Getah virus Nsp3 binds G3BP to block formation of bona fide stress granules.

Stress granules (SGs) are cytoplasmic aggregates of proteins and mRNA that form in response to diverse environmental stressors, including viral infections. Several viruses possess the ability to block the formation of stress granules by targeting the SGs marker protein G3BP. However, the molecular functions and mechanisms underlying the regulation of SGs formation by Getah virus (GETV) remain unclear. In this study, we found that GETV infection triggered the formation of Nsp3-G3BP aggregates, which differed in composition from SGs. Further studies revealed that the presence of these aggregates was dependent on the activation of the PKR/eIF2α signaling pathway. Interestingly, we found that Nsp3 HVD domain blocked the formation of SGs by binding to G3BP NTF2 domain. Moreover, knockout of G3BP in NCI-H1299 cells had no effect on GETV replication, while overexpression of G3BP to form the genuine SGs significantly inhibited GETV replication. Overall, our study elucidates a novel role GETV Nsp3 to change the composition of SG as well as cellular stress response.

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer with18F-FDG PET/CT images.

Objective. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly important for improving patient prognosis. This study aimed to investigate the feasibility of a multimodal radiomics-based method using 3D neural networks trained on18F-FDG PET/CT images to predict BMs in NSCLC patients.Approach. We included 226 NSCLC patients who underwent18F-FDG PET/CT scans of areas, including the lung and brain, prior to EGFR-TKI therapy. Moreover, clinical data (age, sex, stage, etc) were collected and analyzed. Shallow lung features and deep lung-brain features were extracted using PyRadiomics and 3D neural networks, respectively. A support vector machine (SVM) was used to predict BMs. The receiver operating characteristic (ROC) curve and F1 score were used to assess BM prediction performance.Main result. The combination of shallow lung and shallow-deep lung-brain features demonstrated superior predictive performance (AUC = 0.96 ± 0.01). Shallow-deep lung-brain features exhibited strong significance (P < 0.001) and potential predictive performance (coefficient > 0.8). Moreover, BM prediction by age was significant (P < 0.05).Significance. Our approach enables the quantitative assessment of medical images and a deeper understanding of both superficial and deep tumor characteristics. This noninvasive method has the potential to identify BM-related features with statistical significance, thereby aiding in the development of targeted treatment plans for NSCLC patients.

GREMI: an Explainable Multi-omics Integration Framework for Enhanced Disease Prediction and Module Identification.

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort. Code and data are available at https://github.com/Yaolab-fantastic/GREMI.

Fusion of shallow and deep features from 18F-FDG PET/CT for predicting EGFR-sensitizing mutations in non-small cell lung cancer.

Background: Non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor-sensitizing (EGFR-sensitizing) mutations exhibit a positive response to tyrosine kinase inhibitors (TKIs). Given the limitations of current clinical predictive methods, it is critical to explore radiomics-based approaches. In this study, we leveraged deep-learning technology with multimodal radiomics data to more accurately predict EGFR-sensitizing mutations. Methods: A total of 202 patients who underwent both flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans and EGFR sequencing prior to treatment were included in this study. Deep and shallow features were extracted by a residual neural network and the Python package PyRadiomics, respectively. We used least absolute shrinkage and selection operator (LASSO) regression to select predictive features and applied a support vector machine (SVM) to classify the EGFR-sensitive patients. Moreover, we compared predictive performance across different deep models and imaging modalities. Results: In the classification of EGFR-sensitive mutations, the areas under the curve (AUCs) of ResNet-based deep-shallow features and only shallow features from different multidata were as follows: RES_TRAD, PET/CT vs. CT-only vs. PET-only: 0.94 vs. 0.89 vs. 0.92; and ONLY_TRAD, PET/CT vs. CT-only vs. PET-only: 0.68 vs. 0.50 vs. 0.38. Additionally, the receiver operating characteristic (ROC) curves of the model using both deep and shallow features were significantly different from those of the model built using only shallow features (P<0.05). Conclusions: Our findings suggest that deep features significantly enhance the detection of EGFR-sensitizing mutations, especially those extracted with ResNet. Moreover, PET/CT images are more effective than CT-only and PET-only images in producing EGFR-sensitizing mutation-related signatures.

