A bias voltage controlled electrode-molecule interface in single-molecule junctions.

Tuning the electrode-molecule interface stands at the heart of functional single-molecule devices. Herein, we report that the electrode-molecule interface of difluoro-substituted benzothiadiazole (FBTZ)-based single-molecule junctions can be modulated by the bias voltage. At low bias voltage (100 mV), the dative Au-N linkage is formed and at high bias voltage (600 mV), a covalent Au-C linkage is constructed. These junctions show distinct conductance. Interestingly, dominant charge carriers in Au-N- and Au-C-based junctions are different, as evidenced by dft calculations. These results provide a new strategy for regulating the electrode-molecule interface, which will advance the development of molecular electronics.

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico.

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (ß = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

Fundamental, mechanism and development of hydration lubrication: From bio-inspiration to artificial manufacturing.

Friction and lubrication are ubiquitous in all kinds of movements and play a vital role in the smooth operation of production machinery. Water is indispensable both in the lubrication systems of natural organisms and in hydration lubrication systems. There exists a high degree of similarity between these systems, which has driven the development of hydration lubrication from biomimetic to artificial manufacturing. In particular, significant advancements have been made in the understanding of the mechanisms of hydration lubrication over the past 30 years. This enhanced understanding has further stimulated the exploration of biomimetic inspiration from natural hydration lubrication systems, to develop novel artificial hydration lubrication systems that are cost-effective, easily transportable, and possess excellent capability. This review summarizes the recent experimental and theoretical advances in the understanding of hydration-lubrication processes. The entire paper is divided into three parts. Firstly, surface interactions relevant to hydration lubrication are discussed, encompassing topics such as hydrogen bonding, hydration layer, electric double layer force, hydration force, and Stribeck curve. The second part begins with an introduction to articular cartilage in biomaterial lubrication, discussing its compositional structure and lubrication mechanisms. Subsequently, three major categories of bio-inspired artificial manufacturing lubricating material systems are presented, including hydrogels, polymer brushes (e.g., neutral, positive, negative and zwitterionic brushes), hydration lubricant additives (e.g., nano-particles, polymers, ionic liquids), and their related lubrication mechanism is also described. Finally, the challenges and perspectives for hydration lubrication research and materials development are presented.

Suppressing the Conductance of Single-Molecule Junctions Fabricated by sp2 C-H Bond Metalation.

High-conducting single-molecule junctions have attracted a great deal of attention, but insulating single-molecule junctions, which are critical in molecular circuits, have been less investigated due to the long-standing challenges. Herein, the in situ formation of a Au-C linker via electrical-potential-mediated sp2 C-H bond metalation of polyfluoroarenes with the assistance of scanning tunneling microscope-based break junction technique is reported. This metalation process is bias-dependent and occurs with an electropositive electrode, and the formed junction is highly oriented. Surprisingly, these polyfluoroarenes exhibit unexpected low conductance even under short molecular lengths and are superior molecular insulators. Flicker noise analysis and DFT calculations confirm that the insulating properties of polyfluoroarenes are ascribed to their multiple fluorine substituents. Our results pave a way for constructing oriented asymmetric molecular junctions and provide an efficient strategy to suppress the single-molecule conductance, which will aid in the design of molecular insulators and advance the development of self-integrating functional molecular circuits.

In defense of local descriptor-based few-shot object detection.

State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.

[Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection].

BACKGROUND: Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model. METHODS: The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA). RESULTS: Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit. CONCLUSIONS: Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Polymer Coating with Balanced Coordination Strength and Ion Conductivity for Dendrite-Free Zinc Anode.

