ENZYME-LINKED IMMUNOSORBENT ASSAY OF HUMAN PLACENTA TYPE GLUTATHIONE S-TRANSFERASE AND ITS APPLICATION IN THE DIAGNOSIS OF HEPATOCARCINOMA

GST-π was purified from human placenta and its antiserum was raised in rabbits. The antibody IgC was purified and degraded into Fab' fragment which was conjugated with horseradish peroxidase (HRP) using N-succinimidyl-4-(N-maleimido-methyl) cyclo-hexane-1-carboxylate (SMCC) as crosslinking reagent to produce Fab'-HRP conjugate. A sandwich ELISA was established for the microquantitative determination of GST-π. The sensitivity was 11 pg/tube, which was far more sensitive than the radioimmunoassay so far reported. Using this method, the serum GST-π of 41 cases normal adult was found to be 1.06±0.94 ng/ml. The upper limit of the normal value was 2.6 ng/ml. In 30 cases of primary hepatocarcinoma, the level of serum GST-π was 24.4± 17.4 ng/ml, which was 23 times higher than the normal average value (P<0.01). The positive rate was 90%. In contrast, serum GST-π in 25 cases of chronic hepatitis was determined to be 1.74±1.16 ng/ml, which was not significantly different from the normal value (P>0.05). The pseudo-positive rate was 12.0%.

THE APPLICATION OF SEROTONIN AND 5-HVDROXYINDOLE-3-ACETIC ACID IN THE DIAGNOSIS OF APUDOMA

In our report, the values of whole blood serotonin (5-HT) in 87 apudoma patients (diagnosed by operation and pathology) were summarized. Among them, the levels of urinary 5-hydroxyindole-3-acetic acid (5-HIAA) of 44 patients were also tested. The results showed that both parametres of apudoma patients were higher than those of non-apudoma, post-operative patients of apudoma as well as the normal. The increasing extent of the levels of whole blood 5-HT and urinary 5-HIAA in small intestinal carcihoid was the most obvious one but that of rectum was not. The referable diagnostic values suggested were: 5-HT>130 ng ml, 5-HIAA>30 mg/ 24 hours.

Application of Otopoint Detection to Qualitative Diagnosis of Tumour

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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.

Helicobacter pylori neutrophil-activating protein:From molecular pathogenesis to clinical applications

Helicobacter pylori (H. pylori) neutrophil-activating protein (HP-NAP) was originally identified as a virulence factor of H. pylori for its ability to activate neutrophils to generate respiratory burst by releasing reactive oxygen species. Later on, HP-NAP was also found to be involved in the protection of H. pylori from DNA damage, supporting the survival of H. pylori under oxidative stress. This protein is highly conserved and expressed by virtually all clinical isolates of H. pylori. The majority of patients infected with H. pylori produced antibodies specific for HP-NAP, suggesting its important role in immunity. In addition to acting as a pathogenic factor by activating the innate immunity through a wide range of human leukocytes, including neutrophils, monocytes, and mast cells, HP-NAP also mediates adaptive immunity through the induction of T helper cell type&#x02005;I&#x02005;responses. The pro-inflammatory and immunomodulatory properties of HP-NAP not only make it play an important role in disease pathogenesis but also make it a potential candidate for clinical use. Even though there is no convincing evidence to link HP-NAP to a disease outcome, recent findings supporting the pathogenic role of HP-NAP will be reviewed. In addition, the potential clinical applications of HP-NAP in vaccine development, clinical diagnosis, and drug development will be discussed.

CLINICAL APPLICATION OF MODEL CLRH-A AURICULAR POINT DETECTOR

In this study, we have observed the conditions of clinical application of Model CLRHA Auricular Point Detector from the aspects of accessory diagnosis and effective comparison of acupuncture at positive point and non-positive point. The preliminary results show that using this device to seek reaction point is quick and reliable, the coincidence rate of diagnosis is higher (76. 77 % ); the indix is objective. It can effectively direct doctors to select acupoints correctly for treatment, resulting in improving the therapeutic effect. The results of contrastive observation show that the therapeutic effect of positive point group is much better than that of non-positive point group (P < 0. 001 ). This instrument has following characteristics, i. e. it is simple, safe, small and exquisite to be portable, and economic. It is more suitable to the clinical needs, possessing a higher practical value.

