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OBJECTIVE To clarify whether systemic lymph node dissection (LND) influences the safety of surgery and the survival of patients with locally advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemoradiotherapy (nCRT). SUMMARY BACKGROUND DATA Prognostic impact of systemic lymphadenectomy during surgery after nCRT for ESCC is still uncertain and requires clarification. METHODS This is a secondary analysis of NEOCRTEC5010 trial which compared nCRT followed by surgery versus surgery alone for locally advanced ESCC. Relationship between number of LND and perioperative, recurrence and survival outcomes were analyzed in the nCRT group. RESULTS Three-year overall survival was significantly better in the nCRT group than the S group (75.2% vs 61.5%; P=0.011). In the nCRT group, greater number of LND was associated with significantly better overall survival (HR, 0.358; P < 0.001) and disease-free survival (HR, 0.415; P=0.001), but without any negative impact on postoperative complications. Less LND (< 20 vs ≥ 20) was significantly associated with increased local recurrence (18.8% vs 5.2%, P=0.004) and total recurrence rates (41.2% vs 25.8%, P=0.027). Compared to patients with persistent nodal disease, significantly better survival was seen in patients with complete response and with LND ≥ 20, but not in those with LND < 20. CONCLUSIONS Systemic lymph node dissection does not increase surgical risks after nCRT in ESCC patients. And it is associated with better survival and local disease control. Therefore, systemic lymphadenectomy should still be considered as an integrated part of surgery after nCRT for ESCC.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the $l_{2,1}$ -norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule, and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy.

Albeit great success has been achieved in image defocus blur detection, there are still several unsolved challenges, e.g., interference of background clutter, scale sensitivity and missing boundary details of blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We first fuse the features from different layers of FCN as shallow features and semantic features, respectively. Then, the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions, and the fused semantic features are propagated to shallow layers to assist in better locating blur regions. The fusion and refinement are carried out recurrently. In order to narrow the gap between low-level and high-level features, we embed a feature adaptation module before feature propagating to exploit the complementary information as well as reduce the contradictory response of different feature layers. Since different feature channels are with different extents of discrimination for detecting blur regions, we design a channel attention module to select discriminative features for feature refinement. Finally, the output of each layer at last recurrent step are fused to obtain the final result. We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study. Extensive experiments on two commonly used datasets and our newly collected one are conducted to demonstrate both the efficacy and efficiency of DeFusionNet.

Graphical abstract, Introduction The COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. Objectives We assumed that antibodies may serve as biomarkers for predicting the clinical outcome of hospitalized COVID-19 patients on admission. Methods By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1,034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. Results Nonsurvivors (n=955) induced higher levels of IgG responses against most of non-structural proteins than survivors (n=79) on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The area under curves (AUCs) for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Conclusions Our findings might have important implications for improving clinical management of COVID-19 patients.

Automatic matching of multimodal images remains a critical challenging task in many remote sensing and computer vision applications. Due to significant nonlinear radiation distortions (NRDs) between multimodal images, it is difficult for many traditional feature matching methods which are sensitive to NRD to achieve satisfactory matching performance. To cope with this problem, this letter proposes a novel feature matching method named nonlinear consistent feature transform (NCFT) that is robust to large NRD. There are three main contributions to NCFT. First, we propose a new consistent feature map instead of image intensity for feature point detection and description, the consistent feature map encodes the structure information and provides a rich and robust feature. Second, we propose a mean-residual maximum index map (MR-MIM) for feature description and the MR-MIM is constructed from the Log-Gabor convolution sequence on the consistent feature map. Finally, the structure descriptors are built according to the MR-MIM, and multimodal image matching is achieved by computing the correspondence. The extensive experimental results demonstrate that NCFT can effectively overcome the problem of NRD, NCFT outperforms other state-of-the-art methods and improves the matching accuracy and robustness on different multimodal image datasets.

