Examining the unique relationships between problematic use of the internet and impulsive and compulsive tendencies: network approach.

BACKGROUND: Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI. AIMS: The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community. METHOD: We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms. RESULTS: Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence. CONCLUSIONS: The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans.

Introduction: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Results and discussion: Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Generalized Multifunctional Coating Strategies Based on Polyphenol-Amine-Inspired Chemistry and Layer-by-Layer Deposition for Blood Contact Catheters.

Blood-contacting catheters play a pivotal role in contemporary medical treatments, particularly in the management of cardiovascular diseases. However, these catheters exhibit inappropriate wettability and lack antimicrobial characteristics, which often lead to catheter-related infections and thrombosis. Therefore, there is an urgent need for blood contact catheters with antimicrobial and anticoagulant properties. In this study, we employed tannic acid (TA) and 3-aminopropyltriethoxysilane (APTES) to create a stable hydrophilic coating under mild conditions. Heparin (Hep) and poly(lysine) (PL) were then modified on the TA-APTES coating surface using the layer-by-layer (LBL) technique to create a superhydrophilic TA/APTES/(LBL)4 coating on silicone rubber (SR) catheters. Leveraging the superhydrophilic nature of this coating, it can be effectively applied to blood-contacting catheters to impart antibacterial, antiprotein adsorption, and anticoagulant properties. Due to Hep's anticoagulant attributes, the activated partial thromboplastin time and thrombin time tests conducted on SR/TA-APTES/(LBL)4 catheters revealed remarkable extensions of 276 and 103%, respectively, when compared to uncoated commercial SR catheters. Furthermore, the synergistic interaction between PL and TA serves to enhance the resistance of SR/TA-APTES/(LBL)4 catheters against bacterial adherence, reducing it by up to 99.9% compared to uncoated commercial SR catheters. Remarkably, the SR/TA-APTES/(LBL)4 catheter exhibits good biocompatibility with human umbilical vein endothelial cells in culture, positioning it as a promising solution to address the current challenges associated with blood-contact catheters.

Construction and validation of a predictive model for major adverse cardiovascular events in the long term after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction.

PURPOSE: Construction of a prediction model to predict the risk of major adverse cardiovascular events (MACE) in the long term after percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI). METHOD: Retrospective analysis of STEMI patients treated with PCI from April 2018 to April 2021 in Fuyang People's Hospital. Lasso regression was used to screen the risk factors for the first occurrence of MACE in patients, and multifactorial logistic regression analysis was used to construct a prediction model. The efficacy was evaluated by area under the ROC curve (AUC), Hosmer-Lemeshow deviance test, calibration curve, clinical decision curve (DCA) and clinical impact curve (CIC). RESULTS: Logistic regression results showed that hypertension, diabetes mellitus, left main plus three branches lesion, estimated glomerular filtration rate and medication adherence were influential factors in the occurrence of distant MACE after PCI in STEMI patients (P < 0.05). The AUC was 0.849 in the modeling group and 0.724 in the validation group; the calibration curve had a good fit to the standard curve, and the result of the Hosmer-Lemeshow test of deviance was x2 = 7.742 (P = 0. 459); the DCA and the CIC indicated that the predictive model could provide a better net clinical benefit for STEMI patients. CONCLUSION: A prediction model constructed from a total of five predictor variables, namely hypertension, diabetes, left main + three branches lesions, eGFR and medication adherence, can be used to assess the long-term prognosis after PCI in STEMI patients and help in early risk stratification of patients.

Non-invasive in vivo study of morphology and mechanical properties of the median nerve.

