KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery.

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

gutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions.

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

Large-Area Layered Membranes with Precisely Controlled Nano-Confined Channels.

Two-dimensional (2D) nanosheets-based membranes, which have controlled 2D nano-confined channels, are highly desirable for molecular/ionic sieving and confined reactions. However, it is still difficult to develop an efficient method to prepare large-area membranes with high stability, high orientation, and accurately adjustable interlayer spacing. Here, we present a strategy to produce metal ion cross-linked membranes with precisely controlled 2D nano-confined channels and high stability in different solutions using superspreading shear-flow-induced assembly strategy. For example, membranes based on graphene oxide (GO) exhibit interlayer spacing ranging from 8.0 ± 0.1 Å to 10.3 ± 0.2 Å, with a precision of down to 1 Å. At the same time, the value of the orientation order parameter (f) of GO membranes is up to 0.95 and GO membranes exhibit superb stability in different solutions. The strategy we present, which can be generalized to the preparation of 2D nano-confined channels based on a variety of 2D materials, will expand the application scope and provide better performances of membranes.

Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches.

MOTIVATION: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. RESULTS: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. CONTACT: liangcheng@hrbmu.edu.cn. AVAILABILITY AND IMPLEMENTATION: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

Identifying drug-target interactions based on graph convolutional network and deep neural network.

Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug-protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Prediction and collection of protein-metabolite interactions.

Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein-metabolite interactions (PMIs). Compared with the rich knowledge of protein-protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.

Design, Screening, and Characterization of Engineered Phage Endolysins with Extracellular Antibacterial Activity against Gram-Negative Bacteria.

Phage-encoded endolysins are emerging antibacterial agents based on their ability to efficiently degrade peptidoglycan on Gram-positive bacteria, but the envelope characteristics of Gram-negative bacteria limit their application. Engineering modifications of endolysins can improve the optimization of their penetrative and antibacterial properties. This study constructed a screening platform to screen for engineered Artificial-Bp7e (Art-Bp7e) endolysins with extracellular antibacterial activity against Escherichia coli. An oligonucleotide of 20 repeated NNK codons was inserted upstream of the endolysin gene Bp7e to construct a chimeric endolysin library in the pColdTF vector. The chimeric Art-Bp7e proteins were expressed by transforming the plasmid library into E. coli BL21 and released by chloroform fumigation, and the protein activities were evaluated by the spotting method and the colony-counting method to screen for promising proteins. Sequence analysis showed that all screened proteins with extracellular activities had a chimeric peptide with a positive charge and an α-helical structure. Also, a representative protein, Art-Bp7e6, was further characterized. It exhibited broad antibacterial activity against E. coli (7/21), Salmonella enterica serovar Enteritidis (4/10), Pseudomonas aeruginosa (3/10), and even Staphylococcus aureus (1/10). In the transmembrane process, the chimeric peptide of Art-Bp7e6 depolarized the host cell envelope, increased the permeability of the cell, and facilitated the movement of Art-Bp7e6 across the envelope to hydrolyze the peptidoglycan. In conclusion, the screening platform successfully screened for chimeric endolysins with extracellular antibacterial activities against Gram-negative bacteria, which provides methodological support for the further screening of engineered endolysins with high extracellular activities against Gram-negative bacteria. Also, the established platform showed broad application prospects and can be used to screen various proteins. IMPORTANCE The presence of the envelope in Gram-negative bacteria limits the use of phage endolysins, and engineering endolysins is an efficient way to optimize their penetrative and antibacterial properties. We built a platform for endolysin engineering and screening. A random peptide was fused with the phage endolysin Bp7e to construct a chimeric endolysin library, and engineered Artificial-Bp7e (Art-Bp7e) endolysins with extracellular activity against Gram-negative bacteria were successfully screened from the library. The purposeful Art-Bp7e had a chimeric peptide with an abundant positive charge and an α-helical structure, which led Bp7e to acquire the ability for the extracellular lysis of Gram-negative bacteria and showed a broad lysis spectrum. The platform provides a huge library capacity without the limitations of reported proteins or peptides. It can be utilized for the further screening of optimal endolysins against Gram-negative bacteria as well as for the screening of additional proteins with specific modifications.

