Integration of 1H-NMR-Based Metabolomics Approach with Traditional Chinese Pulse Diagnosis to Evaluate the Qi-Enhancing Activity of Herbal Tea

Human metabolism is intricately linked to an individual’s health status. Regardless of living habits, it will be reflected in the metabolic characteristics of urine. The utilization of the 1H NMR-based metabolomics method has enabled examine the metabolomic changes in urine under various physiology conditions, providing valuable insights into metabolites. In this particular study, volunteers were divided into two groups based on the strength of their spleen pulses, using the pulse diagnosis method employed in traditional Chinese medicine. Subsequently, their urine samples were analyzed, revealing notable variances in urea, creatinine, citric acid, succinic acid, trimethylamine-N-oxide (TMAO), alanine, hippuric acid, and glycine between the two groups. Interestingly, individuals with weak spleen pulses showed significant improvements after consuming herbal tea. Furthermore, we conducted LC-MS analysis on herbal tea and performed adenosine triphosphate (ATP) activity tests on the C2C12 mouse skeletal muscle cell line. The results indicated that within a reasonable concentration range, exposure to herbal tea led to an increase in the mitochondrial ATP production capacity of C2C12 cells. These findings shed light on the relationship between traditional Chinese medicine pulse diagnosis and urine metabolites, highlighting their potential as non-invasive and straightforward health assessment indicators. They can aid in the preliminary determination of necessary dietary and lifestyle changes to enhance overall bodily health.

Doctors’Dilemma in Auspicious Pulse Diagnosis Represented in Ming-Qing Fiction

Auspicious pulse diagnosis/pregnancy diagnosis in traditional Chinese medicine involves such issues as medical skills,narrative skills,family decency,and ethics.It is an excellent case for the exploration of ethical dilemmas in traditional Chinese medical practice.The early classical medical texts such as Su Wen(Basic Questions)and Ling Shu Jing(Spiritual Pivot Canon)provide a principle-based ethical guide for doctor-patient communication,while popular fiction such as Hong Lou Meng(A Dream of Red Mansions),Yu Mu Xing Xin Bian(Stories:Entertain to Enlighten),and Feng Yue Meng(Courtesans and Opium)in the Ming and Qing dynasties present literary examples for solving ethical dilemmas.This article will analyze these texts from three perspectives.First,the doctors in the text were subject to gender order and other delicate etiquette and customs,therefore were unable to make the diagnosis without embarrassing the patients and jeopardizing family decency.Second,the narrator tends to attribute pregnancy misdiagnosis to three reasons:incomplete patient information,doctors’poor narrative competence,and doctors’corrupted medical ethics.Finally,the Ming-Qing fiction proposes three methods to solve this moral dilemma:clear pulse reading,tactful speech,and taboo challenging.This discussion of moral dilemmas in pregnancy diagnosis in traditional Chinese medical practice can be used as a reference for the localization of narrative medicine.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features

ABSTRACT Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility of novel types of signal and to develop appropriate feature representation for diagnosis. In this paper, we present a study on computerized pulse diagnosis based on blood flow velocity signal. First, the blood flow velocity signal is collected using Doppler ultrasound device and preprocessed. Then, by locating the fiducial points, we extract the spatial features of blood flow velocity signal, and further present a Hilbert-Huang transform-based method for spectrum feature extraction. Finally, support vector machine is applied for computerized pulse diagnosis. Experiment results show that the proposed method is effective and promising in distinguishing healthy people from patients with cho- lecystitis or nephritis.

The value of pulse wave velocity in the diagnosis of coronary heart disease

Objective: Using receiver operating characteristics (ROC) curve to evaluate the value of pulse wave velocity (PWV) in the diagnosis of coronary heart disease (CHD). Methods: By using coronary angiography as golden diagnostic standard of CHD, 218 patients were divided into both CHD group (n=121) and non-CHD group (n = 97). All these patients received PWV test. The efficacy of PWV of each artery segments in the diagnosis of CHD was evaluated by ROC curve. The sensitivity and specificity were calculated with the golden diagnostic standard of CHD. Results:The PWV of right carotid to femoral artery (Rc-f), left carotid to femoral artery (Lc-f), right radial to carotid artery (Rc-r), left radial to carotid artery (Lc-r) in CHD group were significantly higher than that of non-CHD group (9. 31±1. 75 vs 7.60±1.59, P<0. 01; 9. 02±1.71 vs 7. 52±1.50, P<0. 01; 8. 69±1. 37 vs 8. 00±1. 27, P<0. 01; 8.52±1. 03 vs 8. 03±1. 2, P<0. 01 respectively). However, the PWV of both right and left femoral to ankle artery (Rf-a and Lf-a) had no significant differences between the two groups. We then compared the area under curve (AUC) of each ROC(AUCROC) of PWV of Rc-f, Lc-f Rc-r and Lc-r to evaluate their diagnostic efficacy for CHD. We found that AUCROC of Rc-f PWV was the biggest (AUCROC = 0. 818), at the peak point of its ROC curve, the PWV was 8. 32 m/s. PWV>8. 32 m/s of Rc-f could predict the presence of CHD with a sensitivity of 79% and specificity of 77%. Conclusion: The PWV of Rc-f, Lc-f, Rc-r, Lc-r are significantly higher in CHD group than that in non-CHD group, and PWV of Rc-f is the most accurate in the detection of CHD. The PWV>8. 32 m/s of RC-F is a valuable predictor of CHD.

