Objective To summarize the new progress of circulating tumor cell markers and their clinical application. Methods The related literatures about the detection and clinical application of circulating tumor cell markers in recent years were reviewed. Resuts Epithelial markers, other markers such as human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and immuno-checkpoint genes have also become a useful means to detect the ability of malignant metastasis of circulating tumor cells (CTCs). Moreover, the epithelial-to-mesenchymal transition (EMT) and cancer stem cells (CSCs) have also received increasing attention as important CTCs markers owing to their roles in the biological progression of metastasis. Conclusions Through the detection of peripheral blood CTCs, to identify early cancer, monitoring the metastasis and recurrence of cancer and its treatment on all has the very good guiding significance. Signs of circulating tumor cells has great clinical application value in diagnosis and treatment of tumor and prognosis.
Transcranial magneto-acoustic electrical stimulation (TMAES) is a novel method of brain nerve regulation and research, which uses induction current generated by the coupling of ultrasound and magnetic field to regulate neural electrical activity in different brain regions. As the second special envoy of nerve signal, calcium plays a key role in nerve signal transmission. In order to investigate the effect of TMAES on prefrontal cortex electrical activity, 15 mice were divided into control group, ultrasound stimulation (TUS) group and TMAES group. The TMAES group received 2.6 W/cm2 and 0.3 T of magnetic induction intensity, the TUS group received only ultrasound stimulation, and the control group received no ultrasound and magnetic field for one week. The calcium ion concentration in the prefrontal cortex of mice was recorded in real time by optical fiber photometric detection technology. The new object recognition experiment was conducted to compare the behavioral differences and the time-frequency distribution of calcium signal in each group. The results showed that the mean value of calcium transient signal in the TMAES group was (4.84 ± 0.11)% within 10 s after the stimulation, which was higher than that in the TUS group (4.40 ± 0.10)% and the control group (4.22 ± 0.08)%, and the waveform of calcium transient signal was slower, suggesting that calcium metabolism was faster. The main energy band of the TMAES group was 0?20 Hz, that of the TUS group was 0?12 Hz and that of the control group was 0?8 Hz. The cognitive index was 0.71 in the TMAES group, 0.63 in the TUS group, and 0.58 in the control group, indicating that both ultrasonic and magneto-acoustic stimulation could improve the cognitive ability of mice, but the effect of the TMAES group was better than that of the TUS group. These results suggest that TMAES can change the calcium homeostasis of prefrontal cortex nerve clusters, regulate the discharge activity of prefrontal nerve clusters, and promote cognitive function. The results of this study provide data support and reference for further exploration of the deep neural mechanism of TMAES.
The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.
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.
Tuberculosis caused by Mycobacterium tuberculosis is the leading infectious killer posing a major public health threat. The clinical manifestations of ocular tuberculosis are highly heterogeneous. Currently, the diagnosis of ocular tuberculosis still heavily relies on comprehensive clinical judgment and response to anti-tuberculosis therapy. Tuberculosis-specific T-cell detection quantifies the intensity of antigen-specific T-cell responses, providing indirect evidence for the diagnosis of tuberculosis infection. It has become a key auxiliary examination in the diagnosis and management of ocular tuberculosis but must be closely integrated with clinical manifestations and imaging features. A positive result suggests the involvement of a tuberculous immune response but cannot alone confirm a diagnosis of ocular tuberculosis. Future efforts should integrate T-SPOT.TB testing with other diagnostic tools, standardize diagnostic procedures, and explore the mechanisms linking T-cell subset functions with the intraocular immune microenvironment. Further elucidation of the relationship between T-cell responses and ocular tuberculosis phenotypes will help advance personalized treatment.
Blood biochemical indicators are an important basis for the diagnosis and treatment by doctors. The performance of related instruments, the qualification of operators, the storage method and time of blood samples and other factors will affect the accuracy of test results. However, it is difficult to meet the clinical needs of rapid detection and early screening of diseases with currently available methods. Point-of-care testing (POCT) is a new diagnostic technology with the characteristics of instant, portability, accuracy and efficiency. Microfluidic chips can provide an ideal experimental reaction platform for POCT. This paper summarizes the existing detection methods for common biochemical indicators such as blood glucose, lactic acid, uric acid, dopamine and cholesterol, and focuses on the application status of POCT based on microfluidic technology in blood biochemistry. It also summarizes the advantages and challenges of existing methods and prospects for development. The purpose of this paper is to provide relevant basis for breaking through the technical barriers of microfluidic and POCT product development in China.
As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
Medical magnetic nanoparticles are nano-medical materials with superparamagnetism, which can be collected in the tumor tissue through blood circulation, and magnetic particle imaging technology can be used to visualize the concentration of magnetic nanoparticles in the living body to achieve the purpose of tumor imaging. Based on the nonlinear magnetization characteristics of magnetic particles and the frequency characteristics of their magnetization, a differential detection method for the third harmonic of magnetic particle detection signals is proposed. It was modeled and analyzed, to study the nonlinear magnetization response characteristics of magnetic particles under alternating field, and the spectral characteristics of magnetic particle signals. At the same time, the relationship between each harmonic and the amount of medical magnetic nanoparticle samples was studied. On this basis, a signal detection experimental system was built to analyze the spectral characteristics and power spectral density of the detected signal, and to study the relationship between the signal and the excitation frequency. The signal detection experiment was carried out by the above method. The experimental results showed that under the alternating excitation field, the medical magnetic nanoparticles would generate a spike signal higher than the background sensing signal, and the magnetic particle signal existed in the odd harmonics of the detected signal spectrum. And the spectral energy was concentrated at the third harmonic, that is, the third harmonic magnetic particle signal detection that meets the medical detection requirement could be realized. In addition, the relationship between each harmonic and the particle sample volume had a positive growth relationship, and the detected medical magnetic nanoparticle sample volume could be determined according to the relationship. At the same time, the selection of the excitation frequency was limited by the sensitivity of the system, and the detection peak of the third harmonic of the detection signal was reached at the excitation frequency of 1 kHz. It provides theoretical and technical support for the detection of medical magnetic nanoparticle imaging signals in magnetic particle imaging research.
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.