Peng Qian

Metabolic markers and microecological characteristics of tongue coating in patients with chronic gastritis

BackgroundIn Traditional Chinese Medicine (TCM), tongue diagnosis has been an important diagnostic method for the last 3000 years. Tongue diagnosis is a non-invasive, simple and valuable diagnostic tool. TCM treats the tongue coating on a very sensitive scale that reflects physiological and pathological changes in the organs, especially the spleen and stomach. Tongue coating can diagnose disease severity and determine the TCM syndrome (“Zheng” in Chinese). The biological bases of different tongue coating appearances are still poorly understood and lack systematic investigation at the molecular level.MethodsTongue coating samples were collected from 70 chronic gastritis patients and 20 normal controls. 16S rRNA denatured gradient gel electrophoresis (16S rRNA–DGGE) and liquid chromatography and mass spectrometry (LC–MS) were designed to profile tongue coatings. The statistical techniques used were principal component analysis and partial least squares–discriminate analysis.ResultsTen potential metabolites or markers were found in chronic gastritis patients, including UDP-D-galactose, 3-ketolactose, and vitamin D2, based on LC–MS. Eight significantly different strips were observed in samples from chronic gastritis patients based on 16S rRNA–DGGE. Two strips, Strips 8 and 10, were selected for gene sequencing. Strip 10 sequencing showed a 100% similarity to Rothia mucilaginosa. Strip 8 sequencing showed a 96.2% similarity to Moraxella catarrhalis.ConclusionsChanges in glucose metabolism could possibly form the basis of tongue coating conformation in chronic gastritis patients. The study revealed important connections between metabolic components, microecological components and tongue coating in chronic gastritis patients. Compared with other diagnostic regimens, such as blood tests or tissue biopsies, tongue coating is more amenable to, and more convenient for, both patients and doctors.

Computer-aided disease diagnosis system in TCM based on facial image analysis

Diagnosing diseases through observing the personal face is the Traditional Chinese Medicine (TCM), which is a unique diagnostic method. Clinical experiences in TCM have proved that facial complexion can tell whether the one is healthy. A novel Computer-Aided Disease Diagnosis System (CADDS), acquiring the facial complexion information based on analysing image captured by digital camera, is proposed in this paper. System architecture and image analysis method are carefully illustrated. CADDS was developed for assistant facial-complexion-based diagnosis, which standardised the method to acquire the facial complexion information and avoids depending on the physicians experience and illumination condition. This study is helpful in promoting quantitative-analysis-based diagnosis in TCM.

Application of multilabel learning using the relevant feature for each label in chronic gastritis syndrome diagnosis.

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Analysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning method

ObjectiveTo analyze the diagnostic consistency of Chinese medicine (CM) specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.MethodsUsing self-developed CM clinical scales to collect cases, inquiry information, complexity, tongue manifestation and pulse manifestation were assessed. The number of cases collected was 2,218. Firstly, each case was differentiated by two CM specialists according to the same diagnostic criteria. The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed. Secondly, take the same diagnosis syndromes of two specialists as the results of the cases. According to injury information in the CM scale “yes” or “no” was assigned “1” or “0”, and according to the syndrome type in each case “yes” or “no” was assigned “1” or “0”. CM information data on cardiovascular disease cases were established. We studied CM syndrome classification and identification based on the relevant feature for each label (REAL) learning method, and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5, 10, 15, 20, 30, 50, 70, and 100, respectively.ResultsThe syndromes with good diagnostic consistency were Heart (Xin)-qi deficiency, Heart-yang deficiency, Heart-yin deficiency, phlegm, stagnation of blood and stagnation of qi. Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver (Gan). The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency. A different number of features, such as 5, 10, 15, 20, 30, 40, 50, 70, and 100, respectively, were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy. The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.ConclusionsCM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency. The REAL method fully considers the relationship between syndrome types and injury symptoms, and is suitable for the establishment of models for CM syndrome classification and identification. This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.

