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The high‐resolution model of elastic properties is of great significance for fine reservoir characterization and precise oil and gas exploration. However, it is difficult to obtain a satisfactory high‐resolution reservoir model with the existing technologies. In this paper, a novel high‐resolution stochastic modelling strategy based on the fast Fourier transform moving average is proposed. In this strategy, several structural parameters are optimized to improve the rationality of the stochastic model, including vertical autocorrelation length, horizontal autocorrelation length, roughness factor and angle parameter. Among them, the optimization of the vertical autocorrelation length is crucial for vertical high‐resolution modelling. To this end, a nonlinear optimal inversion strategy of the vertical autocorrelation length is designed based on the idea of minimizing the spectral Jensen–Shannon divergence between the modelling result and the logging curve. However, nonlinear inversion is usually unstable, so it is necessary to introduce a regularization operator in the inversion to improve the stability. Considering that the heterogeneity of the subsurface medium is consistent or gradual within the stratums, but discontinuous and abrupt at the interfaces, edge‐preserving regularization is applied to obtain a blocky estimation of the vertical autocorrelation length. The optimal estimation experiment of the vertical autocorrelation length based on the measured logging data shows that the edge‐preserving regularization significantly improves the stability of the nonlinear optimal inversion, and blocky estimation results with sharp edges are obtained. Then, the optimized vertical autocorrelation length and the other structural parameters are applied to fast Fourier transform moving average modelling on an actual reservoir profile. The result shows that the resolution of the model is significantly improved, which realizes the fine reservoir characterization. In addition, the optimized structural parameters effectively constrain the small‐scale heterogeneity and ensure the rationality of the stochastic model. [ABSTRACT FROM AUTHOR]

Introduction: Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women. Methods: First, the Gene Expression Omnibus database's gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses. Results: According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy. Conclusion: The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women. [ABSTRACT FROM AUTHOR]

Microvascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC. A total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Logistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility. The nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions. [ABSTRACT FROM AUTHOR]

Coumarin possesses the aromatic group and showed plentiful activities, such as antioxidant, preventing asthma and antisepsis. In addition, coumarin derivatives usually possess good solubility, low cytotoxicity and excellent cell permeability. In our study, we synthesized the compound bridge methylene tacrine (BMT), which has the classical pharmacophore structure of Tacrine (THA). Based on the principle of active substructure splicing, BMT was used as a lead compound and synthesized coumarin–BMT hybrids by introducing coumarin to BMT. In this work, 21 novel hybrids of BMT and coumarin were synthesized and evaluated for their inhibitory activity on AChE. All obtained compounds present preferable inhibition. Compound 8b was the most active compound, with the value of Ki as 49.2 nM, which was higher than Galantamine (GAL) and lower than THA. The result of molecular docking showed that the highest binding free energy was −40.43 kcal/mol for compound 8b, which was an identical trend with the calculated Ki. [ABSTRACT FROM AUTHOR]

Abstract Rheumatoid arthritis (RA) is a chronic inflammatory disorder characterized by destructive polyarthritis and systemic complications. It increases cardiovascular morbidity and mortality. However, the mechanism underlying RA-related cardiac damage remains largely unknown. Here, we found and characterized a non-human primate (NHP) model with spontaneous RA similar to the human conditions. Compared with the control group, the cardiac function in RA monkeys showed progressively deterioration; histologically, we found significantly increased inflammatory cell infiltration, cell death, and fibrosis in RA monkey heart tissue. Mechanistically, the upregulated receptor-interacting protein kinase 1 (RIPK1) in RA monkey heart tissue bound to voltage-dependent anion-selective channel 1 (VDAC1), increased VDAC1 oligomerization, and subsequently induced cardiac cell death and functional impairment. These findings identified that RIPK1-VDAC1 pathway is a promising target to treat cardiac impairment in RA. This unique model of RA will provide a valuable tool for mechanistic and translational studies. Highlights • RA monkeys show sustained systemic inflammation and progressive deterioration of cardiac systolic function. • Cardiac cell death, inflammatory cell infiltration and fibrosis are increased in RA monkey heart tissues. • RIPK1, Caspase-8 and VDAC1 expression and their complex formation are upregulated in RA monkey hearts. • RIPK1 promotes VDAC1 oligomerization, and subsequently induces cardiac cell death. • RIPK1 D324 site is important for VDAC1 oligomerization. [ABSTRACT FROM AUTHOR]

Three kinds of nanostructured polyanilines (PANIs) were prepared through interfacial polymerization by using ammonium persulfate (APS) as a single oxidant, and APS/FeCl 3 , APS/K 2 Cr 2 O 7 as composite oxidants, respectively. It is observed that faster formation process and higher yield of nanostructured PANIs could be achieved in the presence of FeCl 3 and K 2 Cr 2 O 7 . The as-prepared PANIs were characterized by field emission scanning electron microscopy, ultraviolet–visible absorption spectroscopy, Fourier transform infrared and Raman spectroscopy, X-ray diffraction analysis and electrochemical measurements including cyclic voltammetry and galvanostatic charge/discharge measurement. The influence of composite oxidants on the morphology, microstructure, and electrical and electrochemical properties of PANIs was discussed. Interestingly, when APS/K 2 Cr 2 O 7 was used as the composite oxidants, PANI exhibited petal-like structure with high yield of 57.35% instead of general nanofibrous morphology formed in interfacial polymerization. Compared with those nanofibrous PANIs obtained by using APS as a single oxidant or APS/FeCl 3 as composite oxidants, petal-like PANIs exhibited the largest specific capacitance (692.4 F/g at scan rate of 5 mV/s) and highest cycle stability among them. It provides a new insight into the control of PANI nanostructures with high yield and energy storage ability by simply selecting suitable composite oxidants in interfacial polymerization. [ABSTRACT FROM AUTHOR]