Solute carrier organic anion transporter family member 4A1 (SLCO4A1-AS1), a newly discovered lncRNA, may exert effects in tumors. Since its role in gastric cancer remains obscure, we sought to explore the mechanism of SLCO4A1-AS1 in gastric cancer. The relationship among SLCO4A1-AS1, miR-149-5p, and STAT3 was detected by bioinformatics, dual luciferase analysis, and Pearson’s test, and the expressions of these genes were determined by quantitative real-time PCR and Western blot. Moreover, CCK-8, flow cytometry, wound healing assay, and Transwell analysis were performed to verify the function of SLCO4A1-AS1 in gastric cancer. Rescue experiments were used to detect the role of miR-149-5p. The expressions of SLCO4A1-AS1 and STAT3 were increased, while the expression of miR-149-5p was suppressed in gastric cancer tissues and cell lines. In addition, STAT3 expression was negatively correlated with miR-149-5p expression but was positively correlated with SLCO4A1-AS1 expression. Overexpression of SLCO4A1-AS1 promoted cell viability, migration, invasion, and STAT3 expression but suppressed apoptosis, while knockdown of SLCO4A1-AS1 had the opposite effect. SLCO4A1-AS1 bound to miR-149-5p and targeted STAT3. Moreover, miR-149-5p mimic inhibited the malignant development of gastric cancer cells and obviously reversed the function of SLCO4A1-AS1 overexpression. Our research reveals that abnormally increased SLCO4A1-AS1 expression may be an important molecular mechanism in the development of gastric cancer.

With the rapid development of genetic sequencing and DNA microarray technologies, a large number of gene expression data has been generated, which provides an important reference for tumor diagnosis. However, it is challenging to classify these gene expression data due to the high-dimensionality and small number of data samples. In this work, we propose an effective method to select the most discriminative genes from high-dimensional microarray data for benefiting tumor classification. In detail, each gene is regarded as a feature dimension and we build a novel computational model based on dual latent feature representation learning, referred as DLRL briefly, which can capture both the internal association of data samples and the relationship between different genes. Instead of measuring the importance of genes in original data space, we perform gene selection in the learned latent representation space which is more robust to noisy and redundant information. We first construct the affinity matrices for both samples and genes, which can represent the correlation information between data samples and genes, respectively. Then the dual latent representation learning is modelled via non-negative matrix factorization of the two affinity matrices. The low-dimensional latent representation matrix of sample space is treated as a pseudo-label matrix to guide the latent space projection of original data. Meanwhile, the sample projection matrix is unified with the latent representation matrix of gene space. An alternating algorithm is carefully designed to solve the resultant optimization problem. Extensive experiments on six commonly used publicly microarray datasets are conducted to demonstrate that the proposed method can steadily outperform other state-of-the-art methods in terms of microarray data classification. In addition, we also test the proposed model on a face image dataset as well as a digit image dataset to validate its efficacy for general unsupervised feature selection.

Objectives Previous literature has shown that chronic diseases and psychological distress are correlated. However, the mediating and moderating mechanisms underlying this relationship have not been sufficiently studied. The purpose of this study was to explore the role played by activities of daily living (ADL) and perceived social support. Method Face-to-face questionnaire-based surveys were conducted with 3250 valid participants (age ≥60 years). Participants were assessed using the Barthel Index, Perceived Social Support Scale, and Depression Anxiety Stress Scale-21. Results 1) Chronic diseases had a significant direct effect on psychological distress in older adults; 2) the relationship between chronic diseases and psychological distress was partially mediated by ADL; and 3) the impact of chronic diseases on psychological distress was significantly moderated by perceived social support. More importantly, perceived social support was a protective factor that could effectively alleviate the adverse effects of chronic diseases on psychological distress. Conclusion The results add to the existing literature by uncovering the underlying mechanisms between chronic diseases and mental health. These findings have implications for early intervention and prevention of mental health problems in older adults.

Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.

