Neural Network-Based Study about Correlation Model between TCM Constitution and Physical Examination Indexes Based on 950 Physical Examinees

Purpose. To establish the correlation model between Traditional Chinese Medicine (TCM) constitution and physical examination indexes by backpropagation neural network (BPNN) technology. A new method for the identification of TCM constitution in clinics is proposed, which is trying to solve the problem like shortage of TCM doctor, complicated process, low efficiency, and unfavorable application in the current TCM constitution identification methods. Methods. The corresponding effective samples were formed by sorting out and classifying the original data which were collected from physical examination indexes and TCM constitution types of 950 physical examinees, who were examined at the affiliated hospital of Chengdu University of TCM. The BPNN algorithm was implemented using the C# programming language and Google’s AI library. Then, the training group and the test (validation) group of the effective samples were, respectively, input into the algorithm, to complete the construction and validation of the target model. Results. For all the correlation models built in this paper, the accuracy of the training group and the test group of entire physical examination indexes-constitutional-type network model, respectively, was 88% and 53%, and the error was 0.001. For the other network models, the accuracy of the learning group and the test group and error, respectively, was as follows: liver function (31%, 42%, and 11.7), renal function (41%, 38%, and 6.7), blood routine (56%, 42%, and 2.4), and urine routine (60%, 40%, and 2.6). Conclusions. The more the physical examination indexes are used in training, the more accurate the network model is established to predict TCM constitution. The sample data used in this paper showed that there was a relatively strong correlation between TCM constitution and physical examination indexes. Construction of the correlation model between physical examination indexes and TCM constitution is a kind of study for the integration of Chinese and Western medicine, which provides a new approach for the identification of TCM constitution, and it may be expected to avoid the existing problem of TCM constitution identification at present

Influencing factors analysis of dynamic change of TCM constitution based on multiple methods

Objective: This study aimed to explore the influencing factors of dynamic changes in traditional Chinese medicine (TCM) constitution based on general statistics, Apriori-DEMATEL algorithm, and DoWhy causal inference framework methods. Methods: Dynamic collection of TCM constitution identification data was conducted from the population aged 18 − 60, containing collection time and constitution type, and 11 constitution influencing factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, stress level, living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. General statistical analysis was used to analyze the relative percentage of corresponding influencing factors of different types of constitution changes, the Apriori-DEMATEL algorithm was used to analyze the correlation between 11 constitution influencing factors such as dietary habit and constitution changes, and the DoWhy causal inference framework was used to analyze the causality between dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, explore the frequency of constitution type transformation-change factors, and determine the key influencing factors causing dynamic changes in constitution type. Results: After preprocessing, 13536 valid data points were obtained. Based on the Apriori-DEMATEL algorithm, the factors were divided into six original factors including dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level, and five result factors including living environment, work/life calamity, family atmosphere, business trip frequency, and overtime situation. Combining with general statistics, we found that among the original factors, changes in dietary habit, sleeping habit, sleeping duration, and stress level had a greater impact on other factors. In the process of constitution conditioning, attention should be paid to these four factors to maintain constitution balance. Among the five result factors, the absolute values of work/life calamity and family atmosphere were relatively large, indicating that these two factors were easily influenced by other factors. The dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level have higher centrality in changes, indicating that these six factors had important in constitution changes. According to the statistical frequency of constitution changes corresponding to each factor, we found that the changes of these six factors accounted for a large proportion of the constitution transformation frequency among Qi deficiency constitution, balanced constitution, and allergic constitution, indicating that the changes of these six factors played an important role in the changes of the three constitution types. Combined with the results of the Apriori-DEMATEL algorithm, and DoWhy causal inference framework analysis, it was inferred that dietary habit and sleeping duration indirectly lead to constitution changes by affecting the changes of other factors. Conclusion: This study explored the influencing factors of dynamic changes in TCM constitution from the perspective of dynamic data and multiple analysis methods, and the results showed that the changes of dietary habit, sleeping habit, sleeping duration, exercise habit, emotion state, and stress level had a great impact on the changes of Qi deficiency constitution, balanced constitution and allergic constitution. Attention should be paid to the changes of these six factors in daily life, and corresponding improvement plans should be formulated to reduce the probability of transforming into biased constitution. Our study also provided data support and objective analysis reference for the analysis of influencing factors of dynamic changes in TCM constitution types

