Ultrastrong and High Thermal Insulating Porous High-Entropy Ceramics up to 2000 °C.

High mechanical load-carrying capability and thermal insulating performance are crucial to thermal-insulation materials under extreme conditions. However, these features are often difficult to achieve simultaneously in conventional porous ceramics. Here, for the first time, it is reported a multiscale structure design and fast fabrication of 9-cation porous high-entropy diboride ceramics via an ultrafast high-temperature synthesis technique that can lead to exceptional mechanical load-bearing capability and high thermal insulation performance. With the construction of multiscale structures involving ultrafine pores at the microscale, high-quality interfaces between building blocks at the nanoscale, and severe lattice distortion at the atomic scale, the materials with an ≈50% porosity exhibit an ultrahigh compressive strength of up to ≈337 MPa at room temperature and a thermal conductivity as low as ≈0.76 W m-1 K-1. More importantly, they demonstrate exceptional thermal stability, with merely ≈2.4% volume shrinkage after 2000 °C annealing. They also show an ultrahigh compressive strength of ≈690 MPa up to 2000 °C, displaying a ductile compressive behavior. The excellent mechanical and thermal insulating properties offer an attractive material for reliable thermal insulation under extreme conditions.

Electric-Field-Induced Selective Directed Transport of Diverse Droplets.

Droplet directional transport is one of the central topics in microfluidics and lab-on-a-chip applications. Selective transport of diverse droplets, particularly in another liquid phase environment with controlled directions, is still challenging. In this work, we propose an electric-field gradient-driven droplet directional transport platform facilitated by a robust lubricant surface. On the platform, we clearly demonstrated a liquid-inherent critical frequency-dominated selective transport of diverse droplets and a driving mechanism transition from electrowetting to liquid dielectrophoresis. Enlightened by the Kelvin-Helmholtz theory, we first realize the directional droplet transport in another liquid phase whenever a permittivity difference exists. Co-transport of multiple droplets and various combinations of droplet types, as well as multifunctional droplet transport modes, are realized based on the presented powerful electric-field gradient-driven platform, overcoming the limitations of the surrounding environment, liquid conductivity, and intrinsic solid-liquid wetting property existing in traditional droplet transport strategies. This work may inspire new applications in liquid separation, multiphase microfluidic manipulation, chemical reagent selection, and so on.

Deformation of confined liquid interfaces by inhomogeneous electric fields and localized particle forces.

HYPOTHESIS: Oil-water interfaces that are created by confining a certain amount of oil in a square shaped pixel (∼200 x 200 µm2 with a height of ∼10 µm) topped by a layer of water, have a curvature that depends on the amount of oil that happens to be present in the confining area. Under the application of an electric field normal to the interface, the interface will deform due to inhomogeneities in the electric field. These inhomogeneities are expected to arise from the initial curvature of the meniscus, from fringe fields that emerge at the confining pixel walls and, if applicable, from interfacially adsorbed particles. MODELING AND EXPERIMENTS: We model the shape of the confined oil-water interface invoking capillarity and electrostatics. Furthermore, we measure the initial curvature by tracking the position of interfacially adsorbed particles depending on sample tilt. FINDINGS: We found that the pixels exhibited meniscus curvature radii ranging from 0.6-7 mm. The corresponding model based minimum oil film thicknesses range between 0.7 and 9 µm. Furthermore, the model shows that the initial meniscus curvature can increase up to 76 percent relative to the initial curvature by the electric field before the oil film becomes unstable. The pixel wall and particles are shown to have minimal impact on the interface deformation.

Strong yet flexible ceramic aerogel.

Ceramic aerogels are highly efficient, lightweight, and chemically stable thermal insulation materials but their application is hindered by their brittleness and low strength. Flexible nanostructure-assembled compressible aerogels have been developed to overcome the brittleness but they still show low strength, leading to insufficient load-bearing capacity. Here we designed and fabricated a laminated SiC-SiOx nanowire aerogel that exhibits reversible compressibility, recoverable buckling deformation, ductile tensile deformation, and simultaneous high strength of up to an order of magnitude larger than other ceramic aerogels. The aerogel also shows good thermal stability ranging from -196 °C in liquid nitrogen to above 1200 °C in butane blow torch, and good thermal insulation performance with a thermal conductivity of 39.3 ± 0.4 mW m-1 K-1. These integrated properties make the aerogel a promising candidate for mechanically robust and highly efficient flexible thermal insulation materials.

Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients.

BACKGROUND: The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP. METHODS: A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models. RESULTS: The deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009). CONCLUSION: Based on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Analysis of the risk factors of radiation pneumonitis and the predictive ability of dosiomics in non-small-cell lung cancer.

Purpose: This prospective study investigated the incidence of radiation pneumonitis (RP) after immunotherapy followed by radiotherapy in non-small-cell lung cancer, analyzed the risk factors for RP, and explored the predictive performance of dosimetry and dosiomics. Methods & materials: Risk factors for grade ≥2 RP were calculated by using a logistic regression model. Predictive performance was compared on the basis of area under the curve values. Results: Grade ≥2 RP occurred in 16 cases (26.7%). The AUC values of V5 Gy, gray-level dependence matrix-small dependence high gray-level emphasis (GLDM-SDHGLE) and combined features were 0.685, 0.724 and 0.734, respectively. Conclusion: Smoking history, bilateral lung V5 Gy and GLDM-SDHGLE were independent risk factors for RP. Dosiomics can effectively predict RP.

The Impact of Shared Information Presentation Time on Users' Privacy-Regulation Behavior in the Context of Vertical Privacy: A Moderated Mediation Model.

Combining data-sharing models and algorithm technologies has led to new data flow structures and usage patterns. In this context, the presentation time of shared low-sensitivity information across platforms has become a crucial factor that affects user perception and privacy-regulation behavior. However, previous studies have not conducted an in-depth exploration of this issue. Based on privacy process theory, this study discusses the impact and potential mechanism of the presentation time (immediate or delayed) of shared low-sensitivity information across platforms on privacy-regulation behavior. Through a pre-study and two online survey experimental studies, which included 379 participants in total, we verified that the immediate information presentation time has a significantly higher impact on online vigilance and privacy-regulation behavior than the delayed condition, ßdirect = 0.5960, 95% CI 0.2402 to 0.9518; ßindirect = 0.1765, 95% CI 0.0326 to 0.3397, and users' perceived control as the moderating role influences online vigilance and privacy-regulation behaviors (preventive or corrective), ßpreventive = -0.0562, 95% CI -0.1435 to -0.0063; ßcorrective = -0.0581, 95% CI -0.1402 to -0.0065. Based on these results, we suggest that the presentation time of using shared low-sensitivity information across platforms should be concerned by companies' recommendation algorithms to reduce users' negative perceptions and privacy behaviors and improve user experience.

Accurate and Wide-Voltage-Range Modeling of Electrowetting with a Lattice Boltzmann Approach.

The lattice Boltzmann method (LBM) has been widely used in multi-phase fluid mechanics and is known to be more computationally efficient than the traditional method of numerically solving Navier-Stokes and Cahn-Hilliard equations. Electrowetting is an important component of interfacial sciences, in which the liquid-liquid and solid-liquid interfaces are tuned by electrostatics. Modeling electrowetting using the LBM can be categorized into surface and bulk methods. By modifying the surface tension scalar, the surface method easily reproduces the fundamental Young-Lippmann (YL) equation at low voltages but fails to capture contact angle saturation at high voltages. With fully coupled hydrodynamics and electrostatics in the form of spatially dependent matrices, the bulk method can successfully show contact angle saturation, but it is often unable to reproduce the YL equation due to its intrinsic inaccuracies. The inaccuracies are mainly due to the fact that while the hydrodynamics are all described by continuous physical quantities in the framework of diffusive interfaces, the interfacial electrostatics are governed by discontinuous electric fields caused by sheet charge density. In this paper, we show that accurately modeling electrowetting using the LBM is non-trivial. Additional modeling work, especially the treatment of interfacial electric fields, is needed to recover the fundamental YL equation at low voltages and predict contact angle saturation at high voltages, with a systematic model validation over key parameters and applications.

An edge-based approach for virtual network embedding based on the graph edit distance.

Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

Highly cross-linked carbon tube aerogels with enhanced elasticity and fatigue resistance.

