EPIC-1042 as a potent PTRF/Cavin1-caveolin-1 interaction inhibitor to induce PARP1 autophagic degradation and suppress temozolomide efflux for glioblastoma.

BACKGROUND: Temozolomide (TMZ) treatment efficacy in glioblastoma is determined by various mechanisms such as TMZ efflux, autophagy, base excision repair (BER) pathway, and the level of O6-methylguanine-DNA methyltransferase (MGMT). Here, we reported a novel small-molecular inhibitor (SMI) EPIC-1042 (C20H28N6) with the potential to decrease TMZ efflux and promote PARP1 degradation via autolysosomes in the early stage. METHODS: EPIC-1042 was obtained from receptor-based virtual screening. Co-immunoprecipitation and pull-down assays were applied to verify the blocking effect of EPIC-1042. Western blotting, co-immunoprecipitation, and immunofluorescence were used to elucidate the underlying mechanisms of EPIC-1042. In vivo experiments were performed to verify the efficacy of EPIC-1042 in sensitizing glioblastoma cells to TMZ. RESULTS: EPIC-1042 physically interrupted the interaction of PTRF/Cavin1 and caveolin-1, leading to reduced secretion of small extracellular vesicles (sEVs) to decrease TMZ efflux. It also induced PARP1 autophagic degradation via increased p62 expression that more p62 bound to PARP1 and specially promoted PARP1 translocation into autolysosomes for degradation in the early stage. Moreover, EPIC-1042 inhibited autophagy flux at last. The application of EPIC-1042 enhanced TMZ efficacy in glioblastoma in vivo. CONCLUSION: EPIC-1042 reinforced the effect of TMZ by preventing TMZ efflux, inducing PARP1 degradation via autolysosomes to perturb the BER pathway and recruitment of MGMT, and inhibiting autophagy flux in the later stage. Therefore, this study provided a novel therapeutic strategy using the combination of TMZ with EPIC-1042 for glioblastoma treatment.

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.

Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology.

Background: The prevalence of diabetes increases year by year, posing a severe threat to human health. Current treatments are difficult to prevent the progression of diabetes and its complications. It is imperative to carry out individualized treatment of diabetes, but current diagnostic methods are difficult to specify an individualized treatment plan. Objective: Clarify the distribution law of tongue features of the diabetic population, and provide the diagnostic basis for individualized treatment of traditional Chinese medicine (TCM) in the treatment of diabetes. Methods: We use the TFDA-1 tongue diagnosis instrument to collect tongue images of people with diabetes and accurately calculate the color features, texture features, and tongue coating ratio features through the Tongue Diagnosis Analysis System (TDAS). Then, we used K-means and Self-organizing Maps (SOM) networks to analyze the distribution of tongue features in diabetic people. Statistical analysis of TDAS features was used to identify differences between clusters. Results: The silhouette coefficient of the K-means clustering result is 0.194, and the silhouette coefficient of the SOM clustering result is 0.127. SOM Cluster 3 and Cluster 4 are derived from K-means Cluster 1, and the intersections account for (76.7% 97.5%) and (22.3% and 70.4%), respectively. K-means Cluster 2 and SOM Cluster 1 are highly overlapping, and the intersection accounts for the ratios of 66.9% and 95.0%. K-means Cluster 3 and SOM Cluster 2 are highly overlaid, and the intersection ratio is 94.1% and 82.1%. For the clustering results of K-means, TB-a and TC-a of Cluster 3 are the highest (P < 0.001), TB-a of Cluster 2 is the lowest (P < 0.001), and TB-a of Cluster 1 is between Cluster 2 and Cluster 3 (P < 0.001). Cluster 1 has the highest TB-b and TC-b (P < 0.001), Cluster 2 has the lowest TB-b and TC-b (P < 0.001), and TB-b and TC-b of Cluster 3 are between Cluster 1 and Cluster 2 (P < 0.001). Cluster 1 has the highest TB-ASM and TC-ASM (P < 0.001), Cluster 3 has the lowest TB-ASM and TC-ASM (P < 0.001), and TB-ASM and TC-ASM of Cluster 2 are between the Cluster 1 and Cluster 3 (P < 0.001). CON, ENT, and MEAN show the opposite trend. Cluster 2 had the highest Per-all (P < 0.001). SOM divides K-means Cluster 1 into two categories. There is almost no difference in texture features between Cluster 3 and Cluster 4 in the SOM clustering results. Cluster 3's TB-L, TC-L, and Per-all are lower than Cluster 4 (P < 0.001), Cluster 3's TB-a, TC-a, TB-b, TC-b, and Per-part are higher than Cluster 4 (P < 0.001). Conclusions: The precise tongue image features calculated by TDAS are the basis for characterizing the disease state of diabetic people. Unsupervised learning technology combined with statistical analysis is an important means to discover subtle changes in the tongue features of diabetic people. The machine vision analysis method based on unsupervised machine learning technology realizes the classification of the diabetic population based on fine tongue features. It provides a diagnostic basis for the designated diabetes TCM treatment plan.