Abstract: Solid precipitate containing phthalate esters was obtained from rice-husk-derived oil through a basification–acidification process. After separation by column chromatography, the solid precipitate was divided into two mono-component fractions, two bi-component fractions and a tetra-component fraction. The major compounds of the five fractions were all consisted of phthalate esters. Especially, phthalate esters accounted for a proportion higher than 80% in both Fractions I and II. The generation and precipitation mechanisms of phthalate esters were proposed. Phthalate esters were considered to be derived from a series of complicated chemical reactions of small molecules in the biomass pyrolysis process, and precipitated from bio-oil by catalytic hydrolysis and esterification. [ABSTRACT FROM AUTHOR]

• Fabricating multifunctional cotton fabric by low–cost and environmentally–friendly spray–assisted layer–by–layer assembly. • Achieving simultaneously excellent self–extinguishing ability, antistatic property and antimicrobial activity. • Designing especially trap structure through the growth of polyaniline nanowire arrays on the cotton and graphene nanosheets. • Bringing into full play the compound synergetic effect and unique structural advantage in the protective coating. Multifunctional protective coating with a novel trap structure containing Graphene oxide (GO) and Polyaniline (PANI) arrays is constructed on the surface of cotton fabrics through in–situ polymerization and spray assisted layer–by–layer assembly. The construction of nanoscale protective coatings with controllable thickness and different compounds on the surface of the cotton fabrics through spray assisted layer–by–layer assembly can better exert the molecular interaction between different layers and synergistic effect of different elements. More importantly, unique structure of PANI arrays are grown on the surface of the cotton fibers and GO sheets play very important role on the improvement of multifunctional performances for the cotton fabrics. PANI arrays can accept more APP molecules to promote the flame retardant behavior and construct nearly closed subspace to resist heat and oxygen, accompanying with the formation of inorganic barrier layer, excellent self–extinguishing ability can be achieved through nanoscale coating. Particularly, (GO–PANI 1.0) 3 coated cotton fabric with a weight gain of 12.7 wt% possesses the excellent self–extinguished ability and minimizes the total heat release value about 82.5% of pristine cotton. In addition, the PANI and GO reduced partially during in–situ polymerization of aniline can present good conductive pathway, as a result, the coated fabric also exhibits significant antistatic property, while can remarkably inhibit the growth of E. coli and S. aureus. Therefore this novel strategy with facile and scalable features can provide reference for developing various kinds of cheap composite nanocoating for multifunctional advanced textiles. [ABSTRACT FROM AUTHOR]

• Presenting facile template–free strategy for preparing PANI nanobelts in fully aqueous system. • Using ferric chloride and cumene hydroperoxide as oxidation–reduction initiation system. • Discussing the influences of four different inorganic acids on the morphologies and structures of PANIs. • Obtaining 38.8 % improvement on the specific capacitance of the nanobelts by adding sulfuric acid. Polyaniline (PANI) nanobelts were efficiently prepared through template–free chemical polymerization by using ferric chloride and cumene hydroperoxide as the oxidation–reduction initiation system in the presence of a small amount of inorganic acids including sulfuric acid, phosphoric acid, hydrochloric acid and nitric acid. The influences of the nature of inorganic acids on the morphologies, microstructures and capacitive properties of PANI nanobelts were investigated by scanning electron microscopy, transmitting electron microscopy, atomic force microscopy, ultraviolet–visible spectroscopy, Fourier transform infrared spectroscopy, X–ray diffraction analysis as well as electrical and electrochemical measurements. It is found that although all products have ultrathin nanobelt morphologies with relatively high yield, their microstructure and properties were significantly sensitive to the existence of additional inorganic acids. Moreover, the specific capacitance of the nanobelts decreased according to the order of sulfuric acid, phosphoric acid, hydrochloric acid and nitric acid, and the largest specific capacitance of 584.1 F g–1 at the current density of 1 A g–1 with the specific capacitance retention of 77.21 % after 2000 cycles can be achieved by introducing sulfuric acid. Furthermore, the differences in the capacitances of the nanobelts are discussed based on capacitive and diffusion controlled contribution. These results show that a simple and green approach is proposed for bulk quantity production of PANI nanobelts without the use of any template in fully aqueous solution, and would also stimulate the exploration of applications of these nanobelts especially as promising electrode materials for energy storage devices. [ABSTRACT FROM AUTHOR]

Objective: Intestinal mucositis is one of the common side effects of anti-cancer chemotherapy. However, the molecular mechanisms involved in mucositis development remain incompletely understood. In this study, we investigated the function of receptor-interacting protein kinase 3 (RIP3/RIPK3) in regulating doxorubicin-induced intestinal mucositis and its potential mechanisms. Methods: Intestinal mucositis animal models were induced in mice for in vivo studies. Rat intestinal cell line IEC-6 was used for in vitro studies. RNA‑seq was used to explore the transcriptomic changes in doxorubicin-induced intestinal mucositis. Intact glycopeptide characterization using mass spectrometry was applied to identify α-1,2-fucosylated proteins associated with mucositis. Results: Doxorubicin treatment increased RIP3 expression in the intestine and caused severe intestinal mucositis in the mice, depletion of RIP3 abolished doxorubicin-induced intestinal mucositis. RIP3-mediated doxorubicin-induced mucositis did not depend on mixed lineage kinase domain-like (MLKL) but on α-1,2-fucosyltransferase 2 (FUT2)-catalyzed α-1,2-fucosylation on inflammation-related proteins. Deficiency of MLKL did not affect intestinal mucositis, whereas inhibition of α-1,2-fucosylation by 2-deoxy-D-galactose (2dGal) profoundly attenuated doxorubicin-induced inflammation and mucositis. Conclusions: RIP3-FUT2 pathway is a central node in doxorubicin-induced intestinal mucositis. Targeting intestinal RIP3 and/or FUT2-mediated α-1,2-fucosylation may provide potential targets for preventing chemotherapy-induced intestinal mucositis. [ABSTRACT FROM AUTHOR]

