An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

Analysis of the Correlation Between Visceral Fat Area and Insulin Resistance in Patients with Type 2 Diabetes and Abdominal Obesity

Objective: To analyze the correlation between visceral fat area and insulin resistance index (HOMA-IR) in patients with type 2 diabetes mellitus (T2DM) and abdominal obesity and to provide a reference for screening and related research of such patients. Methods: Two hundred patients with T2DM admitted to Guandu People’s Hospital of Kunming were included. The study was carried out from October 2022 to December 2023. The patients were divided into three groups according to different abdominal visceral fat areas (VFA): Group A (n = 65) was less than 75cm2, Group B (n = 75) was 75-100 cm2, and Group C (n = 60) was greater than 100 cm2. The subjects in the three groups were all tested for glycated hemoglobin (HbA1c), fasting insulin (FINS), and fasting blood glucose (FPG). Height and weight were measured to calculate body mass index (BMI). The HOMA-IR and TYG (fasting triglyceride and glycemic index) were also calculated. Changes in the BMI, VFA, HOMA-IR, and TYG levels were observed in the three groups. Results: The VFA, BMI, HbA1c, FPG, FINS, HOMA-IR, and TYG of the patients all increased, with a more significant increase in the BMI, FINS, HOMA-IR, and TYG levels (P < 0.01). Multiple linear stepwise regression analyses used visceral fat area (VFA) as the dependent variable. The results showed that VFA was closely related to BMI, FINS, HOMA-IR, and TYG. Conclusion: Early reduction of VFA to reduce insulin resistance may be a better treatment and effective method for T2DM, providing powerful measures and new strategies for effective blood sugar control and early prevention in the treatment of metabolic diseases.

Incorporation of Mg-phenolic networks as a protective coating for magnesium alloy to enhance corrosion resistance and osteogenesis in vivo

Magnesium(Mg) and its alloys have been intensively studied to develop the next generation of bone implants recently, but their clinical application is restricted by rapid degradation and unsatisfied osteogenic effect in vivo. A bioactive chemical conversion Mg-phenolic networks complex coating(e EGCG) was stepwise incorporated by epigallocatechin-3-gallate(EGCG) and exogenous Mg^(2+)on Mg-2Zn magnesium alloy. Simplex EGCG induced chemical conversion coating(c EGCG) was set as compare group. The in vitro corrosion behavior of Mg-2Zn alloy, c EGCG and e EGCG was evaluated in SBF using electrochemical(PDP, EIS) and immersion test. The cytocompatibility was investigated with rat bone marrow mesenchymal stem cells(r BMSCs). Furthermore, the in vivo tests using a rabbit model involved micro computed tomography(Micro-CT) analysis, histological observation, and interface analysis. The results showed that the e EGCG is Mgphenolic multilayer coating incorporated Mg-phenolic networks, which is rougher, more compact and much thicker than c EGCG. The e EGCG highly improved the corrosion resistance of Mg-2Zn alloy, combined with its lower average hemolytic ratios, continuous high scavenging effect ability and relatively moderate contact angle features, resulting in a stable and suitable biological environment, obviously promoted r BMSCs adhesion and proliferation. More importantly, Micro-CT, histological and interface elements distribution evaluations all revealed that the e EGCG effectively inhibited degradation and enhanced bone tissue formation of Mg alloy implants. This study puts forward a promising bioactive chemical conversion coating with Mg-phenolic networks for the application of biodegradable orthopedic implants.

Performance comparison of training algorithms for the estimation of B?hme abrasion resistance using neural networks

Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.

