Jianpi Qinghua Formula Alleviates Diabetic Myocardial Injury Through Inhibiting JunB/c-Fos Expression.

OBJECTIVE: Diabetic cardiomyopathy (DCM) represents a substantial risk factor for heart failure and increased mortality in individuals afflicted with diabetes mellitus (DM). DCM typically manifests as myocardial fibrosis, myocardial hypertrophy, and impaired left ventricular diastolic function. While the clinical utility of the Jianpi Qinghua (JPQH) formula has been established in treating diabetes and insulin resistance, its potential efficacy in alleviating diabetic cardiomyopathy remains uncertain. This study aims to investigate the impact and underlying molecular mechanisms of the JPQH formula (JPQHF) in ameliorating myocardial injury in nonobese diabetic rats, specifically focusing on apoptosis and inflammation. METHODS: Wistar rats were assigned as the normal control group (CON), while Goto-Kakizaki (GK) rats were randomly divided into three groups: DM, DM treated with the JPQHF, and DM treated with metformin (MET). Following a 4-week treatment regimen, various biochemical markers related to glucose metabolism, cardiac function, cardiac morphology, and myocardial ultrastructure in GK rats were assessed. RNA sequencing was utilized to analyze differential gene expression and identify potential therapeutic targets. In vitro experiments involved high glucose to induce apoptosis and inflammation in H9c2 cells. Cell viability was evaluated using CCK-8 assay, apoptosis was monitored via flow cytometry, and the production of inflammatory cytokines was measured using quantitative real-time PCR (qPCR) and ELISA. Protein expression levels were determined by Western blotting analysis. The investigation also incorporated the use of MAPK inhibitors to further elucidate the mechanism at both the transcriptional and protein levels. RESULTS: The JPQHF group exhibited significant reductions in interventricular septal thickness at end-systole (IVSs) and left ventricular internal diameter at end-systole and end-diastole (LVIDs and LVIDd). JPQHF effectively suppressed high glucose-induced activation of IL-1ß and caspase 3 in cardiomyocytes. Furthermore, JPQHF downregulated the expression of myocardial JunB/c-Fos, which was upregulated in both diabetic rats and high glucose-treated H9c2 cells. CONCLUSION: The JPQH formula holds promise in mitigating diabetic myocardial apoptosis and inflammation in cardiomyocytes by inhibiting JunB/c-Fos expression through suppressing the MAPK (p38 and ERK1/2) pathway.

Ultra-high performance liquid chromatography coupled to tandem mass spectrometry-based metabolomics study of diabetic distal symmetric polyneuropathy.

AIMS/INTRODUCTION: Distal symmetric polyneuropathy (DSPN) is a common complication of type 2 diabetes mellitus, but the underlining mechanisms have not yet been elucidated. The current study was designed to screen the feature metabolites classified as potential biomarkers, and to provide deeper insights into the underlying distinctive metabolic changes during disease progression. MATERIALS AND METHODS: Plasma metabolite profiles were obtained by the ultra-high liquid chromatography coupled to tandem mass spectrometry method from healthy control participants, patients with type 2 diabetes mellitus and patients with DSPN. Potential biomarkers were selected through comprehensive analysis of statistically significant differences between groups. RESULTS: Overall, 938 metabolites were identified. Among them, 12 metabolites (dimethylarginine, N6-acetyllysine, N-acetylhistidine, N,N,N-trimethyl-alanylproline betaine, cysteine, 7-methylguanine, N6-carbamoylthreonyladenosine, pseudouridine, 5-methylthioadenosine, N2,N2-dimethylguanosine, aconitate and C-glycosyl tryptophan) were identified as the specific biomarkers. The content of 12 metabolites were significantly higher in the DSPN group compared with the other two groups. Additionally, they showed good performance to discriminate the DSPN state. Correlation analyses showed that the levels of 12 metabolites might be more closely related to the glucose metabolic changes, followed by the levels of lipid metabolism. CONCLUSIONS: The finding of the 12 signature metabolites might provide a novel perspective for the pathogenesis of DSPN. Future studies are required to test this observation further.

JianPi-QingHua formula attenuates nonalcoholic fatty liver disease by regulating the AMPK/SIRT1/NF-κB pathway in high-fat-diet-fed C57BL/6 mice.

