The Prognostic and Immune Significance of BZW2 in Pan-Cancer and its Relationship with Proliferation and Apoptosis of Cervical Cancer.

BACKGROUND: Basic leucine zipper and W2 domains 2 (BZW2), a member of the basic domain leucine zipper superfamily of transcription factors, has been implicated in the development and progression of various cancers. However, the precise biological role of BZW2 in pan-cancer datasets remains to be explored. This study aimed to assess the prognostic significance of BZW2 and its immune-related signatures in various tumors. METHODS: Our study investigated the expression, epigenetic modifications, and clinical prognostic relevance of BZW2 using multi-omics data in different cancer types. Additionally, the immunological characteristics, tumor stemness, drug sensitivity, and correlation of BZW2 with immunotherapy response were explored. Finally, in vitro experiments were conducted to assess the impact of BZW2 knockdown on Hela cells, a cell line derived from cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). RESULTS: BZW2 exhibited elevated expression levels in various tumor tissues and significantly impacted the prognosis of different cancer types. BZW2 emerged as an independent prognostic factor in CESC. We found that copy number amplification and methylation levels of BZW2 were associated with its mRNA expression. Immunological analyses revealed that BZW2 shapes a non-inflamed immunosuppressive tumor microenvironment across multiple cancers. Furthermore, our cell experiments demonstrated that BZW2 knockdown reduced proliferation, migration, and apoptosis activities in CESC cells. CONCLUSIONS: BZW2 promotes cancer progression by shaping a non-inflamed immunosuppressive tumor microenvironment. Additionally, BZW2 was shown to significantly influence the proliferation, migration, and apoptosis of CESC cells.

Structural distortion-induced monoclinic sodium iron hexacyanoferrate as a high-performance electrode for rocking-chair desalination batteries.

Sodium iron hexacyanoferrate (NaFeHCF) has been recognized as a promising Prussian blue analogue (PBA)-based electrode for electrochemical desalination; however, its application potential is limited by its unsatisfactory desalination capacity and cycling stability. Herein, the structurally distorted high-quality monoclinic NaFeHCF with fewer defects in the framework was synthesized by a crystal-controlled coprecipitation method via tuning the crystallization reaction conditions and applied to seawater desalination. Physicochemical characterization and desalination experiments show that the NFHFC-2 with minimized defects possesses enhanced electrochemical activity of Fe2+ and electrochemical kinetics, thus achieving higher desalination performance (specific capacity of 75.0 mA h g-1 and capacity retention of 85.3% after 50 cycles). Furthermore, a symmetrical NFHCF-2 RCDB is assembled, and the operation parameters (including various salinities and electrode spacing) are optimized to achieve a remarkable salt removal capacity (SRC) of 108.9 mg g-1 and a salt removal rate (SRR) of 2.22 mg g-1 min-1 with low energy consumption (0.056 kW h kg-1-NaCl) and outstanding cycling stability (almost no capacity attenuation in 150 cycles). Impressively, the RCDB further exhibits favorable technical feasibility in the simultaneous removal of univalent/bivalent ions from the natural seawater. This study inspires the design of high-quality PBA-based electrodes with optimized crystal structures for electrochemical desalination.

Inositol supplementation for the prevention and treatment of gestational diabetes mellitus: a meta-analysis of randomized controlled trials.

BACKGROUND: Inositol is a potential new therapeutic agent for gestational diabetes mellitus (GDM), but its effectiveness is still controversial. The aim of the report was to evaluate the effectiveness of inositol to preventing or reducing the severity of GDM. METHODS: We searched PubMed, EmBase, Web of science, Cochrane library databases, Clinicaltrials.gov, and International Clinical Trials Registry Platform for randomized controlled trials (RCTs) assessing the effectiveness of inositol supplementation to prevent and treat GDM. This meta-analysis was performed using the random-effects model. RESULTS: A total of 7 RCTs (1319 pregnant women at high risk of GDM) were included in the meta-analysis. The meta-analysis found that inositol supplementation resulted in a significantly lower incidence of GDM in the inositol versus the control group (odds ratio [OR] 0.40; 95% confidence interval [CI] 0.24-0.67; P = 0.0005). The inositol group had improved fasting glucose oral glucose tolerance test (FG OGTT; mean difference [MD] = - 3.20; 95% CI - 4.45 to - 1.95; P < 0.00001), 1-h OGTT (MD = - 7.24; 95% CI - 12.23 to - 2.25; P = 0.004), and 2-h OGTT (MD = - 7.15; 95% CI - 12.86 to - 1.44; P = 0.01) results. Inositol also reduced the risk of pregnancy-induced hypertension (OR 0.37; 95% CI 0.18-0.75; P = 0.006) and preterm birth (OR 0.35; 95% CI 0.18-0.69; P = 0.003). A meta-analysis of 4 RCTs including 320 GDM patients showed that the patients' insulin resistance (P < 0.05) and neonatal hypoglycemia risk (OR 0.10, 95% CI 0.01-0.88; P = 0.04) were lower in the inositol than in the control group. CONCLUSIONS: Inositol supplementation during pregnancy has the potential to prevent GDM, improve glycemic control, and reduce preterm birth rates.

