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A large size BiVO4 photoanode with high stability for efficient photoelectrochemical water oxidation and TCH degradation coupled with H2 evolution.

A NMNTL, which presents an S-scheme electrons transfer mode, facilitating the migration of e−/h+ pairs and suppressing the reoxidation of CO, thus manifesting high photocatalytic CO2-to-CO activity in H2O media is synthesized for the first time.

Human glucose-6-phosphate dehydrogenase (G6PD) is a kind of polymerase, which can bind cofactors β-D-glucose-6-phosphate (G6P) and catalyzes NADP+ to produce NADPH. Therefore, G6PD plays a crucial role in maintaining reduced glutathione to protect cells from oxidative stress disorders. Aurine tricarboxylic acid (ATA) is a potential metal binding inhibitor. Here, we found that G6P and ATA have a common potential binding site in the G6PD receptor conformation through molecular docking. The results of molecular dynamics (MD) simulation showed that, compared with apo-G6PD, G6P-G6PD and ATA-G6PD had lower root mean square deviation (RMSD), radius of gyration (Rg), and solvent accessible surface area (SASA), indicating that binding ligand molecules could promote the overall conformation of G6PD to be more stable. These results suggest that ATA may compete for the binding sites of G6P, thereby inhibiting the catalytic function of G6PD. This study revealed the mechanism of interaction between ATA inhibitors and G6PD at the atomic conformation level, providing a theoretical basis for investigating the effects of cellular oxidative stress.

The maculopathy in highly myopic eyes is complex. Its clinical diagnosis is a huge workload and subjective. To simply and quickly classify pathologic myopia (PM), a deep learning algorithm was developed and assessed to screen myopic maculopathy lesions based on color fundus photographs.This study included 10,347 ocular fundus photographs from 7606 participants. Of these photographs, 8210 were used for training and validation, and 2137 for external testing. A deep learning algorithm was trained, validated, and externally tested to screen myopic maculopathy which was classified into four categories: normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM. The area under the precision-recall curve, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Cohen's kappa were calculated and compared with those of retina specialists.In the validation data set, the model detected normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM with AUCs of 0.98, 0.95, 0.99, and 1.00, respectively; while in the external-testing data set of 2137 photographs, the model had AUCs of 0.99, 0.96, 0.98, and 1.00, respectively.We developed a deep learning model for detection and classification of myopic maculopathy based on fundus photographs. Our model achieved high sensitivities, specificities, and reliable Cohen's kappa, compared with those of attending ophthalmologists.

Background. Fu Fang Zhen Zhu Tiao Zhi (FTZ) is a traditional Chinese herbal prescription widely used to treat dyslipidemia, metabolic diseases, and diabetic coronary disorders. Cardiomyocyte death and loss of regenerative ability cause cardiac dysfunction and heart failure. FTZ can effectively treat diabetic cardiomyopathy and macrovascular diseases; however, the mechanism behind the phenomenon is still unclear. Here, we determined the mechanism of action of FTZ in treating myocardial infarction. Methods. Male C57BL/6 mice were treated with 2.4 or 1.2 g/kg FTZ, or administered saline by oral gavage daily for four weeks, and a 24-hour ligation was administered to the artery. Echocardiography was used to evaluate cardiac function. Hematoxylin and eosin and Evans blue/triphenyltetrazolium chloride staining were carried out by staining the cardiac tissue, used to evaluate cardiac function and infarct size. Using western blotting and reverse transcriptase-polymerase chain reaction, we determined the relative levels of NOD-like receptor protein (NLRP) 3, ASC, cleaved caspase-l (C-Caspase-1), GSDMD, and GSDMD-N. TUNEL, immunohistochemical, and immunofluorescence staining were used to determine cell death and NLRP3 expression. An enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of interleukin (IL)-1β and IL-18. Results. FTZ reduced ischemia-induced cardiomyocyte cell death in vivo and H2O2-induced cell death in vitro by maintaining cardiac architecture and restoring cardiac function. FTZ decreased the NLRP3 expression and inhibited pyroptosis-correlated genes, including NLRP3, ASC, GSDMD, C-Caspase-1, and GSDMD-N. NLRP3 overexpression impaired the efficacy of FTZ by inducing pyroptosis. Conclusion. FTZ could preserve cardiac function resulting from ischemic insult by inhibiting pyroptosis, which was partially reversed by NLRP3 overexpression, indicating that NLRP3 could be a potential target of FTZ in treating myocardial infarction.

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.