CONTEXT: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease, accompanied by liver lipid accumulation and inflammation. JianPi-QingHua formula (JPQH), a Chinese herbal formula, exhibits effects on obesity and T2DM. However, the hepatoprotective effect of JPQH has not been elucidated. OBJECTIVE: To investigate the hepatoprotective effect of JPQH in NAFLD induced by a high-fat diet (HFD) in mice. MATERIALS AND METHODS: C57BL/6J mice were divided into four groups and fed a normal-fat diet (ND), high-fat diet (HFD), HFD + JPQH (2.5 g/kg), or HFD + metformin (300 mg/kg) for 6 weeks, respectively. Furthermore, the body weight, epididymal fat mass, blood glucose, and liver weight were measured. Serum total cholesterol (TC), triglycerides (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were performed. Hematoxylin and eosin staining and Oil Red O staining were observed in hepatic histopathological changes. Western blotting and quantitative real-time polymerase chain reaction were utilized to assess the key protein expression of hepatic lipid metabolism and inflammation. RESULTS: Compared with the HFD group, JPQH could reduce body weight, epididymal fat mass, blood glucose and liver weight (p < 0.05), and markedly decreased the levels of serum TC, TG, ALT, AST (p < 0.05). Additionally, JPQH improved liver pathological changes. Consistent with the hepatic histological analysis, JPQH intervention suppressed lipid accumulation and inflammatory responses. Mechanistically, JPQH boosted SIRT1/AMPK signalling, and attenuated NF-κB pathway, which suppressed inflammatory responses. DISCUSSION AND CONCLUSIONS: These findings indicate that JPQH supplementation protected against HFD-induced NAFLD by regulating SIRT1/AMPK/NF-κB pathway, which provides a theoretical basis for the clinical treatment of patients with NAFLD.

The Interaction Between Age and Risk Factors for Diabetes and Prediabetes: A Community-Based Cross-Sectional Study.

Objective: This study aimed to investigate the interaction between age groups and risk factors for diabetes and prediabetes in Shanghai communities and to identify the effect of age on other risk factors for diabetes and prediabetes. Methods: This study recruited 3540 participants with undiagnosed diabetes or prediabetes in 14 communities in Shanghai from February to August 2019. All participants underwent a comprehensive examination, including filling out a detailed questionnaire, physical examination, 75 g oral glucose tolerance test, and blood sample collection. In addition, logistic regression was used to analyze the interaction between age and risk factors for prediabetes and diabetes. Results: The statistical analysis included 2776 people. In this study, the prevalence of diabetes and prediabetes were 15.1% and 52.3%, respectively. The prevalence of diabetes and prediabetes is higher in the elderly than in the middle-aged group. Among the risk factors for diabetes, overweight was associated with higher age (P-interaction 0.028). In addition, among the risk factors for prediabetes, a high level of education was associated with higher age (P-interaction 0.039) and elevated serum cholesterol level was associated with lower age (P-interaction 0.019). Conclusion: This study confirmed an interaction between age and other influencing factors, which may be important in explaining differences in risk factors for diabetes and prediabetes in the middle-aged and elderly populations. Community health facilities can provide health guidance to people of different age groups to prevent and control prediabetes and diabetes.

Tandem mass tag-based proteomic profiling revealed potential therapeutic targets and mechanisms of liraglutide for the treatment of impaired glucose tolerance.

Objective: Based on the tandem mass tag (TMT) technique, our study investigated the potential therapeutic targets of Liraglutide (LIRA) on streptozotocin (STZ) induced impaired glucose tolerance (IGT) in rats and discuss the biological mechanism of the drug against IGT. Methods: 10 rats were randomly selected from 31 male wistar rats of specific pathogen free (SPF) grade as control group and fed with conventional chow, offered the remaining rats a high fat and high sugar (HFSD) diet combined with an intraperitoneal injection of STZ to establish the IGT model, and excluded 2 non-model rats. Specifically, the model rats were randomly divided into Model group (n=10) and LIRA group (n=9). In addition, the LIRA group was subcutaneously injected with 0.06 mg/kg LIRA, during which the metabolic parameters including body weight and fasting blood glucose were recorded. After 8 weeks, samples were taken under anesthesia. Then, the cell morphology was observed using HE staining, and immunofluorescence was performed on the pancreatic tissues of the three groups of rats. Besides, the expression of differential proteins in pancreatic tissues of the three groups of rats was determined by the TMT proteomic labeling. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological function analysis were performed on the intersection of Model and LIRA differential proteins. Results: LIRA could not only significantly reduce blood glucose levels but also improve islet cell morphology and function in IGT rats. Among the differential proteins between the model group and the blank group, 44 were reversed after LIRA treatment, of which 14 were up-regulated, while 30 were down-regulated, including PPIF, MPRIP, CYP51, TXNL1, BCL-2, etc. (FC>1.1 or<0.909, P<0.05). According to the GO and KEGG analysis results, it was related to biological processes such as fatty acid metabolism and adipocyte generation, which involved multiple signaling pathways regulating the function of islet cells, such as MAPK, PI, Ras, FcγR, and unsaturated fatty acids, and pyruvate metabolism. Conclusion: To sum up, LIRA participated in anti-IGT therapy through regulation of multiple target proteins and biological functions. This study is of great reference for further exploring the mechanism of action of LIRA at the protein level of IGT.