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation

BackgroundNowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy. ObjectiveThis study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases. MethodsThe relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data. ResultsThe data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%. ConclusionsThe main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM

Herb-symptom analysis of Erchen decoction combined with Xiebai powder formula and its mechanism in the treatment of chronic obstructive pulmonary disease

Background: In recent years, the incidence and mortality rates of chronic obstructive pulmonary disease (COPD) have increased significantly. Erchen Decoction combined with Xiebai Powder (ECXB) formula is mainly used to treat lung diseases in traditional Chinese medicine (TCM). However, the active ingredients of ECXB formula, COPD treatment-related molecular targets, and the mechanisms are still unclear. To reveal its underlying action of mechanism, network pharmacology, molecular docking, and molecular dynamic (MD) simulation approaches were used to predict the active ingredients and potential targets of ECXB formula in treating COPD. As a result, Herb-Symptom analysis showed that the symptoms treated by both TCM and modern medicine of ECXB formula were similar to the symptoms of COPD. Network pharmacology identified 170 active ingredients with 137 targets, and 7,002 COPD targets was obtained. 120 targets were obtained by intersection mapping, among which the core targets include MAPK8, ESR1, TP53, MAPK3, JUN, RELA, MAPK1, and AKT1. Functional enrichment analysis suggested that ECXB formula might exert its treat COPD pharmacological effects in multiple biological processes, such as cell proliferation, apoptosis, inflammatory response, and synaptic connections, and ECXB formula treated COPD of the KEGG potential pathways might be associated with the TNF signaling pathway, cAMP signaling pathway, and VEGF signaling pathway. Molecular docking showed that ECXB formula treatment COPD core active ingredients can bind well to core targets. MD simulations showed that the RELA-beta-sitosterol complex and ESR1-stigmasterol complex exhibited higher conformational stability and lower interaction energy, further confirming the role of ECXB formula in the treatment of COPD through these core components and core targets. Our study analyzed the medication rule of ECXB formula in the treatment of COPD from a new perspective and found that the symptoms treated by both TCM and modern medicine of ECXB formula were similar to the symptoms of COPD. ECXB formula could treat COPD through multi-component, multi-target, and multi-pathway synergistic effects, providing a scientific basis for further study on the mechanism of ECXB formula treatment of COPD. It also provides new ideas for drug development

A Flexible Temperature Sensor Integrated at Needle Tip for In Situ Acupoint Temperature Monitoring

Temperature can reflect vital activities, and researchers have attempted to guide Chinese medicine diagnosis and treatment by observing acupoint temperature changes. Integrating a temperature sensor at the needle tip enables in situ acupoint temperature measurement. However, the sensor needles for acupoint temperature monitoring designed in previous studies were fabricated by manually soldering thermistor beads and metal wires, making mass production difficult. In this work, using MEMS manufacturing technology, a flexible temperature sensor that can be integrated at the needle tip is proposed and can be mass-produced on silicon wafers. The sensor uses a Pt thermistor as the temperature-sensing element and has a slender flexible structure with dimensions of 125 μm width by 3.2 cm length. As the sensor is inserted into a hollow needle, the Pt thermistor is glued to the needle tip. In the temperature range of 30 °C to 50 °C, the fabricated temperature sensor has a sensitivity of 5.00 Ω∙°C−1, a nonlinearity of ±0.39%FS, and a repeatability error of ±2.62%FS. Additionally, the sensor has been applied to in vivo acupoint temperature monitoring experiments in rats and demonstrated good performance, suggesting its promise for future research on acupoint temperature

Design and Performance Evaluation of a Home-Based Automatic Acupoint Identification and Treatment System