Immunogenicity and Protective Capacity of Sugar ABC Transporter Substrate-Binding Protein against Streptococcus suis Serotype 2, 7 and 9 Infection in Mice.

Background:Streptococcus suis (S. suis) is a Gram-positive bacterium that causes substantial disease in pigs. S. suis is also an emerging zoonoses in humans, primarily in Asia, through the consumption of undercooked pork and the handling of infected pig meat as well as carcasses. The complexity of S. suis epidemiology, characterized by the presence of multiple bacterial serotypes and strains with diverse sequence types, identifies a critical need for a universal vaccine with the ability to confer cross-protective immunity. Highly conserved immunogenic proteins are generally considered good candidate antigens for subunit universal vaccines. Methods: In this study, the cross-protection of the sugar ABC transporter substrate-binding protein (S-ABC), a surface-associated immunogenic protein of S. suis, was examined in mice for evaluation as a universal vaccine candidate. Results: S-ABC was shown to be highly conserved, with 97% amino acid sequence identity across 31 S. suis strains deposited in GenBank. Recombinantly expressed S-ABC (rS-ABC) was recognized via rabbit sera specific to S. suis serotype 2. The immunization of mice with rS-ABC induced antigen-specific antibody responses, as well as IFN-γ and IL-4, in multiple organs, including the lungs. rS-ABC immunization conferred high (87.5% and 100%) protection against challenges with S. suis serotypes 2 and 9, demonstrating high cross-protection against these serotypes. Protection, albeit lower (50%), was also observed in mice challenged with S. suis serotype 7. Conclusions: These data identify S-ABC as a promising antigenic target within a universal subunit vaccine against S. suis.

Two Enterococcus faecium Isolates Demonstrated Modulating Effects on the Dysbiosis of Mice Gut Microbiota Induced by Antibiotic Treatment.

Broad-spectrum antibiotics are frequently used to treat bacteria-induced infections, but the overuse of antibiotics may induce the gut microbiota dysbiosis and disrupt gastrointestinal tract function. Probiotics can be applied to restore disturbed gut microbiota and repair abnormal intestinal metabolism. In the present study, two strains of Enterococcus faecium (named DC-K7 and DC-K9) were isolated and characterized from the fecal samples of infant dogs. The genomic features of E. faecium DC-K7 and DC-K9 were analyzed, the carbohydrate-active enzyme (CAZyme)-encoding genes were predicted, and their abilities to produce short-chain fatty acids (SCFAs) were investigated. The bacteriocin-encoding genes in the genome sequences of E. faecium DC-K7 and DC-K9 were analyzed, and the gene cluster of Enterolysin-A, which encoded a 401-amino-acid peptide, was predicted. Moreover, the modulating effects of E. faecium DC-K7 and DC-K9 on the gut microbiota dysbiosis induced by antibiotics were analyzed. The current results demonstrated that oral administrations of E. faecium DC-K7 and DC-K9 could enhance the relative abundances of beneficial microbes and decrease the relative abundances of harmful microbes. Therefore, the isolated E. faecium DC-K7 and DC-K9 were proven to be able to alter the gut microbiota dysbiosis induced by antibiotic treatment.

Transparent Cellulose/Multi-Walled Carbon Nanotube Hybrids with Improved Ultraviolet-Shielding Properties Prepared from Cotton Textile Waste.

Plastics offer many advantages and are widely used in various fields. Nevertheless, most plastics derived from petroleum are slow to degrade due to their stable polymer structure, posing serious threats to organisms and ecosystems. Thus, developing environmentally friendly and biodegradable plastics is imperative. In this study, biodegradable cellulose/multi-walled carbon nanotube (MCNT) hybrid gels and films with improved ultraviolet-shielding properties were successfully prepared using cotton textile waste as a resource. It was proven that MCNTs can be dispersed evenly in cellulose without any chemical or physical pretreatment. It was found that the contents of MCNTs had obvious effects on the structures and properties of hybrid films. Particularly, the averaged transmittance of cellulose/MCNT composite films in the range of 320-400 nm (T320-400) and 290-320 nm (T290-320) can be as low as 19.91% and 16.09%, when the content of MCNTs was 4.0%, much lower than those of pure cellulose films (T320-400: 84.12% and T290-320: 80.03%). Meanwhile, the water contact angles of the cellulose/MCNT films were increased by increasing the content of MCNTs. Most importantly, the mechanical performance of cellulose/MCNT films could be controlled by the additives of glycerol and MCNTs. The tensile strength of the cellulose/MCNT films was able to reach as high as 20.58 MPa, while the elongation at break was about 31.35%. To summarize, transparent cellulose/MCNT composites with enhanced ultraviolet-shielding properties can be manufactured successfully from low-cost cotton textile waste, which is beneficial not only in terms of environmental protection, but also the utilization of natural resources.