Pulse signal analysis of patients with coronary heart diseases using Hilbert-Huang transformation and time-domain method

ObjectiveTo explore characteristic of pulse signal to distinguish patients with coronary heart diseases (CHD) from patients without CHD and healthy adults, and accordingly evaluate the potential role of pulse signal to diagnosis CHD.MethodsTotally 407 patients enrolled from 4 collaborating medical centers were assigned to a CHD group (205 patients) and a non-CHD group (202 patients). The healthy control group (62 adults) enrolled from Shanghai University of Traditional Chinese Medicine. Pulse signals were collected using the synchronous multiplex pulse signal acquisition system. The pulse signals were analyzed and extracted using Hilbert-Huang transformation (HHT) and time-domain, respectively. The time-domain parameters of pulse signal were processed by the analysis of variance (SNK test).ResultsSpecial patterns in the CHD group pulse signal were found in this study: (1) time-domain parameters of pulse signal, h1, h3, h4, h3/h1, ts, t4/t were increased and w was wider; (2) 44% of C2 waves in HHT were chaotic and disordered and 72% of C waves were exhibited irregularly with average amplitude over 10 g-forces, which were all significantly different from controls.ConclusionCharacteristic wave and time-domain parameters of pulse signal were extracted using HHT and time-domain which could be served as a non-invasive approach for assessing patients with CHD.

The Serum Analysis of Dampness Syndrome in Patients with Coronary Heart Disease and Chronic Renal Failure Based on the Theory of “Same Syndromes in Different Diseases”

Aim To analyze the serum metabolites in patients with coronary heart disease (CHD) showing dampness syndrome and patients with chronic renal failure (CRF) showing dampness syndrome and to seek the substance that serves as the underlying basis of dampness syndrome in “same syndromes in different diseases.” Methods. Metabolic spectrum by GC-MS was performed using serum samples from 29 patients with CHD showing dampness syndrome and 32 patients with CRF showing dampness syndrome. The principal component analysis and statistical analysis of partial least squares were performed to detect the metabolites with different levels of expression in patients with CHD and CRF. Furthermore, by comparing the VIP value and data mining in METLIN and HMDB, we identified the common metabolites in both patient groups. Results (1) Ten differential metabolites were found in patients with CHD showing dampness syndrome when compared to healthy subjects. Meanwhile, nine differential metabolites were found in patients with CRF showing dampness syndrome when compared to healthy subjects. (2) There were 9 differential metabolites identified when the serum metabolites of the CHD patients with dampness syndrome were compared to those of CRF patients with dampness syndrome. There were 4 common metabolites found in the serums of both patient groups.

Tongue, complexion and pulse research of Chronic Gastritis based on spectral clustering and modularity

Objective: Exploring the regular pattern in combination characteristics of tongue, complexion and pulse in Traditional Chinese Medicine (TCM) and further discussing the standardization of syndrome differentiation of Chronic Gastritis. Methods: Based on 919 Chronic Gastritis cases collected, we combined the algorithms of SM spectral clustering with modularity to identifying sign groups for the involved 112 signs of Chronic Gastritis based on similarity matrix computed by the method of mutual information. Results: The 112 signs were finally clustered into 13 categories. The results of the study suggested that the locations of the disease involved were spleen, stomach, kidney, etc. The general syndrome elements of disease nature included qi asthenia, yang asthenia and blood stasis, etc. Conclusion: These groups consisting of tongue, complexion and pulse identified by spectral clustering with modularity are consistent with clinical experience and TCM theories. It could provide the reference for the research of standardization of the syndrome and syndrome elements differentiation of TCM.

Study the Effect of Different Traditional Chinese Medicine Treatment which to the Elasticity Modulus of Asthma Rats’ Lung

Elasticity modulus is the most important and most characteristic of the mechanical properties which express the degree of materials. The work aimed to explore the changes of elasticity modulus in asthma rats lung and the effect of different Traditional Chinese Medicine (TCM) treatment, such as lung-diffusing therapy, body resistance-strengthening therapy, integrated therapy of lung-diffusing and body resistance-strengthening, to them. We copied rat asthma model using ovalbumin, then gavaged treatment with three Chinese medicine, After 4 weeks, we researched the changes of modulus elasticity in asthma rats lung based on the biological elasticity modulus detection system, and researched the impact of three TCM treatment to them. We found the elasticity modulus of rats lung in normal group is less than those in other groups, and there is statistically significant difference between normal group and model group, prednisone group, body resistance-strengthening group. There also is statistically significant difference between prednisone group and the combination of lung-diffusing and body resistance-strengthening group. TCM treatment can improve the elastic modulus of asthmatic rats lung, and it probable is one of the mechanisms of the treatment of asthma.