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

To obtain α-glucosidase inhibitors with high activity, 19 NB-DNJDs (N-benzyl-deoxynojirimycin derivatives) were designed and synthesized. The results indicated that the 19 NB-DNJDs displayed different inhibitory activities towards α-glucosidase in vitro. Compound 18a (1-(4-hydroxy-3-methoxybenzyl)-2-(hydroxymethyl) piperidine-3,4,5-triol) showed the highest activity, with an IC50 value of 0.207 ± 0.11 mM, followed by 18b (1-(3-bromo-4-hydroxy-5-methoxybenzyl)-2-(hydroxymethyl) piperidine-3,4,5-triol, IC50: 0.276 ± 0.13 mM). Both IC50 values of 18a and 18b were significantly lower than that of acarbose (IC50: 0.353 ± 0.09 mM). According to the structure-activity analysis, substitution of the benzyl and bromine groups on the benzene ring decreased the inhibition activity, while methoxy and hydroxyl group substitution increased the activity, especially with the hydroxyl group substitution. Molecular docking results showed that three hydrogen bonds were formed between compound 18a and amino acids in the active site of α-glucosidase. Additionally, an arene–arene interaction was also modelled between the phenyl ring of compound 18a and Arg 315. The three hydrogen bonds and the arene–arene interaction resulted in a low binding energy (−5.8 kcal/mol) and gave 18a a higher inhibition activity. Consequently, compound 18a is a promising candidate as a new α-glucosidase inhibitor for the treatment of type Ⅱ diabetes. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has prompted global concern due to its profound impact on public health and the economy. Effective treatment of COVID-19 patients in the acute phase or of those with long COVID is a major challenge. Using data-independent acquisition (DIA) technology, we performed proteomic profiling on plasma samples from 22 COVID-19 patients and six healthy controls at Dazhou Central Hospital. Random forest and least absolute shrinkage and selection operator algorithms were used for analysis at various COVID-19 treatment stages. We identified 79 proteins that were differentially expressed between COVID-19 patients and healthy controls, mainly involving pathways associated with cell processes and binding. Across different treatment stages of COVID-19, five proteins—PI16, GPLD1, IGFBP3, KRT19, and VCAM1—were identified as potential molecular markers for dynamic disease monitoring. Furthermore, the proteins BTD, APOM, IGKV2-28, VWF, C4BPA, and C7 were identified as candidate biomarkers for distinguishing between SARS-CoV-2 positivity and negativity. Analysis of protein change profiles between the follow-up and healthy control groups highlighted cardiovascular changes as a concern for patients recovering from COVID-19. Our study revealed the infection profiles of SARS-CoV-2 at the protein expression level comparing different phases of COVID-19. DIA mass spectrometry analysis of plasma samples from COVID-19 patients undergoing treatment identified key proteins involved in signaling pathways that might be used as markers of the recovery phase. These findings provide insight for the development of therapy options and suggest potential blood biomarkers for COVID-19. [ABSTRACT FROM AUTHOR]

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n  = 517, Nanchong Central Hospital n  = 120 and the Cancer Genome Atlas (TCGA) n  = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC. [ABSTRACT FROM AUTHOR]

A sedentary lifestyle affects the diversity and composition of the gut microbiota, but previous studies have mainly focused on bacteria instead of fungi. Here, we compared both the fecal bacterial and fungal microbiota compositions and functions in sedentary persons and controls. Subjects from the China Railway Corporation, including 99 inspectors and 88 officials, were enrolled in our study. Fecal microbiota communities were analyzed using 16S rRNA gene sequencing for bacteria and ITS sequencing for fungi. We found that the diversity of the gut microbiota of the sedentary group was significantly lower than that of the control group (P < 0.05). The sedentary group had a higher abundance of Firmicutes, a lower abundance of Actinobacteria and Proteobacteria and a higher abundance of Ascomycota, and a lower abundance of Basidiomycota. Furthermore, functional prediction analysis of the fungal microbiota revealed more L-tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, more phospholipid remodeling (phosphatidylethanolamine, yeast), and more L-tyrosine degradation I, as well as less pentose phosphate pathway (non-oxidative branch), less adenosine nucleotide biosynthesis and less L-valine biosynthesis in the sedentary group (P < 0.05). Thus, a sedentary lifestyle changes the composition and function of the gut microbiota. It may change the pentose phosphate pathway (non-oxidative branch), nucleic acid and amino acid biosynthesis and phospholipid metabolism in fungi. [ABSTRACT FROM AUTHOR]

P53 protein tumor suppressor gene plays a guiding role in the treatment and prognosis of colorectal cancer (CRC). This paper aimed at proposing a feature selection method based on variable clustering to improve positive and negative discrimination of P53 protein in CRC patients. In this approach, we cluster the preprocessed dataset with variables, and then find the features with the largest information value (IV) for each cluster to form a feature subset. We call this method as IV_Cluster. In the actual medical data test, compared with the information value feature selection method, the accuracy of the 10-fold cross-validation logistic regression model increased by 4.4%, 2.0%, and 5.8%, and Kappa values increased by 21.8%, 8.6%, and 22.4%, respectively, under 5, 10, and 15 feature sets. [ABSTRACT FROM AUTHOR]