AtPIP1;4 and AtPIP2;4 Cooperatively Mediate H2O2 Transport to Regulate Plant Growth and Disease Resistance.

The rapid production of hydrogen peroxide (H2O2) is a hallmark of plants' successful recognition of pathogen infection and plays a crucial role in innate immune signaling. Aquaporins (AQPs) are membrane channels that facilitate the transport of small molecular compounds across cell membranes. In plants, AQPs from the plasma membrane intrinsic protein (PIP) family are utilized for the transport of H2O2, thereby regulating various biological processes. Plants contain two PIP families, PIP1s and PIP2s. However, the specific functions and relationships between these subfamilies in plant growth and immunity remain largely unknown. In this study, we explore the synergistic role of AtPIP1;4 and AtPIP2;4 in regulating plant growth and disease resistance in Arabidopsis. We found that in plant cells treated with H2O2, AtPIP1;4 and AtPIP2;4 act as facilitators of H2O2 across membranes and the translocation of externally applied H2O2 from the apoplast to the cytoplasm. Moreover, AtPIP1;4 and AtPIP2;4 collaborate to transport bacterial pathogens and flg22-induced apoplastic H2O2 into the cytoplasm, leading to increased callose deposition and enhanced defense gene expression to strengthen immunity. These findings suggest that AtPIP1;4 and AtPIP2;4 cooperatively mediate H2O2 transport to regulate plant growth and immunity.

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.

BACKGROUND: Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data. METHODS: We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy prediction. RESULTS: Extensive experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our model. CONCLUSIONS: The proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction.

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Neuroprotective effects of dipeptidyl peptidase 4 inhibitor on Alzheimer's disease: a narrative review.

Insulin resistance in brain and amyloidogenesis are principal pathological features of diabetes-related cognitive decline and development of Alzheimer's disease (AD). A growing body of evidence suggests that maintaining glucose under control in diabetic patients is beneficial for preventing AD development. Dipeptidyl peptidase 4 inhibitors (DDP4is) are a class of novel glucose-lowering medications through increasing insulin excretion and decreasing glucagon levels that have shown neuroprotective potential in recent studies. This review consolidates extant evidence from earlier and new studies investigating the association between DPP4i use, AD, and other cognitive outcomes. Beyond DPP4i's benefits in alleviating insulin resistance and glucose-lowering, underlying mechanisms for the potential neuroprotection with DPP4i medications were categorized into the following sections: (Ferrari et al., Physiol Rev, 2021, 101, 1,047-1,081): the benefits of DPP4is on directly ameliorating the burden of ß-amyloid plaques and reducing the formation of neurofibrillary tangles; DPP4i increasing the bioactivity of neuroprotective DPP4 substrates including glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic peptide (GIP), and stromal-derived factor-1α (SDF-1α) etc.; pleiotropic effects of DPP4is on neuronal cells and intracerebral structure including anti-inflammation, anti-oxidation, and anti-apoptosis. We further revisited recently published epidemiological studies that provided supportive data to compliment preclinical evidence. Given that there remains a lack of completed randomized trials that aim at assessing the effect of DPP4is in preventing AD development and progression, this review is expected to provide a useful insight into DPP4 inhibition as a potential therapeutic target for AD prevention and treatment. The evidence is helpful for informing the rationales of future clinical research and guiding evidence-based clinical practice.

Growth performance, lipid metabolism, and systemic immunity of weaned piglets were altered by buckwheat protein through the modulation of gut microbiota.