Angiogenesis involves temporo-spatially coordinated endothelial cell (EC) proliferation, differentiation, migration, and sprouting. Notch signaling is essential in regulating EC behaviors during angiogenesis, but its downstream mechanisms remain incompletely defined. In the current study, we show that miR-223-3p is a downstream molecule of Notch signaling and mediates the role of Notch signaling in regulating EC migration and sprouting. In human umbilical vein endothelial cells (HUVECs), Notch activation by immobilized Dll4, a Notch ligand, upregulated miR-223-3p, and Notch activation-mediated miR-223-3p upregulation could be blocked by a γ-secretase inhibitor (DAPT). miR-223-3p overexpression apparently repressed HUVEC migration, leading to attenuated lumen formation and sprouting capacities. Transcriptome comparison and subsequent qRT-PCR validation further indicated that miR-223-3p downregulated the expression of multiple genes involved in EC migration, axon guidance, extracellular matrix remodeling, and angiogenesis. In addition, miR-223-3p antagonist transfection abolished Notch-mediated repression of EC migration and sprouting. By quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blotting, and reporter assay analysis, we confirmed that miR-223-3p directly targeted F-box and WD repeat domain-containing 7 (Fbxw7). Meanwhile, Fbxw7 overexpression could efficiently rescue the impaired migration capacity of ECs under miR-223-3p overexpression. In summary, these results identify that Notch activation-induced miR-223-3p suppresses EC migration and sprouting via Fbxw7.

Exploiting highly-efficient and metal-free photocatalyst for CO2 conversion into useful chemicals is a promising pathway to solve the energy and environmental crises. In this work, through a facile exfoliation process, an ultra-thin and short-range order g-C3N4 nanosheet with rich terminal oxygen defects is successfully constructed, which presents total electron yield of 36.30 μmol g-1h−1, 3.05 times higher than that of bulk one. The results affirms that both the van der Waals forces between the C3N4 layers and the C N bonds on the periodic heptazine units could be disrupted during the sonication process, thus achieving the ultra-thin and ultra-small g-C3N4 nanosheet, which enables the improvement of optical absorption and carrier separation abilities. The π-conjugated triazine rings structure is still remained but the terminal active C radicals tend to transform into oxygen defects which become the sites to bind and activate CO2. The in-situ DRIFTS provides the direct evidence that the size regulation and oxygen-defects design strategy can effectively promote the CO2 adsorption and activation process upon the photocatalyst, thus turning out to boost the reactivity toward CO2.

Background Resolvin D1 could reduce the inflammatory, catabolic response of OA chondrocyte, and promote the repair of various tissues.Our goal was to explore whether RvD1 could inhibit NLRP3/caspase-1 signaling pathway, slow down the occurrence of pyroptosis of OA chondrocytes, and then promote the proliferation of OA chondrocytes and repair of articular cartilage.Methods Animal care and use protocols comply with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.After the rat osteoarthritis model was established, RvD1 was injected and a control group trial was set up, Joint specimens were then collected.The micro-CT system was used to determine the acquisition of a 3D model of the rat knee joint. Chondrocytes were stained with toluidine blue, Then the cover slip was examined under a light microscope. EDU was used to detect the proliferation of chondrocytes.Western Blot,RT-qPCR and Immunofluorescence was used to detect markers in experiments.Results Compared with the control group, RvD1 can promote OA chondrocytes multiplication and inhibit chondrocytes pyrosis by regulating the cell cycle.Belnacasan is a specific inhibitor of caspase-1, Treatment of OA chondrocytes with Belnacasan and RvD1 showed that Belnacasan could specifically inhibit the conduction of pyroptosis pathway induced by caspase-1, and the synergistic inhibitory effect with RvD1 was more significant.Conclusion RvD1 promotes the proliferation of OA chondrocytes by inhibiting the expression of caspase-1 to regulate NLRP3/caspase-1 signaling − 8 pathway. At the same time, RvD1 promoted the repair of articular cartilage and retarded the progression of OA in rats.