Herein, we reported the facile synthesis of dicarboxylic esters from biomass derived gamma-valerolactone (GVL) aiming for nylon monomer preparation via a novel synthetic route which improved the efficiency and overcame the need for toxic carbon monoxide for the synthesis of dicarboxylic esters from GVL. [ABSTRACT FROM AUTHOR]

Objectives: Sjögren's syndrome (SS), a systemic autoimmune disorder, is characterized by dry mouth and eyes. However, SS pathogenesis is poorly understood. We performed bioinformatics analysis to investigate the potential targets and molecular pathogenesis of SS. Methods: Gene expression profiles (GSE157159) and methylation data (GSE110007) associated with SS patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially methylated positions (DMPs) and differentially expressed genes (DEGs) were identified by the R package limma. The potential biological functions of DEGs were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Key DMPs were selected by overlap and the shrunken centroid algorithm, and corresponding genes were identified as hub genes, with their diagnostic value assessed by receiver operating characteristic (ROC) curves. The potential molecular mechanisms of hub genes were analyzed by protein–protein interaction (PPI) networks and single-gene gene set enrichment analysis (GSEA). Peripheral blood mononuclear cells (PBMCs) were collected from control and SS patients at The Affiliated Hospital of Southwest Medical University and Dazhou Central Hospital. The mRNA levels of hub genes were verified by quantitative real-time polymerase chain reaction (qRT–PCR). Results: We identified 788 DMPs and 2457 DEGs between the two groups. Functional enrichment analysis suggested that the DEGs were significantly enriched in T cell activation, leukocyte cell–cell adhesion, and cytokine–cytokine receptor interaction. TSS200, TSS1500, and 1stExon were identified as highly enriched areas of differentially methylated promoter CpG islands (DMCIs). In total, 61 differentially methylated genes (DMGs) were identified by the overlap of 2457 DEGs and 507 genes related to DMPs (DMPGs), of which 21 genes located near TSS200, TSS1500, and 1stExon were selected. Then, three key DMPs and the corresponding hub genes (RUNX3, HLA-DPA1, and CD6) were screened by the shrunken centroid algorithm and calculated to have areas under the ROC curve of 1.000, 0.931, and 0.986, respectively, indicating good diagnostic value. The GSEA results suggested that all three hub genes were highly associated with the immune response. Finally, positive mRNA expression of the three hub genes in clinical SS samples was verified by qRT–PCR, consistent with the GSE157159 data. Conclusions: The identification of three hub genes provides novel insight into molecular mechanisms and therapeutic targets for SS. Key Points • Hub genes were screened by DNA methylation and transcriptome analyses. • The relative expression of hub genes in peripheral blood samples was verified by qRT–PCR. • HLA-DPA1 was correlated with the pathogenic mechanism of SS. [ABSTRACT FROM AUTHOR]

We synthesized a series of quinazolinone derivates as tyrosinase inhibitors and evaluated their inhibition constants. We synthesized 2-(2,6-dimethylhepta-1,5-dien-1-yl)quinazolin-4(3H)-one (Q1) from the natural citral. The concentration, which led to 50% activity loss of Q1, was 103 ± 2 μM (IC50 = 103 ± 2 μM). Furthermore, we considered Q1 to be a mixed-type and reversible tyrosinase inhibitor, and determined the KI and KIS inhibition constants to be 117.07 μM and 423.63 μM, respectively. Our fluorescence experiment revealed that Q1 could interact with the substrates of tyrosine and L-DOPA in addition to tyrosinase. Molecular docking studies showed that the binding of Q1 to tyrosinase was driven by hydrogen bonding and hydrophobicity. Briefly, the current study confirmed a new tyrosinase inhibitor, which is expected to be developed into a novel pigmentation drug. [ABSTRACT FROM AUTHOR]

Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation. [ABSTRACT FROM AUTHOR]

To explore the metabolic mechanism of differential plasma interleukin (IL)-6 expression in patients with rheumatoid arthritis (RA). A total of 240 RA patients were enrolled in the non-target metabolomics study cohort and 69 healthy volunteers were included as healthy controls (HCs). Plasma IL-6 levels were detected by electrochemiluminescence assay. Plasma metabolites were detected by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Patients with active RA (n = 20) and remissive RA (n = 20) and 20 HCs were enrolled in the targeted validation cohort. Metabolites identified by non-target metabolomics were quantitatively analyzed by ultra-performance liquid chromatography-triple quadrupole tandem mass spectrometry. Effects of 1-oleoyl- sn -glycero-3-phosphocholine (OGPC) associated with IL-6 on MH7A cells were assessed. After 24-h or 48-h induction by TNF-α, the supernatants were collected for IL-6 quantification by enzyme-linked immunosorbent assay. Furthermore, Western blot was performed to investigate the relative JAK2 and p -JAK2 expressions. With an increasing IL-6 level, OGPC shown to be related to the glycerophospholipid metabolism pathway by Kyoto Encyclopedia of Genes and Genomes analysis displayed a significant decrease. In the validating RA cohort, the OGPC concentrations in remissive RA group and active RA group decreased compared with HC group. OGPC down-regulated IL-6 secretion and p -JAK2 expression in TNF-α-induced MH7A cells in vitro. In conclusion, glycerophospholipid metabolism is the main metabolic pathway associated with the differential IL-6 expression in RA patients. The down-regulated OGPC is a promoting factor for the increased IL-6 plasma level in RA patients, which further affects the downstream JAK signaling pathway. • Abnormal glycerophospholipid metabolism occurs in patients with rheumatoid arthritis. • OGPC negatively correlated with IL-6 in patients with rheumatoid arthritis. • OGPC had an inhibitory effect on MH7A cells through the IL-6/JAK signaling pathway. [ABSTRACT FROM AUTHOR]