Decorating Zn anodes with functionalized polymers is considered as an effective strategy to inhibit dendrite growth. However, this normally brings extra interfacial resistance rendering slow reaction kinetics of Zn2+ . Herein, a poly(2-vinylpyridine) (P2VP) coating with modulated coordination strength and ion conductivity for dendrite-free Zn anode is reported. The P2VP coating favors a high electrolyte wettability and rapid Zn2+ migration speed (Zn2+ transfer number, tZn 2+ = 0.58). Electrostatic potential calculation shows that P2VP mildly coordinates with Zn2+ (adsorption energy = -0.94 eV), which promotes a preferential deposition of Zn along the (002) crystal plane. Notably, the use of partially (26%) quaternized P2VP (q-P2VP) further reduces the interfacial resistance to 126 Ω, leading to a high ion migration speed (tZn 2+ = 0.78) and a considerably low nucleation overpotential (18 mV). As a result of the synergistic effect of mild coordination and partial electrolysis, the overpotential of the q-P2VP-decorated Zn anode retains at a considerably low level (≈46 mV) over 1000 h at a high current density of 10 mA cm-2 . The assembled (NH4 )2 V6 O16 ·1.5H2 O || glass fiber || q-P2VP-Zn full cell reveals a lower average capacity decay rate of only 0.018% per cycle within 500 cycles at 1 A g-1 .

Association between MC1R gene and coat color segregation in Shanxia long black pig and Lulai black pig.

BACKGROUND: Coat color, as a distinct phenotypic characteristic of pigs, is often subject to preference and selection, such as in the breeding process of new breed. Shanxia long black pig was derived from an intercross between Berkshire boars and Licha black pig sows, and it was bred as a paternal strain with high-quality meat and black coat color. Although the coat color was black in the F1 generation of the intercross, it segregated in the subsequent generations. This study aims to decode the genetic basis of coat color segregation and develop a method to distinct black pigs from the spotted in Shanxia long black pig. RESULTS: Only a QTL was mapped at the proximal end of chromosome 6, and MC1R gene was picked out as functional candidate gene. A total of 11 polymorphic loci were identified in MC1R gene, and only the c.67_68insCC variant was co-segregating with coat color. This locus isn't recognized by any restriction endonuclease, so it can't be genotyped by PCR-RFLP. The c.370G > A polymorphic locus was also significantly associated with coat color, and has been in tightly linkage disequilibrium with the c.67_68insCC. Furthermore, it is recognized by BspHI. Therefore, a PCR-RFLP method was set up to genotype this locus. Besides the 175 sequenced individuals, another more 1,391 pigs were genotyped with PCR-RFLP, and all of pigs with GG (one band) were black. CONCLUSION: MC1R gene (c.67_68insCC) is the causative gene (mutation) for the coat color segregation, and the PCR-RFLP of c.370G > A could be used in the breeding program of Shanxia long black pig.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Li3Bi/Li2O layer with uniform built-in electric field distribution for dendrite free lithium metal batteries.

Lithium metal batteries have garnered significant attention as a promising energy storage technology, offering high energy density and potential applications across various industries. However, the formation of lithium dendrites during battery cycling poses a considerable challenge, leading to performance degradation and safety hazards. This study aims to address this issue by investigating the effectiveness of a protective layer on the lithium metal surface in inhibiting dendrite growth. The hypothesis is that continuous lithium consumption during battery cycling is a primary contributor to dendrite formation. To test this hypothesis, a protective layer of Li3Bi/Li2O was applied to the lithium foil through immersion in a BiN3O9 solution. Experimental techniques including kelvin probe force microscopy (KPFM) and density functional theory (DFT) calculations were employed to analyze the structural and electronic properties of the Li3Bi/Li2O layer. The findings demonstrate successful doping of Bi into the Li coating, forming Bi-Bi and Bi-O bonds. KPFM measurements reveal a higher work function of Li3Bi/Li2O, indicating its potential as an effective protective layer. DFT calculations further support this observation by revealing a greater adsorption energy of lithium on the Li3Bi/Li2O layer compared to the bulk material. Charge density analysis suggests that the adsorption of Li atoms onto the Li3Bi/Li2O layer induces a redistribution of charge, resulting in increased electron availability on the surface and preventing electrode-electrolyte contact. This study provides insights into the role of the Li3Bi/Li2O protective layer in inhibiting dendrite growth in lithium metal batteries. By mitigating dendrite formation, the protective layer holds promise for enhancing battery performance and longevity. These findings contribute to the development of strategies for improving the stability and reliability of lithium metal batteries, facilitating their wider adoption in energy storage applications.

Alcohol-Induced Strong Metal-Support Interactions in a Supported Copper/ZnO Catalyst.

