Objective To evaluate the utility of collagen-gel droplet embedded-culture drug sensitivity test (CD-DST) in pancreatic carcinoma cell by compared with WST-8. Methods The chemosensitivity to 5-fluorouracil (5-FU), gemzar (GEM) and oxaliplatin (OXA) of pancreatic adenocarcinoma cells SW1990, PCT-3 and ASPC-1 were tested by WST-8 and CD-DST respectively. Results In a certain living cell number range (500-10 000), there was a linear correlation (r=0.991 1, P＜0.05) between the integral optical density in CD-DST and the cell number. The inhibition ratios of three kinds of cell growth tested by CD-DST were higher than those tested by WST-8 (P＜0.05). The results of drug chemosensitivity to 5-FU， GEM and OXA detected by two methods were uniform. Conclusion The CD-DST can be used to assay the drug chemosensitivity in vitro for pancreatic carcinoma.
In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.
Based on the capacitance coupling principle, we studied a capacitive way of non-contact electrocardiogram (ECG) monitoring, making it possible to obtain ECG on the condition that a patient is habilimented. Conductive fabric with a good electrical conductivity was used as electrodes. The electrodes fixed on a bed sheet is presented in this paper. A capacitance comes into being as long as the body gets close to the surface of electrode, sandwiching the cotton cushion, which acts as dielectric. The surface potential generated by heart is coupled to electrodes through the capacitance. After being processed, the signal is suitable for monitoring. The test results show that 93.5% of R wave could be detected for 9 volunteers and ECG with good signal quality could be acquired for 2 burnt patients. Non-contact ECG is harmless to skin, and it has advantages for those patients to whom stickup electrodes are not suitable. On the other hand, it is convenient to use and good for permanent monitoring.
In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2 048×2 048, the method of CPU needs 5 2641 ms, but the GPU needs only 4 384 ms.
In the evaluation of tear film stability based on corneal topography, a pretreatment algorithm for tear film video was proposed for eye movement, eyelash reflection and background interference. First, Sobel operator was used to detect the blur image. Next, the target image with highlighted ring pattern was obtained by the morphological open operation performed on the grayscale image. Then the ring pattern frequency of the target image was extracted through the Hough circle detection and fast Fourier transform, and a band-pass filter was applied to the target image according to the ring pattern frequency. Finally, binarization and morphological closed operation were used for the localization of the ring pattern. Ten tear film videos were randomly selected from the database and processed frame by frame through the above algorithm. The experimental results showed that the proposed algorithm was effective in removing the invalid images in the video sequence and positioning the ring pattern, which laid a foundation for the subsequent evaluation of tear film stability.
目的:观察脑出血急性期血凝动态变化规律,为治疗提供理论依据。方法:检测36例脑出血患者病后第1天、第3天、第5天、第10天、凝固启动时间(CST)、凝固达峰值时间(MCT)、最大凝固程度(MCE)、凝血酶原(FⅡ)、纤维蛋白原(Fg)和44例健康体检者的相同指标。结果:与对照组比较,脑出血组病后第1天、第3天、第5天,第10天的MCE、Fg、FⅡ增高(Plt;0.05)。结论:脑出血病后10天血凝显著增高,提示脑出血患者急性期应慎用止血剂和清除脑血肿。
In this study, loop-mediated isothermal amplification (LAMP) assay in conjunction with calcein for visualized detection of Mycobacterium tuberculosis (MTB) was established. Firstly, four LAMP primers were designed according to the region of 16S rDNA sequences of MTB. Secondly, clinical sputum samples were collected, decontaminated and their DNA was extracted. Thirdly, standard MTB strains were used to evaluate the specificity and sensitivity of LAMP. At the same time, electrophoresis was used for products detection and calcein was used for visualized verification. At last, Chi-squared test function in SPSS 17.0 software was used for consistency evaluation of LAMP assay as compared with the gold standard (culture method). Results showed that there was no nonspecific amplification appeared in the specificity assay and the detection limit was 10 copies/tube in the sensitivity assay. In addition, visualized method by calcein had a comparable sensitivity with that of electrophoresis method. After evaluation of clinical practicability, the sensitivity of LAMP was calculated as 94.74% and the specificity was 90%, respectively. And Chi-squared test showed that LAMP and culture method had no statistic difference, and the two methods were in good consistency (P>0.05). In conclusion, LAMP assay introduced in our study has the characteristics of high efficiency and visualized detection so that this technique has great application prospects in the resource-limited environment, such as work field and primary care hospitals.
ObjectiveTo investigate a more convenient and safe sampling method for viral nucleic acid detection of coronavirus disease 2019.MethodsAn oropharyngeal swab and nasopharyngeal swab were simultaneously taken from 100 patients with coronavirus disease 2019 in a hospital in Wuhan. Then the efficacies of two sampling methods were compared on the positive rates of viral nucleic acid detection.ResultsThe positive rate for SARS-CoV-2 was 54% in oropharyngeal swabs, while 89% positive in nasopharyngeal swabs. There was a significant difference in the detection rate between oropharyngeal swab and nasopharyngeal swab (χ2=3.850 4, P=0.049 7).ConclusionsThe positive rate for nucleic acid testing from nasopharyngeal swabs are significantly better than that from oropharyngeal swabs. Therefore, sampling by nasopharyngeal swabs, rather than oropharyngeal swabs, should be chosen as the preferred virological screening method for patients with coronavirus disease 2019.
The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.