Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Target Controllability of Multi-Layer Networks With High-Dimensional

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Multi-layer phenomena in petawatt laser-driven acceleration of heavy ions

Laser-accelerated high-flux-intensity heavy-ion beams are important for new types of accelerators.A particle-in-cell program(Smilei) is employed to simulate the entire process of Station of Extreme Light(SEL) 100 PW laser-accelerated heavy particles using different nanoscale short targets with a thickness of 100 nm Cr, Fe, Ag, Ta, Au, Pb, Th and U, as well as 200 nm thick Al and Ca. An obvious stratification is observed in the simulation. The layering phenomenon is a hybrid acceleration mechanism reflecting target normal sheath acceleration and radiation pressure acceleration, and this phenomenon is understood from the simulated energy spectrum,ionization and spatial electric field distribution. According to the stratification, it is suggested that high-quality heavy-ion beams could be expected for fusion reactions to synthesize superheavy nuclei. Two plasma clusters in the stratification are observed simultaneously, which suggest new techniques for plasma experiments as well as thinner metal targets in the precision machining process.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

Blast wave characteristics of multi-layer composite charge:Theoretical analysis,numerical simulation,and experimental validation

This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.

Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.

Explosive synchronization of multi-layer complex networks based on star connection between layers with delay

Explosive synchronization(ES)is a kind of first-order jump phenomenon that exists in physical and biological systems.In recent years,researchers have focused on ES between single-layer and multi-layer networks.Most research on complex networks with delay has focused on single-layer or double-layer networks,multi-layer networks are seldom explored.In this paper,we propose a Kuramoto model of frequency weights in multi-layer complex networks with delay and star connections between layers.Through theoretical analysis and numerical verification,the factors affecting the backward critical coupling strength are analyzed.The results show that the interaction between layers and the average node degree has a direct effect on the backward critical coupling strength of each layer network.The location of the delay,the size of the delay,the number of network layers,the number of nodes,and the network topology are revealed to have no direct impact on the backward critical coupling strength of the network.Delay is introduced to explore the influence of delay and other related parameters on ES.

Evaluation of the efficacy and safety of endoscopic band ligation in the treatment of bleeding from mild to moderate gastric varices type 1

BACKGROUND According to practice guidelines,endoscopic band ligation(EBL)and endoscopic tissue adhesive injection(TAI)are recommended for treating bleeding from esophagogastric varices.However,EBL and TAI are known to cause serious complications,such as hemorrhage from dislodged ligature rings caused by EBL and hemorrhage from operation-related ulcers resulting from TAI.However,the optimal therapy for mild to moderate type 1 gastric variceal hemorrhage(GOV1)has not been determined.Therefore,the aim of this study was to discover an individualized treatment for mild to moderate GOV1.AIM To compare the efficacy,safety and costs of EBL and TAI for the treatment of mild and moderate GOV1.METHODS A clinical analysis of the data retrieved from patients with mild or moderate GOV1 gastric varices who were treated under endoscopy was also conducted.Patients were allocated to an EBL group or an endoscopic TAI group.The differences in the incidence of varicose relief,operative time,operation success rate,mortality rate within 6 wk,rebleeding rate,6-wk operation-related ulcer healing rate,complication rate and average operation cost were compared between the two groups of patients.RESULTS The total effective rate of the two treatments was similar,but the efficacy of EBL(66.7%)was markedly better than that of TAI(39.2%)(P<0.05).The operation success rate in both groups was 100%,and the 6-wk mortality rate in both groups was 0%.The average operative time(26 min)in the EBL group was significantly shorter than that in the TAI group(46 min)(P<0.01).The rate of delayed postoperative rebleeding in the EBL group was significantly lower than that in the TAI group(11.8%vs 45.1%)(P<0.01).At 6 wk after the operation,the healing rate of operation-related ulcers in the EBL group was 80.4%,which was significantly greater than that in the TAI group(35.3%)(P<0.01).The incidence of postoperative complications in the two groups was similar.The average cost and other related economic factors were greater for the EBL than for the TAI(P<0.01).CONCLUSION For mild to moderate GOV1,patients with EBL had a greater one-time varix eradication rate,a greater 6-wk operation-related ulcer healing rate,a lower delayed rebleeding rate and a lower cost than patients with TAI.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks,Liaohe Western Depression,China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks.Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition,high total organic carbon(TOC)content,various kerogen types,and a wide range of thermal maturity.Consequently,their hydrocarbon generation potential and resource estimation can be misinterpreted.In this study,geochemical tests,numerical analysis,hydrocarbon generation kinetics,and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks.Optimized hydrocarbon generation and expulsion(HGE)models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance(R_(o))datasets.Three sets of good-excellent source rocks deposited in the fourth(Es4),third(Es3),and first(Es1)members of Paleogene Shahejie Formation,are predominantly types I-II_(1),II_(1)-II_(2),and II-III,respectively.The activation energy of types I-II_(2)kerogen is concentrated(180-230 kcal/mol),whereas that of type III kerogen is widely distributed(150-280 kcal/mol).The original hydrocarbon generation potentials of types I,II_(1),II_(2),and III kerogens are 790,510,270,and 85 mg/g TOC,respectively.The Ro values of the hydrocarbon generation threshold for type I-III source rocks gradually increase from 0.42%to 0.74%,and Ro values of the hydrocarbon expulsion threshold increase from 0.49%to 0.87%.Types I and II_(1)source rocks are characterized by earlier hydrocarbon generation,more rapid hydrocarbon expulsion,and narrower hydrocarbon generation windows than types II_(2)and III source rocks.The kerogen types also affect the HGE history and resource potential.Three types(conventional,tight,and shale oil/gas)and three levels(realistic,expected,and prospective)of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated.Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources.This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types,and facilitate a quick and accurate assessment of hydrocarbon resources,providing strategies for future oil and gas exploration.