Carbon aerogels are elastic, mechanically robust and fatigue resistant and are known for their promising applications in the fields of soft robotics, pressure sensors etc. However, these aerogels are generally fragile and/or easily deformable, which limits their applications. Here, we report a synthesis strategy for fabricating highly compressible and fatigue-resistant aerogels by assembling interconnected carbon tubes. The carbon tube aerogels demonstrate near-zero Poisson's ratio, exhibit a maximum strength over 20 MPa and a completely recoverable strain up to 99%. They show high fatigue resistance (less than 1.5% permanent degradation after 1000 cycles at 99% strain) and are thermally stable up to 2500 °C in an Ar atmosphere. Additionally, they possess tunable conductivity and electromagnetic shielding. The combined mechanical and multi-functional properties offer an attractive material for the use in harsh environments.

An evolutionary game analysis of digital currency innovation and regulatory coordination.

The innovation and regulatory coordination of digital currency is an important proposition in the new era of Fintech. There is increasing competition between traditional currencies and new digital currencies, so a spontaneous game model of currencies is analyzed. By introducing the role of financial coordination, this paper revises the evolutionary game model of digital currency innovation, and analyzes their competition strategies through case and simulation. The results show that: first, the dominant result of digital currency spontaneous game is that both parties tend to digital cooperation strategy. Second, with the introduction of financial regulation, the dominant result of digital currency tripartite evolutionary game is that financial institutions tend to participate in coordination and both currency parties tend to cooperate. Third, the choice strategy of currency is more sensitive to the changes of willingness to participate in cooperation, cooperation costs and cooperation benefits of financial coordination. The selection strategy of financial coordination institutions for digital currency is more influenced by changes in cooperation costs and incentive return in the process of participating in cooperation.

Hierarchical SiC-Graphene Composite Aerogel-Supported Ni-Mo-S Nanosheets for Efficient pH-Universal Electrocatalytic Hydrogen Evolution.

MoS2 exhibits good prospects in electrocatalytic hydrogen evolution. Whereas, the electrocatalytic property of MoS2 is restrained by its insufficient active sites, low electrical conductivity, and slow water dissociation processes. Herein, an aerogel composed of silicon carbide (SiC) and graphene (SiCnw-RGO) was constructed by growing SiC nanowires (SiCnw) in the graphene aerogel (RGO) via the CVD method, and then Ni-Mo-S nanosheets were hydrothermally synthesized on the SiCnw-RGO composite aerogel to develop an efficient pH-universal electrocatalyst. Ni-Mo-S nanosheets supported on SiCnw-RGO (Ni-Mo-S@SiCnw-RGO) exhibit an interesting hierarchical three-dimensional interconnected structure of composite aerogel. The optimal Ni-Mo-S@SiCnw-RGO electrocatalyst exhibits excellent catalytic performance with low Tafel slopes of 60 mV/dec under acidic conditions and 90 mV/dec under alkaline conditions. Density functional theory calculations demonstrate a composite catalyst exhibits advantageous hydrogen adsorption free energy and water dissociation energy barrier. This study provides a reference to design an efficient hierarchical aerogel electrocatalyst.

GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma.

Background: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. Methods: By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers. Results: Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid. Conclusions: To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

TaTPP-7A positively feedback regulates grain filling and wheat grain yield through T6P-SnRK1 signalling pathway and sugar-ABA interaction.

Grain size and filling are two key determinants of grain thousand-kernel weight (TKW) and crop yield, therefore they have undergone strong selection since cereal was domesticated. Genetic dissection of the two traits will improve yield potential in crops. A quantitative trait locus significantly associated with wheat grain TKW was detected on chromosome 7AS flanked by a simple sequence repeat marker of Wmc17 in Chinese wheat 262 mini-core collection by genome-wide association study. Combined with the bulked segregant RNA-sequencing (BSR-seq) analysis of an F2 genetic segregation population with extremely different TKW traits, a candidate trehalose-6-phosphate phosphatase gene located at 135.0 Mb (CS V1.0), designated as TaTPP-7A, was identified. This gene was specifically expressed in developing grains and strongly influenced grain filling and size. Overexpression (OE) of TaTPP-7A in wheat enhanced grain TKW and wheat yield greatly. Detailed analysis revealed that OE of TaTPP-7A significantly increased the expression levels of starch synthesis- and senescence-related genes involved in abscisic acid (ABA) and ethylene pathways. Moreover, most of the sucrose metabolism and starch regulation-related genes were potentially regulated by SnRK1. In addition, TaTPP-7A is a crucial domestication- and breeding-targeted gene and it feedback regulates sucrose lysis, flux, and utilization in the grain endosperm mainly through the T6P-SnRK1 pathway and sugar-ABA interaction. Thus, we confirmed the T6P signalling pathway as the central regulatory system for sucrose allocation and source-sink interactions in wheat grains and propose that the trehalose pathway components have great potential to increase yields in cereal crops.