Effects of vitamin D supplementation on glucose and lipid metabolism in patients with type 2 diabetes mellitus and risk factors for insulin resistance

BACKGROUND Type 2 diabetes mellitus(T2DM)is a chronic metabolic disease featured by insulin resistance(IR)and decreased insulin secretion.Currently,vitamin D deficiency is found in most patients with T2DM,but the relationship between vitamin D and IR in T2DM patients requires further investigation.AIM To explore the risk factors of IR and the effects of vitamin D supplementation on glucose and lipid metabolism in patients with T2DM.METHODS Clinical data of 162 T2DM patients treated in First Affiliated Hospital of Harbin Medical University between January 2019 and February 2022 were retrospectively analyzed.Based on the diagnostic criteria of IR,the patients were divided into a resistance group(n=100)and a non-resistance group(n=62).Subsequently,patients in the resistance group were subdivided to a conventional group(n=44)or a joint group(n=56)according to the treatment regimens.Logistic regression was carried out to analyze the risk factors of IR in T2DM patients.The changes in glucose and lipid metabolism indexes in T2DM patients with vitamin D deficiency were evaluated after the treatment.RESULTS Notable differences were observed in age and body mass index(BMI)between the resistance group and the non-resistance group(both P<0.05).The resistance group exhibited a lower 25-hydroxyvitamin D_(3)(25(OH)D_(3))level,as well as notably higher levels of 2-h postprandial blood glucose(2hPG),fasting blood glucose(FBG),and glycosylated hemoglobin(HbA1c)than the non-resistance group(all P<0.0001).Additionally,the resistance group demonstrated a higher triglyceride(TG)level but a lower high-density lipoprotein-cholesterol(HDL-C)level than the non-resistance group(all P<0.0001).The BMI,TG,HDL-C,25(OH)D_(3),2hPG,and HbA1c were found to be risk factors of IR.Moreover,the posttreatment changes in levels of 25(OH)D_(3),2hPG,FBG and HbA1c,as well as TG,total cholesterol,and HDL-C in the joint group were more significant than those in the conventional group(all P<0.05).CONCLUSION Patients with IR exhibit significant abnormalities in glucose and lipid metabolism parameters compared to the noninsulin resistant group.Logistic regression analysis revealed that 25(OH)D_(3)is an independent risk factor influencing IR.Supplementation of vitamin D has been shown to improve glucose and lipid metabolism in patients with IR and T2DM.

Mechanism of Gegen Qinlian Decoction in Treating Type 2 Diabetes Mellitus Complicated with NAFLD Based on Network Pharmacology

[Objectives]To explore the mechanism of Gegen Qinlian Decoction in treating type 2 diabetes mellitus(T2DM)complicated with non-alcoholic fatty liver disease(NAFLD)by analyzing the effective components of Gegen Qinlian Decoction.[Methods]TCMSP database was used to analyze the active components of Gegen Qinlian Decoction,and pubchem and Swiss ADME databases were also used to predict drug targets,extract T2DM complicated with NAFLD targets from OMIM and Genecards databases.Venny plot was drawn to obtain intersection targets,and finally Cytoscape was used to make core target maps and drug-target-disease network maps.Using DAVID and Metascape database to analyze the intersection targets,the gene ontology information of Go and KEGG was obtained.Microbial informatics technology was used to visualize GO,and Cytoscape was used to make drug-target-disease network map-enrichment pathway map.[Results]The network pharmacological analysis showed that Gegen Qinlian Decoction acted on the key targets of type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease,such as ALB and ALT1,through many components,and achieved the purpose of treating this disease.The chemical constituents of the drug include formononetin,5-hydroxyisomucronulatol-2,5-2-O-glucoside,cholesteryl laurate,isoliquiritigenin,etc.[Conclusions]This study provides a new idea and theoretical support for future drug research and clinical practice.

