The clinical implication and translational research of OSCC differentiation.

BACKGROUND: The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine. METHODS: Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system. RESULTS: Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO. CONCLUSIONS: OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy.

Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.

BACKGROUND: The grading of oral epithelial dysplasia is often time-consuming for oral pathologists and the results are poorly reproducible between observers. In this study, we aimed to establish an objective, accurate and useful detection and grading system for oral epithelial dysplasia in the whole-slides of oral leukoplakia. METHODS: Four convolutional neural networks were compared using the image patches from 56 whole-slide of oral leukoplakia labeled by pathologists as the gold standard. Sequentially, feature detection models were trained, validated and tested with 1,000 image patches using the optimal network. Lastly, a comprehensive system named E-MOD-plus was established by combining feature detection models and a multiclass logistic model. RESULTS: EfficientNet-B0 was selected as the optimal network to build feature detection models. In the internal dataset of whole-slide images, the prediction accuracy of E-MOD-plus was 81.3% (95% confidence interval: 71.4-90.5%) and the area under the receiver operating characteristic curve was 0.793 (95% confidence interval: 0.650 to 0.925); in the external dataset of 229 tissue microarray images, the prediction accuracy was 86.5% (95% confidence interval: 82.4-90.0%) and the area under the receiver operating characteristic curve was 0.669 (95% confidence interval: 0.496 to 0.843). CONCLUSIONS: E-MOD-plus was objective and accurate in the detection of pathological features as well as the grading of oral epithelial dysplasia, and had potential to assist pathologists in clinical practice.

Self-Standing Covalent Organic Polymer Membrane with High Stability and Enhanced Ion-Sieving Effect for Flow Battery.

Fabrication of ion-conducting membranes with continuous sub-nanometer channels holds fundamental importance for flow batteries in achieving safe integration of renewable energy into grids. Self-standing covalent organic polymer (COP) membranes provide feasibility due to their rapid and selective ion transport. However, the development of a scale-up possible, mechanically robust and chemically stable membranes remains a significant challenge. Herein, using irreversible strong secondary amine linkage, we propose a self-standing COP membrane with sub-nanometer pores ranging from 4.5 to 6.4 Å, by a simple and efficient in-situ polymerization approach. This membrane exhibits enhanced selectivity for proton and vanadium ions, especially excellent electrochemical stability, delivering an energy efficiency of over 80% at the current density of 200 mA cm-2 over 1000 cycles for an all-vanadium redox flow battery (VFB). This study provides novel insights for COP-based ion-sieving membranes in sustainable energy fields.

Nanoemulsion-Coated Ni-Fe Hydroxide Self-Supported Electrode as an Air-Breathing Cathode for High-Performance Zinc-Air Batteries.

To improve the energy conversion efficiency and durability of zinc-air batteries (ZABs) for large-scale implementations, here we propose an "air-breathing" strategy to significantly enlarge triple-interfaces with intensified mass transfer. By dip-coating the aerophilic perfluorochemical compounds (PFC) and amphiphilic ionomers into the self-supported electrodes, (1) the high solubility of O2 in the PFC nanoemulsions greatly increases triple-phase boundaries and facilitates the efficient supply/removal of O2 from the electrolyte; (2) the ionomers with hydrophobic and hydrophilic functionalities enable fast gas, water, and ion transport to the triple-phase boundaries; and (3) the self-supported electrode without binder ensures fast electron transfer while the firm integration prevents catalyst shedding. By applying this strategy, the ZABs show a high power density of 115 mW cm-2 and a narrow discharge/charge gap of 0.64 V at 10 mA cm-2 and a long-cycling durability (over 1000 h). This work provides a universal approach to promote gas-evolving reactions for electrochemical applications.

Supramolecular Polymer Ion Conductor with Weakened Li Ion Solvation Enables Room Temperature All-Solid-State Lithium Metal Batteries.