Background Anastomotic leakage and postoperative pancreatic fistula (POPF) may occur after pancreatic head resection, also in the setting of pancreato-gastric reconstruction. For adequate complication management, a variety of non-standardized treatments are available. Still, data on clinical evaluation of endoscopic methods remain scarce. Based on our interdisciplinary experience on endoscopic treatment of retro-gastric fluid collections after left-sided pancreatectomies, we developed an innovative endoscopic concept with internal peri-anastomotic stent placement for patients with anastomotic leakage and/or peri-anastomotic fluid collection. Methods Over the period of 6 years (2015–2020) we retrospectively evaluated 531 patients after pancreatic head resections at the Department of Surgery, Charité–Unversitätsmedizin Berlin. Of these, 403 received reconstruction via pancreatogastrostomy. We identified 110 patients (27.3%) with anastomotic leakage and/or peri-anastomotic fluid collection and could define four treatment groups which received either conservative treatment (C), percutaneous drainage (PD), endoscopic drainage (ED), and/or re-operation (OP). Patients were grouped in a step-up approach for descriptive analyses and in a stratified, decision-based algorithm for comparative analyses. The study’s primary endpoints were hospitalization (length of hospital stay) and clinical success (treatment success rate, primary/secondary resolution). Results We characterized an institutional, post-operative cohort with heterogenous complication management following pancreato-gastric reconstruction. The majority of patients needed interventional treatments (n = 92, 83.6%). Of these, close to one-third (n = 32, 29.1%) were treated with endoscopy-guided, peri-anastomotic pigtail stents for internal drainage as either primary, secondary and/or tertiary treatment modality. Following a decision-based algorithm, we could discriminate superior primary—(77,8% vs 53.7%) and secondary success rates (85.7% vs 68.4%) as well as earlier primary resolutions (11.4 days, 95%CI (5.75–17.13) vs 37.4 days, 95%CI (27.2–47.5)] in patients receiving an endoscopic compared to percutaneous management. Conclusion This study underscores the importance of endoscopy-guided approaches for adequate treatment of anastomotic leakage and/or peri-anastomotic fluid collections after pancreatoduodenectomy. We herein report a novel, interdisciplinary concept for internal drainage in the setting of pancreato-gastric reconstruction.

Background The antibiotic resistome is the collection of all the antibiotic resistance genes (ARGs) present in an individual. Whether an individual’s susceptibility to infection and the eventual severity of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is influenced by their respiratory tract antibiotic resistome is unknown. Additional, whether a relationship exists between the respiratory tract and gut antibiotic resistance genes composition has not been fully explored. Method We recruited 66 patients with COVID-19 at three disease stages (admission, progression and recovery) and conducted a metagenome sequencing analysis of 143 sputum and 97 fecal samples obtained from them. Respiratory tract, gut metagenomes, and peripheral blood mononuclear cell (PBMC) transcriptomes are analyzed to compare the gut and respiratory tract ARGs of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between ARGs and immune response. Results Among the respiratory tract ARGs, we found that Aminoglycoside, Multidrugand Vancomycin are increased in ICU patients compared with nICU patients. In the gut, we found that Multidrug, Vancomycin and Fosmidomycinwere increased in ICU patients. Upon further investigation a significantly positive correlation was found between the relative abundance in ARGs (i.e., subtypes of the Aminoglycoside and Tetracyclinetypes) in the respiratory tract and gut. We discovered that the relative abundances of Multidrug were significantly correlated with clinical indices, and there was a significantly positive correlation between ARGs and microbiota in respiratory tract and gut. We found that immune related pathways in PBMC were enhanced, and they were significantly correlated with the relative abundance of Multidrug, Vancomycin and Tetracycline ARGs. Based on the relative abundance of ARG types, we built a respiratory tract-gut ARG combined random-forest classifier to distinguish ICU COVID-19 patients from nICU patients with an AUC of 0.969. The level of Aminoglycoside and Vancomycinin the gut was regarded as the most prominent biomarker. Conclusions Cumulatively, our findings provide some of the first insights into the dynamic alterations of respiratory tract and gut antibiotic resistome in the progression of COVID-19 and disease severity. They also provide a better understanding of how this disease affects different cohorts of patients. As such, these findings should contribute to better diagnosis and treatment scenarios.

Background: PMOD and the HeartSee software are commonly used to quantify myocardial perfusion. PMOD typically uses a one-tissue compartment model, whereas, HeartSee uses a simple retention model that is considered a special case of the one-tissue compartment model. We explored agreement in the absolute quantification of myocardial perfusion and the diagnostic performance of coronary microvascular dysfunction by comparison of PMOD and HeartSee in non-obstructive patients. Results: The rest myocardial blood flow of PMOD was higher than that of HeartSee (1.02±0.22 vs. 0.92±0.23, pvs.2.64±0.51, ppConclusions: The absolute quantification values obtained by PMOD and HeartSee were different. However, the diagnostic accuracy of HeartSee for abnormal myocardial flow reserve with a PET myocardial flow reserve less than two was 72%.