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize(Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases.Over the last three decades,many quantitative trait loci(QTL)mapping studies reported numerous QTL for fungal disease resistance(FDR)in maize.However,different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies.The meta-QTL(MQTL)analysis is the meta-analysis of multiple QTL experiments,which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits.In the present study,128(33.59%)out of 381 reported QTL(from 82 studies)for FDR could be projected on the maize genome through MQTL analysis.It revealed 38 MQTL for FDR(12 diseases)on all chromosomes except chromosome 10.Five MQTL namely 1_4,2_4,3_2,3_4,and 5_4 were linked with multiple FDR.Total of 1910 candidate genes were identified for all the MQTL regions,with protein kinase gene families,TFs,pathogenesis-related,and disease-responsive proteins directly or indirectly associated with FDR.The comparison of physical positions of marker-traits association(MTAs)from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases.The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies.The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network,which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

High Water Resistance and Enhanced Mechanical Properties of Bio-Based Waterborne Polyurethane Enabled by in-situ Construction of Interpenetrating Polymer Network

In this study,acrylic acid was used as a neutralizer to prepare bio-based WPU with an interpenetrating polymer network structure by thermally induced free radical emulsion polymerization.The effects of the content of acrylic acid on the properties of the resulting waterborne polyurethane-poly(acrylic acid)(WPU-PAA)dispersion and the films were systematically investigated.The results showed that the cross-linking density of the interpenetrating network polymers was increased and the interlocking structure of the soft and hard phase dislocations in the molecular segments of the double networks was tailored with increasing the content of acrylic acid,leading to enhancement of the mechanical properties and water resistance of WPU-PAA films.Notably,with the increase in content of acrylic acid,the tensile strength,Young’s modulus,and toughness of the WPU-PAA-110 film increased by 3 times,and 8 times,and 2.4 times compared with WPU-PAA-80,respectively.The WPU-PAA-100 film showed the best water resistance,and the water absorption rate at 96 h was only 3.27%.This work provided a new design scheme for constructing bio-based WPU materials with excellent properties.

Exploration of the molecular mechanism of Qishen decoction in regulating miR-495/FTO pathway mediated macrophage polarization to improve insulin resistance therapy of type 2 diabetes

Objective:To observe the effect of Qishen decoction on macrophage polarization mediated by miR-495/FTO signaling pathway,and to clarify the molecular mechanism of Qishen decoction in improving insulin resistance in the treatment of type 2 diabetes.Methods:THP-1 was induced to differentiate macrophages with phorbol ester.It was divided into the control group,the model group,the Qishen decoction group,the miR-495 inhibitor group,and the Qishen decoction+miR-495 inhibitor group.Except for the control group,the remaining groups were stimulated with 30 mmol/L glucose to construct a macrophage polarization model,and corresponding drugs were given for intervention.Cells were collected from each group for 24 hours and the content of inflammatory factors(IL-6,IL-1β,IL-4,and IL-10)were detected using enzyme-linked immunosorbent assay.The expression of macrophage polarization marker molecules,miR-495,and FTO were detected by flow cytometry,qPCR,and Western blot to detect.Results:Compared with the control group,there was no significant change in the activity of macrophages in the control serum,Qishen decoction containing serum,and miR-495 inhibitor transfected serum,and the difference was not statistically significant(P>0.05).In addition,compared to the control group,the content of IL-6 and IL-1β,the expression levels of CD68,iNOS,COX-2,miR-495,and the ratio of CD68/CD206,were significantly increased(P<0.01).While the content of IL-4 and IL-10,as well as the expression of CD206,Arg-1,YM-1,and FTO were significantly reduced(P<0.01).Compared with the model group,the QiShen decoction significantly reduced the contents of IL-6 and IL-1β,and the expression levels of CD68,iNOS,COX-2,and miR-495,as well as the ratio of CD68/CD206,while the content of IL-4 and IL-10,as well as the expression of CD206,Arg-1,YM-1,and FTO were significantly increased(P<0.01).Conclusion:Qishen decoction upregulate the expression of FTO to promote M2 type polarization of macrophages,thereby inhibiting inflammation and improving insulin resistance by inhibiting the expression of miR-495.

