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.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

BACKGROUND: Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. OBJECTIVE: The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. METHODS: Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. RESULTS: There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. CONCLUSION: The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

[Advances in traditional Chinese medicine treatment of non-alcoholic fatty liver disease via farnesoid X receptor].

Non-alcoholic fatty liver disease(NAFLD) is a chronic metabolic condition with rapidly increasing incidence, becoming a public health issue of worldwide concern. Studies have shown that farnesoid X receptor(FXR)-based modulation of downstream targets can improve liver function and metabolic status in the patients with NAFLD and may be a potential drug target for treating this di-sease. Great progress has been achieved in the development of drugs targeting FXR for the treatment of NAFLD. A number of studies have explored the traditional Chinese medicine and their active ingredients for the treatment of NAFLD via FXR considering the high safety and efficacy and mild side effects. This paper systematically describes the mechanism of traditional Chinese medicines in the treatment of NAFLD via FXR and the downstream targets, aiming to provide precise targets for the drug development and clinical treatment of NAFLD.

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup.

Background: Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods: A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results: The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion: The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

A multi-step approach for tongue image classification in patients with diabetes.

BACKGROUND: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility. OBJECTIVE: Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis. METHODS: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. RESULTS: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%. CONCLUSIONS: The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

A Study of Logistic Regression for Fatigue Classification Based on Data of Tongue and Pulse.

Methods: The Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect the tongue image and sphygmogram of the subhealth fatigue population (n = 252) and disease fatigue population (n = 1160), and we mainly analyzed the tongue and pulse characteristics and constructed the classification model by using the logistic regression method. Results: The results showed that subhealth fatigue people and disease fatigue people had different characteristics of tongue and pulse, and the logistic regression model based on tongue and pulse data had a good classification effect. The accuracies of models of healthy controls and subhealth fatigue, subhealth fatigue and disease fatigue, and healthy controls and disease fatigue were 68.29%, 81.18%, and 84.73%, and the AUC was 0.698, 0.882, and 0.924, respectively. Conclusion: This study provided a new noninvasive method for the fatigue diagnosis from the perspective of objective tongue and pulse data, and the modern tongue diagnosis and pulse diagnosis have good application prospects.

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.

Acupuncture combined with western medicine for the treatment of hypertension: A protocol for an updated systematic review and meta-analysis.

BACKGROUND: Hypertension is a kind of cardiovascular syndrome with the main clinical manifestation of continuous increase of systemic arterial blood pressure. Hypertension coexists with other cardiovascular risk factors and is an important risk factor for cardiovascular and cerebrovascular diseases. Acupuncture is an important part of Traditional Chinese Medicine intervention. The antihypertensive effect of acupuncture on hypertension is based on the neuroendocrine system, characterized by multichannel and multitarget. This study aims to provide latest and updated proof of systematic review to assess the effectiveness and safety of acupuncture for hypertension. METHODS: We will systematically search 9 databases from their inceptions to February 2021. Only randomized controlled trials of acupuncture combined with western medicine in the treatment of hypertension will meet the inclusion criteria. The main outcome measures we focus on include clinical efficacy, syndrome efficacy, Traditional Chinese Medicine syndrome score, diastolic and systolic blood pressure changes, blood pressure variability, heart rate variability, pulse rate variability, and adverse reactions. The research screening, data extraction, and risk of bias assessment will be employed by 2 reviewers independently, and disagreement will be decided by a third senior reviewer. The Revman 5.3 software will be used for meta-analysis. The confidence of proof will be rated adopting grading of recommendations assessment, development and evaluation tool and methodological quality of this research will be assessed using assessment of multiple systematic reviews-2 and risk of bias in systematic reviews. The publication quality will be evaluated by preferred reporting items for systematic reviews and meta-analyses (PRISMA). RESULTS: This systematic review (SR) will provide evidence-based medical evidence for hypertension therapy by acupuncture combined with western medicine and we will submit the findings of this SR for peer-review publication. CONCLUSIONS: This SR will provide latest and updated summary proof for assessing the effectiveness and safety of acupuncture for hypertension. REGISTRATION NUMBER: INPLASY 202150047.