Diabetes mellitus in patients with type 1 autoimmune pancreatitis at diagnosis and after corticosteroid therapy

Background:A high prevalence of diabetes mellitus(DM)coexisting with autoimmune pancreatitis(AIP)is observed.However,evidence on the circumstances under which corticosteroid therapy(CST)for AIP improves or worsens DM is scarce.This study aimed to demonstrate and identify predictors of DM control under the influence of CST.Methods:Patients diagnosed with type 1 AIP were enrolled from a prospectively maintained cohort and were classified into three groups according to the chronology in which AIP and DM were diagnosed:pre-existing DM(pDM),concurrent DM(cDM),and non-DM(nDM).The responses of DM to CST were assessed when corticosteroid was ceased or tapered to a maintenance dose and classified as‘improvement’and‘non-improvement’(including‘no change’and‘exacerbation’).Results:Among 101 patients with type 1 AIP,52(51.5%)patients were complicated with DM at the time of AIP diagnosis,with 36 patients in the cDM group and 16 patients in the pDM group.The incidences of diffuse pancreatic swelling(72.2%)and pancreatic body/tail involvement(91.7%)were significantly higher in the cDM group than in both the pDM and nDM groups.Of the 52 patients with DM,CST was administered in 48 cases.Multivariate logistic analysis identified that elevated serum gamma-glutamyl transferase(GGT)level at AIP diagnosis[odds ratio(OR)=0.032,95%confidence interval(CI):0.003-0.412,P=0.008]and pancreatic atrophy after CST(OR=0.027,95%CI:0.003-0.295,P=0.003)were negatively associated with DM control improvement.Conclusions:Patients with diffuse pancreatic swelling and pancreatic body/tail involvement in pancreatitis tended to be complicated with cDM at AIP diagnosis.CST exerted a beneficial effect on the clinical course of DM in nearly half of the AIP patients complicated with DM at diagnosis,particularly in those without elevated serum GGT levels at diagnosis and who did not experience pancreatic atrophy after CST.

Analysis of the influencing factors and clinical related characteristics of pulmonary tuberculosis in patients with type 2 diabetes mellitus