Simiao Wan alleviates obesity-associated insulin resistance via PKCε/IRS-1/PI3K/Akt signaling pathway based on network pharmacology analysis and experimental validation

Background:The purpose of the study was to investigatethe active ingredients and potential biochemicalmechanisms of Simiao Wan(SMW)in obesity-associated insulin resistance.Methods:An integrated network pharmacology method to screen the active compoundsand candidate targets,construct the protein-protein-interaction network,and ingredients-targets-pathways network was constructed for topological analysis to identify core targets and main ingredients.To find the possible signaling pathways,enrichment analysis was performed.Further,a model of insulin resistance in HL-7702 cells was established to verify the impact of SMW and the regulatory processes.Results:An overall of 63 active components and 151 candidate targets were obtained,in which flavonoids were the main ingredients.Enrichment analysis indicated that the PI3K-Akt signaling pathway was the potential pathway regulated by SMW in obesity-associated insulin resistance treatment.The result showed that SMW could significantly ameliorate insulin sensitivity,increase glucose synthesis and glucose utilization and reduce intracellular lipids accumulation in hepatocytes.Also,SMW inhibited diacylglycerols accumulation-induced PKCεactivity and decreased its translocation to the membrane.Conclusion:SMW ameliorated obesity-associated insulin resistance through PKCε/IRS-1/PI3K/Akt signaling axis in hepatocytes,providing a new strategy for metabolic disease treatment.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

THEORY AND APPLICATION OF πBRIDGE HYDRAULIC RESISTANCE NETWORKS

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Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.

Comparative transcriptome analysis of Ts(Resistant genotype)and Ma(Susceptible genotype)marigold(Tagetes erecta L.)leaves in response to Alternaria tagetica

Marigold black spot caused by Alternaria tagetica is a major disease that can decrease marigold production by 40%,resulting in serious economic losses.In this study,we identified many genes responsive to A.tagetica in the resistant and susceptible marigold genotypes.Analyses of differentially expressed genes,expression trends,and a weighted gene co-expression network revealed a series of hub genes with key roles in different A.tagetica infection stages.Additionally,1216 unigenes encoding transcription factors from eight families were differentially expressed between Ts and Ma.Moreover,R genes fromvarious families(e.g.,N,NL,RLP,and TNL)were differentially expressed in the twomarigold genotypes before and after the inoculation with A.tagetica.Pathway diagrams were used to visualize the leaf transcriptional changes in the two marigold genotypes infected by A.tagetica to clarify the effects of A.tagetica on the expression patterns of genes involved in phosphatidylinositol signaling,plant–pathogen interactions,and plant hormone signal transduction.We identified candidate genes related to disease resistance and generated valuable resources for analyzing the candidate gene functions related to black spot resistance in marigold.The study data may be useful for the molecular marker-assisted screening and breeding of marigold lines with increased disease resistance.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Oxidative stress,insulin resistance,dyslipidemia and type 2 diabetes mellitus

Oxidative stress is increased in metabolic syndrome and type 2 diabetes mellitus(T2DM) and this appears to underlie the development of cardiovascular disease,T2 DM and diabetic complications.Increased oxidative stress appears to be a deleterious factor leading toinsulin resistance,dyslipidemia,β-cell dysfunction,impaired glucose tolerance and ultimately leading to T2 DM.Chronic oxidative stress,hyperglycemia and dyslipidemia are particularly dangerous for β-cells from lowest levels of antioxidant,have high oxidative energy requirements,decrease the gene expression of key β-cell genes and induce cell death.If β-cell functioning is impaired,it results in an under production of insulin,impairs glucose stimulated insulin secretion,fasting hyperglycemia and eventually the development of T2 DM.

Adiponectin and its association with insulin resistance and type 2 diabetes

Adiponectin is an adipokine, which is expressed in adipose tissue and is thought to play an important role in glucose metabolism. Hypoadiponectinemia can cause reduction of fatty acid oxidation, decreased glucose uptake in skeletal muscle cells, and increased gluconeogenesis in hepatic cells. The level of plasma glucose can be increased. On the other hand, the decrease of fatty acid oxidation increases the level of free fatty acid （FFA）, which increases the insulin resistance, and then decreases the glucose uptake, which ultimately causes increased plasma glucose and type 2 diabetes （T2D）. This review describes the process from hypoadiponectinemia to T2D and the genesis of hypoadiponectinemia at a molecular level.