• The deep learning radiomics model showed good performance in lymph node metastasis (LNM) prediction of colorectal cancer (CRC) patients. • The genes correlated to selected deep learning features mainly enriched in catabolic processes and immune-related pathways in CRC patients. • The genes in the immune pathways significantly decreased from non-LNM to LNM in CRC patients. The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC. A total of 423 CRC patients were divided into cohort 1 (training set, n = 238, testing set, n = 101) and cohort 2 (validation set, n = 84). Among them, 84 patients' tumour tissues were collected for RNA sequencing. The DPL features were extracted from enhanced venous-phase computed tomography of CRC using an autoencoder. A DPL model was constructed with the least absolute shrinkage and selection operator algorithm. Carcinoembryonic antigen and carbohydrate antigen 19-9 were incorporated into the DPL model to construct a combined model. The model performance was assessed by receiver operating characteristic curves, calibration curves and decision curves. The correlations between DPL features, which have been selected, and genes were analysed by Spearman' correlation, and the genes correlated with DPL features were used to transcriptomic analysis. The DPL model, integrated with 20 DPL features, showed a good discrimination performance in predicting the LNM, with areas under the curves (AUCs) of 0.79, 0.73 and 0.70 in the training set, testing set and validation set, respectively. The combined model had a better performance, with AUCs of 0.81, 0.77 and 0.73 in the three sets, respectively. Decision curve analysis confirmed the clinical application value of the DPL model and combined model. Furthermore, catabolic processes and immune-related pathways were identified and related with the selected DPL features. This study presented a DPL model and a combined model for LNM prediction. We explored the potential genomic phenotypes related with DPL features. In addition, the model could potentially be utilized to facilitate the individualized prediction of LNM in CRC. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods: Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results: Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions: We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis. [ABSTRACT FROM AUTHOR]

Background and Purpose: The aim of this meta‐analysis study was to assess the predictive effects of RNF213 p.R4810K on phenotype in moyamoya disease (MMD). Methods: Electronic databases (e.g., Pubmed and EMBASE) were searched, and relevant articles published up to August 2020 were retrieved. Review Manager 5.3 and Stata 12.0 were used for all statistical analyses. Pooled odds ratios, with 95% confidence intervals, and three comparison models were evaluated to analyze the association between RNF213 pR4810K variant and clinical characteristics of MMD patients using a fixed‐effects model. Results: A total of 2798 patients with MMD were selected and the effects of the heterozygous or homozygous RNF213 p.R4810K variant on 18 clinical features were identified. There were more patients aged <15 years in the GA and AA groups (AA vs GA: p = 0.009; AA vs GG: p = 0.003; GA vs GG: p = 0.001). Among homozygous patients, the majority experienced MMD onset before the age of 4 years (AA vs. GA: p < 0.00001; AA vs GG: p < 0.00001). The frequency of infarctions and transient ischemic attack was significantly higher in homozygotes and heterozygotes，respectively. However, the frequency of intracerebral/intraventricular hemorrhage was lower in patients with the GA than the GG genotype. More MMD patients with AA and GA genotypes had a family history of the disease (p = 0.003, p < 0.00001, respectively). Posterior cerebral artery involvement was more common in patients with the GA genotype (p < 0.00001). Conclusion: The homozygous or heterozygous RNF213 variant may be an efficient biomarker with which to classify different clinical phenotypes of MMD. [ABSTRACT FROM AUTHOR]

Background: The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients.Methods: Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared.Results: Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P <  0·0001), and to have more frequent lymphopenia (79·2% vs 43·7%, P = 0·003) and eosinopenia (84·2% vs 57·0%, P = 0·046).Conclusions: The symptoms of patients in west China were relatively mild, and an appreciable proportion of infected cases had no fever, warranting special attention. [ABSTRACT FROM AUTHOR]

The lung lesions of this COVID‐19 patient were slowly absorbed, and the clinical symptoms with shortness of breath were improved slowly in the recovery period. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) and diabetic retinopathy (DR) are the major factors of morbidity and mortality in the patients with diabetes mellitus (DM). Growing studies have investigated the relationship between the TNF-α-308G/A polymorphism and the susceptibility to DN and DR, without achieving consensus. Thus, we conducted this meta-analysis to reach more comprehensive conclusions for these issues. Eligible studies were retrieved through electronic databases such as PubMed, Embase, Web of Science and China National Knowledge Infrastructure. Summary of odds ratios (OR) and 95% confidence intervals (CIs) were generated to evaluate the intensity of the associations. Statistical analyses were performed by STATA 11.0 and RevMan 5.2. There are fourteen eligible publications involving nineteen studies in this meta-analysis. TNF-α-308G/A polymorphism was significantly related to increasing risk of DN under recessive model (OR=1.37, 95% CI=1.03–1.83) and homozygous model (OR=1.54, 95% CI=1.15–2.06). Moreover, the similar results were also obtained in Asian groups for DN (recessive: OR=1.69, 95% CI=1.18–2.42; homozygous: OR=1.99, 95% CI=1.38–2.86; respectively), and significant association was also detected between TNF-α-308G/A and DN susceptibility in type 2 DM in recessive model (OR=1.39, 95% CI=1.02–1.89). No significant association was observed between TNF-α-308G/A and DR susceptibility in total analyses and subgroup analyses by ethnicity and type of DM. TNF-α-308G/A polymorphism may enhance the susceptibility to diabetic nephropathy, especially in Asian population and in T2DM patients, but not diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore the possibility of serum tumor markers (TMs) combinations in assessing tumor metastasis in patients with lung cancer. Methods: We performed a retrospective analysis of 541 patients diagnosed with lung cancer between January 2016 and December 2017 at the Pneumology Department of Dazhou Central Hospital. Serum carcinoembryonic antigen (CEA), carbohydrate antigen (CA)125, CA153, CA199, CA724, cytokeratin 19 fragment (CYFRA), and neuron‐specific enolase (NSE) levels were quantified in each patient at the time of lung cancer diagnosis. Metastasis was confirmed by computed tomography, and/or positron emission tomography, and/or surgery or other necessary methods. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the performance of the model. Results: Of the 541 patients eligible for final analysis, 253 were detected with metastasis and 288 were detected without metastasis. Compared with those in nonmetastatic patients, the serum CEA, CA125, CA199, CA153, CYFRA, and NSE levels were notably higher in metastatic patients (P <.05). The ROC curve demonstrated that the CEA‐CA125‐CA199‐CA153‐CYFRA‐NSE‐CA724 combination based on the cut‐off value had an optimal area under the curve and specificity in assessing tumor metastasis. The decision tree model is a convenient and valuable tool for guiding the appropriate application of our model to assess metastasis in lung cancer patients. Conclusions: Our study suggested that the nomogram of the regression model is valuable for assessing tumor metastasis in newly diagnosed lung cancer patients before traditional standard methods are used. These findings could aid in the evaluation of metastasis in the clinic. [ABSTRACT FROM AUTHOR]

