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| 1 | Treatment effect of Bushen Huayu extract on postmenopausal osteoporosisin vivo显示文摘 | Lu Ouyang Qiufang Zhang Xuzhi Ruan Yibin Feng Xuanbin Wang | 2014 | Experimental and Therapeutic Medicine2014，，6： | 1 |
| 2 | Inducible and cardiac specific PTEN inactivation protects ischemia/reperfusion injury显示文摘 | Hongmei Ruan Jian Li Shuxun Ren Jing Gao Guangping Li Rachel Kim Hong Wu Yibin Wang | | Journal of Molecular and Cellular Cardiology0，，： | 1 |
| 3 | Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs显示文摘Background Pork quality can directly affect customer purchase tendency and meat quality traits have become valu-able in modern pork production.However,genetic improvement has been slow due to high phenotyping costs.In this study,whole genome sequence(WGS)data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction(GBLUP)for meat quality in large-scale crossbred commercial pigs.Results We produced WGS data(18,695,907 SNPs and 2,106,902 INDELs exceed quality control)from 1,469 sequenced Duroc×(Landrace×Yorkshire)pigs and developed a reference panel for meat quality including meat color score,marbling score,L*(lightness),a*(redness),and b*(yellowness)of genomic prediction.The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population.Using different marker density panels derived from WGS data,accuracy differed substantially among meat quality traits,varied from 0.08 to 0.47.Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39%to 75%.We optimized the marker density and found medium-and high-density marker panels are beneficial for the estimation of heritability for meat quality.Moreover,we conducted genotype imputation from 50K chip to WGS level in the same population and found average concord-ance rate to exceed 95%and r^(2)=0.81.Conclusions Overall,estimation of heritability for meat quality traits can benefit from the use of WGS data.This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction. | Zhanwei Zhuang Jie Wu Yibin Qiu Donglin Ruan Rongrong Ding Cineng Xu Shenping Zhou Yuling Zhang Yiyi Liu Fucai Ma Jifei Yang Ying Sun Enqin Zheng Ming Yang Gengyuan Cai Jie Yang Zhenfang Wu | 2023 | Journal of Animal Science and Biotechnology2023，14，5： | 1 |
| 4 | Enhanced saccharide sensing based on simple phenylboronic acidre- ceptor by coupling to Suzuki homocoupling reaction 显示文摘 | Xu Suying Ruan Yibin Luo Xingxing | 2010 | Chemical Communications2010，46，32： | 1 |
| 5 | FGF8-mediated signaling regulates tooth developmental pace during odontogenesis显示文摘The developing human and mouse teeth constitute an ideal model system to study the regulatory mechanism underlying organ growth control since their teeth share highly conserved and well-characterized developmental processes, and their developmental tempo varies notably. In the current study, we manipulated heterogenous recombination between human and mouse dental tissues and demonstrated that the dental mesenchyme dominates the tooth developmental tempo and FGF8 could be a critical player during this developmental process. Forced activation of FGF8 signaling in the dental mesenchyme of mice promoted cell proliferation, prevented cell apoptosis via p38 and perhaps PI3 K-Akt intracellular signaling,and impelled the transition of the cell cycle from G1-to S-phase in the tooth germ, resulting in the slowdown of the tooth developmental pace. Our results provide compelling evidence that extrinsic signals can profoundly affect tooth developmental tempo, and the dental mesenchymal FGF8 could be a pivotal factor in controlling the developmental pace in a non-cell-autonomous manner during mammalian odontogenesis. | Chensheng Lin Ningsheng Ruan Linjun Li Yibin Chen Xiaoxiao Hu YiPing Chen Xuefeng Hu Yanding Zhang | 2022 | Journal of Genetics and Genomics2022，49，1： | 1 |