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.

Implementation intention training for prospective memory in schizophrenia: A 3-month follow-up study.

The beneficial effect of implementation intentions (II) on prospective memory (PM) deficits in patients with schizophrenia has been reported. However, these studies were limited to brief interventions such that the transfer and long-term effects of II training remains unclear. This study examined whether a 10-session II programme could improve PM performance, social functioning and functional capacity in patients with schizophrenia immediately after training and at 3-month follow-up. Patients with schizophrenia (n = 42) recruited from the community were randomly assigned to II training (n = 21) or treatment as usual (TAU) (n = 21). Participants in the II group learned the verbal and imagery component of II and were encouraged to apply these strategies in their daily lives. We found that the II group performed better than the TAU group on computer-based PM tasks and a daily life PM task (telephone call at specified date and time) at post-treatment and at 3-month follow-up. The II group also exhibited better working ability than the TAU group at post-treatment. Our results suggest that the II intervention programme may have lasting beneficial effects in PM performance and significant transfer effects to functional capacity in schizophrenia patients.

Analysis of Pulse Signals Based on Array Pulse Volume.

OBJECTIVE: To collect and analyze multi-dimensional pulse diagram features with the array sensor of a pressure profile system (PPS) and study the characteristic parameters of the new multi-dimensional pulse diagram by pulse diagram analysis technology. METHODS: The pulse signals at the Guan position of left wrist were acquired from 105 volunteers at the Shanghai University of Traditional Chinese Medicine. We obtained the pulse data using an array sensor with 3×4 channels. Three dimensional pulse diagrams were constructed for the validated pulse data, and the array pulse volume (APV) parameter was computed by a linear interpolation algorithm. The APV differences among normal pulse (NP), wiry pulse (WP) and slippery pulse (SP) were analyzed using one-way analysis of variance. The coefficients of variation (CV) were calculated for WP, SP and NP. RESULTS: The APV difference between WP and NP in the 105 volunteers was statistically significant (6.26±0.28 vs. 6.04±0.36, P=0.048), as well as the difference between WP and SP (6.26±0.28 vs. 6.07±0.46, P=0.049). However, no statistically significant difference was found between NP and SP (P=0.75). WP showed a similar CV (4.47%) to those of NP (5.96%) and SP (7.58%). CONCLUSION: The new parameter APV could differentiate between NP or SP and WP. Accordingly, APV could be considered an useful parameter for the analysis of array pulse diagrams in Chinese medicine.

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.

OBJECTIVE: In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). METHOD: The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. RESULT: Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. CONCLUSION: Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.

Pulse Wave Cycle Features Analysis of Different Blood Pressure Grades in the Elderly.

BACKGROUND AND OBJECTIVE: The same range of blood pressure values may reflect different vascular functions, especially in the elderly. Therefore, a single blood pressure value may not comprehensively reveal cardiovascular function. This study focused on identifying pulse wave features in the elderly that can be used to show functional differences when blood pressure values are in the same range. METHODS: First, pulse data were preprocessed and pulse cycles were segmented. Second, time domain, higher-order statistics, and energy features of wavelet packet decomposition coefficients were extracted. Finally, useful pulse wave features were evaluated using a feature selection and classifier design. RESULTS: A total of 6,075 pulse wave cycles were grouped into 3 types according to different blood pressure levels and each group was divided into 2 categories according to a history of hypertension. The classification accuracy of feature selection in the 3 groups was 97.91%, 95.24%, and 92.28%, respectively. CONCLUSION: Selected features could be appropriately used to analyze cardiovascular function in the elderly and can serve as the basis for research on a cardiovascular risk assessment model based on Traditional Chinese Medicine pulse diagnosis.

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images.

Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

Use of metal-enhanced fluorescence spectroscopy for detection of polycyclic aromatic hydrocarbons in diesel oil emulsions in artificial seawater.