Radio frequency identification (RFID) has been widely used in many fields such as object tracking and inventory management. For RFID systems, grouping is a fundamental issue which can support efficient multicast transmissions, dynamic tag management, and accurate aggregate queries. Existing grouping protocols have drawbacks of unknown theoretical communication time, high computational cost on the server end and inability to deal with unexpected tags which are those tags whose IDs have not been collected by readers. In this paper, we would like to address the above limitations and consider a more general grouping problem that allows an arbitrary number of unexpected tags to present. Our objective is to design a protocol that guarantees the reader to efficiently and correctly notify each known tag of its group-ID, while the probability that an unexpected tag is mistakenly notified of any group-ID is smaller than a pre-determined value. In this paper, we first obtain a lower bound on the communication time for solving this generalized grouping problem. Then, we propose a near-optimal protocol, called OPT-G, and prove that its communication time approximately equals the lower bound. Finally, we report extensive simulation results that demonstrate OPT-G's near-optimal performance and its superiority over existing baseline schemes.

Depression is the most frequent psychiatric disorder among the older adults, the purpose of this study is to explore the mediation effect of health promoting lifestyle on the relationship between Socioeconomic Status (SES) and depression, so as to provide practical reference for effectively preventing and alleviating depression of the older adults.A multi-stage random sampling method was conducted in Shanxi Province. A total 8526 older adults were included in the analysis. Regression analysis was used to explore the relationship between the socioeconomic status, health promoting lifestyle and depression, and the mediation effect test process was used to verify the mediating mechanism of health promoting lifestyle on this relationship.62.1% of the older adults had depression symptoms in this study, and there were significant differences in depression scores among the older adults with gender, age, residence, marital status, endowment pattern, self-care ability, exercise frequency, physical examination frequency, daily diet rules(P0.05). The results of mediating effect analysis showed that health promoting lifestyle as mediation effect between social economic status and depression was established, and the mediating effect was accounted for (a*b/c) 63.1%.Given the cross-sectional study, it is not possible to make causal inferences.There were different degrees of depression in the older adults. The lower the SES is, the higher the risk of depression is. This result once again confirms the social causation theory. Combined with the theory of health risk behavior, the study further confirmed the applicability of lifestyle mechanism in the intermediate mechanism of the relationship between SES and depression in the elderly. Thus, in addition to providing material support and financial assistance, helping the elderly develop a healthy lifestyle is also effective measure to improve their depression.

Obesity is epidemiologically linked to 13 forms of cancer. The local and systemic obese environment is complex and likely affect tumors through multiple avenues. This includes modulation of cancer cell phenotypes and the composition of the tumor microenvironment. A molecular understanding of how obesity links to cancer holds promise for identifying candidate genes for targeted therapy for obese cancer patient. Herein, we review both the cell-autonomous and non-cell-autonomous mechanisms linking obesity and cancer as well as provide an overview of the mouse model systems applied to study this.

Accurate demand prediction of bike-sharing is an important prerequisite to reducing the cost of scheduling and improving the user satisfaction. However, it is a challenging issue due to stochasticity and non-linearity in bike-sharing systems. In this paper, a model called pseudo-double hidden layer feedforward neural networks is proposed to approximately predict actual demands of bike-sharing. Specifically, to overcome limitations in traditional back-propagation learning process, an algorithm, an extreme learning machine with improved particle swarm optimization, is designed to construct learning rules in neural networks. The performance is verified by comparing with other learning algorithms on the dataset of Streeter Dr bike-sharing station in Chicago.

Objective This study was aimed at exploring whether miR-30a enhanced sensitivity of non-small-cell lung cancer (NSCLC) cells against neoadjuvant chemotherapy through an autophagy-dependent way. Methods We totally recruited 304 NSCLC patients who have underwent chemotherapy, as well as 185 NSCLC patients who did not receive chemotherapy. NSCLC cell lines (i.e. H1299 and H460) were also purchased, and they were transfected by miR-30a mimic/inhibitor. Furthermore, cisplatin (DDP)/pemetrexed (PEM) resistance of NSCLC cells was assessed utilizing MTT assay, and autophagic proteins isolated from NSCLC tissues and cells were quantitated by western blotting. Results Lowly expressed miR-30a was reflective of lymph node metastasis, advanced TNM stage and poor 5-year survival among NSCLC patients treated by neoadjuvant chemotherapy (i.e. combined treatment of DDP and PEM) (P Conclusions Intentionally elevating miR-30a expression was conducive to improving NSCLC prognosis after neoadjuvant chemotherapy, for its depressing drug-caused autophagy and resistance.

