Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

BACKGROUND: Pulse width, which can reflect qi, blood excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis. METHODS: The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability. RESULTS: The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater. CONCLUSIONS: The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the "guan" location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann-Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APV h1, APV h3, APV h4, APV h3/h1 (P < 0.01), and APV h4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APV h4, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) and the mean APV h3, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APV h4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

Correlation Analysis of Data of Tongue and Pulse in Patients With Disease Fatigue and Sub-health Fatigue.

Fatigue is one of the most common subjective symptoms of abnormal health state, there is still no reliable and stable evaluation method to distinguish disease fatigue and non-disease fatigue. Studies have shown that tongue diagnosis and pulse diagnosis are the reflection of overall state of the body. This study aims to explore the distribution rules and correlation of data of tongue and pulse in population with disease fatigue and sub-health fatigue and provide a new method of clinical diagnosis of fatigue from the perspective of tongue diagnosis and pulse diagnosis. In this study, a total of 736 people were selected and divided into healthy controls (n = 250), sub-health fatigue group (n = 242), and disease fatigue group (n = 244). TFDA-1 tongue diagnosis instrument and PDA-1 pulse diagnosis instrument were used to collect tongue image and sphygmogram, simple correlation analysis and canonical correlation analysis were used to analyze the correlation of tongue and pulse data about the two groups of fatigue people. The study had shown that tongue and pulse data could provide a certain reference for the diagnosis of different types of fatigue, tongue and pulse data in disease fatigue and sub-health fatigue population had different distribution rules, and there was a simple correlation and canonical correlation in the disease fatigue population, the coefficient of canonical correlation was .649 (P <.05).

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.

OBJECTIVE: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. METHODS: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. RESULTS: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. CONCLUSIONS: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

Female serotonin transporter-knockout rat: A potential model of irritable bowel syndrome.

Irritable bowel syndrome (IBS) is a common functional gastrointestinal disease. Although visceral hypersensitivity (VH) and disturbed gastrointestinal motility are typical pathophysiological features of IBS, the pathological mechanisms underlying this disease remain unclear. Serotonin system abnormalities are considered to play an important role in the pathomechanisms of IBS. Here, we hypothesize that similar alterations, including VH and colonic motility, induced by serotonin transporter (SERT) knockout result from altered serotonin signaling. We sought to determine the molecular mechanism underlying VH and colonic dysmotility induced by SERT knockout. We found that female SERT (slc6a4)-knockout (KO; ie, slc6a4-/- ) rats exhibited lower pain pressure thresholds (PPTs) than wild-type (WT; ie, slc6a4+/+ ) rats in response to colorectal distension (CRD). Significantly increased fecal pellet output and reduced concentration of serum tryptophan were observed in the female SERT KO rats. The concentrations of 5-hydroxytryptamine (5-HT) in platelet-rich plasma (PRP) and serum in SERT KO rats were lower than those in WT rats, but the numbers of enterochromaffin cells (ECs) and the concentrations of 5-HT in colon of SERT KO rats were higher than those of WT rats. Finally, increased expression levels of 5-HT1B receptors, 5-HT2C receptors, 5-HT3A receptors, 5-HT3B receptors, 5-HT6 receptors, 5-HT7 receptors, and glycosylated dopamine transporters (DATs) were found in the female SERT KO rats. We concluded that alterations in the serotonin system induced by the knockout of slc6a4 might result in VH and accelerated gastrointestinal motility in female SERT KO rats, which can be used as an animal model of IBS.

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.

BACKGROUND: Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health. OBJECTIVE: Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics. METHODS: Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness. RESULTS: Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94. CONCLUSIONS: Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.

BACKGROUND: Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. METHODS: In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. RESULTS: Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. CONCLUSIONS: The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.

BACKGROUND: Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis. METHODS: Tongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue. RESULTS: The results showed that all the four classifiers over "Tongue & Pulse" joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively. CONCLUSION: This study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

Shugan Decoction Alleviates Colonic Dysmotility in Female SERT-Knockout Rats by Decreasing M3 Receptor Expression.

BACKGROUND: Irritable bowel syndrome (IBS) is a functional gut disease characterized by visceral hypersensitivity and gut motor dysfunction. Serotonin (5-hydroxytryptamine, 5-HT) is an important enteric neurotransmitter. High levels of 5-HT aggravate IBS symptoms. The serotonin reuptake transporter (SERT) is a membrane-embedded transporter involved in IBS pathogenesis that plays an important role in regulating 5-HT signaling. AIM: We investigated whether gut motor function was altered in SERT-knockout (SERT-KO) rats. Additionally, we sought to determine whether Shugan decoction (SGD), a clinically experienced prescription for the treatment of IBS, exerts regulatory effects on intestinal motility in SERT-KO rats, and attempted to identify the mechanisms involved. METHOD: SERT-KO rats were produced by transcription activator-like effector nuclease (TALEN) technology. Fecal pellet output was measured for ten consecutive days to estimate distal colonic motility. Small intestinal motility was measured by charcoal-meal experiments. The colonic and small intestinal muscle contractile activities were measured by organ bath study. Western blot was used to analyze the muscarinic receptor expression in colon tissue. RESULT: Compared with that in wild-type (WT) rats, the defecation amount, amplitude of spontaneous contraction, and the tension of ACh-induced contraction of colonic longitudinal smooth muscle in SERT-KO rats were significantly increased. The expression of muscarinic receptor subtype-3 (M3R) in the colons of SERT-KO rats was also elevated. SGD can decrease defecation of SERT-KO rats. Moreover, SGD reduced the amplitude of spontaneous contraction, the frequency and tension of ACh-induced contraction of colonic longitudinal smooth muscle, and the expression of M3R in the colon in SERT-KO rats. CONCLUSIONS: SERT-KO rats showed increased defecation accompanied by enhanced colonic motility and M3R expression. The findings suggest that SGD modifies colonic dysmotility and reduces defecation in SERT-KO rats by down-regulating M3R expression in the colon.

The Efficacy and Potential Mechanisms of Chinese Herbal Medicine on Irritable Bowel Syndrome.

Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders (FBD), which is characterized by recurrent abdominal pain, abdominal bloating/distention associated with defecation or changed bowel habits. Currently, there is no evidence of obvious anatomic or physiologic abnormalities on the routine diagnostic examinations. There are multiple pathological factors involved in IBS responsible for its heterogeneous nature, although the exact etiology and pathology of IBS are not well known and it is disappointed to develop biomarkers for this disorder. These factors including low-grade inflammation, activation of immune system, changed intestinal microorganism, small intestinal bacterial overgrowth (SIBO), malabsorption of bile acid (BA), increased number of mast cells (MCs). Current pharmacologic treatment for IBS focuses on alleviation of its symptoms, but not on the elimination of its cause. Although there are a lot of conventional chemical medicines for IBS available, due to the limited clinical benefits, high medical expenses and severe side effects, many IBS patients have turned to alternative medicine, particularly Chinese herbal medicine (CHM). Chinese herbal therapies have been used for thousand years in eastern Asia and have been provided that they are effective in relieving symptoms among IBS patients. Generally, traditional Chinese herbal formulae (CHF) consisting of CHM can be easily adjusted in accordance with concrete conditions, which means the treatment is based on syndrome differentiation and varied from individual to individual. Meanwhile, CHF/CHM containing many different ingredients may act on multiple sites/pathways with potential synergistic effects and chemical reactions.