Improved durability, enhanced interfacial stability, and room temperature applicability are desirable properties for all-solid-state lithium metal batteries (ASSLMBs), yet these desired properties are rarely achieved simultaneously. Here, in this work, it is noticed that the huge resistance at Li metal/electrolyte interface dominantly impeded the normal cycling of ASSLMBs especially at around room temperature (<30 °C). Accordingly, a supramolecular polymer ion conductor (SPC) with "weak solvation" of Li+ was prepared. Benefiting from the halogen-bonding interaction between the electron-deficient iodine atom (on 1,4-diiodotetrafluorobenzene) and electron-rich oxygen atoms (on ethylene oxide), the O-Li+ coordination was significantly weakened. Therefore, the SPC achieves rapid Li+ transport with high Li+ transference number, and importantly, derives a unique Li2 O-rich SEI with low interfacial resistance on lithium metal surface, therefore enabling stable cycling of ASSLMBs even down to 10 °C. This work is a new exploration of halogen-bonding chemistry in solid polymer electrolyte and highlights the importance of "weak solvation" of Li+ in the solid-state electrolyte for room temperature ASSLMBs.

Construction of Integrated Electrodes with Transport Highways for Pure-Water-Fed Anion Exchange Membrane Water Electrolysis.

The design of high-performance and durable electrodes for the oxygen evolution reaction (OER) is crucial for pure-water-fed anion exchange membrane water electrolysis (AEMWE). In this study, an integrated electrode with vertically aligned ionomer-incorporated nickel-iron layered double hydroxide nanosheet arrays, used on one side of the liquid/gas diffusion layer, is fabricated for the OER. Transport highways in the fabricated integrated electrode, significantly improve the transport of liquid/gas, hydroxide ions, and electron in the anode, resulting in a high current density of 1900 mA cm-2 at 1.90 V in pure-water-fed AEMWE. Specifically, three-electrode and single-cell measurement results indicate that an anion-exchange ionomer can increase the local OH- concentration on the integrated electrodes surface and facilitate the OER for pure-water-fed AEMWE. This study highlights a new approach to fabricating and understanding electrode architecture with enhanced performance and durability for pure-water-fed AEMWE.

SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis.

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to unseen datasets that hinder clinical translation. Endoscopic imaging data is characterised by large inter- and intra-patient variability that makes these models more challenging to learn representative features for downstream tasks. Thus, despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin within the cosine similarity metrics. Our novel approach enables models to learn to cluster similar representations, thereby improving their ability to provide better separation between different classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets. We evaluated our approach for classification, detection, and segmentation. SSL-CPCD attains notable Top 1 accuracy of 79.77% in ulcerative colitis classification, an 88.62% mean average precision (mAP) for detection, and an 82.32% dice similarity coefficient for segmentation tasks. These represent improvements of over 4%, 2%, and 3%, respectively, compared to the baseline architectures. We demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting over 7% improvement.

A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling.

With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex scheduling models with strong constraints that are difficult to tackle using existing scheduling methods. To optimize maximum completion time and effluent quality simultaneously, this article proposes a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm to address the WWTP effluent scheduling problem (WWTP-ESP). First, an effective encoding and decoding method is designed to effectively map solutions to feasible schedules using temporal and spatial information. Second, a restructured set-based discrete particle swarm algorithm is introduced to enhance the searching ability in discrete solution space via restructuring the solution set. Third, a constraint handling strategy based on violation degree ranking is designed to reduce the waste of computational resources. Fourth, a Sobel filter based local search is proposed to guide particle search direction to enhance search efficiency ability. The RS-DPSO provides a novel method for solving WWTP-ESP problems with complex discrete solution space. The comparative experiments indicate that the novel designs are effective and the proposed algorithm has superior performance over existing algorithms in solving the WWTP-ESP.

Potential toxic effects of perfluorobutanesulfonyl fluoride analysis based on multiple-spectroscopy techniques and molecular modelling analysis.

Perfluorobutanesulfonyl fluoride (PBSF) has been used in the manufacture of fluorochemicals. Since PBSF is not biodegradable, the predicted environmental levels of PBSF are also expected to rise over time. In recent years, there has been a rise in the levels of PBSF in humans. In order to clarify the impact of PBSF on the accumulation of substances in the human body, we examined the interaction mechanism between PBSF and bovine serum albumin (BSA). To investigate the interaction mechanism between PBSF and BSA, we utilized a range of methods including UV-visible spectrophotometry, fluorescence spectroscopy, circular dichroism, molecular docking simulation, and molecular dynamics (MD) simulation. The inherent fluorescence of BSA was effectively suppressed by PBSF through fluorescence quenching analysis, using a static mechanism. The Ka value of 1.34 × 105 mol-1 L indicated a strong binding between PBSF and BSA. Further analysis of the interaction between PBSF and BSA involved examining thermodynamic parameters, fluorescence resonance energy transfer, and conducting other theoretical calculations. These investigations produced results that were in strong accordance with the experimental observations. The participation of hydrophobic interactions between BSA and PBSF was uncovered through molecular docking and MD simulation investigations. Furthermore, this investigation explored the impact of copper ions (Cu2+) and calcium ions (Ca2+) on the interaction between PBSF and BSA, establishing a vital basis for comprehending the mechanism by which PBSF affects proteins in the human surroundings.