Intestinal uric acid excretion contributes to serum uric acid decrease during acute gout attack.

OBJECTIVE: Spontaneous serum uric acid (SUA) decrease has been found in many patients during acute gout attacks, but its mechanism remains unclear. METHODS: The spontaneous regulation of SUA during a gout attack and its possible causes were evaluated in patients with gout. The mechanism of the spontaneous SUA decrease was further studied in Caco2 cells and a monosodium urate (MSU)-induced gout model of wild-type mice and ABCG2-/- mice. The urate transport function of intestinal epithelial cells was detected by transwell culture of Caco2 cells. Expression of ATP-binding cassette super-family G member 2 (ABCG2), IL-1ß and phosphoinositide 3-kinase (PI3K)/Akt was analysed using real-time PCR, western blotting, or immunofluorescence assays. RESULTS: SUA decreased during acute gout attacks in both the gout patients and MSU-induced gouty mice. Increased serum CRP and IL-1ß levels were correlated with the SUA decrease. Intestinal uric acid excretion and expression of ABCG2 were upregulated in the mice during acute gout attacks. In the ABCG2-/- mice, intestinal uric acid excretion significantly decreased during gout attacks. In an in vitro study of a transwell culture, ABCG2 and its upstream PI3K/Akt pathway were significantly upregulated in intestinal epithelial cells. However, ABCG2 expression and its associated intestinal uric acid transport were inhibited when PI3K/Akt was blocked by a PI3K inhibitor, LY294002. CONCLUSIONS: Increased intestinal urate excretion resulted in spontaneous SUA downregulation during acute gout attacks. Inflammation-induced PI3K/Akt activation and ABCG2 expression in epithelial cells might contribute to the upregulation of intestinal uric acid excretion.

Aging-accelerated differential production and aggregation of STAT3 protein in neuronal cells and neural stem cells in the male mouse spinal cord.

Aging-affected cellular compositions of the spinal cord are diverse and region specific. Age leads to the accumulation of abnormal protein aggregates and dysregulation of proteostasis. Dysregulated proteostasis and protein aggregates result from dysfunction of the ubiquitin-proteasome system (UPS) and autophagy. Understanding the molecular mechanisms of spinal cord aging is essential and important for scientists to discover new therapies for rejuvenation. We found age-related increases in STAT3 and decreases in Tuj1 in aging mouse spinal cords, which was characterized by increased expression of P16. Coaggregation of lysine-48 and lysine-63 ubiquitin with STAT3 was revealed in aging mouse spinal cords. STAT3-ubiquitin aggregates formed via lysine-48 and lysine-63 linkages were increased significantly in the aging spinal cords but not in central canal ependymal cells or neural stem cells in the spinal cord. These results highlight the increase in STAT3 and its region-specific aggregation and ubiquitin-conjugation during spinal cord aging.

Does the quality of street greenspace matter? Examining the associations between multiple greenspace exposures and chronic health conditions of urban residents in a rapidly urbanising Chinese city.