Tuning the strong metal-support interaction (SMSI) in metal catalysts is a promising strategy to improve their catalytic performance. In this article, we systematically investigated the influences of different alcohol/water mixtures on the evolution of the interfacial structure of Cu/ZnO catalysts in the reduction stage. A series of in situ characterization and theoretical simulation studies were performed to elucidate the various mechanisms of alcohol induced SMSI. It was found that when methanol/water is added to H2 during the reduction pretreatment, more oxygen vacancies are formed on the ZnO support, which facilitates the dissociation of H2 O and the hydroxylation of ZnO species. Such promotion eventually favors the SMSI between Cu and ZnO and increases the catalytic activity for the methanol steam reforming reaction. In contrast, the addition of ethanol/water and 1-propanol/water during reduction leads to a physical blockage of the catalyst by alcohol molecules, poisoning the active Cu sites and limiting the migration of ZnO species.

Integrated Construction Improving Electrochemical Performance of Stretchable Supercapacitors Based on Ant-Nest Amphiphilic Gel Electrolytes.

Aqueous integrated stretchable supercapacitors (ISSCs) have attracted extensive attention due to the intrinsic safety in future wearable electronics. However, aqueous ISSCs usually suffer from low energy density and poor dynamic deformation stability owing to the conventional hydrogel electrolytes' narrow electrochemical stability window (ESW) and dissatisfied interface bonding. Herein, an ant-nest amphiphilic polyurethane hydro/organogel electrolyte (sAPUGE) with a wide ESW (≈2.2 V) and superb self-adhesion is prepared by electrospinning, which interacts with carbon-based stretchable electrodes for the construction of flame-retardant PU-based sAPUGE-ISSC. Benefitting from the synergistic effect of chemical bonding and mechanical meshing between the electrode and gel electrolyte interface, as-assembled sAPUGE-ISSC delivers a high energy density of 13.7 mWh cm-3 (at a power density of 0.126 W cm-3 ) and outstanding dynamic deformation stability (98.3% capacitance retention after 500 stretching cycles under 100% strain). This unique hydro/organogel electrolyte provides a pathway toward the next generation of wearable energy products in modern electronics.

A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation.

Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent. Deep learning methods in industrial quality detection have been widely proposed recently. However, due to the particularity of industrial scenarios, the existing deep learning-based general object detection methods have shortcomings in industrial applications. This study designs two isomorphic industrial detection models to solve these problems: T-model and S-model. Both proposed models combine swin-transformer with convolution in the backbone and design a residual fusion path. In the neck, this study designs a dual attention module to improve feature fusion. Second, this study presents a knowledge distiller based on the dual attention module to improve the detection accuracy of the lightweight S-model. According to the analysis of the experimental results on four public industrial defect detection datasets, the model in this study is more advantageous in industrial defect detection.

Tuning Functionalized Ionic Liquids for CO2 Capture.

The increasing concentration of CO2 in the atmosphere is related to global climate change. Carbon capture, utilization, and storage (CCUS) is an important technology to reduce CO2 emissions and to deal with global climate change. The development of new materials and technologies for efficient CO2 capture has received increasing attention among global researchers. Ionic liquids (ILs), especially functionalized ILs, with such unique properties as almost no vapor pressure, thermal- and chemical-stability, non-flammability, and tunable properties, have been used in CCUS with great interest. This paper focuses on the development of functionalized ILs for CO2 capture in the past decade (2012~2022). Functionalized ILs, or task-specific ILs, are ILs with active sites on cations or/and anions. The main contents include three parts: cation-functionalized ILs, anion-functionalized ILs, and cation-anion dual-functionalized ILs for CO2 capture. In addition, classification, structures, and synthesis of functionalized ILs are also summarized. Finally, future directions, concerns, and prospects for functionalized ILs in CCUS are discussed. This review is beneficial for researchers to obtain an overall understanding of CO2-philic ILs. This work will open a door to develop novel IL-based solvents and materials for the capture and separation of other gases, such as SO2, H2S, NOx, NH3, and so on.

Covalent Organic Frameworks with Ionic Liquid-Moieties (ILCOFs): Structures, Synthesis, and CO2 Conversion.