A new interpretation of TCM pulse diagnosis based on quantum physical model of the human body

Following the quantum theory-based physical model of the human body,a new interpretation of the traditional Chinese medicine(TCM)principle of"Cunkou reads viscera"is presented.Then,a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes,and to quantitatively capture the degree of YinYang attributes of 13 pulse shapes.Furthermore,the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity,to characterize the hierarchical resonance between the human body and the environment.The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state,and diagnose its health state according to the pulse deviation from its equilibrium state,to truly achieve the high level of TCM:"knowing the normal state and reaching the change".

Nonlinear Response Analysis for Insulation Diagnosis of Water-tree Aged 10 kV XLPE Cable Using Pulse Voltage

The diagnosis of water trees of cable insulation is of great importance as the water-treeing is a primary cause of aging breakdown for the middle voltage cables. In this paper, it is described how the water-tree-aged 10 kV XLPE cables were diagnosed. The cables were subjected to electrical stress of 5.9 kV/mm and a thermal load cycle in a curved water-filled tube for 3, 6 and 12 months of aging in accor- dance with the accelerated water-tree test method. The aged cables were used as the samples for water-tree diagnosis. First, the water-tree degraded cable, was charged by a DC voltage, and then the cable was grounded while a pulse voltage was applied to it for releasing the space charge trapped in the water trees. The amount of the space charge, which corresponds to the deterioration degree of the water trees, was calculated. The effects of DC voltage amplitude, pulse voltage repetition rate and aging conditions on the amount of the space charge were studied. Obtained results show that the amount of the space charge has a positive correlation with the applied DC voltage and the ag- ing time of the cables, and that a peak value of space charge appears with the increase of the pulse voltage repetition rate. An optimum pulse voltage repetition rate under which the space charge can be released rapidly is obtained. Furthermore, the releasing mechanism of space charge by the pulse voltage is discussed. Accumulated results show that the presented method has a high resolution for the diagnosis of water tree degradation degree and is expected to be applied in practice in future.

A correlation between pulse diagnosis of human body and health monitoring of structures

The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.

Spectroscopic diagnosis of plasma in atmospheric pressure negative pulsed gas-liquid discharge with nozzle-cylinder electrode

The plasma characteristics of a gas-liquid phase discharge reactor were investigated by optical and electrical methods.The nozzle-cylinder electrode in the discharge reactor was supplied witha negative nanosecond pulsed generator.The optical emission spectrum diagnosis revealed that OH（A2∑＋ → X2Π,306–309 nm）,N32（CΠ→B3Πg,337 nm）,O（3p5p→3s-5s-0,777.2 nm）and O（3p3p→3s3s0,844.6 nm）were produced in the discharge plasma channels.The electron temperature（Te）was calculated from the emission relative intensity ratio between the atomic O 777.2 nm and 844.6 nm,and it increased with the applied voltage and the pulsed frequency and fell within the range of 0.5–0.8 e V.The gas temperature（Tg）that was measured by Lifbase was in a range from 400 K to 600 K.

Automated Pulse-Based Diagnosis: Role of TIM Diagnostic Features

Emanated from the idea of reinvestigating ancient medical system of Ayurveda—Traditional Indian Medicine (TIM), our recent study had shown significant applications of analysis of arterial pulse waveforms for non-invasive diagnosis of cardiovascular functions. Here we present results of further investigations analyzing the relation of pulse-characteristics with some clinical and pathological parameters and other features that are of diagnostic importance in Ayurveda.

Ultrafast two-dimensional x-ray imager with temporal fiducial pulses for laser-produced plasmas

It is challenging to make an ultrafast diagnosis of the temporal evolution of small and short-lived plasma in two dimensions. To overcome this difficulty, we have developed a well-timed diagnostic utilizing an x-ray streak camera equipped with a row of multi-pinhole arrays. By processing multiple sets of one-dimensional streaked image data acquired from various pinholes, we are capable of reconstructing high-resolution two-dimensional images with a temporal resolution of 38 ps and a spatial resolution of 18 μm. The temporal fiducial pulses accessed from external sources can advance the precise timing and accurately determine the arrival time of the laser. Moreover, it can correct the nonlinear sweeping speed of the streak camera. The effectiveness of this diagnostic has been successfully verified at the Shenguang-II laser facility,providing an indispensable tool for observing complex physical phenomena, such as the implosion process of laser-fusion experiments.

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform

This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.