Individualized Assessment of Brain Aß Deposition With fMRI Using Deep Learning.

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-ß deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-ß PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aß-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aß-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.

BAG2 Is a Novel Prognostic Biomarker and Promising Immunotherapy Target in Uveal Melanoma.

BACKGROUND: The importance of BAG2 in malignancy is gradually being recognized, however, information on its role in uveal melanoma (UVM) is limited. We aimed to elucidate its function and potential mechanism of action in UVM. METHODS: Using the Cancer Genome Atlas (TCGA) and GEO-related datasets, we analyzed the differential expression of BAG2 in tumors, combined with clinical information and methylation data to analyze the prognostic value of BAG2, differential methylation and its association with UVM metastasis. In addition, correlation analysis explored the immunological characteristics of BAG2 in UVM and the response to immunotherapy. Finally, a prognostic model of ferroptosis- related genes was constructed and validated. RESULTS: BAG2 is significantly downregulated in multiple cancers including UVM. Prognostic analysis showed that BAG2 was an independent prognostic factor for UVM. Abnormal methylation of BAG2 may affect the metastasis of UVM and be significantly associated with poor prognosis. Immune analysis clarified that BAG2 was significantly associated with UVM immune cell infiltration and multiple immune checkpoints, and low expression of BAG2 was more beneficial in immunotherapy. In addition, the prognostic model of ferroptosis we constructed has good performance in predicting overall survival and metastasis-free survival of UVM. CONCLUSIONS: BAG2 is an independent prognostic factor for UVM and may be a potential immune checkpoint for UVM.

The prognostic and immune significance of C15orf48 in pan-cancer and its relationship with proliferation and apoptosis of thyroid carcinoma.

Background: C15orf48 was recently identified as an inflammatory response-related gene; however there is limited information on its function in tumors. In this study, we aimed to elucidate the function and potential mechanism of action of C15orf48 in cancer. Methods: We evaluated the pan-cancer expression, methylation, and mutation data of C15orf48 to analyze its clinical prognostic value. In addition, we explored the pan-cancer immunological characteristics of C15orf48, especially in thyroid cancer (THCA), by correlation analysis. Additionally, we conducted a THCA subtype analysis of C15orf48 to determine its subtype-specific expression and immunological characteristics. Lastly, we evaluated the effects of C15orf48 knockdown on the THCA cell line, BHT101, by in vitro experimentation. Results: The results of our study revealed that C15orf48 is differentially expressed in different cancer types and that it can serve as an independent prognostic factor for glioma. Additionally, we found that the epigenetic alterations of C15orf48 are highly heterogeneous in several cancers and that its aberrant methylation and copy number variation are associated with poor prognosis in multiple cancers. Immunoassays elucidated that C15orf48 was significantly associated with macrophage immune infiltration and multiple immune checkpoints in THCA, and was a potential biomarker for PTC. In addition, cell experiments showed that the knockdown of C15orf48 could reduce the proliferation, migration, and apoptosis abilities of THCA cells. Conclusions: The results of this study indicate that C15orf48 is a potential tumor prognostic biomarker and immunotherapy target, and plays an essential role in the proliferation, migration, and apoptosis of THCA cells.

Removal of gaseous volatile organic compounds via vacuum ultraviolet photodegradation: Review and prospect.

Volatile organic compounds (VOCs) have attracted much attention for decades as they are the precursors of photochemical smog and are harmful to the environment and human health. Vacuum ultraviolet (VUV) photodegradation is a simple and effective method to decompose VOCs (ranging from tens to hundreds of ppmV) without additional oxidants or catalysts in the air at atmospheric pressure. In this paper, we review the research progress of VOCs removal via VUV photodegradation. The fundamentals are outlined and the key operation factors for VOCs degradation, such as humidity, oxygen content, VOCs initial concentration, light intensity, and flow rate, are discussed. VUV photodegradation of VOCs mixture is elucidated. The application of VUV photodegradation in combination with ozone-assisted catalytic oxidation (OZCO) and photocatalytic oxidation (PCO) systems, and as the pre-treatment technique for biological purification are illustrated. Based on the summary, we propose the challenges of VUV photodegradation and perspectives for its future development.