Background The aim of the present study was to analyze the association of programmed cell death-ligand 1 (PD-L1) and vascular endothelial growth factor receptor 2 (VEGFR2) expression levels with clinicopathological characteristics and survival to provide a treatment strategy for rectal cancer. Methods Immunohistochemical staining of VEGFR2 and PD-L1 was carried out, and the association of PD-L1 and VEGFR2 expression levels with clinicopathological characteristics and survival were investigated in 77 pair-matched rectal cancer patients. Results PD-L1 and VEGFR2 expression levels in surgical tumor tissues were higher than those in paired adjacent normal tissues, respectively (both P

Type 2 diabetes (T2D) is a chronic endocrine disorder with rapidly increasing prevalence worldwide. Genetic instability leading to metabolic dysfunction plays an important role in T2D susceptibility and progression. Structural alteration in genome, that is, copy number variation (CNV) is emerging as the inherent marker for disease identification. Previous genomic CNV array revealed that protein tyrosine phosphatase non-receptor type 22 (PTPN22) gene was overlapped with a CNV region, however, whether this CNV affected T2D risk remains to be further elucidated. In this study, we first identified divergent distributions of the PTPN22 copy number (CN) between T2D patients and healthy controls in Chinese population (p < 0.01). Risk assessment analysis revealed that the CN gain (OR = 3.28, p < 0.001) was the promising risk factor for T2D. Also, significantly positive correlations of the PTPN22 CNV with fasting plasma glucose and glycated hemoglobin were demonstrated in T2D patients. Statistical association analysis investigated that the T2D individuals carrying CN gain showed higher plasma glucose and lower insulin levels than those carrying CN normal and loss at 60 min/120 min/180 min during an OGTT test. In addition, the PTPN22 CNV had an effect on total cholesterol, and the CN gain presented higher values than the other two CN types. These results suggested that the CN gain types of the PTPN22 gene accompany with the glycometabolism dysregulation, and finally predispose their carriers to T2D; therefore, the PTPN22 CNV may be a promising biomarker for predicting T2D risk, or a clinical target for T2D diagnosis and therapy.

Accompanying with diabetes mellitus-induced osteoporosis (DM-OS), diabetic patients show poor peri-implant osteogenesis after implantation for dentition defect. Berberine (BBR), a candidate oral hypoglycemic agent, is a promising agent for treating DM-OS. In this study, BBR was applied on DM rats and high-glucose-cultured bone mesenchymal stem cells (BMSCs) to investigate its therapeutic mechanism on DM-OS, thus laying a theoretical basis for the future application of BBR in implant restoration. Phenotypes were assessed in the DM rats after 4 w of gavage with BBR. Furthermore, BMSCs were cultured with high glucose and BBR. Cell Counting Kit-8, 2',7'-dichlorofluorescin diacetate (H2 DCF-DA), quantitative real-time PCR (qRT-PCR), and western blot were performed to estimate the cell proliferation, oxidative stress, and osteogenic differentiation. Moreover, the DM rats treated with BBR and insulin receptor substrate-1 anti-sense oligonucleotide (IRS-1-ASO) underwent a 4-w implant-healing period and then micro computed tomography (Micro-CT) and histology were performed to verify the mechanism. Results showed that the 4-w administration of BBR markedly improved the glucose metabolism and bone metabolism in the DM rats. in vitro experiments revealed that BBR alleviated high-glucose-inhibited osteogenesis of the BMSCs by upregulating reactive oxygen species (ROS)-mediated IRS-1 signaling. Besides, injection of IRS-1-ASO abolished the BBR promotion of implant osseointegration in the DM rats. In conclusion, targeting ROS-mediated IRS-1 signaling, BBR acted as an efficient agent to advance osseointegration in DM, which indicated that BBR use is a good strategy for future implants restoration in diabetic patients.