In this study, we employed the metal-enhanced fluorescence (MEF) method for the detection of polycyclic aromatic hydrocarbons (PAHs) in diesel oil emulsions in artificial seawater. Silver nanoparticles with an average diameter of 80 nm were immobilized onto silanized quartz as MEF substrates. Excited by illumination at 355 nm, a 4.6-fold increase in the fluorescence signals of PAHs was recorded when MEF substrates were used. The finite-difference time-domain (FDTD) method was used to provide mechanistic insights into the fluorescence enhancement. Monitoring water pollution has environmental significance and MEF spectroscopy is finding direct applications in this field.

A novel strain of Beet western yellows virus infecting sugar beet with two distinct genotypes differing in the 5'-terminal half of genome.

The complete genomic sequences of two distinct Beet western yellows virus (BWYV) genotypes infecting sugar beet in Beijing, named as BWYV-BJ(A) and BWYV-BJ(B) (GenBank accession number HM804471, HM804472, respectively), were determined by RT-PCR sub-cloning approach. BWYV-BJ(A) and BWYV-BJ(B) were 5674 and 5626nt in length, respectively. BWYV-BJ(B) was 48nt shorter than BWYV-BJ(A) in the regions 1589-1615 and 1629-1649nt. Sequence alignment analysis showed that the full length of BWYV-BJ(A) and BWYV-BJ(B) shared 93% nucleotide sequence identity, with relatively high variability within ORFs 0, 1, 2 (at the nucleotide level was 86.3-88.8%) and high conservation within ORFs 3, 4, 5 (at the nucleotide level was 99.3-99.5%). The complete nucleotide sequences of BWYV-BJ(A) and BWYV-BJ(B) were most related to BWYV-US (80.6 and 79.0%, respectively). ORFs 1, 2 of BWYV-BJ(A) and BWYV-BJ(B) shared the highest homology with BWYV-US (nucleotide identity 91.2-93.3, 86.7-89.5%, respectively) and their ORFs 3, 4 were more closely related to BWYV-IM. However, their ORF5 were more closely related to that of Cucurbit aphid-borne yellows virus China strain (CABYV-CHN), with 68.1 and 68.5% nucleotide identity, respectively. Based on the sequence and phylogenetic analysis, we proposed that BWYV-BJ was at least a novel strain of BWYV, and BWYV-BJ(A), BWYV-BJ(B) were two distinct genotypes of BWYV-BJ. In addition, phylogenetic analysis and recombination analysis suggested that BWYV-BJ(A) and BWYV-BJ(B) might be recombinant viruses.

[Neuroprotective effect of epigallocatechin gallate on oxidative-stress-injured retinal cells].

OBJECTIVE: To investigate the neuroprotective effect of epigallocatechin gallate(EGCG), the main extract from green tea, on the oxidative-stress-injured retinal ganglion cells. METHODS: Rat retinal ganglion cells (RGC-5) were cultured into 3 groups (normal control; H2O2; H2O2 + EGCG or Trolox or NU1025). In-situ TUNEL was used to detect the apoptosis of the RGC-5 cells. Dihydroethidium (DHE) assay was used to observe the intracellular ROS generation. The activation of nuclear enzyme, PARP-1 was quantitatively detected by Western blot and the cell viability was measured by MT method. RESULTS: Hydrogen peroxide reduced RGC-5 cell viability in a time-concentration-dependent manner. The treatment of 500 micromol/L H2O2 for 24 hours reduced RGC-5 cell viability by about 50% of control. Hydrogen peraoxide caused apoptosis of the RGC-5 cell, obviously increased intracellular ROS generation and up-regulated the PARP-1 expression. The pretreatment with EGCG was able to markedly reduce the number of apoptotic cells, attenuate intracellular ROS generation. Furthermore, MTT assay showed that the pretreatment with EGCG (50 micromol/L) increased the most cell viability to 87% of control, but pretreatment with Trolox (100 micromol/L) and NU1025 (100 micromol/L, a PARP-1 inhibitor) recovered the most cell viability to 62% and 71% of control respectively. CONCLUSION: EGCG is able to effectively protect retinal ganglion cell against oxidative-stressed injury and can be used as a very potential neuroprotective drug.