Conservatively transmitted alleles of key agronomic genes provide insights into the genetic basis of founder parents in bread wheat (Triticum aestivum L.).

BACKGROUND: Founder parents play extremely important roles in wheat breeding. Studies into the genetic basis of founder parents and the transmission rules of favorable alleles are of great significance in improving agronomically important traits in wheat. RESULTS: Here, a total of 366 founder parents, widely grown cultivars, and derivatives of four representative founder parents were genotyped based on efficient kompetitive allele-specific PCR (KASP) markers in 87 agronomically important genes controlling yield, quality, adaptability, and stress resistance. Genetic composition analysis of founder parents and widely grown cultivars showed a consistently high frequency of favorable alleles for yield-related genes. This analysis further showed that other alleles favorable for resistance, strong gluten, dwarf size, and early heading date were also subject to selective pressure over time. By comparing the transmission of alleles from four representative founder parents to their derivatives during different breeding periods, it was found that the genetic composition of the representative founder parents was optimized as breeding progressed over time, with the number and types of favorable alleles carried gradually increasing and becoming enriched. There are still a large number of favorable alleles in wheat founder parents that have not been fully utilized in breeding selection. Eighty-seven agronomically important genes were used to construct an enrichment map that shows favorable alleles of four founder parents, providing an important theoretical foundation for future identification of candidate wheat founder parents. CONCLUSIONS: These results reveal the genetic basis of founder parents and allele transmission for 87 agronomically important genes and shed light on breeding strategies for the next generation of elite founder parents in wheat.

Lightweight and Strong Ceramic Network with Exceptional Damage Tolerance.

Lightweight materials such as porous ceramics have attracted increasing attention for applications in energy conservation, aerospace and automobile industries. However, porous ceramics are usually weak and brittle; in particular, tiny defects could cause catastrophic failure, which affects their reliability and limits the potential use greatly. Here we report a SiC/SiO2 nanowire network constructed from numerous well-bonded SiC nanowires coated by a biphasic structure consisting of amorphous SiO2 and nanocrystal SiC. The as-obtained SiC/SiO2 nanowire network is lightweight (360 ± 10 mg cm-3), mechanically strong (compressive strength of 16 MPa), and damage-tolerant. The high strength of the network is attributed to the biphasic mixed structure of the binding coating which can restrict the deformation of nanowires upon compression. The lightweight and strong SiC/SiO2 nanowire network shows potential for engineering applications in harsh environments.

An intravenous anesthetic drug-propofol, influences the biological characteristics of malignant tumors and reshapes the tumor microenvironment: A narrative literature review.

Malignant tumors are the second leading cause of death worldwide. This is a public health concern that negatively impacts human health and poses a threat to the safety of life. Although there are several treatment approaches for malignant tumors, surgical resection remains the primary and direct treatment for malignant solid tumors. Anesthesia is an integral part of the operation process. Different anesthesia techniques and drugs have different effects on the operation and the postoperative prognosis. Propofol is an intravenous anesthetic that is commonly used in surgery. A substantial number of studies have shown that propofol participates in the pathophysiological process related to malignant tumors and affects the occurrence and development of malignant tumors, including anti-tumor effect, pro-tumor effect, and regulation of drug resistance. Propofol can also reshape the tumor microenvironment, including anti-angiogenesis, regulation of immunity, reduction of inflammation and remodeling of the extracellular matrix. Furthermore, most clinical studies have also indicated that propofol may contribute to a better postoperative outcome in some malignant tumor surgeries. Therefore, the author reviewed the chemical properties, pharmacokinetics, clinical application and limitations, mechanism of influencing the biological characteristics of malignant tumors and reshaping the tumor microenvironment, studies of propofol in animal tumor models and its relationship with postoperative prognosis of propofol in combination with the relevant literature in recent years, to lay a foundation for further study on the correlation between propofol and malignant tumor and provide theoretical guidance for the selection of anesthetics in malignant tumor surgery.

Cytotoxic Sesquiterpenoids from Atractylodes chinensis (DC.) Koidz.