Relationships Between Diurnal Changes of Tongue Coating Microbiota and Intestinal Microbiota.

The oral cavity and the intestine are the main distribution locations of human digestive bacteria. Exploring the relationships between the tongue coating and gut microbiota, the influence of the diurnal variations of the tongue coating microbiota on the intestinal microbiota can provide a reference for the development of the disease diagnosis and monitoring, as well as the medication time. In this work, a total of 39 healthy college students were recruited. We collected their tongue coating microbiota which was collected before and after sleep and fecal microbiota. The diurnal variations of tongue coating microbiota are mainly manifested on the changes in diversity and relative abundance. There are commensal bacteria in the tongue coating and intestines, especially Prevotella which has the higher proportion in both sites. The relative abundance of Prevotella in the tongue coating before sleep has a positive correlation with intestinal Prevotella; the r is 0.322 (p < 0.05). Bacteroides in the intestine had the most bacteria associated with the tongue coating and had the highest correlation coefficient with Veillonella in the oral cavity, which was 0.468 (p < 0.01). These results suggest that the abundance of the same flora in the two sites may have a common change trend. The SourceTracker results show that the proportion of intestinal bacteria sourced from tongue coating is less than 1%. It indicates that oral flora is difficult to colonize in the intestine in healthy people. This will provide a reference for the study on the oral and intestinal microbiota in diseases.

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).

Application of computer tongue image analysis technology in the diagnosis of NAFLD.

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

BACKGROUND: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease. OBJECTIVE: Our objective was to establish the predictive model that can be applied to evaluate people with blood glucose in high and critical state. METHODS: We established the diabetes risk prediction model formed by a combined TCM tongue diagnosis with machine learning techniques. 1512 subjects were recruited from the hospital. After data preprocessing, we got the dataset 1 and dataset 2. Dataset 1 was used to train classical machine learning model, while dataset 2 was used to train deep learning model. To evaluate the performance of the prediction model, we used Classification Accuracy(CA), Precision, Recall, F1-score, Precision-Recall curve(P-R curve), Area Under the Precision-Recall curve(AUPRC), Receiver Operating Characteristic curve(ROC curve), Area Under the Receiver Operating Characteristic curve(AUROC), then selected the best diabetes risk prediction model. RESULTS: On the test set of dataset 1, the CA of non-invasive Stacking model was 71 %, micro average AUROC was 0.87, macro average AUROC was 0.84, and micro average AUPRC was 0.77. In the critical blood glucose group, the AUROC was 0.84, AUPRC was 0.67. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.83. On the validation set of dataset 2, the CA of ResNet50 model was 69 %, micro average AUROC was 0.84, macro average AUROC was 0.83, and micro average AUPRC was 0.73. In the critical blood glucose group, AUROC was 0.88, AUPRC was 0.71. In the high blood glucose group, AUROC was 0.80, AUPRC was 0.76. On the test set of dataset 2, the CA of ResNet50 model was 65 %, micro average AUROC was 0.83, macro average AUROC was 0.82, and micro average AUPRC was 0.71. In the critical blood glucose group, the prediction of AUROC was 0.84, AUPRC was 0.60. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.71. CONCLUSIONS: Tongue features can improve the prediction accuracy of the diabetes risk prediction model formed by classical machine learning model significantly. In addition to the excellent performance, Stacking model and ResNet50 model which were recommended had non-invasive operation and were easy to use. Stacking model and ResNet50 model had high precision, low false positive rate and low misdiagnosis rate on detecting hyperglycemia. While on detecting blood glucose value in critical state, Stacking model and ResNet50 model had a high sensitivity, a low false negative rate and a low missed diagnosis rate. The study had proved that the differential changes of tongue features reflected the abnormal glucose metabolism, thus the diabetes risk prediction model formed by a combined TCM tongue diagnosis and machine learning technique was feasible.