Candidate metabolite markers of peripheral neuropathy in Chinese patients with type 2 diabetes.

OBJECTIVES: To analyze the serum and urine metabolites present in type 2 diabetes mellitus (T2DM) patients and T2DM patients with diabetic peripheral neuropathy (DPN) and to select differentially expressed biomarkers for early diagnosis of DPN. METHODS: Serum and urine metabolites from 74 T2DM patients with peripheral neuropathy and 41 without peripheral neuropathy were analyzed using gas chromatograph system with time-of-flight mass spectrometer metabolomics to detect biomarkers of peripheral neuropathy in T2DM. RESULTS: There were increased serum triglycerides, alanine aminotransferase, and decreased C-peptide, and total cholesterol levels in T2DM patients with DPN compared to those without peripheral neuropathy. Metabolomic analysis revealed visible differences in metabolic characteristics between two groups, and overall 53 serum differential metabolites and 56 urine differential metabolites were identified with variable influence on projection (VIP) >1 and P<0.05. To further analyze the correlation between the identified metabolites and DPN, four serum metabolites and six urine metabolites were selected with VIP>2, and fold change (FC) >1, including serum ß-alanine, caproic acid, ß-alanine/L-aspartic acid, and L-arabinose/L-arabitol, and urine gluconic acid, erythritol, galactonic acid, guanidoacetic acid, cytidine, and aminoadipic acid. Furthermore, five serum biomarkers and six urine biomarkers were found to show significant changes (P<0.05, VIP>1, and FC>1) respectively in patients with mild, moderate, and severe DPN. In addition, we found that glyoxylate and dicarboxylate metabolism was a differential metabolic pathway not only between T2DM and DPN, but also among different degrees of DPN. The differential metabolites such as ß-alanine and caproic acid are expected to be biomarkers for DPN patients, and the significant changes in glyoxylate and dicarboxylate metabolism may be related to the pathogenesis of DPN. CONCLUSION: There were serum and urine spectrum metabolomic differences in patients with DPN, which could serve as biomarkers for T2DM and DPN patients.

Identifying Distinct Risk Thresholds of Glycated Hemoglobin and Systolic Blood Pressure for Rapid Albuminuria Progression in Type 2 Diabetes From NHANES (1999-2018).

Background: It is widely recognized that glycated hemoglobin (HbA1c) and systolic blood pressure (SBP) are two key risk factors for albuminuria and renal function impairment in patients with type 2 diabetes mellitus (T2DM). Our study aimed to identify the specific numerical relationship of albumin/creatinine ratio (ACR) with HbA1c and SBP among a large population of adults with T2DM. Method: A total of 8,626 patients with T2DM were included in the data analysis from the National Health and Nutrition Examination Surveys (NHANES) (1999-2018). The multiple linear regressions were used to examine the associations of ACR with HbA1c and SBP. Generalized additive models with smooth functions were performed to identify the non-linear relations between variables and interactions were also tested. Results: Significantly threshold effects were observed between ACR and HbA1c or SBP after multivariable adjustment, with the risk threshold values HbA1c = 6.4% and SBP = 127 mmHg, respectively. Once above thresholds were exceeded, the lnACR increased dramatically with higher levels of HbA1c (ß = 0.23, 95 CI%:0.14, 0.32, P < 0.001) and SBP (ß = 0.03, 95 CI%:0.03, 0.04, P < 0.001). Subgroup analysis showed high protein diet was related to higher ACR. In addition, a higher risk of ACR progression was observed in central obesity participants with HbA1C ≥ 6.4% or hyperuricemia participants with SBP ≥ 127 mmHg among patients withT2DM. Conclusion: We identified thresholds of HbA1c and SBP to stratify patients with T2DM through rapid albuminuria progression. These might provide a clinical reference value for preventing and controlling diabetes kidney disease.