Four new sesquiterpenoids named atrchiterpenes A-D (1-4), a new natural product (5), and twelve known compounds (6-17) were isolated from Atractylodes chinensis (DC.) Koidz. Compound 1 was a rare N-containing eudesmane-type sesquiterpenoid. Structure elucidation was performed by spectroscopic techniques, including 1D, 2D NMR spectra, and HR-ESI-MS. Compounds 6-11, 14, and 17 were reported from Atractylodes for the first time. All the isolated compounds were evaluated for cytotoxicity activity. Compound 16 showed moderate cytotoxicity against HepG2 cells with an IC50 value of 5.81±0.47.

Increased levels of serum adenosine deaminase and increased risk of diabetic peripheral neuropathy in type 2 diabetes.

Background: Increased serum adenosine deaminase (ADA) levels have been shown to be involved in metabolic abnormalities and immune disequilibrium, which may in turn contribute to inflammatory diseases. This study aimed to determine whether increased serum ADA levels are related to diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2D). Methods: This study was part of a series exploring the potential risks for DPN. All patients received DPN assessment based on neuropathic symptoms, neuropathic signs, and nerve conduction studies to calculate the composite Z score of nerve latency, amplitude and conduction velocity (NCV). DPN was confirmed by both at least a presentation of neuropathic symptoms/signs and an abnormal nerve conduction index. Serum ADA levels were also synchronously detected. Results: A total of 384 eligible patients with T2D were recruited for this study, and 24.5% (n=94) were determined to have DPN. Increases in serum ADA levels were closely associated with increases in composite Z score of latency (ß=0.263, t=5.273, p<0.001) and decreases in composite Z score of amplitude (ß=-0.126, t=-2.352, p=0.019) and NCV (ß=-0.201, t=-3.841, p<0.001) after adjusting for other clinical covariates. Moreover, each 5 U/L increase in serum ADA levels was associated with a 1.781-fold increased adjusted odds ratio of having DPN (95% confidence interval: 1.271-2.495). Furthermore, the optimal cut-off value of serum ADA levels to discriminate DPN was ≥14.2 U/L (sensitivity=59.57%, specificity=75.52% and Youden index=0.351) after analysis by receiver operating characteristic curve. Conclusions: Increased serum ADA levels may be a potential risk factor for DPN in patients with T2D.

Increased plasma D-dimer levels may be a promising indicator for diabetic peripheral neuropathy in type 2 diabetes.

Background: Increased plasma D-dimer levels have been reported to be associated with a range of adverse health outcomes. This study aimed to determine whether plasma D-dimer is connected to diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2D). Methods: This study was part of a series exploring the potential risks for DPN. All patients were questioned for neurologic symptoms, examined for neurologic signs, and received nerve conduction studies to collect nerve action potential onset latency, amplitude, and nerve conduction velocity (NCV). Composite Z scores of latency, amplitude, and NCV were calculated. DPN was confirmed as both at least a neurologic symptom/sign and an abnormality of nerve conduction studies. Coagulation function indices, such as plasma D-dimer levels, were also synchronously detected. Results: We finally recruited 393 eligible patients for this study, of whom 24.7% (n = 97) were determined to have DPN. The plasma D-dimer level was found to be closely associated with the composite Z score of latency, amplitude, and NCV after adjusting for other coagulation function indices and clinical covariates (latency: ß = 0.134, t = 2.299, p = 0.022; amplitude: ß = -0.138, t = -2.286, p = 0.023; NCV: ß = -0.139, t = -2.433, p = 0.016). Moreover, the prevalence of DPN in the first, second, third, and fourth quartiles (Q1, Q2, Q3, and Q4) of the D-dimer level was 15.2%, 15.9%, 26.4%, and 42.7%, respectively (p for trend < 0.001). The corresponding adjusted odds ratios and 95% CIs for DPN in D-dimer quartiles were 1, 0.79 (0.21-2.99), 1.75 (0.49-6.26), and 5.17 (1.38-19.42), respectively. Furthermore, the optimal cutoff value of the plasma D-dimer level to discriminate DPN was ≥0.22 mg/L (sensitivity = 67.01%, specificity = 58.78%, and Youden index = 0.26) after analysis by the receiver operating characteristic curve. Conclusions: Increased plasma D-dimer levels may be a promising indicator for DPN in patients with T2D.