A model for automatic identification of human pulse signals

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition

Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

MODELING AND ANALYSIS OF HUMAN PULSE

Pulse diagnosis is a special method of diagnosing patient’s disease.Amathematical model of the pulse is presented in this paper.The feature parameters ofthe sphygmogram are extracted based on the signal model.The discrimination andprincipal analysis are employed to identify the pulses.The results are matching withthose of traditional methods.

Comparative study of pulsed breakdown processes and mechanisms in self-triggered four-electrode pre-ionized switches

This work investigates the pulsed breakdown processes and mechanisms of self-triggered preionized switches with a four-electrode structure in nitrogen through intensified charge coupled device photographs.The diameter of the trigger plane hole mainly determines the switch’s electric field distribution.Two configurations with minimum and maximum trigger plane holes are adopted for comparison.In the switch with a minimum trigger plane hole,the maximum electric field distributes at the surfaces of the main electrodes.Although charged particles in the triggering spark channel cannot drift out,homogeneous discharges can be stimulated from both the cathode and anode surfaces through ultraviolet illumination.Two sub-gaps are likely to break down simultaneously.In the switch with a maximum trigger plane hole,the maximum electric field locates near the trigger electrodes.Discharges in both sub-gaps initiate from the trigger electrodes in the form of a positive or negative streamer.Due to the lower breakdown voltage and electric field threshold for discharge initiation,the cathode side sub-gap breaks down first.The analysis of two extreme examples can be referenced in the future design and improvement of self-triggered four-electrode switches with different trigger electrode structures.

A Protocol for Digitalized Collection of Traditional Chinese Medicine(TCM)Pulse Information Using Bionic Pulse Diagnosis Equipment

Pulse diagnosis equipment used in Traditional Chinese Medicine(TCM)has long been developed for collecting pulse information and in TCM research.However,it is still difficult to implement pulse taking automatically or efficiently in clinical practice.Here,we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment,which ensures high efficiency,reliability and data integrity of pulse diagnosis information.A four-degree-of-freedom pulse taking platform together with a wrist bracket can satisfy the spatial positioning and angle requirements for individually adaptive pulse acquisition.Three-dimensional reconstruction of a wrist surface and an image localization model are combined to provide coordinates of the acquisition position and detection direction automatically.Three series elastic joints can not only simulate the TCM pulse taking method that“Three fingers in a straight line,the middle finger determining the‘Guan’location and finger pulp pressing on the radial artery,”but also simultaneously carry out the force-controlled multi-gradient pressing process.In terms of pulse information integrity,this proposed protocol can generate rich pulse information,including basic individual information,pulse localization distribution,multi-gradient dynamic pulse force time series,and objective pulse parameters,which can help establish the fundamental data sets that are required as the pulse phenotype for subsequent comprehensive analysis of pulse diagnosis.The implementation of this scheme is beneficial to promote the standardization of the digitalized collection of pulse information,the effectiveness of detecting abnormal health status,and the promotion of the fundamental and clinical research of TCM,such as TCM pulse phenomics.

Micro-piezoelectric pulse diagnoser and frequency domain analysis of human pulse signals

Background:The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse.However,pulses can vary markedly from person to person.Further,pulse diagnosis is difficult to learn and requires one-on-one teaching.Methods:To address this problem,we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis.Our pulse diagnoser was composed of a piezoelectric transducer,differential amplifier,data acquisition instrument,and a Matlab analysis program.The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer.The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis.Simulations were performed to assess the factors that affected the pulse,including phase shifts of reflected pulse waves (generate sub-peaks in pulses),inconsistent heart rates (deform pulse waves),the vessel stiffness (alter harmonic frequencies of the pulses),and the wave amplitudes (determined by the pulse strength).Results:By comparing a published report and our simulation findings,we characterized the pulse types and the effects of various factors,and then applied the findings to study actual pulses in patients.Three types of pulses were determined from the frequency spectrumchoppy pulse (Se mai) without apparent harmonic peaks,the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency,and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency.A normal pulse and a slippery pulse were differentiated by a phase shift,but not by assessing the frequency spectrum.Conclusion:These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain.Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.

基于最优频段循环脉冲指数谱的轴承故障诊断方法

针对滚动轴承多故障冲击共振频带的非一致性及相互交叠影响问题,提出了一种基于循环脉冲指数(CPI)谱的轴承多故障同步诊断方法。该方法引入短时脉冲峰值矩的变异系数对轴承故障冲击进行量化表征,结合冗余提升小波包对轴承故障信号进行频带塔式分解与频带信号CPI计算,构建了故障信号的CPI比值谱图(CPIRgram);根据CPI比值最大原则对轴承故障信号的最优共振频带进行自适应选择,并采用最优频段循环脉冲谱对轴承各故障特征频率进行了统一表征。仿真与故障试验分析结果表明,本文方法无需故障先验知识与分解参数的优化设置,在强噪声及随机瞬态干扰情况下,也能够准确地对多故障特征频率进行同步检测,检测出的故障频率与其理论值误差均小于1.6 Hz,且对故障冲击强度大小及冲击模式变化具有较好的鲁棒性,有较好的应用前景。