BACKGROUND: Numerous studies have demonstrated that greenspace(GS) exposure is associated with health improvements in individuals with hypertension and diabetes. However, studies examining the associations between multiple GS exposures and chronic health conditions in developing countries are limited. METHODS: Geospatial data and spatial analysis were employed to objectively measure the total neighbourhood vegetative cover (mean value of normalised difference vegetation index [NDVI] within specific buffer zone) and proximity to park-based GS (network distance from home to the entrance of park-based GS). Street view imagery and machine learning techniques were used to measure the subjective perceptions of street GS quality. A multiple linear regression model was applied to examine the associations between multiple GS exposures and the prevalence of hypertension and diabetes in neighbourhoods located in Qingdao, China. RESULTS: The model explained 29.8% and 28.2% of the prevalence of hypertension and diabetes, respectively. The results suggested that: 1) the total vegetative cover of the neighbourhood was inversely correlated with the prevalence of hypertension (ß = -0.272, p = 0.013, 95% confidence interval (CI): [-1.332, -0.162]) and diabetes (ß = -0.230, p = 0.037, 95% CI: [-0.720, -0.008]). 2) The street GS quality was negatively correlated with the prevalence of hypertension (ß = -0.303, p = 0.007, 95% CI: [-2.981, -0.491]) and diabetes (ß = -0.309, p = 0.006, 95% CI: [-1.839, -0.314]). 3) Proximity to park-based GS and the prevalence of hypertension and diabetes mellitus were not significantly correlated. CONCLUSIONS: This study used subjective and objective methods to comprehensively assess the greenspace exposure from overhead to eye level, from quantity, proximity to quality. The results demonstrated the beneficial relationships between street GS quality, total vegetative cover, and chronic health in a rapidly urbanising Chinese city. Furthermore. the effect of street GS quality was more pronounced in potentially mitigating chronic health problems, and improving the quality of street GS might be an efficient and effective intervention pathway for addressing chronic health issues in densely populated cities.

SC2disease: a manually curated database of single-cell transcriptome for human diseases.

SC2disease (http://easybioai.com/sc2disease/) is a manually curated database that aims to provide a comprehensive and accurate resource of gene expression profiles in various cell types for different diseases. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering cellular heterogeneity of different tissues for different diseases has become feasible by profiling transcriptomes across cell types at the cellular level. In particular, comparing gene expression profiles between different cell types and identifying cell-type-specific genes in various diseases offers new possibilities to address biological and medical questions. However, systematic, hierarchical and vast databases of gene expression profiles in human diseases at the cellular level are lacking. Thus, we reviewed the literature prior to March 2020 for studies which used scRNA-seq to study diseases with human samples, and developed the SC2disease database to summarize all the data by different diseases, tissues and cell types. SC2disease documents 946 481 entries, corresponding to 341 cell types, 29 tissues and 25 diseases. Each entry in the SC2disease database contains comparisons of differentially expressed genes between different cell types, tissues and disease-related health status. Furthermore, we reanalyzed gene expression matrix by unified pipeline to improve the comparability between different studies. For each disease, we also compare cell-type-specific genes with the corresponding genes of lead single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) to implicate cell type specificity of the traits.

High-density lipoprotein cholesterol modifies the association between blood lead and uric acid: results from NHANES 2005-2016.

BACKGROUND: The association between blood lead (PbB) and uric acid (SUA) remains unclear in US adults without a high level of lead exposure. Additionally, the effects of high-density lipoprotein cholesterol (HDL-C) modifying this association are still unclear. Therefore, this study aims to assess the effect of modification of high-density lipoprotein cholesterol on the association between PbB and SUA. METHOD: This research analyzed National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2016. Through several screenings, 18,578 participants over the age of 20 were eligible for the analysis. Multivariable linear regression was used to evaluate the association between PbB and SUA. By having stratified participants based on the HDL-C intake category (low HDL-C intake < 50 mg/dl; high HDL-C intake ≥ 50 mg/dl), effect modification by HDL-C was assessed through a likelihood ratio test between PbB and SUA. RESULT: Multivariable linear regression indicated that PbB positively affects SUA (ß = 0.19, 95% CI 0.16-0.22). The relationship between PbB and SUA was different in the low and high HDL-C intake group (ß 0.12 95% Cl 0.08-0.16 vs. ß 0.26 95% Cl 0.22 ~ - 0.30). Furthermore, high-density lipoprotein cholesterol significantly modified the relationship between PbB and SUA in all models which indicates that the interaction of lead exposure and HDL-C is more dangerous than the sum of the individual effects. CONCLUSIONS: Our study shows that high-density lipoprotein cholesterol and blood lead have an interactive effect on increasing uric acid, which may have great importance for clinical medication.

A retrospective study on application of a classification criterion based on relative intervertebral tension in spinal fusion surgery for lumbar degenerative diseases.

