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.
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 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.
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.
目的 建立测定胎盘灌流液中格列苯脲浓度的高效液相色谱（HPLC）检测方法。方法 采用的色谱柱为Symmetry Shield RP C18（150 mm×4.6 mm，5 μm），柱温40℃，流动相为NaH2PO4缓冲盐（25 mmol/L，pH值5.2）︰乙腈=1︰1；内标为格列齐特，流速1.0 mL/min，检测波长228 nm，采用液-液萃取预处理方法测定胎盘灌流液中格列苯脲的浓度。 结果 格列苯脲浓度线性范围为2.0～25.0 μg/mL，线性方程为：y=0.226x+0.002，r=0.999 9 （n=6），日内相对标准偏差（RSD）＜3.1%，日间RSD＜9.5%，方法学回收率为95.32%～103.35%。 结论 HPLC检测方法灵敏、简便，可用于胎盘灌流液中格列苯脲浓度的检测。
Objective To study the comparability of liver function results between the two detection systems. Methods Based on the NCCLS EP9-A document, 8 fresh serum samples were collected daily for the assay of 10 routine liver function parameters by utilizing the Olympus AU1000 and Backman CX7 detection systems respectively. The results were recorded over 5 days consecutively. Linearity equation and relative deviations were calculated. The comparability between the results obtained from different detection systems was evaluated according to the systematic error at medical decision level of CLIA, 88. Results The paired t-test showed that the results of the fresh serum samples had no significant difference between the two detection systems (Pgt;0.05). Except that the systematic error of albumin at low medical decision level exceeded the allowable error, all the other systematic errors at medical decision level were acceptable. Conclusion The results of liver function are comparable between the two detection systems, and the systematic errors between the two detection systems are clinically acceptable.
Clinical studies had demonstrated that early diagnosis of lesion could significantly reduce the risk of cancer. Magneto-acoustic-electrical tomography (MAET) is expected to become a new detection method due to its advantages of high resolution and high contrast. Based on thinking of modular design, a low-cost, digital magneto-acoustic conductivity detection system was designed and implemented in this study. The theory of MAET using chirp continuous wave excitation was introduced. The results of homogeneous phantom experiment with 0.5% NaCl clearly showed that the conductivity curve of homogeneous phantom was highly consistent with the actual physical size, which indicated that the chirp excitation theory in our proposed system was correct and feasible. Besides, the resolution obtained by 1 000 μs sweep time was better than that obtained by 500 μs and 1 500 μs, which means that sweep time is an important factor affecting the detection resolution of the conductivity. The same result was obtained in the experiments carried out on homogeneous phantoms with different concentrations of NaCl, which demonstrated the repeatability of our proposed MAET system.
Antimicrobial resistance is a rigorous health issue around the world. Because of the short turn-around-time and broad pathogen spectrum, culture-independent metagenomic next-generation sequencing (mNGS) is a powerful and highly efficient tool for clinical pathogen detection. The increasing question is whether mNGS is practical in the prediction of antimicrobial susceptibility. This review summarizes the current mNGS-based antimicrobial susceptibility testing technologies. The critical determinants of mNGS-based antibacterial resistance prediction have been comprehensively analyzed, including antimicrobial resistance databases, sequence alignment tools, detection tools for genomic antimicrobial resistance determinants, as well as resistance prediction models. The clinical challenges for mNGS-based antibacterial resistance prediction have also been reviewed and discussed.
Neurosyphilis is a group of clinical syndromes in which Treponema pallidum invades the nervous system and causes damage to the meninges, blood vessels, brain parenchyma or spinal cord. At present, there is no highly specific and sensitive method for the diagnosis of neurosyphilis, and its diagnosis mainly depends on clinical manifestations, abnormal cerebrospinal fluid and the comprehensive judgment of clinicians. Current studies show that some cytokines and chemokines are promising for laboratory detection of neurosyphilis. This article reviews the research progress of neurosyphilis from the aspects of traditional laboratory testing, polymerase chain reaction testing, cytokine and chemokine testing, and existing diagnostic criteria for neurosyphilis, in order to provide a reference for clinical testing and follow-up research.