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 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.

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.

Discovery of hCES2A inhibitors from Glycyrrhiza inflata via combination of docking-based virtual screening and fluorescence-based inhibition assays.

Human carboxylesterase 2 (hCES2A) is a key target to ameliorate the intestinal toxicity triggered by irinotecan that causes severe diarrhea in 50%-80% of patients receiving this anticancer agent. Herbal medicines are frequently used for the prevention and treatment of the intestinal toxicity of irinotecan, but it is very hard to find strong hCES2A inhibitors from herbal medicines in an efficient way. Herein, an integrated strategy via combination of chemical profiling, docking-based virtual screening and fluorescence-based high-throughput inhibitor screening assays was utilized. Following the screening of a total of 73 herbal products, licorice (the dried root of Glycyrrhiza species) was found with the most potent hCES2A inhibition activity. Further investigation revealed that the chalcones and several flavonols in licorice displayed strong hCES2A inhibition activities, while isoliquiritigenin, echinatin, naringenin, gancaonin I and glycycoumarin exhibited moderate inhibition of hCES2A. Inhibition kinetic analysis demonstrated that licochalcone A, licochalcone C, licochalcone D and isolicoflavonol potently inhibited hCES2A-mediated fluorescein diacetate hydrolysis in a reversible and mixed inhibition manner, with Ki values less than 1.0 µM. Further investigations demonstrated that licochalcone C, the most potent hCES2A inhibitor identified from licorice, dose-dependently inhibited intracellular hCES2A in living HepG2 cells. In summary, this study proposed an integrated strategy to find hCES2A inhibitors from herbal medicines, and our findings suggested that the chalcones and isolicoflavonol in licorice were the key ingredients responsible for hCES2A inhibition, which would be very helpful to develop new herbal remedies or drugs for ameliorating hCES2A-associated drug toxicity.

[Analysis on pulse diagram characteristics of subjects with subhealth state].

OBJECTIVE: To study the pulse diagram parameters of subjects with subhealth state and to find the pulse parameters for subhealth state evaluation. METHODS: A total of 1 275 subjects without diagnosed diseases were recruited and their health conditions were assessed with Health Evaluating Questionnaire H20 V2009. The subjects were assigned to health group or subhealth group according to the scale score. Subjects' syndrome in the subhealth group was differentiated using score of "subhealth state of syndrome differentiation V2010". Another 121 patients with cardiovascular diseases were enrolled as a control. The pulse information was collected with YJJ-101 subhealth pulse monitoring system and the parameters include amplitude of main wave (h1), amplitude of repeat wave (h5) and its front wave (h3), 1/3 or 1/5 width of main wave (w1) or (w2), time of rapid ejection phase (t2), period of pulse (t), pulse pressure (Pp), square (S), area in systole (As) and area in diastole (Ad) of pulse diagram and ratios of h3/h1, h5/h1, w1/t, w2/t and h1/t1. RESULTS: Pulse diagram analysis showed significant differences among health, subhealth and disease group in Pp, h1, S and As and ratios of h5/h1 and w2/t. Compared with the health group, the values of w1/t and w2/t of the subhealth group increased (P<0.05), and Pp, h1, h5, h5/h1, S, As and Ad decreased (P<0.05). Compared with health group, the parameters of pulse of the subhealth group were increased in Pp and h5/h1 (P<0.05) and decreased in h1, w2/t, S and As (P<0.05). Compared with health group, pulse parameters h3/h1, w1, w1/t, w2/t of excess and deficiency syndrome group increased, and h1, h5, h1/t 1and h5/h1 decreased. Among different syndromes of subhealth state, pulse diagram parameters h1, h5, h3/h1, h5/h1 and w1/t of yin deficiency, qi deficiency, liver stagnation and excess heat group were significantly different (P<0.05) from the health group, for example, pulse parameters h1 and h5 of stagnation, yin deficiency, qi deficiency and excess heat group declined in order, and pulse parameters h3/h1 and w1/t of liver stagnation, excess heat, yin deficiency and qi deficiency group increased in order. Pulse index h1 in the kidney deficiency group was higher than that in the health group and the other syndrome groups. CONCLUSION: Results of analyzing sphygmogram parameters showed different characteristics among different health status and the subhealth state due to different syndromes. Sphygmogram parameters may be used for objective evaluation of health status or subhealth syndrome differentiation.