BACKGROUND In China,the prevalence of type 2 diabetes mellitus(T2DM)among diabetic patients is estimated to be between 90%-95%.Additionally,China is among the 22 countries burdened by a high number of tuberculosis cases,with approximately 4.5 million individuals affected by active tuberculosis.Notably,T2DM poses a significant risk factor for the development of tuberculosis,as evidenced by the increased incidence of T2DM coexisting with pulmonary tuberculosis(T2DMPTB),which has risen from 19.3%to 24.1%.It is evident that these two diseases are intricately interconnected and mutually reinforcing in nature.AIM To elucidate the clinical features of individuals diagnosed with both T2DM and tuberculosis(T2DM-PTB),as well as to investigate the potential risk factors associated with active tuberculosis in patients with T2DM.METHODS T2DM-PTB patients who visited our hospital between January 2020 and January 2023 were selected as the observation group,Simple DM patients presenting to our hospital in the same period were the control group,Controls and case groups were matched 1:2 according to the principle of the same sex,age difference(±3)years and disease duration difference(±5)years,patients were investigated for general demographic characteristics,diabetes-related characteristics,body immune status,lifestyle and behavioral habits,univariate and multivariate analysis of the data using conditional logistic regression,calculate the odds ratio(OR)values and 95%CI of OR values.RESULTS A total of 315 study subjects were included in this study,including 105 subjects in the observation group and 210 subjects in the control group.Comparison of the results of both anthropometric and biochemical measures showed that the constitution index,systolic blood pressure,diastolic blood pressure and lymphocyte count were significantly lower in the case group,while fasting blood glucose and high-density lipoprotein cholesterol levels were significantly higher than those in the control group.The results of univariate analysis showed that poor glucose control,hypoproteinemia,lymphopenia,TB contact history,high infection,smoking and alcohol consumption were positively associated with PTB in T2DM patients;married,history of hypertension,treatment of oral hypoglycemic drugs plus insulin,overweight,obesity and regular exercise were negatively associated with PTB in T2DM patients.Results of multivariate stepwise regression analysis found lymphopenia(OR=17.75,95%CI:3.40-92.74),smoking(OR=12.25,95%CI:2.53-59.37),history of TB contact(OR=6.56,95%CI:1.23-35.03)and poor glycemic control(OR=3.37,95%CI:1.11-10.25)was associated with an increased risk of developing PTB in patients with T2DM,While being overweight(OR=0.23,95%CI:0.08-0.72)and obesity(OR=0.11,95%CI:0.02-0.72)was associated with a reduced risk of developing PTB in patients with T2DM.CONCLUSION T2DM-PTB patients are prone to worse glycemic control,higher infection frequency,and a higher proportion of people smoking,drinking alcohol,and lack of exercise.Lymphopenia,smoking,history of TB exposure,poor glycemic control were independent risk factors for T2DM-PTB,and overweight and obesity were associated with reduced risk of concurrent PTB in patients with T2DM.

Detecting Phishing Using a Multi-Layered Social Engineering Framework

As businesses develop and expand with a significant volume of data,data protection and privacy become increasingly important.Research has shown a tremendous increase in phishing activities during and after COVID-19.This research aimed to improve the existing approaches to detecting phishing activities on the internet.We designed a multi-layered phish detection algorithm to detect and prevent phishing applications on the internet using URLs.In the algorithm,we considered technical dimensions of phishing attack prevention and mitigation on the internet.In our approach,we merge,Phishtank,Blacklist,Blocklist,and Whitelist to form our framework.A web application system and browser extension were developed to implement the algorithm.The multi-layer phish detector evaluated ten thousandURLs gathered randomly from the internet(five thousand phishing and five thousand legitimate URLs).The system was estimated to detect levels of accuracy,true-positive and false-positive values.The system level accuracy was recorded to be 98.16%.Approximately 49.6%of the websites were detected as illegitimate,whilst 49.8%were seen as legitimate.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

BACKGROUND Among older adults,type 2 diabetes mellitus(T2DM)is widely recognized as one of the most prevalent diseases.Diabetic nephropathy(DN)is a frequent com-plication of DM,mainly characterized by renal microvascular damage.Early detection,aggressive prevention,and cure of DN are key to improving prognosis.Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.AIM To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.METHODS The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed.According to whether the patients had DN,they were divided into the DN group(complicated with DN)and the non-DN group(without DN).Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM.The data were randomly split into a training set(n=147)and a test set(n=63)in a 7:3 ratio using a random function.The training set was used to construct the nomogram,decision tree,and random forest models,and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity,specificity,accuracy,recall,precision,and area under the receiver operating characteristic curve.RESULTS Among the 210 patients with T2DM,74(35.34%)had DN.The validation dataset showed that the accuracies of the nomogram,decision tree,and random forest models in predicting DN in patients with T2DM were 0.746,0.714,and 0.730,respectively.The sensitivities were 0.710,0.710,and 0.806,respectively;the specificities were 0.844,0.875,and 0.844,respectively;the area under the receiver operating characteristic curve(AUC)of the patients were 0.811,0.735,and 0.850,respectively.The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models(P<0.05),whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant(P>0.05).CONCLUSION Among the three prediction models,random forest performs best and can help identify patients with T2DM at high risk of DN.