| 6 | Composition,function,and timing:exploring the early‑life gut microbiota in piglets for probiotic interventions显示文摘Background The establishment of a robust gut microbiota in piglets during their early developmental stage holds the potential for long-term advantageous effects.However,the optimal timeframe for introducing probiotics to achieve this outcome remains uncertain.Results In the context of this investigation,we conducted a longitudinal assessment of the fecal microbiota of 63 piglets at three distinct pre-weaning time points.Simultaneously,we gathered vaginal and fecal samples from 23 sows.Employing 16S rRNA gene and metagenomic sequencing methodologies,we conducted a comprehensive analysis of the fluctuation patterns in microbial composition,functional capacity,interaction networks,and colonization resistance within the gut microbiota of piglets.As the piglets progressed in age,discernible modifications in intestinal microbial diversity,composition,and function were observed.A source-tracking analysis unveiled the pivotal role of fecal and vaginal microbiota derived from sows in populating the gut microbiota of neonatal piglets.By D21,the microbial interaction network displayed a more concise and efficient configuration,accompanied by enhanced colonization resistance relative to the other two time points.Moreover,we identified three strains of Ruminococcus sp.at D10 as potential candidates for improving piglets’weight gain during the weaning phase.Conclusions The findings of this study propose that D10 represents the most opportune juncture for the introduction of external probiotic interventions during the early stages of piglet development.This investigation augments our comprehension of the microbiota dynamics in early-life of piglets and offers valuable insights for guiding forthcoming probiotic interventions. | Jianping Quan Cineng Xu Donglin Ruan Yong Ye Yibin Qiu Jie Wu Shenping Zhou Menghao Luan Xiang Zhao Yue Chen Danyang Lin Ying Sun Jifei Yang Enqin Zheng Gengyuan Cai Zhenfang Wu Jie Yang | 2024 | Journal of Animal Science and Biotechnology2024，15，2： | 0 |
| 7 | Nicotine and menthol independently exert neuroprotective effects against cisplatin-or amyloid-toxicity by upregulating Bcl-xl via JNK activation in SH-SY5Y cells显示文摘Nicotine and menthol,agonists of nicotinic acetylcholine receptor(nAChR)and transient receptor potential melastatin type 8(TRPM8),serve important roles in the prevention of cell death-involved neurodegenerative diseases.However,the potential synergistic effects of nicotine and menthol on anti-apoptotic ability are still uncertain.In the present study,the potential synergistic effects of nicotine and menthol on cisplatin or amyloidβ1-42 induced cell model of the neurodegenerative diseases were explored by assessing cell viability,TNF-αexpression,caspase-3 activation,and the collapse of mitochondrial membrane potential in human SH-SY5Y neuroblastoma cells.Statistical significance was tested using Student’s t-test or one-way ANOVA with post hoc Newman-Keuls test.The results showed that:Firstly,SH-SY5Y cell viability was obviously increased by the treatments with nicotine and menthol.Secondly,nicotine and menthol independently alleviated cisplatin or amyloidβ1-42 induced TNF-αup-regulation.Thirdly,nicotine and menthol abrogated the effect of cisplatin and amyloidβ25-35 on caspase-3 activation.Interestingly,the effect of cisplatin and amyloidβ1-42 on the collapse of mitochondrial membrane potential was efficiently attenuated by nicotine and menthol treatments.Most importantly,the inhibition of c-jun kinase(JNK)activation abolished the effect of cisplatin,and amyloidβ1-42 stimulated Bcl-xl expression.All these findings indicate that nicotine and menthol independently exert neuroprotective effects by upregulating Bcl-xl via JNK activation.Nicotine and menthol augmented Bcl-xl expression and JNK phosphorylation,and thus they are potential therapeutic targets for altering the progress of neurodegenerative diseases. | YIBIN RUAN ZHONGMING XIE QIONG LIU LIXIAO ZHANG XIKUI HAN XIAOYAN LIAO JIAN LIU FENGGUANG GAO | 2021 | BIOCELL2021，45，4： | 0 |
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