SCRN: Single-cell Gene Regulatory Network Identification in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.

Conductive imprinted polymeric interfacially modified electrochemical sensors based on covalently bonded layer-by-layer assembly of Gr/Au with flower-like morphology for sensitive detection of 2,4,6-TCP.

Polymeric membrane sensors based on molecular imprinted polymers (MIPs) have been attractive analytical tools for detecting organic species. However, the MIPs in electrochemical sensors developed so far are usually prepared by in situ polymerization of pre-polymers and non-covalent adsorption on the surface of the working electrode. Meanwhile, the MIPs in the electrochemical sensors developed are typically made of a non-conductive polymer film. This results in a relatively low current due to the lack of electron transfer. Additionally, the smoothness of the traditional electrochemical substrate results in a low specific surface area, which reduces the sensitivity of the electrochemical sensor. Here, we describe a novel electrochemical sensor with a conductive interface and MIPs modification. The electrochemical sensor was modified by covalent coupled layer by layer self-assembly with the imprinted polymer film. The incorporation of these two conductive functional materials improves the conductivity of the electrodes and provides interface support materials to obtain high specific surface area. By using 2,4,6-trichlorophenol as the model, the sensitivity of the developed conductive sensor was greatly improved compared to that of the traditional MIPs sensor. We believe that the proposed MIPs-based sensing strategy provides a general and convenient method for making sensitive and selective electrochemical sensors.

Interfacial and Vacancy Engineering on 3D-Interlocked Anode Catalyst Layer for Achieving Ultralow Voltage in Anion Exchange Membrane Water Electrolyzer.

Developing a high-efficiency and stable anode catalyst layer (CL) is crucial for promoting the practical applications of anion exchange membrane (AEM) water electrolyzers. Herein, a hierarchical nanosheet array composed of oxygen vacancy-enriched CoCrOx nanosheets and dispersed FeNi layered double hydroxide (LDH) is proposed to regulate the electronic structure and increase the electrical conductivity for improving the intrinsic activity of the oxygen evolution reaction (OER). The CoCrOx/NiFe LDH electrodes require an overpotential of 205 mV to achieve a current density of 100 mA cm-2, and they exhibit long-term stability at 1000 mA cm-2 over 7000 h. Notably, a breakthrough strategy is introduced in membrane electrode assembly (MEA) fabrication by transferring CoCrOx/NiFe LDH to the surface of an AEM, forming a 3D-interlocked anode CL, significantly reducing the overall cell resistance and enhancing the liquid/gas mass transfer. In AEM water electrolysis, it exhibits an ultralow cell voltage of 1.55 Vcell to achieve a current density of 1.0 A cm-2 in 1 M KOH, outperforming the state-of-the-art Pt/C//IrO2. This work provides a valuable approach to designing high-efficiency electrocatalysts at the single-cell level for advanced alkaline water electrolysis technologies.

Arbuscular mycorrhizal fungi enhanced the drinking water treatment residue-based vertical flow constructed wetlands on the purification of arsenic-containing wastewater.

Arsenic (As) is a toxic metalloid that poses a potential risk to the environment and human health. In this study, drinking water treatment residue (DWTR) and ceramsite-based vertical flow constructed wetlands (VFCWs) were built to purify As-containing wastewater. As a method of bioaugmentation, arbuscular mycorrhizal fungi (AMF) was inoculated to Pteris vittata roots to enhance the As removal of the VFCWs. The results showed that the As removal rates reached 87.82-94.29% (DWTR) and 33.28-58.66% (ceramsite). DWTR and P. vittata contributed 64.33-72.07% and 7.57-29% to the removal of As, while AMF inoculation intensified the As accumulation effect of P. vittata. Proteobacteria, the main As3+ oxidizing bacteria in the aquatic systems, dominated the microbial community, occupying 72.41 ± 7.76%. AMF inoculation increased As-related functional genes abundance in DWTR-based wetlands and provided a reliable means of arsenic resistance in wetlands. These findings indicated that the DWTR-based VFCWs with AMF inoculated P. vittata had a great purification effect on As-containing wastewater, providing a theoretical basis for the application of DWTR and AMF for As removal in constructed wetlands.