PCMT1 Is a Potential Prognostic Biomarker and Is Correlated with Immune Infiltrates in Breast Cancer.

Background: Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) is involved in the occurrence and development of a variety of malignant tumors. However, the prognostic value of PCMT1 in breast cancer remains unclear. Methods: Based on the Cancer Genome Atlas database, we assessed the correlation between the expression of PCMT1 and prognosis, immune invasion, and tumor mutation burden in a variety of cancers. The expression level, mutation, immune correlation, and coexpression of PCMT1 in breast cancer were studied using the following databases: UALCAN database, Human Protein Atlas database, cBioPortal database, TIMER database, and LinkedOmics database. Kaplan-Meier Plotter was used for survival analysis. Receiver operating characteristic (ROC) curves and nomograms were drawn using the R software package. P < 0.05 was considered statistically significant. Results: Pancancer analysis showed that PCMT1 is highly expressed in a variety of cancers and is significantly related to the prognosis of a variety of cancers. PCMT1 is significantly related to the tumor mutation burden of a variety of cancers. PCMT1 is significantly high in breast cancer, and it is significantly related to the abundance of immune infiltration. Survival analysis revealed that high PCMT1 expression is significantly associated with shorter overall survival (OS), relapse-free survival (RFS), and postprogression survival (PPS) in breast cancer patients. ROC curves and nomograms verify the effectiveness of PCMT1 as a prognostic biomarker for breast cancer. Conclusions: PCMT1 can be used as a potential prognostic biomarker of breast cancer, and it is significantly related to the abundance of breast cancer immune infiltration.

Amino-acid modulated hierarchical In/H-Beta zeolites for selective catalytic reduction of NO with CH4 in the presence of H2O and SO2.

Selective catalytic reduction of NO with CH4 (CH4-SCR) has been studied over a series of amino-acid mediated hierarchical beta zeolites with indium exchange. Amino acid mesoporogens greatly affect the NO reduction (DeNOx) efficiency of In/H-Beta catalysts. Mesoporous In/H-Beta-P synthesized using proline exhibits the highest NOx removal efficiency of 40% in excess oxygen and poisonous SO2 and H2O, 10% higher than our previously optimized In/H-Beta catalyst using commercial beta zeolites with a similar Si/Al ratio. Analyses using XRD, N2 adsorption-desorption, EPR, SEM, TEM, EDX, ICP, 27Al and 29Si MAS NMR, XPS, H2-TPR, NH3-TPD, and Py-IR reveal that amino acids promote beta crystallization, modulate zeolite acid sites and surface oxygen species, and generate hierarchical pore architectures without affecting the Si/Al ratio, indium content, and percentage of the active InO+ species. The mosaic-structured In/H-Beta-P exhibits the strongest Brønsted acidity and surface labile oxygen which enhance the oxyindium interaction with the zeolite framework, promoting CH4-SCR activity. The strong acidity, surface active oxygen species, and mesopores lead to excellent stability of the In/H-Beta-P catalyst in the presence of SO2 and H2O, withstanding several catalytic DeNOx cycles under harsh reaction conditions.

BAG family proteins contributes to autophagy-mediated multidrug resistance of tumor.

Multidrug resistance (MDR) is a significant cause of tumor treatment failure. Accumulating evidence suggests that autophagy plays a significant role in the development of MDR. Autophagy is a conserved mechanism that maintains tumor homeostasis by removing damaged mitochondria. However, the specific regulatory mechanism is unclear. Here, we summarize recent studies on the role of autophagy in the development of MDR and the initiation of mitophagy by Bcl-2-associated athanogene (BAG) family proteins. Additionally, this mini-review emphasizes the regulatory role of BAG family proteins, which maintain mitochondrial homeostasis by regulating the PINK1/Parkin pathway. Elucidation of the regulatory mechanisms of mitophagy may foster the development of clinical therapeutic strategies for MDR tumors.

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R2 significantly increased by 12.35%, 11.94%, 9.21%, and 8.13%, respectively, compared to ANN predictions. The sensitivity analysis based on Shapley (SHAP) values revealed that conductivity (σ), temperature (T), pH, and sewage flow (Flow) contributed significantly to the prediction results of the model. Furthermore, the three input variables including air pressure (AP), land area (A), and population (Pop.) were removed without affecting the prediction performance of the model, which maximized the modeling efficiency and reduced the operational cost. This study provides an economical and feasible technical method for early warning of abnormal heavy metal concentrations in urban industrial sewer networks.