Tartary buckwheat protein (BWP) is well known for the wide-spectrum antibacterial activity and the lipid metabolism- regulating property; therefore, BWP can be applied as feed additives to improve the animal's nutritional supply. With the aim to investigate the bioactive actions of the BWP, growth performance, lipid metabolism and systemic immunity of the weaned piglets were measured, and the alterations of pig gut microbiota were also analyzed. According to the results, the growth performances of the weaned piglets which were calculated as the average daily gain (ADG) and the average daily feed intake (ADFI) were significantly increased when compared to the control group. Simultaneously, the serum levels of the total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) were decreased, while the levels of high-density lipoprotein cholesterol (HDL-C) were increased in the BWP group. Moreover, the relative abundances of Lactobacillus, Prevotella_9, Subdoligranulum, Blautia, and other potential probiotics in the gut microbiota of weaned piglets were obviously increased in the BWP group. However, the relative abundances of Escherichia-Shigella, Campylobacter, Rikenellaceae_RC9_gut_group and other opportunistic pathogens were obviously decreased in the BWP group. In all, BWP was proved to be able to significantly improve the growth performance, lipid metabolism, and systemic immunity of the weaned piglets, and the specific mechanism might relate to the alterations of the gut microbiota. Therefore, BWP could be explored as a prospective antibiotic alternative for pig feed additives.

Trinuclear Cu-based covalent organic framework: π-conjugated framework regulating electron delocalization to promote photoreduction CO2.

Sunlight-driven CO2 reduction to value-added chemicals is an effective strategy to promote carbon recycling. The exploration of catalysts with efficient charge separation is crucially important for highly efficient CO2 photoreduction. In this work, the preparation of metal-cluster-based covalent organic framework (CuABD) integrated features from both metal organic frameworks (MOFs) and covalent organic frameworks (COFs) through the condensation of diamines and functionalized trinuclear copper clusters demonstrate a thoughtful design strategy. The reported yield of 1.3 mmol g-1 h-1 for formic acid (HCOOH) under simulated solar irradiation is impressive, surpassing the performance of many COF- and MOF-based catalysts previously reported. Compared to its isomorphic metal-free structure (named BDFTD) and bare trinuclear Cu cluster which present extremely poor catalytic activities, CuABD displays remarkably enhanced CO2 reduction activity. Experimental and theoretical investigations reveal that the efficient charge transfer between diamine monomer and cyclic trinuclear copper (I) units, and the electron delocalization of the π-conjugated framework are responsible for the appealing catalytic performance. In summary, the work presents a well-structured and scientifically sound exploration of a metal-cluster-based covalent organic framework for efficient CO2 reduction under sunlight.

Genetic Characteristics of Wuxiang Virus in Shanxi Province, China.

Wuxiang virus (WUXV) is the first sandfly-borne Phlebovirus isolated from Phlebotomus chinensis collected in China and has been established as a consistent viral presence in the local sandfly populations of both Wuxiang County and Yangquan City. However, its distribution in the Shanxi Province remains unclear. In this study, three novel WUXV strains were isolated from sandflies collected from Jiexiu City, Shanxi Province, China, in 2022. Subsequently, whole-genome sequences of these novel strains were generated using next-generation sequencing. The open reading frame (ORF) sequences of the WUXV strains from the three locations were subjected to gene analysis. Phylogenetic analysis revealed that WUXV belongs to two distinct clades with geographical differences. Strains from Wuxiang County and Yangquan City belonged to clade 1, whereas strains from Jiexiu City belonged to clade 2. Reassortment and recombination analyses indicated no gene reassortment or recombination between the two clades. However, four reassortments or recombination events could be detected in clade 1 strains. By aligning the amino acid sequences, eighty-seven mutation sites were identified between the two clades, with seventeen, sixty, nine, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Additionally, selection pressure analysis identified 17 positively selected sites across the entire genome of WUXV, with two, thirteen, one, and one site(s) in the proteins RdRp, M, NSs, and N, respectively. Notably, sites M-312 and M-340 in the M segment not only represented mutation sites but also showed positive selective pressure effects. These findings highlight the need for continuous nationwide surveillance of WUXV.

Transcriptomic sequencing analysis of key long noncoding RNAs and mRNAs expression profiles in postoperative recurrence of hepatocellular carcinoma.