Review of eye diagnosis in traditional Chinese medicine:Modernization and future prospects

This article reviews the basic theories, methods, and clinical applications of eye diagnosis in traditional Chinese medicine(TCM). It introduces cutting-edge methods and applications and explains that the modernization of TCM eye diagnosis includes “equipment-assisted diagnosis” and “artificial intelligencebased diagnosis”. The article also notes that while there are many recent studies of the static attributes of eyes in modern TCM eye diagnosis, modern application research on the dynamic attributes of eyes in TCM diagnosis theory is relatively rare. We propose, therefore, that introducing advanced eye-movement detection technology into TCM clinical diagnosis could help to further modernize TCM eye diagnosis.

Diagnosis of Dental Caries Using Terahertz Technology

Recently,the diagnoses of dental caries and other dental issues are in a queue as only X-ray-based techniques are available in most hospitals around the world.Terahertz(THz)parametric imaging(TPI)is the latest technology that can be applied for medical applications,especially dental caries.This technology is harmless and thus suitable for biological samples owing to the low energy of THz emission.In this paper,a developed TPI system is used to investigate the two-dimensional(2 D)and three-dimensional(3D)images of different samples from human teeth.After analyzing the measured images of human teeth,the results suggest that the THz parametric technology is capable of investigating the inner side structure of the teeth.This technique can be useful in detecting the defects in all types of human and animal teeth.The measurement and analytical calculations have been performed by using the TPI system and MATLAB,respectively,and both are in good agreement.The characteristics of THz waves and their interactions with the tooth samples are summarized.And the available THz-based technologies,such as TPI,and their potential applications of diagnoses are also presented.

A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network

Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization.

湘西刘氏小儿推拿流派的学术特色与应用

湖南湘西刘氏小儿推拿是我国主要小儿推拿流派之一,起源于清朝咸丰同治年间,至今已传承六代,目前是省级非物质文化遗产,国家中医药管理局确定其为国家流派传承工作室。湘西刘氏小儿推拿的学术特色主要有:以五行学说为理论指导,首创了“推五经”疗法;在“推拿八法”的基础上,融合苗医、中医技术,创立了“刘氏小儿推拿十法”和各种复式手法;注重开阖有序,阴阳协调;独创分经脉诊法,分别为单经病脉诊、单侧复合病脉诊、双侧复合病脉诊。目前,湘西刘氏小儿推拿在小儿呼吸、消化、神经系统疾病的临床应用较广泛,且治疗效果良好。

Fault diagnosis and accommodation with flight control applications

This paper reviews some main results and process concerning with fault diagnosis and accommodation with flight control applications.The fault detection and diagnosis including the quantitative and qualitative methods and the fault-tolerant control including passive and active schemes are introduced,respectively.The control designs and developments of the fault diagnosis and accommodation for continuous-time systems and discrete-time systems,the fault diagnosis and fault-tolerant control for switched systems and interconnected systems as well as the applied flight control research including hypersonic vehicle,spacecraft and helicopter systems are summarised.

WoundCareLog APP-A new application to record wound diagnosis and healing

The incidence of chronic wounds has been increasing over the past 20 years.However,the standardized diagnosis and treatment practice of chronic refractory wounds have not been established.In addition,the properties of the wound are characterized by morphology and thus correct description of the wound in medical history collection plays a vital role,which directly affects the definitive diagnosis.To develop more accurate format of clinical history record which can correctly reflect a patient's course and treatment progress,and to standardize the medical history record of chronic refractory wounds,at the national or regional level,we designed the WoundCareLog APP.It acts as a recording and communication tool for wound healing specialists at all levels of medical institutions in China.The WoundCareLog APP is fully compatible to meet the criteria and requirements of conventional medical records by embedding 9 modules.In addition,the demands for morphological description of wounds in wound healing diagnosis and treatment have been fulfilled by enroll of digital imaging technology to overcome the inadequacies of trad让ional medical history records.

The application of CRISPR-Cas in disease diagnosis and treatment

Clustered regularly interspaced short palindromic repeat(CRISPR)has been gaining much attention in the modern medical field and has been widely used for the diagnosis and treatment of diseases in recent years.In this review,we will introduce the application of CRISPR in disease diagnosis and treatment,including its use in detecting pathogens,gene mutations,and genetic diseases,as well as its application in gene therapy for single-gene diseases,cancer,viral infectious diseases,and cardiovascular diseases.Additionally,we will discuss the potential future directions and challenges of CRISPR in the diagnosis and treatment of diseases,and provide a thorough overview of the ways in which CRISPR is used for diagnosing and treating diseases.

Energy inefficiency diagnosis for Android applications: a literature review

Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.

Application of 3-D Sonography in the Diagnosis of Prostatic Diseases.

Transbdominal and transrectal 3-D ultrasonography were carried out simultaneously for 301 cases of prostatic