BACKGROUND: As an important part of spinal fusion procedure, the selection of fusion cage size is closely related to the curative effect of the surgery. It mainly depends on the clinical experience of surgeons, and there is still a lack of objective standards. The purpose of this study is to propose the concept of relative intervertebral tension (RIT) for the first time and its grading standards to improve the surgical procedures of lumbar interbody fusion. METHODS: This retrospective study was conducted from January 2018 to July 2019. A total of 83 eligible patients including 45 men and 38 women with lumbar degenerative disease underwent transforaminal lumbar interbody fusion (TLIF) were included in this study. A total of 151 fusion segments were divided into group A, group B and group C according to the grading standards of RIT. In addition, parameters of intervertebral space angle (ISA), intervertebral space height (ISH), intervertebral space foramen (IFH), fusion rates, cage-related complications and cage heights were also compared among the three groups. RESULTS: The ISA in group A was the smallest among three groups in contrast with group C with largest ISA at the final follow-up(P < 0.05). The group A presented the smallest ISH and IFH values(P < 0.05), compared with group B with the largest ISH and IFH values(P < 0.05). These two parameters in the group C were in-between. The fusion rates of group A, group B and group C were 100%, 96.3% and 98.8% at the final follow-up, respectively. No statistical difference in fusion rates and cage-related complications occurred among the three groups(P > 0.05), and a certain correlation between ISH and RIT was also observed. CONCLUSIONS: The concept of RIT and the application of its clinical grading standards could simplify the surgical procedures of spinal fusion and reduce cage-related complications.

A meta-analysis of risk factors for non-superficial surgical site infection following spinal surgery.

BACKGROUND: Surgical site infection (SSI) is the most common complications in spinal surgery. In SSI, non-superficial surgical site infections are more likely to result in poor clinical outcomes. It has been reported that there are multiple factors contributing to postoperative non-superficial SSI, but still remains controversial. Therefore, the aim of this meta-analysis is to investigate the potential risk factors for non-superficial SSI following spinal surgery. METHODS: A systematic database search of PubMed, Embase, Web of Science, Cochrane Library and Clinical Trials was performed for relevant articles published until September 2022. According to the inclusion and exclusion criteria, two evaluators independently conducted literature screening, data extraction and quality evaluation of the obtained literature. The Newcastle-Ottawa Scale (NOS) score was used for quality evaluation, and meta-analysis was performed by STATA 14.0 software. RESULTS: A total of 3660 relevant articles were initially identified and 11 articles were finally included in this study for data extraction and meta-analysis. The results of meta-analysis showed that the diabetes mellitus, obesity, using steroids, drainage time and operative time were related to the non-superficial SSI. The OR values (95%CI) of these five factors were 1.527 (1.196, 1.949); 1.314 (1.128, 1.532); 1.687(1.317, 2.162); 1.531(1.313, 1.786) and 4.255(2.612, 6.932) respectively. CONCLUSIONS: Diabetes mellitus, obesity, using steroids, drainage time and operative time are the current risk factors for non-superficial SSI following spinal surgery. In this study, operative time is the most important risk factor resulting in postoperative SSI.

Insights into reactive oxygen species formation induced by water-soluble organic compounds and transition metals using spectroscopic method.

Ambient particulate matter (PM) can cause adverse health effects via their ability to produce Reactive Oxygen Species (ROS). Water-Soluble Organic Compounds (WSOCs), a complex mixture of organic compounds which usually coexist with Transition Metals (TMs) in PM, have been found to contribute to ROS formation. However, the interaction between WSOCs and TMs and its effect on ROS generation are still unknown. In this study, we examined the ROS concentrations of V, Zn, Suwannee River Fulvic Acid (SRFA), Suwannee River Humic Acid (SRHA) and the mixtures of V/Zn and SRFA/SRHA by using a cell-free 2',7'-Dichlorodihydrofluorescein (DCFH) assay. The results showed that V or Zn synergistically promoted ROS generated by SRFA, but had an antagonistic effect on ROS generated by SRHA. Fluorescence quenching experiments indicated that V and Zn were more prone to form stable complexes with aromatic humic acid-like component (C1) and fulvic acid-like component (C3) in SRFA and SRHA. Results suggested that the underlying mechanism involving the fulvic acid-like component in SRFA more tending to complex with TMs to facilitate ROS generation through π electron transfer. Our work showed that the complexing ability and complexing stability of atmospheric PM organics with metals could significantly affect ROS generation. It is recommended that the research deploying multiple analytical methods to quantify the impact of PM components on public health and environment is needed in the future.