Acupuncture can be effective in relieving pain and reducing drug dependence and abuse caused by chronic pain. However, the cumulative effect of acupuncture requires patients to undergo multiple treatments, which limits its use due to time and economic costs. Currently, existing acupuncture devices include acupoint identification and acupoint stimulation devices. The identification and stimulation functions are separated for these two classes of devices. They were unable to locate the acupoint automatically and required a professional acupuncturist to perform the procedure. Although acupuncture robots that can simultaneously identify the acupoints and initiate treatment are under development, the technology is not yet mature that it is currently not available for general use. To address these issues, we developed an automatic system that combines the identification of acupoints and the administration of treatment. The system is designed to assist users without any knowledge of acupuncture in the management of chronic pain at home. We conducted tests to evaluate the performance of the system in terms of resistance detection accuracy, electroacupuncture accuracy, and acupoint identification test. The results demonstrate that the system significantly improves the accuracy of locating acupoints, indicating its suitability for daily home use as an effective approach to managing chronic pain

Family exposure and the impact of containment measures to children with coronavirus disease 2019 outside Hubei, China: a cross-sectional study.

Background In response to the ongoing epidemic of coronavirus disease 2019 (COVID-19), China has carried out restrictive disease containment measures across the country. Methods In this cross-sectional study, we collected demographic and epidemiological data of 376 laboratory-confirmed cases of COVID-19 among children younger than 18 years of age. Using descriptive statistics and odds ratios, we described the odds of exposure outside the family after the implementation of control measures compared to before. Results Children diagnosed on or after February 4, 2020, had a lower odds of exposure to COVID-19 outside of the family compared to those diagnosed before February 3, 2020 (OR =0.594, 95% CI: 0.391 to 0.904). In the stratified analysis, children aged 0 to 5 years had the lowest odds of exposure outside of the family (OR =0.420, 95% CI: 0.196 to 0.904) compared to the other age groups assessed. Conclusions Our study on the children infected with COVID-19 as well as their exposure within family provided evidence that the implementation of containment measures was effective in reducing the odds of exposure outside of the family, especially for preschool children. Continuation of these efforts, coupled with tailored prevention and health education messaging for younger aged children, may help to reduce the transmission of COVID-19 among children until other therapeutic interventions or vaccines are available

Characterization of microRNAs in Mud Crab <i>Scylla paramamosain</i> under <i>Vibrio parahaemolyticus</i> Infection

<div><p>Background</p><p>Infection of bacterial <i>Vibrio parahaemolyticus</i> is common in mud crab farms. However, the mechanisms of the crab’s response to pathogenic <i>V. parahaemolyticus</i> infection are not fully understood. MicroRNAs (miRNAs) are a class of small noncoding RNAs that function as regulators of gene expression and play essential roles in various biological processes. To understand the underlying mechanisms of the molecular immune response of the crab to the pathogens, high-throughput Illumina/Solexa deep sequencing technology was used to investigate the expression profiles of miRNAs in <i>S</i><i>. paramamosain</i> under <i>V. parahaemolyticus</i> infection.</p> <p>Methodology/Principal Findings</p><p>Two mixed RNA pools of 7 tissues (intestine, heart, liver, gill, brain, muscle and blood) were obtained from <i>V. parahaemolyticus</i> infected crabs and the control groups, respectively. By aligning the sequencing data with known miRNAs, we characterized 421 miRNA families, and 133 conserved miRNA families in mud crab <i>S</i><i>. paramamosain</i> were either identical or very similar to existing miRNAs in miRBase. Stem-loop qRT-PCRs were used to scan the expression levels of four randomly chosen differentially expressed miRNAs and tissue distribution. Eight novel potential miRNAs were confirmed by qRT-PCR analysis and the precursors of these novel miRNAs were verified by PCR amplification, cloning and sequencing in <i>S</i><i>. paramamosain</i>. 161 miRNAs (106 of which up-regulated and 55 down-regulated) were significantly differentially expressed during the challenge and the potential targets of these differentially expressed miRNAs were predicted. Furthermore, we demonstrated evolutionary conservation of mud crab miRNAs in the animal evolution process.</p> <p>Conclusions/Significance</p><p>In this study, a large number of miRNAs were identified in <i>S</i><i>. paramamosain</i> when challenged with <i>V. parahaemolyticus</i>, some of which were differentially expressed. The results show that miRNAs might play some important roles in regulating gene expression in mud crab under <i>V. parahaemolyticus</i> infection, providing a basis for further investigation of miRNA-modulating networks in innate immunity of mud crab.</p> </div