Which is the best TACE agent for patients with different NLR hepatocellular carcinomas? A systematic review and network meta-analysis.

Background: Transarterial chemoembolization (TACE) is a common treatment for hepatocellular carcinoma (HCC), but the best therapeutic agent for TACE treatment has not been determined. The neutrophil/lymphocyte ratio (NLR) is a systemic immune system marker; however, the ability of the NLR to predict the prognosis of patients with HCC is unknown, and no studies have been conducted to determine the most appropriate TACE regimen for HCC patients with different NLRs. Methods: The PubMed, Embase, Web of Science, and CNKI databases were searched through May 28, 2023. Comparisons of overall survival (OS) among cohort studies with different NLRs and different TACE treatment regimens were performed with a random effects model. Findings: Thirty-five studies involving 9210 patients were included in this meta-analysis. The results showed that Group 3-4 (NLR<2.5) patients had a significantly longer OS than Group 1-2 (NLR 2.5-5.0). Among the patients, Group 1-3 (NLR 2.0-5.0) patients had the best survival after treatment with adriamycin (lnHR (95 % CI = 0.48 [0.31, 0.75] and lnHR (95 % CI = 0.41 [0.19, 0.91]). Among the Group 4 patients (NLR<2.0), the best outcome was obtained with platinum + adriamycin (lnHR (95 % CI = 0.59 [0.45, 0.78]), followed by adriamycin. A subgroup analysis of TACE combined with other treatments showed that adriamycin combined with sorafenib was the most effective and superior to the other treatment agents. Interpretation: The NLR can be used to predict the prognosis of HCC patients treated with TACE; the higher the NLR is, the worse the prognosis. Adriamycin may be the best therapeutic agent for HCC patients treated with TACE.

Biomimetic Iron-Based Nanoparticles Remodel Immunosuppressive Tumor Microenvironment for Metabolic Immunotherapy.

Introduction: Immunotherapy has led to a paradigm shift in reinvigorating treatment of cancer. Nevertheless, tumor associated macrophages (TAMs) experience functional polarization on account of the generation of suppressive metabolites, contributing to impaired antitumor immune responses. Methods: Hence, metabolic reprogramming of tumor microenvironment (TME) can synergistically improve the efficacy of anti-tumor immunotherapy. Herein, we engineered an iron-based nanoplatform termed ERFe3O4 NPs. This platform features hollow Fe3O4 nanoparticles loaded with the natural product emodin, the outer layer is coated with red blood cell membrane (mRBCs) inserted with DSPE-PEG2000-galactose. This effectively modulates lactate production, thereby reversing the tumor immune suppressive microenvironment (TIME). Results: The ERFe3O4 NPs actively targeted TAMs on account of their ability to bind to M2-like TAMs with high expression of galectin (Mgl). ERFe3O4 NPs achieved efficient ability to reverse TIME via the production of reducing lactate and prompting enrichment iron of high concentrations. Furthermore, ERFe3O4 NPs resulted in heightened expression of CD16/32 and enhanced TNF-α release, indicating promotion of M1 TAMs polarization. In vitro and in vivo experiments revealed that ERFe3O4 NPs induced significant apoptosis of tumor cells and antitumor immune response. Discussion: This study combines Traditional Chinese Medicine (TCM) with nanomaterials to synergistically reprogram TAMs and reverse TIME, opening up new ideas for improving anti-tumor immunotherapy.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.

INTRODUCTION: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. OBJECTIVES: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. RESULTS: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805-0.883), AUC5-year = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. CONCLUSION: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

The efficacy and safety of PI3K and AKT inhibitors for patients with cancer: A systematic review and network meta-analysis.