The relationship between insulin resistance/β-cell dysfunction and diabetic retinopathy in Chinese patients with type 2 diabetes mellitus： the Desheng Diabetic Eye Study

AIM： To investigate the relationship between insulin resistance （IR）/β-cell dysfunction and diabetic retinopathy （DR） in Chinese patients with type 2 diabetes mellitus （T2DM）, and to explore further whether there were differences in the relationship among diabetic patients with higher and lower body mass index （BMI）. METHODS： Cross-sectional study. A total of 1466 subjects with T2DM were recruited in a local Desheng Community of urban Beijing from November 2009 to June 2012 for the cohort of Beijing Desheng Diabetic Eye Study. Standardized evaluation was carried out for each participant, including questionnaire, ocular and anthropometric examinations, and laboratory tests. Seven fields 30° color fundus photographs were used for DR grading according to the Early Treatment Diabetic Retinopathy Study protocols. Homeostatis Model Assessment （HOMA） method was employed for IR and β-cell function assessment. RESULTS： After excluding those participants who were treated with insulin （n=352） or had missing data of fasting insulin （n=96）, and further excluding those with poor quality of retinal photographs （n=10）, a total of 1008 subjects were included for the final analysis, 406 （40.3%） were men and 602 （59.7%） were women, age ranging fiom 34 to 86 （64.87±8.28）y. Any DR （levels 14 and above） was present in 278 （27.6%） subjects. After adjusting for possible covariates, the presence of any DR did not correlate with HOMA IR [odds ratio （OR） 1.51, 95% confidence interval （Cl） 0.87-2.61, P=0.14] or HOMA β-cell （OR 0.71, 95%CI 0.40-1.26, P=0.25）. After stratification by BMI, the presence of any DR was associated positively with HOMA IR （OR 2.46, 95%CI： 1.18-5.12, P=0.016）, and negatively with HOMA β-cell （OR 0.40, 95%CI： 0.19-0.87, P=0.021） in the group of patients with higher BMI （225 kg/m2）. In the group of patients with lower BMI （〈25 kg/m2）, the presence of any DR was not associated with HOMA IR （OR 1.00, 95%C1： 0.43-2.33, P=I.00） or HOMA β-cell （OR 1.41, 95%CI： 0.60-3.32, P=0.43）. CONCLUSION： The data suggest that higher IR and lower 13-cell function are associated with the presence of DR in the subgroup of diabetic patients with higher BMI. However, this association is not statistically significant in diabetic patients with lower BMI.

Molecular mechanisms of insulin resistance in type 2 diabetes mellitus

Free fatty acids are known to play a key role in promoting loss of insulin sensitivity in type 2 diabetes mellitus but the underlying mechanism is still unclear.It has been postulated that an increase in the intracellular concentration of fatty acid metabolites activates a serine kinase cascade,which leads to defects in insu-lin signaling downstream to the insulin receptor.In addition,the complex network of adipokines released from adipose tissue modulates the response of tissues to insulin.Among the many molecules involved in the intracellular processing of the signal provided by insulin,the insulin receptor substrate-2,the protein kinase B and the forkhead transcription factor Foxo 1a are of particular interest,as recent data has provided strong evidence that dysfunction of these proteins results in insulin resistance in vivo.Recently,studies have revealed that phosphoinositidedependent kinase 1-independent phosphorylation of protein kinase Cε causes a reduction in insulin receptor gene expression.Additionally,it has been suggested that mitochondrial dysfunction triggers activation of several serine kinases,and weakens insulin signal transduction.Thus,in this review,the current developments in understanding the pathophysiological processes of insulin resistance in type 2 diabetes have been summarized.In addition,this study provides potential new targets for the treatment and prevention of type 2 diabetes.