The current literature studied the median nerve (MN) at specific locations during joint motions. As only a few particular parts of the nerve are depicted, the relevant information available is limited. This experiment investigated the morphological and biomechanical properties of the MN. The effects of the shoulder and wrist motions on MN were explored as well. Eight young healthy female individuals were tested with two-dimensional ultrasound and shear wave elastography (SWE). The morphological and biomechanical properties were examined in limb position 1, with the wrist at the neutral position, the elbow extended at 180°, and the shoulder abducted at 60°. In addition, the experiment assessed the differences among the wrist, forearm, elbow, and upper arm with Friedman's test and Bonferroni post hoc analysis. Two groups of limb positions were designed to explore the effects of shoulder movements (shoulder abducted at 90° and 120°) and wrist movements (wrist extended at 45° and flexed at 45°) on the thickness and Young's modulus. Differences among the distributions of five limb positions were tested as well. The ICC3, 1 values for thickness and Young's modulus were 0.976 and 0.996, respectively. There were differences among the MN thicknesses of four arm locations in limb position 1, while Young's modulus was higher at the elbow and wrist than at the forearm and upper arm. Compared to limb position 1, only limb position 4 had an effect on MN thickness at the wrist. Both shoulder and wrist motions affected MN Young's modulus, and the stiffness variations at typical locations all showed a downward trend proximally in all. The distributions of MN thickness and Young's modulus showed fold line patterns but differed at the wrist and the pronator teres. The MN in the wrist is more susceptible to limb positions, and Young's modulus is sensitive to nerve changes and is more promising for the early diagnosis of neuropathy.

Divergence in regulatory mechanisms of GR-RBP genes in different plants under abiotic stress.

The IVa subfamily of glycine-rich proteins (GRPs) comprises a group of glycine-rich RNA binding proteins referred to as GR-RBPa here. Previous studies have demonstrated functions of GR-RBPa proteins in regulating stress response in plants. However, the mechanisms responsible for the differential regulatory functions of GR-RBPa proteins in different plant species have not been fully elucidated. In this study, we identified and comprehensively studied a total of 34 GR-RBPa proteins from five plant species. Our analysis revealed that GR-RBPa proteins were further classified into two branches, with proteins in branch I being relatively more conserved than those in branch II. When subjected to identical stresses, these genes exhibited intensive and differential expression regulation in different plant species, corresponding to the enrichment of cis-acting regulatory elements involving in environmental and internal signaling in these genes. Unexpectedly, all GR-RBPa genes in branch I underwent intensive alternative splicing (AS) regulation, while almost all genes in branch II were only constitutively spliced, despite having more introns. This study highlights the complex and divergent regulations of a group of conserved RNA binding proteins in different plants when exposed to identical stress conditions. These species-specific regulations may have implications for stress responses and adaptations in different plant species.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients.

BACKGROUND: Disulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC. METHODS: The expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort. RESULTS: We identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort. CONCLUSION: The DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Study on Influence of Rare Earth Ce on Micro and Macro Properties of U75V Steel.

Non-metallic inclusions in steel have great influence on the continuity of the steel matrix and the mechanical properties of steel. The precipitation sequence of Ce inclusions in molten steel is predicted by thermodynamic calculations. The results show that Ce content will affect the precipitation sequence of rare earth inclusions in molten steel, and the formation of CeO2, Ce2O3 and CeAlO3 will be inhibited with the increase in Ce content. Our laboratory smelted the test steel without rare earth additive and the test steel with rare earth Ce additive (0.0008%, 0.0013%, 0.0032%, 0.0042%). It was found that the MnS inclusions and inclusions containing Al, Ca, Mg and Si oxides or sulfides in the steel after rare earth addition were modified into complex inclusions containing CeAlO3 and Ce2O2S. The size of inclusion in steel was reduced and the aspect ratio of inclusion was improved. The addition of Ce also improved the grain size of U75V steel and significantly refined the pearlite lamellar spacing. After mechanical property testing of the test steel, it was found that when Ce is increased within 0.0042%, the tensile and impact properties of U75V steel are also improved.

Soft Actuator with Biomass Porous Electrode: A Strategy for Lowering Voltage and Enhancing Durability.

Electroactive artificial muscles with deformability have attracted widespread interest in the field of soft robotics. However, the design of artificial muscles with low-driven voltage and operational durability remains challenging. Herein, novel biomass porous carbon (BPC) electrodes are proposed. The nanoporous BPC enables the electrode to provide exposed active surfaces for charge transfer and unimpeded channels for ion migration, thus decreasing the driving voltage, enhancing time durability, and maintaining the actuation performances simultaneously. The proposed actuator exhibits a high displacement of 13.6 mm (bending strain of 0.54%) under 0.5 V and long-term durability of 99.3% retention after 550,000 cycles (∼13 days) without breaks. Further, the actuators are integrated to perform soft touch on a smartphone and demonstrated as bioinspired robots, including a bionic butterfly and a crawling robot (moving speed = 0.08 BL s-1). This strategy provides new insight into the design and fabrication of high-performance electroactive soft actuators with great application potential.

