1):64-79
Background/Aims:
Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
Methods:
We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.
Results:
The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.
Conclusions
Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.