BACKGROUND: Inhibiting PI3K/AKT pathway activation may hinder the occurrence and progression of cancer. The aim of this study was to evaluate the efficacy and safety of the PI3K/AKT inhibitors and determine the most appropriate inhibitor for different cancer types. METHODS: Electronic databases up to June 2024 were used to examine the efficacy and safety of PI3K inhibitors (alpelisib, copanlisib, duvelisib, and idelalisib) and AKT inhibitors (capivasertib, ipatasertib and MK-2206) for the treatment of cancer. Data was assessed with a random-effect pairwise and network meta-analysis. Randomized controlled trials and retrospective studies were eligible if they compared PI3K or AKT inhibitors with non-PI3K/AKT controls with no restriction. RESULTS: The results were based on 34 studies from 34 published articles and 6 online registration trials (6710 patients). According to pairwise meta-analysis, PI3K/AKT inhibitors showed to be highly effective, especially for treating mutant cancers, but had poor safety profiles. According to our network meta-analysis, PI3K/AKT inhibitors, especially the AKT inhibitor capivasertib, are effective for treating solid cancers such as breast cancer (BC). Moreover, PI3K inhibitors, especially idelalisib, were effective for treating hematologic cancers such as chronic lymphocytic leukemia (CLL). CONCLUSIONS: The PI3K/AKT inhibitors are effective in patients with genetic mutations. For solid cancers such as BC, capivasertib was efficacy and safety. For hematological cancers represented by CLL, idelalisib was efficacy and safety. The above studies can be used when recommending appropriate targeted therapies for patients with different cancer types.

Photoinduced Trifluoromethylselenolation of Aryl Halides with [Me4N][SeCF3].

A visible-light-induced metal-free trifluoromethylselenolation of aryl iodides and bromides with [Me4N][SeCF3] is described. The reaction was conducted at ambient temperature by successfully harnessing the light-sensitive SeCF3 reagent. Mechanistically, the EDA complexes between aryl halide and the -SeCF3 anion or the base might be formed and excited by light, which subsequently undergo intracomplex SET processes to generate aryl and •SeCF3 radicals as key intermediates, allowing a convenient and green access to various aryl trifluoromethyl selenoethers.

Correlation Analysis of Surgical Efficacy and Risk Factors of Cervical Spondylotic Myelopathy with High Signal Intensity on MRI-T2WI.

OBJECTIVES: The purpose of this study was to investigate the surgical efficacy and risk factors of cervical spondylotic myelopathy (CSM) patients with increased signal intensity (ISI) on T2-weighted magnetic resonance imaging (MRI-T2WI). METHODS: We compared the surgical outcomes of CSM patients with and without ISI. In addition, we compared the efficacy of anterior and posterior cervical decompression in CSM patients with ISI. We also analyzed the risk factors of MRI-T2WI ISI in CSM patients. RESULTS: The incidence of ISI among 153 CSM patients was 71.89 %. The JOA score and JOA remission rate were better in the ISI-free than in the ISI group. The postoperative JOA score and JOA remission rate were better in the posterior than the anterior approach surgery group. The disease duration and vertebral canal volume were found to be risk factors for ISI in CSM patients. CONCLUSION: Among patients with CSM, the prognosis is worse for those with ISI than those without ISI. Posterior cervical decompression surgery produces a better curative effect than anterior cervical decompression surgery in CSM patients with ISI. CSM patients with longer disease duration and small vertebral canal volume should undergo surgical treatment as early as possible.

Continuous ammonia electrosynthesis using physically interlocked bipolar membrane at 1000 mA cm-2.

Electrosynthesis of ammonia from nitrate reduction receives extensive attention recently for its relatively mild conditions and clean energy requirements, while most existed electrochemical strategies can only deliver a low yield rate and short duration for the lack of stable ion exchange membranes at high current density. Here, a bipolar membrane nitrate reduction process is proposed to achieve ionic balance, and increasing water dissociation sites is delivered by constructing a three-dimensional physically interlocked interface for the bipolar membrane. This design simultaneously boosts ionic transfer and interfacial stability compared to traditional ones, successfully reducing transmembrane voltage to 1.13 V at up to current density of 1000 mA cm-2. By combining a Co three-dimensional nanoarray cathode designed for large current and low concentration utilizations, a continuous and high yield bipolar membrane reactor for NH3 electrosynthesis realized a stable electrolysis at 1000 mA cm-2 for over 100 h, Faradaic efficiency of 86.2% and maximum yield rate of 68.4 mg h-1 cm-2 with merely 2000 ppm NO3- alkaline electrolyte. These results show promising potential for artificial nitrogen cycling in the near future.