Predicting Brain Amyloid-ß PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity.

BACKGROUND: The detection of amyloid-ß (Aß) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer's disease (AD). However, the current positron emission tomography (PET)-based brain Aß examination suffers from the problems of coarse visual inspection (in many cases, with 2-class stratification) and high scanning cost. OBJECTIVE: 1) To characterize the non-binary Aß deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual Aß deposition grades with non-invasive functional magnetic resonance imaging (fMRI). METHODS: 1) Individual whole-brain Aß-PET images from the OASIS-3 dataset (N = 258) were grouped into three clusters (grades) with t-SNE and k-means. The demographical data as well as global and regional standard uptake value ratios (SUVRs) were compared among the three clusters with Chi-square tests or ANOVA tests. 2) From resting-state fMRI, both conventional functional connectivity (FC) and high-order FC networks were constructed and the topological architectures of the two networks were jointly learned with graph convolutional networks (GCNs) to predict the Aß-PET grades for each individual. RESULTS: We found three clearly separated clusters, indicating three Aß-PET grades. There were significant differences in gender, age, cognitive ability, APOE type, as well as global and regional SUVRs among the three grades we found. The prediction of Aß-PET grades with GCNs on FC for the 258 samples in the AD continuum reached a satisfactory averaged accuracy (78.8%) in the two-class classification tasks. CONCLUSION: The results demonstrated the feasibility of using deep learning on a non-invasive brain functional imaging technique to approximate PET-based Aß deposition grading.

Comparison of the effectiveness and safety of insulin and oral hypoglycemic drugs in the treatment of gestational diabetes mellitus: a meta-analysis of 26 randomized controlled trials.

OBJECTIVE: Oral hypoglycemic drugs for the treatment of gestational diabetes mellitus (GDM) are still controversial because they can pass through the placenta. The purpose of this meta-analysis is to evaluate the safety and effectiveness of oral hypoglycemic drugs. METHODS: PubMed, Ovid Embase, Web of Science, and Cochrane databases were systematically searched (inception to 20 April 2021). Rev Man 5.0 was used to analyze the data. A random-effects model was used to compute the summary risk estimates. RESULTS: There were 26 randomized controlled trials (RCTs) involving 4921 GDM patients which were included in this meta-analysis. Compared with metformin, insulin had a significant increase in the risk of preeclampsia (odds ratio [OR], 1.61; 95% confidence interval [CI], 1.06 to 2.45; I2=40%; p < .05), hypertension (OR, 1.42; 95% CI, 1.02 to 1.99; I2=0%; p < .05), hypoglycemia (OR, 3.93; 95% CI, 1.27 to 12.19; I2=0%; p < .05), neonatal hypoglycemia (OR, 1.92; 95% CI, 1.34 to 2.76; I2=41%; p < .0001), neonatal jaundice (OR, 2.70; 95% CI, 1.12 to 6.52; I2=0%; p < .05), and Neonatal Intensive Care Unit Admission (OR, 1.46; 95% CI, 1.09 to 1.95; I2=39%; p < .05), but the risk of neonatal macrosomia (OR, 1.67; 95% CI, 1.12 to 2.40; I2=0%; p < .05) and neonatal injury (OR, 0.70; 95% CI, 0.55 to 0.89; I2=0%; p < .01) is lower. CONCLUSIONS: Metformin is comparable with insulin in glycemic control and neonatal outcomes and has the potential to replace insulin therapy in clinical practice. Glyburide is behind metformin and insulin, and more RCTs are needed to verify its safety.

Case Report: Genomic Characteristics of the First Known Case of SARS-CoV-2 Imported From Spain to Sichuan, China.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been basically under control in China since March 2020, but the import of domestic SARS-CoV-2 has begun to increase. This study reported the first case of asymptomatic SARS-CoV-2 infection imported from Spain into Sichuan Province, China, on March 11, 2020. The infected male had a body temperature of 37.5°C, normal blood oxygen saturation levels, and a computed tomography (CT) examination showed that his lungs had no shadows. However, a throat swab from the subject tested positive for SARS-CoV-2 using qPCR assay. In this study, we conducted transcriptome sequencing on respiratory throat swabs from the subject and found that the dominant SARS-CoV-2 sequence (Gene Bank ID: MW301121) was a spike protein D614G mutant strain, which is currently popular throughout world. We downloaded and analyzed SARS-CoV-2 sequences collected from cases in China and Spain for comparison and tracing purposes. After March 11, 2020, the Chinese domestic clade was naturally divided into the imported SARS-CoV-2 D614G mutant strain and evolutionarily-related similar sequences and that of sequences collected in the original Wuhan area. The sequence reported in this study was located on a small branch, far from the evolution of Wuhan sequences. As expected, the identified sequence was closely related to the evolution of the SARS-CoV-2 D614G mutant strain circulating in Spain.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas.