Lung cancer (LC), is the most common and lethal cancer worldwide. It affects both sexes and in its early stages is clinically silent, until it reaches a more advanced stage, when it becomes highly incurable. In order to improve the high mortality associated with LC there has been an urgent need for screening high risk patients by low dose CT scan (LDCT) for the early detection of small resectable malignant tumors. However, while highly sensitive to detect small lung nodules, LDCT is non-specific, resulting in a compelling need for a complementary diagnostic tool. For example, a non-invasive blood test or liquid biopsy, (LB), could prove quite useful to confirm a diagnosis of malignancy prior to definitive therapy. With the advent of LB becoming increasingly clinically accepted in the diagnosis and management of LC, there has been an explosion of publications highlighting new technologies for the isolation of and detection of circulating tumor cells (CTCs) and cell free tumor DNA (cfDNA). The enormous potential for LB to play an important role in the diagnosis and management of LC to obtain valuable diagnostic information via an approach that may yield equivalent information to a surgical biopsy, regarding the presence of cancer and its molecular landscape is described.

Colorectal cancer (CRC) has the second highest mortality rate among all cancers worldwide. Surgery, chemotherapy, radiotherapy, molecular targeting and other treatment methods have significantly prolonged the survival of patients with CRC. Recently, the emergence of tumor immunotherapy represented by immune checkpoint inhibitors (ICIs) has brought new immunotherapy options for the treatment of advanced CRC. As the efficacy of ICIs is closely related to the tumor immune microenvironment (TME), it is necessary to clarify the relationship between the immune microenvironment of CRC and the efficacy of immunotherapy to ensure that the appropriate drugs are selected. We herein review the latest research progress in the immune microenvironment and strategies related to immunotherapy for CRC. We hope that this review helps in the selection of appropriate treatment strategies for CRC patients.

The presence of tumor infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLSs) in tumor tissues are of great prognostic significance in several types of human cancer. The present study investigated the density of TILs and TLSs in gastric cancer (GC) tissues and their association with pathological parameters. Moreover, the clinical significance of follicular CD8+ cytotoxic T cells present within the germinal centers of the tumor-associated TLSs was investigated. Immunohistochemistry and H&E staining were used to examine the infiltration and distribution patterns of TILs, TLSs and germinal center (gc) CD8+ TILs in tumor tissues obtained from 63 patients with GC. The number of TILs, TLSs, combination of TILs and TLSs (TILs-TLSs) and gcCD8+ TILs were used to define tumoral immune parameters, and the prognostic value of these parameters was assessed. The analysis revealed that patients with GC with increased levels of TILs, TLSs, or gcCD8+ TILs exhibited improved overall survival. In addition, gcCD8+ TILs levels were significantly associated with patient age, histological grade and pTN stage. Increased levels of TILs-TLSs were positively associated with nerve invasion, tumor thrombus, nodal metastasis and histological grade. Multivariate Cox regression analysis revealed that TILs-TLSs and gcCD8+ TILs were independent prognostic factors. The data obtained in the present study demonstrated that high levels of tumoral immune parameters are important independent prognostic predictors for human GC. The results also suggested a possible role of gcCD8+ TILs in tumor immune surveillance.

Three configurations that incorporate a negative stiffness element and an inerter in parallel connection into the conventional isolator are considered to investigate their vibration characteristics from the point of view of resonance frequencies and displacement transmissibility. Analytical and numerical results are evaluated and compared with each other. It is illustrated that for the configuration of the inerter and an additional viscous damper in series connection which are then connected with the negative stiffness element in parallel, the isolator shows a better vibration isolation performance.

