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RISS 인기검색어
- 검색키워드
- 저자 : Geng Daoying (검색결과 3 건)
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Zhang, Muhan,Wang, Daoying,Geng, Zhiming,Sun, Chong,Bian, Huan,Xu, Weimin,Zhu, Yongzhi,Li, Pengpeng Asian Australasian Association of Animal Productio 2017 Animal Bioscience Vol.30 No.1
Objective: The aim of this study was to investigate the expression of heat shock protein (HSP) 90, 70, and 60 in chicken muscles and their possible relationship with quality traits of meat. Methods: The breast muscles from one hundred broiler chickens were analyzed for drip loss and other quality parameters, and the levels of heat shock protein (HSP) 90, 70, and 60 were determined by immunoblots. Results: Based on the data, chicken breast muscles were segregated into low (drip loss${\leq}5%$), intermediate (5%<drip loss<9.5%) and high (drip loss${\geq}9.5$) drip loss groups. The expression of HSP90 and HSP60 were significantly lower in the high drip loss group compared to that in the low and intermediate drip loss group (p<0.05), while HSP70 was equivalent in abundance in all groups (p>0.05). Conclusion: Results of this study suggests that higher levels of HSP90 and HSP60 may be advantageous for maintenance of cell function and reduction of water loss, and they could act as potential indicator for better water holding capacity of meat.
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Jiang, Yeqing,Xu, Feng,Huang, Lei,Lu, Gang,Ge, Liang,Wan, Hailin,Geng, Daoying,Zhang, Xiaolong The Korean Neurosurgical Society 2021 Journal of Korean neurosurgical society Vol.64 No.2
Objective : This study aims to investigate the relationship between aneurysm wall enhancement and clinical rupture risks based on the magnetic resonance vessel wall imaging (MR-VWI) quantitative methods. Methods : One hundred and eight patients with 127 unruptured aneurysms were prospectively enrolled from Feburary 2016 to October 2017. Aneurysms were divided into high risk (≥10) and intermediate-low risk group (<10) according to the PHASES (Population, Hypertension, Age, Size of aneurysm, Earlier SAH history from another aneurysm, Site of aneurysm) scores. Clinical risk factors, aneurysm morphology, and wall enhancement index (WEI) calculated using 3D MR-VWI were analyzed and compared. Results : In comparison of high-risk and intermediated-low risk groups, univariate analysis showed that neck width (4.5±3.3 mm vs. 3.4±1.7 mm, p=0.002), the presence of wall enhancement (100.0% vs. 62.9%, p<0.001), and WEI (1.6±0.6 vs. 0.8±0.8, p<0.001) were significantly associated with high rupture risk. Multivariate regression analysis revealed that WEI was the most important factor in predicting high rupture risk (odds ratio, 2.6; 95% confidence interval, 1.4-4.9; p=0.002). The receiver operating characteristic (ROC) curve analysis can efficiently differentiate higher risk aneurysms (area under the curve, 0.780; p<0.001) which have a reliable WEI cutoff value (1.04; sensitivity, 0.833; specificity, 0.67) predictive of high rupture risk. Conclusion : Aneurysms with higher rupture risk based on PHASES score demonstrate increased neck width, wall enhancement, and the enhancement intensity. Higher WEI in unruptured aneurysms has a predictive value for increased rupture risk.
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Zheng Yingyan,Xiao Anling,Yu Xiangrong,Zhao Yajing,Lu Yiping,Li Xuanxuan,Mei Nan,She Dejun,Wang Dongdong,Geng Daoying,Yin Bo 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.8
Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67–6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04–0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03–4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76–0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82–0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.
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