An Improved Extreme Learning Machine (ELM) Algorithm for Intent Recognition of Transfemoral Amputees With Powered Knee Prosthesis.

To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this study, resulting in enhanced classification accuracy. The structure of the ELM algorithm is enhanced using the logistic regression (LR) algorithm, significantly reducing the number of hidden layer nodes. Hence, this algorithm can be adopted for real-time human locomotion intent recognition on portable devices with only 234 parameters to store. Additionally, a hybrid grey wolf optimization and slime mould algorithm (GWO-SMA) is proposed to optimize the hidden layer bias of the improved ELM classifier. Numerical results demonstrate that the proposed model successfully recognizes nine daily motion modes including low-, mid-, and fast-speed level ground walking, ramp ascent/descent, sit/stand, and stair ascent/descent. Specifically, it achieves 96.75% accuracy with 5-fold cross-validation while maintaining a real-time prediction time of only 2 ms. These promising findings highlight the potential of onboard real-time recognition of continuous locomotion modes based on our model for the high-level control of powered knee prostheses.

Pyrrolic Nitrogen Boosted H2 Generation from an Aqueous Solution of HCHO at Room Temperature by Metal-Free Carbon Catalysts.

Hydrogen production from organic hydrides represents a promising strategy for the development of safe and sustainable technologies for H2 storage and transportation. Nonetheless, the majority of existing procedures rely on noble metal catalysts and emit greenhouse gases such as CO2/CO. Herein, we demonstrated an alternative N-doped carbon (CN) catalyst for highly efficient and robust H2 production from an aqueous solution of formaldehyde (HCHO). Importantly, this process generated formic acid as a valuable byproduct instead of CO2/CO, enabling a clean H2 generation process with 100% atom economy. Mechanism investigations revealed that the pyrrolic N in the CN catalysts played a critical role in promoting H2 generation via enhancing the transformation of O2 to generate •OO- free radicals. Consequently, the optimized CN catalysts achieved a remarkable H2 generation rate of 13.6 mmol g-1 h-1 at 30 °C. This finding is anticipated to facilitate the development of liquid H2 storage and its large-scale utilization.

ROS Balance Autoregulating Core-Shell CeO2@ZIF-8/Au Nanoplatform for Wound Repair.

Reactive oxygen species (ROS) plays important roles in living organisms. While ROS is a double-edged sword, which can eliminate drug-resistant bacteria, but excessive levels can cause oxidative damage to cells. A core-shell nanozyme, CeO2@ZIF-8/Au, has been crafted, spontaneously activating both ROS generating and scavenging functions, achieving the multi-faceted functions of eliminating bacteria, reducing inflammation, and promoting wound healing. The Au Nanoparticles (NPs) on the shell exhibit high-efficiency peroxidase-like activity, producing ROS to kill bacteria. Meanwhile, the encapsulation of CeO2 core within ZIF-8 provides a seal for temporarily limiting the superoxide dismutase and catalase-like activities of CeO2 nanoparticles. Subsequently, as the ZIF-8 structure decomposes in the acidic microenvironment, the CeO2 core is gradually released, exerting its ROS scavenging activity to eliminate excess ROS produced by the Au NPs. These two functions automatically and continuously regulate the balance of ROS levels, ultimately achieving the function of killing bacteria, reducing inflammation, and promoting wound healing. Such innovative ROS spontaneous regulators hold immense potential for revolutionizing the field of antibacterial agents and therapies.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder.

BACKGROUND: A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom. METHODS: A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network. RESULTS: In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality. CONCLUSIONS: This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

Burnout and depression in college students.

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

Industrial Metaverse for Smart Manufacturing: Model, Architecture, and Applications.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue.Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification.Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy.Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool forinvivo3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Mechanisms of biochar assisted di-2-ethylhexyl phthalate (DEHP) biodegradation in tomato rhizosphere by metabolic and metagenomic analysis.