Leflunomide (LEF) is a conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) for the treatment of rheumatoid arthritis. However, there are few reports on the comparison of efficacy between LEF alone and combined with other csDMARDs. Here, the efficacy and safety of LEF monotherapy (88) and combination (361) therapy groups were evaluated. After 3 months, there were no significant differences in 28-joint disease activity score (DAS28), health assessment questionnaire (HAQ), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) between the monotherapy and combination groups (all P > 0.05). According to the European League Against Rheumatism (EULAR) response criteria, it was found that the DAS28 response rates were similar in the two groups (P > 0.05). Besides, the two groups presented similar safety profiles. Subgroup analysis found that there was no difference in efficacy among the three combined therapies (LEF + methotrexate (MTX), LEF + hydroxychloroquine (HCQ), and LEF + MTX + HCQ) and LEF monotherapy. Furthermore, when the dose of LEF was less than 40 mg/day, no significant difference in efficacy was observed between low and high doses. Overall, these results indicated that low dose LEF monotherapy was not inferior to the combination therapy. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer (PC) is a seriously malignant tumor with a low 5-year survival rate. The relationship between methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and PC has been reported by several studies. However, the results were controversial. Thus, we conducted a meta-analysis to summarize available data on MTHFR gene and PC. Methods: We searched PubMed, Embase, Web of Science, Wanfang, CNKI databases prior to July 2019. Data were analyzed by RevMan 5.3 and STATA 12.0 software. Odds ratio (OR) and 95% confidence interval (CI) were used to evaluate the strength of the association. Subgroup analysis, sensitivity analysis and assessment of publication bias were performed in this study. Results: Ten articles with 17 reports (10 for C677T, 7 for A1298C) were eligible for inclusion in the meta-analysis (1864 cases and 3165 controls for C677T, and 1488 cases and 1946 controls for A1298C). Our meta-analysis detected that C677T was associated with PC for three genetic models (allele model: OR = 1.24, 95% CI: 1.00–1.53, P = 0.047; recessive model: OR = 1.39, 95% CI: 1.04–1.86, P = 0.027; homozygous model: OR = 1.60, 95% CI: 1.04–2.45, P = 0.034). In the stratified analyses according to ethnicity, source of controls and genotyping method, significant association was observed in genotyping method subgroup. For the A1298C polymorphism, no significant association was observed either in overall analysis or in subgroup analysis under all genetic models. Conclusions: MTHFR gene C677T rather than A1298C polymorphism may be associated with PC. Larger sample size studies should be performed to find the association between MTHFR gene and PC. [ABSTRACT FROM AUTHOR]

Aims: To investigate the progression of obesity‐related type 2 diabetes mellitus (T2DM) in rhesus monkeys, especially dynamic changes in insulin and glucagon. Materials and methods: We followed a cohort of 52 rhesus monkeys for 7 years throughout the progression of obesity‐related T2DM. Intravenous glucose tolerance tests were performed every 6 months to evaluate dynamic changes in glucose, insulin and glucagon levels. Results: Obesity in rhesus monkeys increased the overall mortality and T2DM morbidity. During the progression of T2DM, glucagon remained consistently elevated, while insulin initially increased in compensation but then dropped to below normal levels when the monkeys developed overt T2DM. After a glucose challenge, both the first and second phases of insulin secretion increased during the early stage of T2DM; in later stages the first phase was delayed and the second phase was diminished. Conclusion: Our findings showed that, beside the decreased insulin level, hyperglucagonaemia also plays an important role in the development of T2DM. [ABSTRACT FROM AUTHOR]