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.

Efficient mineralization of gaseous benzyl chloride by VUV/UV photodegradation in humid air.

VUV/UV photodegradation technology, which is free of catalysts or oxidants, has been regarded as an efficient method to decompose gaseous VOCs. However, the mineralization of gaseous VOCs by VUV/UV photodegradation has seldom been discussed systematically. In this work, the mineralization of benzyl chloride in humid air is comprehensively investigated and the potential contributors in the system (i.e., light wavelength, O2 and H2O) are discussed. As a result, more than 95.0% benzyl chloride is mineralized at 40 min in humid air with 80% relative humidity (RH) despite its initial concentrations (i.e., ranging from 4 to 20 ppm). It is found that both 185-nm VUV light and H2O significant contribute to the efficient mineralization of benzyl chloride in humid air, while O2 only has a limited effect to the efficient mineralization of benzyl chloride in humid air. The introduction of H2O into the VUV/UV photodegradation can reduce the emission of ozone obviously. These findings are significant inspiration to application of the VUV/UV photodegradation technology on the treatment of gaseous VOCs in the actual air atmosphere.

Rapid photolysis of H2S and the mechanism using high-frequency discharge electrodeless lamp.

In the present study, the H2S photolysis using the self-made high-frequency discharge electrodeless lamp (light distribution was 90% at 254 nm and 10% at 185 nm) was studied and simulated by MATLAB software. Firstly, the effects of the initial H2S concentration, irradiation time, oxygen content and relative humidity on H2S photolysis efficiency were experimentally investigated. The results indicated that the photolysis efficiency decreased from 100% to 90.13% with the increase in the initial concentration from 3 to 30 mg/m3, and the main product was H2SO4. With the relative humidity increased from 0% to 99%, H2S photolysis efficiency was obviously improved under different atmospheres (O2 > air > Ar), indicating the significant effect of relative humidity and oxygen concentration. The simulation results were consistent with the experimental results, indicating the feasibility of the simulation model. Moreover, based on the photoreactions, model simulation and equilibrium analysis of sulphur species, the photodegradation pathway of H2S was further inferred. H2S was oxidized to H2SO4 by O3 and other strong oxidizing radicals excited by 185 nm UV light.

Gas diffusion electrodes for H2O2 production and their applications for electrochemical degradation of organic pollutants in water: A review.

Nowadays, it is a great challenge to minimize the negative impact of hazardous organic compounds in the environment. Highly efficient hydrogen peroxide (H2O2) production through electrochemical methods with gas diffusion electrodes (GDEs) is greatly demand for degradation of organic pollutants that present in drinking water and industrial wastewater. The GDEs as cathodic electrocatalyst manifest more cost-effective, lower energy consumption and higher oxygen utilization efficiency for H2O2 production as compared to other carbonaceous cathodes due to its worthy chemical and physical characteristics. In recent years, the crucial research and practical application of GDE for degradation of organic pollutants have been well developed. In this review, we focus on the novel design, fundamental aspects, influence factors, and electrochemical properties of GDEs. Furthermore, we investigate the generation of H2O2 through electrocatalytic processes and degradation mechanisms of refractory organic pollutants on GDEs. We describe the advanced methodologies towards electrochemical kinetics, which include the enhancement of GDEs electrochemical catalytic activity and mass transfer process. More importantly, we also highlight the other technologies assisted electrochemical process with GDEs to enlarge the practical application for water treatment. In addition, the developmental prospective and the existing research challenges of GDE-based electrocatalytic materials for real applications in H2O2 production and wastewater treatment are forecasted. According to our best knowledge, only few review articles have discussed GDEs in detail for H2O2 production and their applications for degradation of organic pollutants in water.

Recent Advances in Desalination Battery: An Initial Review.

Desalination is one of the most effective strategies to solve the problem of freshwater shortage, which is one of the most critical challenges facing global development. Recently, the desalination battery has become an emerging desalination technology thanks to its high salt-removal capacity enabled by the high capacity of battery electrodes and low energy consumption mainly rooted from the high energy recovery during the discharge process. To promote the development of the desalination battery, we must understand the recent advances and the remaining issues in the field. Herein, we comprehensively review the development of the concept and the electrode materials for a desalination battery, summarize the performance of a full desalination battery, and propose perspectives and guidelines.