Dendrons have well-defined dendritic structures. However, it is a great challenge to preserve their high structural definition after multiple functionalization because the site-selective conjugation of different functional molecules is quite difficult. Scaffold-modifiable dendrons that have orthogonal reactive groups at the scaffold and periphery are ideal for achieving the site-specific bifunctionalization. In this paper, we present a new strategy for synthesizing scaffold-modifiable dendrons via orthogonal amino protection and a solid-phase synthesis method. This strategy renders the reactive sites at the scaffold and periphery of the dendrons a super selectivity, high reactivity, and wide applicability to various reaction types. The fourth-generation dendrons can be facilely synthesized within 2 days without structural defects as demonstrated by mass spectrometry. We conjugated doxorubicin (DOX) and phenylboronic acid (PBA) groups to the scaffold and periphery, respectively. Thanks to the PBA-enhanced lysosome escape, tumor targeting ability, and tumor permeability as well as the high drug loading content larger than 30%, the dendron-based prodrug exhibited extraordinary antitumor efficacy and could eradicate the tumors established in mice by multiple intravenous administration. This work provides a practical strategy for synthesizing scaffold-modifiable dendrons that can be a promising nanoplatform to achieve function integration in a precisely controlled manner.

Purpose Recently, a “U” hazard ratio curve between resting left ventricular ejection fraction (LVEF) and prognosis has been observed in patients referred for routine clinical echocardiograms. The present study sought to explore whether a similar “U” curve existed between resting LVEF and coronary flow reserve (CFR) in patients without severe cardiovascular disease (CVD) and whether impaired CFR played a role in the adverse outcome of patients with supra-normal LVEF (snLVEF, LVEF ≥ 65%). Methods Two hundred ten consecutive patients (mean age 52.3 ± 9.3 years, 104 women) without severe CVD underwent clinically indicated rest/dipyridamole stress electrocardiography (ECG)-gated 13 N-ammonia positron emission tomography/computed tomography (PET/CT). Major adverse cardiac events (MACE) were followed up for 27.3 ± 9.5 months, including heart failure, late revascularization, re-hospitalization, and re-coronary angiography for any cardiac reason. Clinical characteristics, corrected CFR (cCFR), and MACE were compared among the three groups categorized by resting LVEF detected by PET/CT. Dose–response analyses using restricted cubic spline (RCS) functions, multivariate logistic regression, and Kaplan–Meier survival analysis were conducted to evaluate the relationship between resting LVEF and CFR/outcome. Results An inverted “U” curve existed between resting LVEF and cCFR (p = 0.06). Both patients with snLVEF (n = 38) and with reduced LVEF (rLVEF, LVEF n = 66) displayed a higher incidence of reduced cCFR than those with normal LVEF (nLVEF, 55% ≤ LVEF n = 106) (57.9% vs 54.5% vs 34.3%, p p p p = 0.01). Conclusions Patients with snLVEF are prone to impaired cCFR, which may be related to the adverse prognosis. Further investigations are warranted to explore its underlying pathological mechanism and clinical significance.

The innovative design of Fe2TiO5 nanorods and the loading of NiMoO4/CoMoO4 nanosheet cocatalyst greatly improved the performance for oxidation of water.

An enhanced OER catalyst based on Fe doped CePO4 exhibits higher turnover frequency, more active sites and more oxygen vacancies than CePO4.

Well-designed second near-infrared (NIR-II) fluorophores are promising in optical diagnosis and therapy of tumors. In this work, we synthesized a donor-acceptor-donor (D-A-D) NIR-II fluorophore named BBTD-BET with dithienylethene as an electron donor and benzobisthiadiazole as an electron acceptor. To the best of our knowledge, this is the first report of using dithienylethene, a typical photochromic molecule, as a building block for NIR-II fluorophores. We studied the geometrical configuration, electronic state, and optical properties of BBTD-BET by both theoretical and experimental means. BBTD-BET had absorption and emission in the NIR-I and NIR-II spectral ranges, respectively. Using PEGylated BBTD-BET as a theranostic agent, we achieved NIR-II fluorescence/photoacoustic (PA) dual-modal imaging and attained high imaging resolution, desired signal-to-noise ratio, and excellent photothermal therapy (PTT) efficacy. After one PTT treatment, the tumors established in mice were eradicated. This work provides a novel organic conjugated molecule integrating NIR-II/PA dual-modal imaging and PTT functionalities that is very promising in the theranostic of tumors.