Landmark detection has been well developed by deep learning methods, and the cascade-connected neural network (CCNN) stands out as a widely used deep learning landmark detection method. CCNNs consist of several stacked network backbones, where the predictions of the previous network backbone are used as the input of the following one. Due to GPU memory bottlenecks, CCNNs have two limitations. First, the network backbones of CCNNs have limited numbers and depths; thus, the learning ability of CCNNs is limited. Second, CCNNs are usually trained in low-resolution images. However, the neighboring pixels in high-resolution images are usually vital for landmark detection, especially for cephalometric landmark detection. This paper interprets CCNNs as the discrete approximation of ordinary differential equations. Relying on this explanation, we further propose a novel model, called the cascade-refine model , which takes advantage of CCNNs and makes it possible to overcome the limitations of number and depth by sharing parameters among stacked network backbones. Moreover, the proposed model obeys the rule of coarse-to-fine architectures, where a global module is used to generate the coarse landmark locations, and a local module is adopted to tune the pixel error of latent landmark locations in the region of interest. The proposed model is trained in an end-to-end manner. Thus, the neighboring detailed high-resolution pixels are directly exploited for cephalometric landmark detection. The proposed cascade-refine model outperforms state-of-the-art methods on the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset. We also built two private cephalometric X-ray datasets, and experimental results on both datasets demonstrate the good performance of the proposed model. • A cascade training strategy was introduced to model the discrete approximation of an ODE. • The proposed cascade-refine model can continually refine landmark locations. • The cascade-refine model breaks through the limitation of GPU memory. • The landmark label map was proposed to the local module of the cascade-refine model. [ABSTRACT FROM AUTHOR]

Blood and lymphatic vessels form a versatile transport network and provide inductive signals to regulate tissue-specific functions. Blood vessels in bone regulate osteogenesis and hematopoiesis, but current dogma suggests that bone lacks lymphatic vessels. Here, by combining high-resolution light-sheet imaging and cell-specific mouse genetics, we demonstrate presence of lymphatic vessels in mouse and human bones. We find that lymphatic vessels in bone expand during genotoxic stress. VEGF-C/VEGFR-3 signaling and genotoxic stress-induced IL6 drive lymphangiogenesis in bones. During lymphangiogenesis, secretion of CXCL12 from proliferating lymphatic endothelial cells is critical for hematopoietic and bone regeneration. Moreover, lymphangiocrine CXCL12 triggers expansion of mature Myh11+ CXCR4+ pericytes, which differentiate into bone cells and contribute to bone and hematopoietic regeneration. In aged animals, such expansion of lymphatic vessels and Myh11-positive cells in response to genotoxic stress is impaired. These data suggest lymphangiogenesis as a therapeutic avenue to stimulate hematopoietic and bone regeneration. [Display omitted] • Light-sheet imaging of intact bones uncovers lymphatic vessels in bones • Stress-induced IL6 drives lymphatic vessel expansion • Lymphatic vessels mediate bone and hematopoietic regeneration • Lymphangiocrine CXCL12 supports regeneration in bone Lymphatic vessels not only connect secondary lymphoid organs with tissue sites throughout the body but are also now shown directly irrigating bones and participating in their regeneration. [ABSTRACT FROM AUTHOR]

Objectives: Our study aimed to elucidate the role of metabolites, bacteria, and fungi in rheumatoid arthritis (RA) patients with bone destruction (BD(+)) and identify some biomarkers to predicate bone progression of RA.Methods: Plasma metabolites of the 127 RA patients and 69 healthy controls were conducted by using nontargeted liquid chromatography-mass spectrometry (LC-MS). The gut bacteria and fungi were assessed by 16S rRNA and internal transcribed spacer (ITS).Results: Compared with RA patients without bone destruction (BD(-)), some metabolites, bacteria, and fungi were altered in BD(+). Seven metabolites were selected as key metabolites for classifying the BD(+) and BD(-) groups with moderate accuracy (AUC=0.71). Metabolites-groups, metabolites-metabolites, and metabolites-clinical factors had a certain correlation, and 7 metabolites were enriched in glycerophospholipid metabolism and L-arginine and proline metabolism pathways. The bacteria and fungi of the BD(+) group showed significant differences in composition and function compared with BD(-) group. The changed 4 bacteria and 12 fungi yielded accuracy (AUC=0.74 and AUC=0.87, respectively) for the two groups. Taking 7 metabolites, 4 bacteria, and 12 fungi as a panel for AUC analysis, an improved AUC of 0.99 significantly discriminated the two groups. The changed metabolites, gut bacteria, and fungi may affect the pathway related to L-arginine.Conclusions: Our nontargeted LC-MS, 16S rRNA, and ITS highlighted a novel link among the metabolites, bacteria, fungi, and pathology of BD(+), which could contribute to our understanding of the role of metabolites, bacteria, and fungi in BD(+) etiology and offered some novel biomarkers to predict the bone progression of RA. [ABSTRACT FROM AUTHOR]

• Proposing a framework of active learning combined with semi-supervised learning. • Introducing Gaussian Mixture Model to fit distribution of complex logging data. • Probabilistic uncertainty measurement for lithology identification results. Lithology identification is the basis of energy exploration and reservoir evaluation, intelligent and accurate identification of underground lithology is a key issue. The establishment of a machine learning lithology identification model using logging data is a hot research direction in recent years. However, the logging data has a high degree of non-linearity and multi-response characteristics, and there are insufficient numbers of labeled samples in the training data set. These will eventually affect the modeling accuracy and may cause over-fitting. Therefore, a framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes (ALSLINB) is proposed. The contributions are fourfold: (i) The Gaussian mixture model (GMM) based on the EM algorithm is used to estimate the probability density of the log data, which fits the probability distribution of the nonlinear multi-response log data. (ii) A framework combining active learning (AL) and semi-supervised learning is proposed for the expansion of labeled samples in the training data set. (iii) The application of pseudo-labeling detection technology can effectively improve the authenticity of pseudo-label samples. (iv) Different from the general deterministic lithology identification method, the result of the ALSLINB algorithm corresponds to the probability score, which provides an auxiliary basis for the prediction result. Finally, the ALSLINB algorithm is applied to two different data sets for a large number of experiments and compared with the related baseline methods to verify its effectiveness and generalization ability. The result proves that the ALSLINB algorithm can complete the lithology recognition task well and has high accuracy and robustness, which provides a new direction for intelligent lithology identification. [ABSTRACT FROM AUTHOR]