The intensive accumulation of di-2-ethylhexyl phthalate (DEHP) in agricultural soils has resulted in severe environmental pollution that endangers ecosystem and human health. Biochar is an eco-friendly material that can help in accelerating organic pollutant degradation; nevertheless, its roles in enhancing DEHP removal in rhizosphere remain unclear. This work investigated the impacts of biochar dosage (0%-2.0%) on DEHP degradation performance in tomato rhizosphere by comprehensively exploring the change in DEHP metabolites, bacterial communities and DEHP-degrading genes. Our results showed a significant increase of rhizosphere pH, organic matter and humus by biochar amendment, which achieved a satisfactorily higher DEHP removal efficiency, maximally 77.53% in treatments with 1.0% of biochar. Biochar addition also remarkably changed rhizosphere bacterial communities by enriching some potential DEHP degraders of Nocardioides, Sphingomonas, Bradyrhizobium and Rhodanobacter. The abundance of genes encoding key enzymes (hydrolase, esterase and cytochrome P450) and DEHP-degrading genes (pht3, pht4, pht5, benC-xylZ and benD-xylL) were increased after biochar amendment, leading to the change in DEHP degradation metabolism, primarily from benzoic acid pathway to protocatechuic acid pathway. Our findings evidenced that biochar amendment could accelerate DEHP degradation by altering rhizosphere soil physicochemical variables, bacterial community composition and metabolic genes, providing clues for the mechanisms of biochar-assisted DEHP degradation in organic contaminated farmland soils.

A case report of abdominal metastatic dermatofibrosarcoma protuberans misdiagnosed as gastrointestinal stromal tumor.

Dermatofibrosarcoma protuberans (DFSP) is a low-grade malignant soft-tissue tumor that originates from the skin. It has a slow onset in the early stages, non-specific clinical symptoms, low specificity, and can easily be overlooked, missed, or misdiagnosed by clinicians and pathologists. In addition, DFSP is prone to recurrence after local surgical treatment; however, distant metastasis, especially abdominal metastasis, is rare, which is also a challenge for the accurate diagnosis of DFSP when it progresses distantly. Now a case of abdominal metastasis of DFSP is reported. The patient has been treated with imatinib for ten years, and the lesion has shrunk, but because the patient has been receiving imatinib treatment, his abdominal lesion was once misdiagnosed as gastrointestinal stromal tumor. Therefore, we report on this case to enhance the understanding of the diagnosis and treatment of DFSP, and to provide reference for the pathological diagnosis and precise treatment of such patients.

Fe-modified NASICON-type Na3V2(PO4)3 as a cathode material for sodium ion batteries.

The sol-gel method is used to synthesize a new compound called Na3Fe0.8V1.2(PO4)3/C (NFVP/C), which has a crystal structure and belongs to the NASICON-type family. The dimensions of NFVP's unit cell are a = 8.717 (1) Å, c = 21.84 (1) Å, and V = 1437.27 (0) Å3. The Na‖NFVP/C battery provides a discharge potential of 3.43 V compared to Na+/Na, an intriguing rate capability of 76.2 mA h g-1 at 40C, and maintains an impressive capacity of 97.8% after 500 cycles at 5C. The excellent efficiency of Na3Fe0.8V1.2(PO4)3/C can be ascribed to its elevated Na+ conductivity and reduced energy barrier for sodium-ion diffusion. The NASICON-type Na3Fe0.8V1.2(PO4)3/C is a promising material for sodium-ion batteries.

The study of DIEA three dimensional reconstruction technique based on CTA in preoperative perforator selection.

Preoperative selection of perforator is one of the key steps for successful surgery. The purpose of this study is to simulate the selection process of the perforator of the flap using the 3D models of the deep inferior epigastric artery (DIEA). A retrospective study was performed of women who underwent deep inferior epigastric perforator flap breast reconstruction from January 2011 to July 2021. Construct 3D models of the DIEA using computerized tomography angiography images, and then computational fluid dynamics simulations were performed. Correlation and regression analyses were used to analyze the geometric and hemodynamic parameters. Statistical analysis suggested that the outlet flow was positively correlated with the inlet area (r = 0.338, p = 0.000), outlet area (r = 0.840, p = 0.000), the average radius of the perforator (r = 0.592, p = 0.000), and negatively correlated with the length of perforator(r = -0.210, p = 0.024). The results of linear regression analysis showed that the outlet area (p = 0.000), the average radius (p = 0.000), and the length (p = 0.044) of the perforator were the influencing factors on outlet flow. In multiple perforators analysis, there was a significant difference in the total outlet flow among single perforator, double perforators, and triple perforators (p = 0.002). The successful implementation of this experiment provides a new approach for the selection of dominant perforators in the future.