Background Gallbladder torsion is a rare acute abdominal condition that requires emergency surgery. It occurs more commonly in elderly people and in women in the adult population. Diagnosis is a challenge as non-specific symptoms and signs have been reported on ultrasonography, computed tomography and magnetic resonance imaging. Prompt cholecystectomy can decrease the mortality and morbidity of perforation due to gallbladder torsion. Case summary An 82-year-old woman with upper-right quadrant pain and associated nausea and vomiting was diagnosed with ectopic acute calculus cholecystitis. Magnetic resonance cholangiopancreatography (MRCP) showed a V-shaped distortion of the extrahepatic bile ducts and a particularly extended twisted cystic duct, which indicated the presence of gallbladder torsion. Emergency laparoscopic cholecystectomy confirmed the diagnosis and the patient recovered without incident. Conclusion Gallbladder torsion can be diagnosed pre-operatively by MRCP. The specific signs are a V-shaped distortion of the extrahepatic bile ducts and a particularly extended twisted cystic duct which can be called twisting signs.

Introduction Antimicrobial resistance (AMR) of Neisseria gonorrhoeae (N. gonorrhoeae) becomes a grave public health problem in the world. A strengthened Antimicrobial Resistance Surveillance Program is needed to track the trend of AMR development. However, the lack of a proper antimicrobial susceptibility test (AST) method is a barrier to expand the AMR surveillance in China. Traditional agar dilution (AD) method is laborious and E-test strips have no approval license for clinical use. Herein, a Chinese group modified the microdilution (MD) method for clinical ASTs. The objective of this study is to compare the MD method with the AD method for N. gonorrhoeae AST. Materials and methods A total of 166 clinical isolates were tested for antimicrobial susceptibility of ceftriaxone, spectinomycin, azithromycin, ciprofloxacin, tetracycline, and penicillin using MD and AD method simultaneously. Results of MD method were read manually or automatically. Rates of essential agreement (EA), category agreement (CA), minor error, and very major error were compared. Results The total EAs (compared with results read manually) of penicillin, tetracycline, ciprofloxacin, spectinomycin, ceftriaxone, and azithromycin were 90.4%, 97.0%, 85.5%, 100.0%, 94%, and 72.3%; and CAs were 82.5%, 94.0%, 100%, 100%, 95.2%, and 94%, respectively. Conclusion We conclude that the MD method might be an alternative for clinical AST of N. gonorrhoeae in China. In particular, MD method has the potency of accurate differentiation of isolates resistant to ceftriaxone or azithromycin, which were empirically recommended for gonococcal treatment, but its quality remained suboptimal, and further improvement is needed for clinical use.

Background: Lung cancer is the leading cause of cancer-related deaths worldwide, imposing an enormous economic burden on society. Several studies have identified a link between the genetic polymorp...

This paper introduces gMig , an open-source and practical vGPU live migration solution for full virtualization. Taking the advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy mechanism combined with the hashing based Software Dirty Page technique to achieve efficient vGPU live migration. Particularly, we propose three core techniques for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping and adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach with sampling pre-filtering to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) Overlapped Migration Process, which significantly compresses the hanging overhead by overlapping the dirty page verification and transmission concurrently. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by up to 80.0 percent . The design of sampling filter and overlapped processing can bring about further 30.0 and 10.0 percent improvements in page processing.