N6-methyladenosine (m6A) plays a significant role as an epigenetic mechanism, which is involved in various cancers’ progress via regulating mRNA modification. As a crucial m6A “reader”, YTHDF1 is able to alter m6A-modified mRNA and promote the protein translation process in multiple cancers. However, the role of YTHDF1 in lung cancer has not been fully investigated. This study focuses on elucidating the function of YTHDF1 in the development of lung cancer and its underlying mechanism. We demonstrated that YTHDF1 was highly expressed in lung carcinoma progression; then, the loss of function experiments in lung cell lines confirmed that knockdown of YTHDF1 suppressed cell proliferation, migration and invasion and induced ferroptosis of lung cancer cells. Further functional assays showed that ferritin (FTH) was identified as the key target of YTHDF1 in lung cancer cells. Furthermore, the overexpression of ferritin in YTHDF1-depleted cells partially restored lung cancer cell suppression. Collectively, our data suggested that the upregulation of YTHDF1 promotes lung cancer carcinogenesis by accelerating ferritin translation in an m6A-dependent manner. We hope that our findings may provide a new target for lung cancer diagnosis and treatment.

Existing machine learning-based malware traffic recognition techniques can effectively detect abnormal behaviors in the network. However, almost all of them focus on a closed-set scenario in which the data used for training and testing come from the same label space. Since sophisticated malware and advanced persistent threats are evolving, it is impossible to exhaust all attacks to train a complete recognition model under the existing technical conditions. Therefore, recognition in the real network is an open-set problem, i.e., the recognition system should identify unknown and unseen attacks at test time. In this paper, we propose an uncertainty-aware method to identify known malicious traffic accurately and handle unknown traffic effectively. This method employs predictive uncertainty in deep learning as an indicator for unknown class detection. The predictive uncertainty represents the confidence in neural network predictions. In particular, the Deep Evidence Malware Traffic Recognition (DEMTR) model is presented to provide the multi-classification probability and predictive uncertainty in open-set scenarios using evidential deep learning. We demonstrate the performance of DEMTR on the MCFP dataset. Experimental results indicate that the proposed model outperforms the baseline methods in accuracy and F1-score.

Antimony sulfide, as a binary chalcogenide, has attracted great attention in the field of optoelectronics in recent years, particularly in photovoltaics, because of its striking merits such as earth elements abundance, excellent stability, chemical versatility, and solution processability. With the rapid development of fabrication techniques and device engineering, the device performance of Sb

Diabetic retinopathy (DR), as the leading cause of vision loss in the working-age population, exhibits unique metabolite profiles in human plasma and vitreous. However, those in retina are not fully understood. Here, we utilized liquid and gas chromatography–tandem mass spectrometry technology to explore metabolite characteristics of streptozotocin (STZ)-induced diabetic mice retina. A total of 145 metabolites differed significantly in diabetic retinas compared with controls. These metabolites are mainly enriched in the Warburg effect, and valine, leucine and isoleucine degradation pathways. To further identify underlying regulators, RNA sequencing was performed to integrate metabolic enzyme alterations with metabolomics in STZ-induced diabetic retina. Retinol metabolism and tryptophan metabolism are the shared pathways enriched by metabolome and transcriptome. Additionally, transcriptomic analysis identified 71 differentially expressed enzyme-related genes including Hk2, Slc7a5, Aldh1a3 and Tph integrated with altered metabolic pathways. In addition, single nucleotide polymorphisms within 6 out of 71 genes are associated with increased diabetes risk. This study lays the foundation for mechanism research and the therapeutic target development of DR.

Deep learning is successful in providing adequate classification results in the field of traffic classification due to its ability to characterize features. However, malicious traffic captures insufficient data and identity tags, which makes it difficult to reach the data volume required to drive deep learning. The problem of classifying small-sample malicious traffic has gradually become a research hotspot. This paper proposes a small-sample malicious traffic classification method based on deep transfer learning. The proposed DA-Transfer method significantly improves the accuracy and efficiency of the small-sample malicious traffic classification model by integrating both data and model transfer adaptive modules. The data adaptation module promotes the consistency of the distribution between the source and target datasets, which improves the classification performance by adaptive training of the prior model. In addition, the model transfer adaptive module recommends the transfer network structure parameters, which effectively improves the network training efficiency. Experiments show that the average classification accuracy of the DA-Transfer method reaches 93.01% on a small-sample dataset with less than 200 packets per class. The training efficiency of the DA-Transfer model is improved by 20.02% compared to traditional transfer methods.