BACKGROUND: Recurrence is the main cause of death in hepatocellular carcinoma (HCC) patients after liver resection. OBJECTIVE: The long non-coding RNAs (lncRNAs) have been reported participated in progression and prognosis of HCC, however, the vital role of lncRNA in postoperative recurrence of HCC has rarely been systematically identified. METHODS: RNA-sequencing (RNA-seq) was performed between orthotopic model of HCC and hepatoma postoperative recurrent model to comprehensively analyze the integrated transcriptome expression profiles of lncRNA and mRNA. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was then conducted to quantify the expression levels of DElncRNAs and their target mRNAs. RESULTS: In our study, 211 lncRNAs (P-value < 0.05) and 1125 mRNAs (P-adjust < 0.05) were significantly differentially expressed (DE) between two groups. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DElncRNAs and DEmRNAs were mainly enriched in lipid metabolism, including Arachidonic acid metabolism, PPAR signaling pathway, Steroid hormone biosynthesis, Linoleic acid metabolism, Inflammatory mediator regulation of TRP channels, and Fatty acid degradation. Furthermore, we constructed lncRNA-mRNA interaction networks and protein-protein interaction (PPI) network, and verified by qRT-PCR, suggesting that increased DEIncRNAs (XLOC_063499 and XLOC_042016) may prevent HCC recurrence after surgery by upregulating on targeted cytochrome P450 (CYP) family genes in the lipid metabolism pathway, such as cyp3a16, cyp3a44, cyp2c39, cyp2c40 and cyp2c68. CONCLUSION: Overall, Our findings provided new insights for further investigation of biological function in lncRNA related HCC recurrence.

The response of rhubarb to smut infection is revealed through a comparative transcriptome and metabolome study.

MAIN CONCLUSION: Integrated transcriptome and metabolome analysis have unveiled the physiological and molecular responses of rhubarb to infection by smut fungi. Rhubarb is an important medicinal plant that is easily infected by smut fungi during its growth. Thus far, no research on the influence of smut fungi on the growth of rhubarb and its secondary metabolism has been conducted. In this study, petioles of Chinese rhubarb (Rheum officinale) [healthy or infected with smut fungus (Thecaphora schwarzmaniana)] were characterized. Microscopic structure, global gene expression profiling, global metabolic profiling, and key enzyme activity and metabolite levels in infected plants were analyzed. Infection by smut fungi resulted in numerous holes inside the petiole tissue and led to visible tumors on the external surface of the petiole. Through metabolic changes, T. schwarzmaniana induced the production of specific sugars, lipids, and amino acids, and inhibited the metabolism of phenolics and flavonoids in R. officinale. The concentrations of key medicinal compounds (anthraquinones) were decreased because of smut fungus infection. In terms of gene expression, the presence of T. schwarzmaniana led to upregulation of the genes associated with nutrient (sugar, amino acid, etc.) transport and metabolism. The gene expression profiling showed a stimulated cell division activity (the basis of tumor formation). Although plant antioxidative response was enhanced, the plant defense response against pathogen was suppressed by T. schwarzmaniana, as indicated by the expression profiling of genes involved in biotic and abiotic stress-related hormone signaling and the synthesis of plant disease resistance proteins. This study demonstrated physiological and molecular changes in R. officinale under T. schwarzmaniana infection, reflecting the survival tactics employed by smut fungus for parasitizing rhubarb.

Regenerated silk protein based hybrid film electrode with large area specific capacitance, high flexibility and light weight towards high-performance wearable energy storage.