Background The Goto-Kakizaki (GK) rat, developed from repeated inbreeding of glucose-intolerant Wistar rats, has been widely used to explore the development of spontaneous type-2 diabetes mellitus (T2DM). However, the gastric microbiota of GK and Wistar rats are still unclear. This study aimed to understand the gastric microbiota characteristics of GK rats by comparing it with non-diabetic Wistar rats. Materials and Methods Male Wistar rats and GK rats were housed in specific pathogen-free (SPF) environment for 12 weeks with free access to sterilized food and water. Body weight and random blood glucose (BG) levels were determined. At the end of the experiment, the gastric contents of the rats were collected for the identification of gastric microbiota using 16S rRNA gene sequencing. Results The richness of gastric microbiota in GK rats was similar to that of Wistar rats (P > 0.05). The results of Shannon, Simpson, beta diversity indices, and ANOSIM analysis showed that alpha and beta diversity of gastric microbiota in GK rats were significantly lower than that of Wistar rats (P < 0.01). Firmicutes (96.0%), Proteobacteria (1.9%) and Cyanobacteria (0.8%) were the dominant gastric microbiota in GK rats accounting for 72.9%, 14.7% and 10.9%, respectively. Linear discriminant analysis effect size (LEfSe) revealed that phylum Firmicutes and four genera (Anaerovibrio, Collinsella, Prevotellaceae_UCG_001, and Lactobacillus) were significantly abundant in the stomachs of GK rats. In contrast, seven genera (unidentified_Chloroplast, Porphyromonas, Neisseria, Rubrobacter, Veillonella, Lachnospiraceae_UCG_005, and unidentified_Erysipelotrichaceae) were significantly abundant in the stomachs of Wistar rats. Blood glucose was positively correlated with Anaerobibrio and Lactobacillus, and negatively correlated with four genera (Porphyromonas, Rubrobacter, Lachnospiraceae_UCG_005, and unidentified_Erysipelotrichaceae). In addition, chemoheterotrophy and fermentation were the most important functions of gastric microbiota. Conclusion The gastric microbiota of GK rats with spontaneous T2DM showed the typical characteristics of low diversity and significant enrichment of Firmicutes phylum and four genera (Anaerovibrio, Collinsella, Prevotellaceae_UCG_001, and Lactobacillus) compared with gastric microbiota of Wistar rats.

Objectives: We aimed to observe the relationship between serum 25-hydroxyvitamin D (25-[OH] D) and different cognitive domains, and to evaluate the predictive value of 25-(OH) D level for cognitive impairment in patients with white matter lesions (WML). Methods: The differences in clinical data including 25-(OH) D were analyzed between cognitive normality (n = 87) and impairment (n = 139) groups, and variant cognitive domains were analyzed between groups of different levels of serum 25-(OH) D. Risk factors for cognitive impairments were evaluated with multivariate logistic regression analysis; a receiver operating characteristic (ROC) curve of 25-(OH) D levels was used to examine the association between 25-(OH) D and WML with cognitive dysfunction. Results: As the severity of WML increased, the proportion of patients with a low level of serum 25-(OH) D increased (p < 0.05). The total MoCA (Montreal Cognitive Assessment) scores and all domain scores except naming were significantly lower in patients with low levels of serum 25-(OH) D than in patients with high levels of serum 25-(OH) D (p < 0.05). Multivariate logistic regression analyses showed that serum 25-(OH) D levels were independently correlated with cognitive impairment. In the ROC analysis, the optimal cut-off value for 25-(OH) D was 17.53 with 76% sensitivity and 70% specificity (AUC =0.751, 95% CI: 0.674–0.819, p < 0.05). Conclusion: We observed that vitamin D deficiency is associated with multiple areas of cognitive impairment and that it is an independent risk factor for cognitive impairment in WML.

Extending photoabsorption to the near-infrared region (NIR) of the spectrum remains a major challenge for the enhancement of the photoelectric performance of perovskites. In this work, we propose a model of van der Waals heterostructures formed by CH3NH3PbI3 perovskite films and graphdiyne (GDY) to improve the photocurrent in the NIR. To obtain better insights into the properties of GDY/perovskite heterostructures, we first determine its electronic properties using the first principles calculations. The charge transfer between GDY and perovskites leads to a built-in electrical field that facilitates the separation and the transport of the photogenerated carriers. Then, the non-equilibrium Green's function (NEGF) is used to calculate the photocurrents of perovskite slabs with and without GDY. The photocurrents of GDY/perovskite heterostructures are nearly an order of magnitude larger than that of pristine perovskites in NIR due to the synergistic effect between GDY and perovskites. Furthermore, a polarization-sensitive photocurrent is obtained for a GDY/PbI2 heterostructure.