CO2, an acidic gas, is usually emitted from the combustion of fossil fuels and leads to the formation of acid rain and greenhouse effects. CO2 can be used to produce kinds of value-added chemicals from a viewpoint based on carbon capture, utilization, and storage (CCUS). With the combination of unique structures and properties of ionic liquids (ILs) and covalent organic frameworks (COFs), covalent organic frameworks with ionic liquid-moieties (ILCOFs) have been developed as a kind of novel and efficient sorbent, catalyst, and electrolyte since 2016. In this critical review, we first focus on the structures and synthesis of different kinds of ILCOFs materials, including ILCOFs with IL moieties located on the main linkers, on the nodes, and on the side chains. We then discuss the ILCOFs for CO2 capture and conversion, including the reduction and cycloaddition of CO2. Finally, future directions and prospects for ILCOFs are outlined. This review is beneficial for academic researchers in obtaining an overall understanding of ILCOFs and their application of CO2 conversion. This work will open a door to develop novel ILCOFs materials for the capture, separation, and utilization of other typical acid, basic, or neutral gases such as SO2, H2S, NOx, NH3, and so on.

Design and Implementation of a Modular UUV Simulation Platform.

The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before putting it into use. Considering the high cost of the UUV hardware platform and the high risk of underwater experiments, this study aims to evaluate and optimize autonomy schemes in the manner of software-in-loop (SIL) simulation efficiently. Therefore, a self-feedback development framework is proposed and a multi-interface, programmable modular simulation platform for UUV based on a robotic operating system (ROS) is designed. The platform integrates the 3D marine environment, UUV models, sensor plugins, motion control plugins in a modular manner, and reserves programming interfaces for users to test various algorithms. Subsequently, we demonstrate the simulation details with cases, such as single UUV path planning, task scheduling, and multi-UUV formation control, and construct underwater experiments to confirm the feasibility of the simulation platform. Finally, the extensibility of the simulation platform and the related performance analysis are discussed.

Manufacturing and post-engineering strategies of hydrogel actuators and sensors: From materials to interfaces.

Living bodies are made of numerous bio-sensors and actuators for perceiving external stimuli and making movement. Hydrogels have been considered as ideal candidates for manufacturing bio-sensors and actuators because of their excellent biocompatibility, similar mechanical and electrical properties to that of living organs. The key point of manufacturing hydrogel sensors/actuators is that the materials should not only possess excellent mechanical and electrical properties but also form effective interfacial connections with various substrates. Traditional hydrogel normally shows high electrical resistance (~ MΩ•cm) with limited mechanical strength (<1 MPa), and it is prone to fatigue fracture during continuous loading-unloading cycles. Just like iron should be toughened and hardened into steel, manufacturing and post-treatment processes are necessary for modifying hydrogels. Besides, advanced design and manufacturing strategies can build effective interfaces between sensors/actuators and other substrates, thus enhancing the desired mechanical and electrical performances. Although various literatures have reviewed the manufacture or modification of hydrogels, the summary regarding the post-treatment strategies and the creation of effective electrical and mechanically sustainable interfaces are still lacking. This paper aims at providing an overview of the following topics: (i) the manufacturing and post-engineering treatment of hydrogel sensors and actuators; (ii) the processes of creating sensor(actuator)-substrate interfaces; (iii) the development and innovation of hydrogel manufacturing and interface creation. In the first section, the manufacturing processes and the principles for post-engineering treatments are discussed, and some typical examples are also presented. In the second section, the studies of interfaces between hydrogels and various substrates are reviewed. Lastly, we summarize the current manufacturing processes of hydrogels, and provide potential perspectives for hydrogel manufacturing and post-treatment methods.

Preliminary application of three-dimensional printing in congenital uterine anomalies based on three-dimensional transvaginal ultrasonographic data.