Jianpi Qinghua Fomula alleviates insulin resistance via restraining of MAPK pathway to suppress inflammation of the small intestine in DIO mice.

BACKGROUND: Jianpi Qinghua Fomula (JPQHF), a clinically proven prescription,has been applied to cure insulin resistance(IR) and type 2 diabetes (T2DM) for more than 20 years. Here, we will unravel the underlying molecular mechanisms relevant to the therapeutic actions of JPQHF. METHODS: High-fat(HF)diet-induced obesity(DIO)mouse were established in our research, along with insulin resistance. After the administration of JPQHF 5 or 6 weeks, the parameters of the glucose and lipid metabolism were measured. Flow cytometry and Luminex were utilized to assess the inflammation in small intestine,whilst Western blot was used to determine the relative expression levels of the MAPK pathway-related proteins. The glucose and lipid transporter of small intestine was assessed by immunofluorescence and ELISA, and the expression of insulin signaling pathway was detected by Western blot. RESULTS: The metabolic phenotypes of DIO mouse were ameliorated after 6-week oral administration of JPQHF; Meanwhile,JPQHF downregulated levels of IL-1ß,IL-6, TNF-α and IFN-γ but upregulated the ratio of M2/M1 macrophages in the small intestine. The elevated expressions of p-P38 MAPK/P38 MAPK、p-JNK/JNK and p-ERK1/2/ERK1/2 were reversed by JPQHF. Moreover, JPQHF enhanced expression of PI3K,p-AKT/AKT, p-IRS1/ IRS1, p-IRS2/ IRS2 and apoB48 in small intestine, and facilitated the translocation of GLUT2 to the basal side of small intestine epithelial cells. CONCLUSION: JPQHF alleviates insulin resistance in DIO mice, and this effect may be associated with its restraining of inflammation of small intestine via attenuating MAPK pathway, and then diminishes small intestinal glucose and lipid absorption.

Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population.

Aims: This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population. Methods: Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results: Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b'= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639-0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671-0.899) and 0.731 (95% CI 0.619-0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753-0.964) and 0.823 (95% CI 0.730-0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767-0.844) and 0.830 (95% CI 0.788-0.872), respectively, with seven as the cut-off point. Conclusions: The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population.

Hierarchical CNN-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models.

OBJECTIVE: 3D Digitization of dental model is growing in popularity for dental application. Classification of tooth type from single 3D point cloud model without assist of relative position among teeth is still a challenging task. METHODS: In this paper, 8-class posterior tooth type classification (first premolar, second premolar, first molar, second molar in maxilla and mandible respectively) was investigated by convolutional neural network (CNN)-based occlusal surface morphology analysis. 3D occlusal surface was transformed to depth image for basic CNN-based classification. Considering the logical hierarchy of tooth categories, a hierarchical classification structure was proposed to decompose 8-class classification task into two-stage cascaded classification subtasks. Image augmentations including traditional geometrical transformation and deep convolutional generative adversarial networks (DCGANs) were applied for each subnetworks and cascaded network. RESULTS: Results indicate that combing traditional and DCGAN-based augmented images to train CNN models can improve classification performance. In the paper, we achieve overall accuracy 91.35%, macro precision 91.49%, macro-recall 91.29%, and macro-F1 0.9139 for the 8-class posterior tooth type classification, which outperform other deep learning models. Meanwhile, Grad-cam results demonstrate that CNN model trained by our augmented images will focus on smaller important region for better generality. And anatomic landmarks of cusp, fossa, and groove work as important regions for cascaded classification model. CONCLUSION: The reported work has proved that using basic CNN to construct two-stage hierarchical structure can achieve the best classification performance of posterior tooth type in 3D model without assistance of relative position information. The proposed method has advantages of easy training, great ability to learn discriminative features from small image region.

Puerarin suppresses the hepatic gluconeogenesis via activation of PI3K/Akt signaling pathway in diabetic rats and HepG2 cells.