Planar wearable supercapacitors (PWSCs) have sparked intense interest owing to their hopeful application in smart electronics. However, current PWSCs suffered from poor electrochemical property, weak flexibility and/or large weight. To relieve these defects, in this study, we fabricated a high-performance PWSC using silk protein derived film electrodes (PPy/RSF/MWCNTs-2; RSF, PPy and MWCNTs represent regenerated silk film, polypyrrole and multi-walled carbon nanotubes, respectively, while 2 is the mass ratio of silk to MWCNTs), which were developed by 'dissolving-mixing-evaporating' and in situ polymerization. In three-electrode, PPy/RSF/MWCNTs-2 showed a superb area specific capacitance of 8704.7 mF cm-2 at 5 mA cm-2, which surpassed numerous reported PWSC electrodes, and had a decent durability with a capacitance retention of 90.7 % after 5000 cycles. The PPy/RSF/MWCNTs-2 derived PWSC showed a largest energy density of 281.3 µWh cm-2 at 1660.1 µW cm-2, and a power density as high as 13636.4 µW cm-2 at 125.6 µWh cm-2. Furthermore, impressive capacitive-mechanical stability with a capacitance retention of 92 % under bending angles from 0 to 150 was depicted. Thanks to the rational and affordable preparation, our study for the first time prepared RSF electrode that had great capacitive property, high mechanical flexibility and light weight, simultaneously. The encouraging results can not only open up a new path to manufacture high-performance flexible electrodes, but may also help to realize the high-value-added utilization of silk.

Nacre-inspired, strong, tough silk fibroin hydrogels based on biomineralization and the layer-by-layer assembly of ordered silk fabric.

Hydrogels are attractive materials with structures and functional properties similar to biological tissues and widely used in biomedical engineering. However, traditional synthetic hydrogels possess poor mechanical strength, and their applications are limited. Herein, a multidimensional material design method is developed; it includes the in situ gelation of silk fabric and nacre-inspired layer-by-layer assembly, which is used to prepare silk fibroin (SF) hydrogels. The in situ gelation method of silk fabric introduces a directionally ordered fabric network in a silk substrate, considerably enhancing the strength of hydrogels. Based on the nacre structure, the layer-by-layer assembly method enables silk hydrogels to break through the size limit and increase the thickness, realizing the longitudinal extension of the hydrogels. The application of the combined biomineralization and hot pressing method can effectively reduce interface defects and improve the interaction between organic and inorganic interfaces. The multidimensional material design method helps increase the strength (287.78 MPa), toughness (18.43 MJ m-3), and fracture energy (50.58 kJ m-2) of SF hydrogels; these hydrogels can weigh 2000 times their own weight. Therefore, SF hydrogels designed using the aforementioned combined method can realize the combination of strength and toughness and be used in biological tissue engineering and structural materials.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Reduction in gefitinib resistance mediated by Yi-Fei San-Jie pill in non-small cell lung cancer through regulation of tyrosine metabolism, cell cycle, and the MET/EGFR signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The Chinese herbal prescription Yi-Fei San-Jie pill (YFSJ) has been used for adjuvant treatment in patients with lung cancer for a long time. AIM OF THE STUDY: Reports have indicated that the combination of gefitinib (Gef) with YFSJ inhibits the proliferation of EGFR-TKI-resistant cell lines by enhancing cellular apoptosis and autophagy in non-small cell lung cancer (NSCLC). However, the molecular mechanisms underlying the effect of YFSJ on EGFR-TKI resistance and related metabolic pathways remain to be explored. MATERIALS AND METHODS: In our report, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), metabolomics, network pharmacology, bioinformatics, and biological analysis methods were used to investigate the mechanism. RESULTS: The UPLC-MS/MS data identified 42 active compounds of YFSJ extracts. YFSJ extracts can enhance the antitumor efficacy of Gef without hepatic and renal toxicity in vivo. The analysis of the metabolomics pathway enrichment revealed that YFSJ mainly affected the tyrosine metabolism pathway in rat models. Moreover, YFSJ has been shown to reverse Gef resistance and improve the effects of Gef on the cellular viability, migration capacity, and cell cycle arrest of NSCLC cell lines with EGFR mutations. The results of network pharmacology and molecular docking analyses revealed that tyrosine metabolism-related active compounds of YFSJ affect EGFR-TKIs resistance in NSCLC by targeting cell cycle and the MET/EGFR signaling pathway; these findings were validated by western blotting and immunohistochemistry. CONCLUSIONS: YFSJ inhibits NSCLC by inducing cell cycle arrest in the G1/S phase to suppress tumor growth, cell viability, and cell migration through synergistic effects with Gef via the tyrosine metabolic pathway and the EGFR/MET signaling pathway. To summarize, the findings of the current study indicate that YFSJ is a prospective complementary treatment for Gef-resistant NSCLC.