Diversity of microbes and potential exoelectrogenic bacteria on anode surface in microbial fuel cells.

Single-chamber microbial fuel cells (MFCs), inoculated with anaerobic sludge and continuously run with two kinds of organic wastewater influents, were systemically investigated. The diversity of microbes, determined by 16S rDNA analysis, was analyzed on three anodes under different conditions. One anode was in a closed circuit in synthetic wastewater containing glucose. The other two anodes, in open or closed circuits, were fed effluent from an anaerobic reactor treating starch wastewater. The chemical oxygen demand (COD) removal efficiency was about 70%, and the exported voltages were about 450 mV. The 16S rDNA molecular clones of microbes on anode surfaces showed significant changes in Eubacterial structure under different conditions. gamma-Proteobacteria and the high G+C gram-positive groups were predominant in the synthetic wastewater, while epsilon-Proteobacteria predominated in the anaerobic reactor effluent. Known exoelectrogenic bacterial species composition also changed greatly depending on substrate. On the artificial substrate, 28% of the bacterial sequences were affiliated with Aeromonas, Pseudomonas, Geobacter, and Desulfobulbus. On the anaerobic effluent, only 6% were affiliated with Geobacter or Clostridium. Because only a few exoelectrogenic bacteria from MFCs have been directly isolated and studied, we compared the community structures of two bacterial anodes, in open and closed circuits, under the same substrate of anaerobic effluent in order to identify additional exoelectrogenic bacterial strains. Alcaligenes monasteriensis, Comamonas denitrificans, and Dechloromonas sp. were found to be potential exoelectrogenic bacteria worthy of further research.

[Continuous bio-hydrogen production by mesophilic and thermophilic cultures].

Anaerobic biological hydrogen productions were achieved successfully in two lab-scale anaerobic hydrogen production reactors under mesophilic (37 degrees C) and thermophilic (55 degrees C) conditions, respectively. The mesophilic reactor, a CSTR, was operated over 4 months by seeding with river sediments and feeding with glucose solution, in which the highest hydrogen production rate was 8.6 L/(L x d) and the substrate hydrogen production molar ratio (H2/glucose) was 1.98. After seeded with anaerobic methanogenic granules, a UASB reactor was thermophilically operated by feeding with sucrose solution and during its steady operation period, the hydrogen production rate was 6.8 L/(L x d) and the substrate hydrogen production molar ratio (H2/sucrose) was 3.6. Within the produced gas, the H2 percentages were about 43% and others were CO2, no methane could be detected. Thermophilic hydrogen-producing granules were successfully cultivated in the UASB reactor, which were grey-white in color, the diameters were about 0.8 - 1.2 mm, and typical settling velocities were about 30 - 40 m/h. Through SEM a great number of bacilli could be found on the surface of the granules which made the surface rough. Total DNA of these two hydrogen production sludges were extracted and purified, and the PCR and DGGE process were conducted, the results indicate that most of the eubacteria in two sludges are the same, but the dominant species are obviously different with each other.