Poly(aniline-co-pyrrole) was coated on the surface of reduced graphene oxide (rGO) through in situ chemical oxidative polymerization of aniline and pyrrole monomers. For comparison, rGO/polyaniline (PANi) and rGO/polypyrrole (PPy) were also prepared under the similar reaction condition, respectively. The resulting nanocomposites were characterized by field-emission scanning electron microscopy, atomic force microscopy, Fourier transform infrared and Raman spectrometry, and thermogravimetric analysis. Ultrathin layers of conducting polymers on the surface of rGO not only provide more electrochemically active sites but also shorten the distance for ion and electron transport, benefiting from the existence of strong interactions between polymeric chains and rGO. Besides, rGO in the composite can also provide an electron transfer path due to its excellent conductivity and high surface area. What is more, rGO/Copolymer exhibits the highest thermal stability and superior electrochemical stability among these composites, benefiting from its specific chemical structures, intrinsic electrochemical properties of three components, and more importantly, the synergistic effect in the composite. In 1 M Na 2 SO 4 electrolyte, the specific capacitance of rGO/Copolymer was 541 F g −1 at the scan rate of 1 mV s −1 , and 283 F g −1 at the scan rate of 20 mV s −1 , respectively, which is larger than those for rGO/PANi and rGO/PPy. After 500 cycles, capacitance retention was 86% for rGO/Copolymer. These demonstrate that rGO/Copolymer can be applied as a high-performance electrode material for electrochemical supercapacitors. [ABSTRACT FROM AUTHOR]

• Bulk synthesis of polyaniline nanofibers through seed polymerization in totally aqueous solution. • Bifunctional doping effects of sulfonic acid on the oligomer assembly and nanofiber dispersion. • Over 55% and 11% improvements on the specific capacitance and cycle retention by introducing camphorsulfonic acid. Polyaniline (PANI), thanks to low cost, easy synthesis, fast reversible Faradic reaction and especially theoretical charge storage capability, has been extensively studied in the field of energy conversion and storage. However, its development as electrode material in supercapacitor is still be restricted by the need of high–yield production of PANI with robust electrochemical performance. Herein, inspired by the seeding polymerization, and tunable conductivity and solubility with organic acid, one–pot bulk synthesis of PANI nanofibers in fully aqueous solution was realized with the aid of aniline oligomer and sulfonic acid. During the seeding polymerization process, four kinds of sulfonic acids with different functional groups and sizes including 5–sulfosalicylic acid (SSA), p–toluenesulfonic acid (pTSA), p–aminobenzenesulfonic acid (pASA) and camphorsulfonic acid (CSA) were introduced to efficiently control the assembly behavior of oligomers to form PANI nanofibers, meanwhile provide better electrical and electrochemical properties as well as good dispersion in aqueous solution compared with pure hydrochloric acid (HCl) medium. Benefiting from the bifunctional doping effects of sulfonic acid on the oligomer assembly and nanofiber dispersion, great enhancements on the electrochemical performances of PANI nanofibers can be easily achieved. Especially for CSA, high specific capacitance of 600.7 F g–1 at the current density of 1 A g–1 and good cycling stability with retention of 74% could be manifested, which are over 55% and 11% improvements in contrast to neat HCl medium, respectively. These results suggest that sulfonic acid assisted oligomer assembly is highly effective in combining smaller diameter of PANI nanofiber with superior supercapacitor property, and also provides a new strategy to promote the development of conductive polymers in energy related fields. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Background: Nonalcoholic fatty liver disease (NAFLD) is a significant contributor to global hepatic disorders. ADIPOQ gene single‐nucleotide polymorphisms have been associated with NAFLD susceptibility, but with inconsistent results across the studies. This study aimed to investigate the association between ADIPOQ polymorphisms (+276G>T, rs1501299 and −11377C>G, rs266729) and the risk of NAFLD. Methods: PubMed, Embase, Wanfang, Web of Science, and China National Knowledge Infrastructure databases were used to identify the relevant published literature. Statistical analyses were calculated with STATA 11.0 software and RevMan 5.2. Summary odds ratios (OR) and 95% confidence intervals (CIs) were generated to assess the strength of the associations. Results: Eleven relevant articles with a total of 3,644 participants (1,847 cases/1,797 controls) were included. Our meta‐analysis results revealed that ADIPOQ gene +276G>T polymorphism was not associated with NAFLD under various genetic models (allele model: OR = 0.99, 95% CI [0.69, 1.41]; dominant model: OR = 1.06, 95% CI [0.71, 1.58]; recessive model: OR = 0.83, 95% CI [0.42, 1.65]; homozygous model: OR = 0.86, 95% CI [0.38, 1.95]; heterozygous model: OR = 1.10, 95% CI [0.80, 1.53]; respectively). Moreover, no statistical significant association was found between +276G>T and NAFLD risk in the subgroups. ADIPOQ gene −11377C>G polymorphism significantly increased the risk of NAFLD (allele model: OR = 1.49, 95% CI [1.28, 1.75]; dominant model: OR = 1.64, 95% CI [1.35, 1.99]; recessive model: OR = 1.77, 95% CI [1.16, 2.70]; homozygous model: OR = 2.13, 95% CI [1.38, 3.28]; heterozygous model: OR = 1.58, 95% CI [1.29, 1.93]; respectively). Conclusion: ADIPOQ gene −11377C>G may be a risk factor for NAFLD, while there was no association between ADIPOQ gene +276G>T polymorphism and the risk of NAFLD. Further studies are needed to detect the relationship between these ADIPOQ polymorphisms and NAFLD. [ABSTRACT FROM AUTHOR]