Pueraria, a Chinese herbal medicine, plays an important role in many classic prescriptions for the treatment of diabetes. Puerarin is the main component of pueraria. The current in vivo and in vitro research mainly focus on exploring the potential mechanism of puerarin in inhibiting hepatic gluconeogenesis. The type 2 diabetic rats were established by a combination of small dosage of streptozotocin (STZ) injection with high-fat diet. After the administration of puerarin 4 weeks, the parameters of the glucose and lipid metabolism were determined. HepG2 cells were treated by palmitic acid (PA) to induce the insulin resistance in vitro model. After the treatment of puerarin, the glucose consumption and cell viability were examined. Then, the protein expression of PI3K, Akt, pAkt, pFOXO1, FOXO1, PEPCK and G6pase in liver tissue and HepG2 cells were evaluated by western blot. RT-PCR was used to measure the content of PEPCK, G6pase mRNA in liver tissue. The results showed that puerarin administration significantly decrease the level of FBG, HbA1C and triglycerides in diabetic rats. Mechanistic research showed that puerarin activating PI3K/Akt is puerarin-mediated beneficial effects and can be reversed by inhibitor of PI3K or Akt. In conclusion, puerarin inhibits hepatic gluconeogenesis by activating PI3K/Akt signaling pathway.

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.

3D distribution of dental plaque on occlusal surface using 2D-fluorescence-image to 3D-surface registration.

The accumulation of dental plaque on a tooth surface plays a crucial role in developing dental caries. In this paper, fluorescence imaging modality with structured light-based intraoral 3D scanner were combined to investigate the 3D distribution of dental plaque. The traditional fluorescence imaging method only reveals the 2D spatial distribution of the dental plaque on a tooth surface. To visualize the 3D distribution of the dental plaque on an occlusal surface, mapping a 2D fluorescence image to a 3D occlusal surface was investigated. An iterative closest point (ICP)-based contour registration method was proposed. A fluorescence camera was calibrated to obtain intrinsic parameters. The rotation and translation matrices for projecting the 3D occlusal surface were optimized to match the contours of the 2D fluorescence image and the 3D projected model. The 3D distribution of occlusal plaque reveals that dental plaque accumulation relates to the local and global morphology of the tooth surface. Thus, the depth of the pit-and-fissure is not the only parameter used to determine plaque content. The investigation of the 3D distribution of occlusal plaque using 2D-3D registration paves the path for the quantitative analysis of the tooth surface morphology to perform plaque-guided caries risk assessment.

Serum and urine metabolomics reveal potential biomarkers of T2DM patients with nephropathy.

BACKGROUND: Diabetes is a metabolic disease and is often accompanied by severe microvascular and macrovascular complications. A comprehensive understanding of its complex mechanisms can help prevent type 2 diabetes mellitus (T2DM) complications, such as diabetic nephropathy (DN). METHODS: To reveal the systemic metabolic changes related to renal injury, clinical information of T2DM patients with or without nephropathy was collected, and it was found that serum urea levels of DN patients were significantly higher in T2DM patients without nephropathy. Further along the disease progression, the serum urea levels also gradually increased. We used gas chromatograph coupled with time-of-flight mass spectrometry (GC-TOFMS) metabolomics to analyze the serum and urine metabolites of T2DM patients with or without nephropathy to study the metabolic changes associated with the disease. RESULTS: Finally, we identified 61 serum metabolites and 46 urine metabolites as potential biomarkers related to DN (P<0.05, VIP >1). In order to determine which metabolic pathways were major altered in DN, we summarized pathway analysis based on P values from their impact values and enrichment. There were 9 serum metabolic pathways and 12 urine metabolic pathways with significant differences in serum and urine metabolism, respectively. CONCLUSIONS: This study emphasizes that GC-TOFMS-based metabolomics provides insight into the potential pathways in the pathogenesis and progression of DN.

Passively mode-locked Yb fiber laser with PbSe colloidal quantum dots as saturable absorber.

A passively mode-locked Yb fiber laser using PbSe colloidal quantum dots (CQDs) as saturable absorber (SA) is experimentally demonstrated. An all-fiber experimental scheme was designed to understand the SA property of PbSe CQDs. The non-saturable loss, modulation depth, and saturable intensity of SA measured were 23%, 7%, and 12 MW/cm2, respectively. The PbSe CQDs were sandwiched in a fiber connector, which was further inserted into the Yb fiber laser for mode-locking. As the pump power up to 110 mW, the self-starting mode-locking pulses were observed. Under the pump power of 285 mW, a maximum average laser power with fundamental mode-locking operation was obtained to be 21.3 mW. In this situation, the pulse full width at half maximum (FWHM), pulse repetition rate, and spectral FWHM were measured to be 70 ps, 8.3 MHz, and 4.5 nm, respectively.

