ObjectiveTo assess the detection performance of the hematopoietic stem cell enumeration kit developed by BD Biosciences.MethodsCord blood samples were prepared using a hematopoietic stem cell enumeration kit developed by BD Biosciences and Stem-Kit reagents from Beckman Coulter. CD34+ cells were enumerated using a BD FACSCanto instrument and FACSDiva software.ResultsA total of 519 samples were analyzed in this study. The hematopoietic stem cell enumeration kit developed by BD Biosciences yielded absolute counts of CD34-positive cells that were on average 8.7% lower than Beckman Coulter Stem-Kit reagents (range: −5.7% to−14.7%). The BD Biosciences kit yielded relative counts that were on average 9.9% higher compared with Beckman Coulter Stem-Kit reagents (range: −2.1% to +13.8%). The intraclass correlation coefficients for absolute and relative counts of CD34-positive cells were 0.9967 (95% confidence interval [CI]: 0.9961–0.9972) and 0.9512 (95% CI: 0.9423–0.9587) for the BD Biosciences and Beckman Coulter kits, respectively.ConclusionsThe hematopoietic stem cell enumeration kit developed by BD Biosciences can be used to enumerate CD34-positive stem cells from cord blood samples. 相似文献
目的:探讨仑伐替尼与索拉非尼作为一线分子靶向药物治疗晚期肝细胞癌的临床疗效.方法:通过检索PubMed、Web of Science、Cochrane、中国知网、万方等数据库中有关仑伐替尼与索拉非尼作为一线分子靶向药物治疗晚期肝细胞癌临床疗效对比的所有文献.根据制定的纳入和排除文献标准,由2位参研者进行文献筛选及数据整... 相似文献
In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.