BACKGROUND: The three-dimensional (3D) printing technology has remarkable potential as an auxiliary tool for representing anatomical structures, facilitating diagnosis and therapy, and enhancing training and teaching in the medical field. As the most available diagnostic tool and it is routinely used as the first approach in diagnosis of the uterine anomalies, 3D transvaginal ultrasonography (3D-TVS) has been proposed as non-invasive "gold standard" approach for these malformations due to high diagnostic accuracy. Despite holding promise of manufacturing 3D printed models based on 3D-TVS data, relevant reports about 3D-TVS derived gynecological 3D printing haven't been reported to the best of our knowledge. We found an opportunity to explore the feasibility of building 3D printed models for the abnormal uterus based on the data acquired by 3D-TVS. METHODS: The women suspected with congenital uterine anomalies (CUAs) were enrolled in the study. The diagnose of CUAs were made by 3D-TVS scanning and further confirmed under the hysteroscopy examination. One volunteer with normal uterus was enrolled as control. All subjects underwent 3D-TVS scanning for 3D printing data collection. Acquired images were stored and extracted as DICOM files, then processed by professional software to portray and model the boundary of the uterine inner and outer walls separately. After the computer 3D models were constructed, the data were saved and output as STL files for further surface restoration and smoothing. The colors of endometrium and uterine body were specified, respectively, in the print preview mode. Then the uncured photosensitive resin was cleaned and polished to obtain a smooth and transparent solid model after printed models were cooled down. RESULTS: 3D printing models of normal uterus, incomplete septate uterus, complete septate uterus, uterus didelphys and unicornuate uterus were produced on ultrasonographic data of 3D-TVS. CONCLUSIONS: Our research and practice made the first try in modeling CUAs successfully based on ultrasonographic data entirely, verifying that it's a feasible way to build 3D printed models of high-quality through 3D-TVS scanning.

Conformation-dominated surface antifouling and aqueous lubrication.

Antifouling and aqueous lubrication are important properties for biomaterials, especially for those with implantation purposes. In order to better understand the polymer conformation dependence of the surface antifouling and lubrication properties, poly(ethylene glycol) (PEG) polymers with mono-functional and difunctional catechol anchors were designed and anchored on surface to adopt tail and loop conformations. Diblock and triblock copolymers with poly(dopamine methacrylamide) (PDMA) block as anchors and PEG block as the main body were synthesized and anchored on silicon surfaces by a "grafting to" strategy. The chemical composition, film thickness, and surface roughness of both coatings were controlled to be similar to give a direct comparison of looped brushes and tailed analogues. Then, the antifouling and surface friction behaviors were detected to verify the topological conformation effect of PEG polymer brushes. Results showed that PEG triblock copolymer modified surface exhibited an obviously better antifouling property and a lower friction coefficient of ∼0.011 than that of PEG diblock copolymer modified surface. Additionally, calculation and simulation results demonstrated that triblock copolymer had higher adsorption energy and anchored on surface with looped conformation. It is indicated that the strongly anchored PEG loops are effective for excellent antifouling and lubricating properties due to its strong hydration and steric hindrance. The conformation-dominated enhanced antifouling and reduced interfacial friction is an effective method for the development of excellent antifouling surfaces.

Efficacy and safety of 3D printing-assisted percutaneous nephrolithotomy in complex renal calculi.

This study evaluated the efficacy and safety of 3D printing technology combined with percutaneous nephrolithotomy in the treatment of complex renal calculi. Ninety patients with complex renal calculi were randomly divided into a 3D printing group (45 patients) and a control group (45 patients). In the 3D printing group, a patient-specific 1:1 3D printing model was established based on the patient's thin-layer CT scanning data. A 3D printing model was used for preoperative communication between doctors and patients. Preoperative puncture training, channel design, residual stone prediction, and percutaneous nephrolithotomy were performed under the guidance of a 3D printing model and B-ultrasound. The control group was treated with the conventional B-ultrasound-guided puncture method. Results suggest that there was a statistically significant difference between the two groups (P < 0.05). The overall score of the doctor-patient communication objects in the 3D printing group was 19.32 ± 1.57 points, and in the control group, it was 14.51 ± 2.13 points. The operation time of the 3D printing group was 103.21 ± 13.49 min, and that of the control group was 126.12 ± 25.87 min. The calculi clearance rate of the 3D printing group was 96%, while that of the control group was 80%. The incidence of postoperative complications was 6.67% in the 3D printing group and 22.22% in the control group. Compared with traditional percutaneous nephrolithotomy, 3D printing technology combined with percutaneous nephrolithotomy can significantly enhance the effectiveness of doctor-patient communication, shorten operation time, reduce operation bleeding, improve the stone clearance rate, reduce the incidence of complications and shorten the length of hospital stay. The proposed method is thus a safe and effective method to treat complex renal calculi.