Facial Diagnosis of CHB Clinical Syndromes Based on Computer Technology

To objectively describe the facial features of different clinical syndromes of chronic hepatitis B (CHB) with the computer visual and extraction technologies and analyze the changes of facial indexes in facial diagnosis of Traditional Chinese Medicine (TCM). Methods: We used Chinese medicine face consultation digital detection system to acquire face-on photos and analyze the complexion and gloss features. Results: (1) Compared with the normal people, the patients with CHB have different facial indexes. (2)There are differences for facial indexes of patients with CHB of varied clinical syndromes. Conclusion: We extracted objective facial indexes by computer technology, which is a useful method for clinical diagnosis of disease and provides a new thought and method for TCM diagnosis.

Normalized Acquisition System of the Facial Diagnosis in Traditional Chinese Medicine

Background: Facial diagnosis, an important part of clinical diagnosis in Traditional Chinese Medicine (TCM), is a method used to diagnose the functions of Zang-Fu organs by observing the color, luster, shape, and texture of faces. However, the traditional facial diagnosis mainly relies on doctors’ eyes, languages, and personal clinical experiences. Results are not only determined based on the doctors’ diagnostic skills but also by external conditions such as light and temperature. Without objective evaluation criteria, conducting studies on facial diagnosis to widen its application are difficult. To solve this problem, we should find new methods and technologies to realize the objectification and normalization of diagnosis in TCM. In this article, we discuss the results of our study on the normalized acquisition system of facial diagnosis in TCM. Some of the hardware used includes lights, image acquisition equipment, and dark boxes. The software used includes image acquisition and preprocessing. To the best of our knowledge, this study is the first to propose this system and no similar study has been reported yet. Methods: We initially introduced the hardware and the software that we used in this study. The key technologies in this system, including lighting equipment, facial diagnosis device, facial information acquisition scales, image acquisition, and preprocessing were then introduced one by one. The hardware of this system consists of a light emitting diode (LED), a digital camera, a dark box, and a computer. Each of this hardware has its special function: the LED imitates natural light; the camera records facial images; the dark box imitates the consultation room; and the computer stores the images. The software is used to acquire and adjust the images. The image acquisition system uses the computer to control the opening and the closing of the camera, the photography, and the setup of relevant index to obtain the fully automatic photography of faces and information transmission. Results: The normalized acquisition system of facial diagnosis in TCM was tested according to the following procedures. (1) The lighting uniformity of acquisition windows was tested. Results of the uniformity test showed an even distribution of the illumination on the opening of the dark box for facial complexion collection. (2) TCM experts valuated the acquisition environment. The four features basically tallied among the different diagnoses of doctors, in which the lowest consistency was 75% for the lip color and the highest was 95% for the moist/dry lips. These data showed that the LED the natural light can be used efficiently in the image collecting process. (3) The correction of collected images was tested. The sum of the Euclidean distance between the uncorrected color and the standard color was 1296.345, whereas the sum between the corrected color and the standard color was 403.527. The maximum distances before and after correction were 163.68 and 44.69, respectively. The minimum distances before and after correction were 13.3 and 5.9, respectively. (4) We collected 4050 photos of patients using this system, which was proven to be stable. Conclusions: This article introduces an automated acquisition system of facial diagnosis in TCM. The safety of the system can be ensured. By comparing TCM under natural light and in the dark box, this system meets the requirements of clinical application in all of the collected samples (more than 4050). The acquisition system of facial diagnosis in TCM has also been applied efficiently in a few hospitals.