Organohydrogels with High-Speed Lubrication by Confining Polymer Chain Mobility by an Interpenetrated Heteronetwork.

Hydrogels with pure hydrophilic network have received much attention due to their excellent low frictional behavior. However, the lubrication performance of hydrogels is not satisfied under high-speed condition due to the energy dissipation caused by adsorbed polymer chains as well as the failure of lubricating mechanisms accompanied by the transition of lubrication regime. In this work, interpenetrating double-network organohydrogels were constructed by combining hydrophilic and oleophilic polymer networks to modify the physiochemical properties of surface polymer chains, especially the chain mobility. The oleophilic polymer network spatially restricting the mobility of the swollen hydrophilic network in water, resulted in a low coefficient of friction (ca. 0.01) compared with conventional hydrogels at high speed (0.1 m s-1 ). Meanwhile, the organohydrogels had superior wear resistance, with almost no wear observed on the sliding track after 5 k cycles of rubbing at high speed. The design concept of organohydrogels can be extended to a variety of low-wear, highly-lubricating materials.

IL-37 blocks gouty inflammation by shaping macrophages into a non-inflammatory phagocytic phenotype.

OBJECTIVE: Interleukin (IL)-37 is a natural suppressor of inflammation. Macrophages play an important role in acute gout flare by dominating the inflammation and spontaneous relief. We have reported that IL-37 could limit runaway inflammation in gout. Here we focus on whether IL-37 inhibits gouty inflammation by altering macrophage functions, and how it does so. METHODS: Macrophage functions were evaluated in terms of phagocytosis, pyroptosis, polarization and metabolism. Phagocytosis and polarization of macrophages were detected by side scattering and double-labelling induced nitrogen monoxide synthase (iNOS)/arginase-1 (Arg-1) using flow cytometry, respectively. Transcription of pyroptosis-related molecules was detected by qPCR. Metabolomics was performed by liquid chromatograph mass spectrometer. Human IL-37 knock-in mice and a model with point mutation (S9A) at mouse Gsk3b locus were created by CRISPR/Cas-mediated genome engineering. MSU was injected into the paws and peritoneal cavity to model acute gout. Vernier calliper was used to measure the thickness of the paws. The mice paws and human synovium tissues or tophi were collected for pathological staining. Peritoneal fluid of mice was used to enrich macrophages to detect polarization. RESULTS: IL-37 promoted non-inflammatory phagocytic activity of macrophages by enhancing phagocytosis of MSU, reducing transcription of pyroptosis-related proteins and release of inflammatory cytokines, protecting mitochondrial function, and mediating metabolic reprogramming in MSU-treated THP-1 cells. These multifaceted roles of IL-37 were partly depended on the mediation of glycogen synthase kinase-3ß (GSK-3ß). CONCLUSIONS: Our study revealed that IL-37 could shape macrophages into a 'silent' non-inflammatory phagocytic fashion. IL-37 may become a potentially valuable treatment option for patients of chronic gout, especially for those with tophi.

Space optical communication system with a sub-second acquisition time.

Acquisition is the key technology in space laser communication and the nodus in communication-link establishment. Traditional laser communication demands too long an acquisition time to meet the requirements of a space optical communication network and real-time transmission of big data. One novel laser communication system which fuses a laser communication function with a star-sensitive function to achieve precise autonomous calibration of the open-loop pointing direction of line of sight (LOS) is proposed and developed. Theoretical analysis and field experiments proved that the novel, to the best of our knowledge, laser-communication system can achieve sub-second-level scanless acquisition.