Elevated Squamous Cell Carcinoma Antigen, Cytokeratin 19 Fragment, and Carcinoembryonic Antigen Levels in Diabetic Nephropathy.

OBJECTIVE: We aimed to explore whether squamous cell carcinoma antigen (SCC), cytokeratin 19 fragment (Cyfra21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) are elevated in diabetic nephropathy (DN) and the association between urinary albumin-to-creatinine ratio (UACR) and tumor markers in diabetic patients. METHODS: Nondialysis patients with diabetes (n = 261) and 90 healthy controls were enrolled. DN was defined as an UACR ≥ 30 mg/g in the absence of a urinary tract infection or other renal abnormalities. RESULTS: Patients with DN had significantly higher serum SCC, Cyfra21-1, and CEA levels than those with normoalbuminuria and healthy controls. The rates of positive SCC, Cyfra21-1, and CEA significantly increased with increasing urinary albumin excretion (all P for trend < 0.001). In contrast, NSE was not affected by DN. SCC, Cyfra21-1, and CEA were significantly and positively correlated with UACR. In logistic regression, after multivariable adjustment, increased UACR was associated with increased odds ratio of elevated tumor marker levels (all P for trend < 0.05). CONCLUSIONS: Serum levels of SCC, Cyfra21-1, and CEA are markedly increased with increasing urinary albumin excretion, which affects the specificity for diagnosis for lung cancer. Appropriate interpretation of tumor markers in diabetic patients is mandatory to avoid unnecessary and even hazardous biopsies.

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.

Discrimination of Dental Caries Using Colorimetric Characteristics of Fluorescence Spectrum.

The feasibility of colorimetric parameters for the discrimination of the stages of dental caries based on a light-induced autofluorescence spectrum at a 405-nm excitation wavelength was investigated. The fluorescence spectra of 4 groups of tooth samples (10 sound, 10 early-stage decay, 14 established decay, and 10 severe decay), which were classified by the International Caries Detection and Assessment System, were experimentally measured in vitro. The carious lesion samples had an additional fluorescence peak at around 627 nm. The mathematical relation of the fluorescence spectrum and human color perception was established and computed. With increasing severity, the fluorescence color changed from green to yellow according to the colorimetric parameters of the CIE 1931 (x, y) chromaticity coordinates and dominant wavelengths. The results from a one-way ANOVA of the dominant wavelength showed a statistically significant difference among the 4 classified groups. The colorimetric parameters of the light-induced fluorescence spectrum can potentially be applied to evaluate the various carious levels.

A study of the fluorescence characteristics of common cariogenic microorganisms.

OBJECTIVE: To explore the fluorescence characteristics of common cariogenic bacteria: Streptococcus mutans, S. sanguis, Actinomyces viscosus, Prevotella intermedia, Lactobacillus acidophilus, and Candida albicans. METHODS: The bacteria were cultured on brain heart infusion (BHI) agar and BHI blood agar, and bacterial colonies were collected for further amplification in liquid medium. Bacterial suspensions in physiological saline were equally divided into three parts for bacteria counting, fluorescence spectrometry detection, and fluorescence microscope examination. RESULTS: The optimal excitation wavelength of the bacteria was 350 nm; their characteristic fluorescence peak position was at 436 ± 4 nm. There was a significant linear correlation between fluorescence intensity and bacterial concentration. The mean optical density (MOD) of S. mutans and L. acidophilus cultivated in BHI blood was significantly higher than that cultivated in BHI agar (110 ± 10 vs. 57 ± 20; 94 ± 16 vs. 31 ± 12, respectively, P < 0.05). The MOD of S. sanguis, A. viscosus, and P. intermedia cultivated in BHI blood agar was higher than that cultivated in BHI agar (37 ± 12 vs. 36 ± 11; 43 ± 17 vs. 38 ± 6; 86 ± 21 vs. 72 ± 8, respectively, P > 0.05); the opposite was observed for C. albicans. CONCLUSION: At 350 nm excitation wavelength, 436 ± 4 nm is an indicator for detecting six cariogenic bacteria. The fluorescence energy, Q, is a valuable index reflecting bacterial concentration under fluorescence spectrometry detection. Exogenous fluorescence groups have greater influence on fluorescence intensity and little influence on fluorescence peak position detection.

[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.