Hypoglycemic mechanism of Tegillarca granosa polysaccharides on type 2 diabetic mice by altering gut microbiota and regulating the PI3K-akt signaling pathwaye

Type 2 diabetes mellitus(T2DM)is a complex metabolic disease threatening human health.We investigated the effects of Tegillarca granosa polysaccharide(TGP)and determined its potential mechanisms in a mouse model of T2DM established through a high-fat diet and streptozotocin.TGP(5.1×10^(3) Da)was composed of mannose,glucosamine,rhamnose,glucuronic acid,galactosamine,glucose,galactose,xylose,and fucose.It could significantly alleviate weight loss,reduce fasting blood glucose levels,reverse dyslipidemia,reduce liver damage from oxidative stress,and improve insulin sensitivity.RT-PCR and Western blotting indicated that TGP could activate the phosphatidylinositol-3-kinase/protein kinase B signaling pathway to regulate disorders in glucolipid metabolism and improve insulin resistance.TGP increased the abundance of Allobaculum,Akkermansia,and Bifidobacterium,restored the microbiota abundance in the intestinal tracts of mice with T2DM,and promoted short-chain fatty acid production.This study provides new insights into the antidiabetic effects of TGP and highlights its potential as a natural hypoglycemic nutraceutical.

Comparative efficacy of sodium glucose cotransporter-2 inhibitors in the management of type 2 diabetes mellitus:A real-world experience

BACKGROUND Sodium glucose cotransporter-2 inhibitors(SGLT-2i)are a class of drugs with modest antidiabetic efficacy,weight loss effect,and cardiovascular benefits as proven by multiple randomised controlled trials(RCTs).However,real-world data on the comparative efficacy and safety of individual SGLT-2i medications is sparse.AIM To study the comparative efficacy and safety of SGLT-2i using real-world clinical data.METHODS We evaluated the comparative efficacy data of 3 SGLT-2i drugs(dapagliflozin,canagliflozin,and empagliflozin)used for treating patients with type 2 diabetes mellitus.Data on the reduction of glycated hemoglobin(HbA1c),body weight,blood pressure(BP),urine albumin creatinine ratio(ACR),and adverse effects were recorded retrospectively.RESULTS Data from 467 patients with a median age of 64(14.8)years,294(62.96%)males and 375(80.5%)Caucasians were analysed.Median diabetes duration was 16.0(9.0)years,and the duration of SGLT-2i use was 3.6(2.1)years.SGLT-2i molecules used were dapagliflozin 10 mg(n=227;48.6%),canagliflozin 300 mg(n=160;34.3%),and empagliflozin 25 mg(n=80;17.1).Baseline median(interquartile range)HbA1c in mmol/mol were:dapagliflozin-78.0(25.3),canagliflozin-80.0(25.5),and empagliflozin-75.0(23.5)respectively.The respective median HbA1c reduction at 12 months and the latest review(just prior to the study)were:66.5(22.8)&69.0(24.0),67.0(16.3)&66.0(28.0),and 67.0(22.5)&66.5(25.8)respectively(P<0.001 for all comparisons from baseline).Significant improvements in body weight(in kilograms)from baseline to study end were noticed with dapagliflozin-101(29.5)to 92.2(25.6),and canagliflozin 100(28.3)to 95.3(27.5)only.Significant reductions in median systolic and diastolic BP,from 144(21)mmHg to 139(23)mmHg;(P=0.015),and from 82(16)mmHg to 78(19)mmHg;(P<0.001)respectively were also observed.A significant reduction of microalbuminuria was observed with canagliflozin only[ACR 14.6(42.6)at baseline to 8.9(23.7)at the study end;P=0.043].Adverse effects of SGLT-2i were as follows:genital thrush and urinary infection-20(8.8%)&17(7.5%)with dapagliflozin;9(5.6%)&5(3.13%)with canagliflozin;and 4(5%)&4(5%)with empagliflozin.Diabetic ketoacidosis was observed in 4(1.8%)with dapagliflozin and 1(0.63%)with canagliflozin.CONCLUSION Treatment of patients with SGLT-2i is associated with statistically significant reductions in HbA1c,body weight,and better than those reported in RCTs,with low side effect profiles.A review of large-scale real-world data is needed to inform better clinical practice decision making.