In multi-label learning, each object is represented by a single instance and associated with multiple labels simultaneously. Existing multi-label learning approaches mainly construct classification models with a fixed set of target labels ( observed labels ). However, in the big data era, it is difficult to provide a fully complete label set for a data set. In some real applications, there are multiple labels hidden in the data set, especially for those large-scale data sets. In this paper, a novel approach named MLLHL is proposed to not only discover the hidden labels in the training data but also predict these hidden labels and observed labels for unseen examples simultaneously. We assume that the observed labels are just a subset of labels which are selected from the full label set, and the rest ones are omitted by the annotators during the labeling stage. Extensive experiments show the competitive performance of MLLHL against other state-of-the-art multi-label learning approaches.

Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into several separated subgroups via clustering techniques, where the cells in a specific cluster belong to the same cell type based on gene expression patterns. In this work, we present a novel multiple kernel clustering framework for scRNA-seq data clustering via locality preserving kernel alignment. Specifically, we first generate a series of similarity kernel matrices by using different kernel functions. Then we transfer the clustering task to a multiple kernel k-means problem with the kernels aligned in a local manner, i.e., the similarity of a sample to its k-nearest neighbours are aligned with the ideal similarity matrix. In our method, the clustering process focuses on closer sample pairs that shall stay together, and avoids involving unreliable similarity evaluation for farther sample pairs. In addition, we construct a local Laplacian matrix for each sample to constrain that closer samples should be allocated similar labels. In such a manner, the local structure of the data can be well preserved and utilized to produce better alignment for clustering. An alternate updating algorithm with theoretical analysis is developed to solve the proposed problem. We evaluate the performance of the proposed method on various real scRNA-seq data, and the results show that our method can obtain superior results when compared with other state-of-the-art approaches.

Objective To determine the prognostic impact of pathologic lymph node (LN) status and investigate risk factors of recurrence in esophageal squamous cell carcinoma (ESCC) patients with pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT). Summary background data There are no large-scale prospective study data regarding ypN status and recurrence after pCR in ESCC patients receiving NCRT. Methods The NEOCRTEC5010 trial was a prospective multicenter trial that compared the survival and safety of NCRT plus surgery (S) with S in patients with locally advanced ESCC. The relationships between survival and cN, pN, and ypN status were assessed. Potential prognostic factors in patients with ypN+ and pCR were identified. Results A total of 389 ESCC patients (NCRT: 182; S: 207) were included. Patients with pN+ in the S group and ypN+ in the NCRT group had decreased overall survival (OS) and disease-free survival (DFS) compared with pN0 and ypN0 patients, respectively. Partial response at the primary site [hazard ratio (HR), 2.09] and stable disease in the LNs (HR, 3.26) were independent risk factors for lower DFS, but not OS. For patients with pCR, the recurrence rate was 13.9%. Patients with distant LN metastasis had a median OS and DFS of 16.1 months and 14.4 months, respectively. Failure to achieve the median total dose of chemotherapy was a significant risk factor of recurrence and metastasis after pCR (HR, 44.27). Conclusions Persistent pathologic LN metastasis after NCRT is a strong poor prognostic factor in ESCC. Additionally, pCR does not guarantee a cure; patients with pCR should undergo an active strategy of surveillance and adjuvant therapy.

Background IFIT2 (interferon-induced proteins with tetratricopeptide repeats 2), also known as ISG54, is an important interferon-stimulated gene family protein, which has been confirmed to play a crucial role in anti-cancer as well as anti-virus process. In the present study, we aimed to investigate the IFIT2 expression in human non-small-cell cancer (NSCLC) tissues and its clinical implications. Methods The immunohistochemistry assay was used to identify the clinical significance and prognostic value of IFIT2 expression in NSCLC tissues. The depletion of IFIT2 was achieved using RNAi approach to assess the role of IFIT2 in the regulation of biological behaviors in human lung cancer cell lines. Results Decreased IFIT2 expression was found in human NSCLC tissues (both in lung adenocarcinoma and lung squamous cell carcinoma) in contrast to the adjacent normal tissues (both P

Radiofrequency ablation (RFA) is an important strategy for treatment of advanced hepatocellular carcinoma (HCC). However, the prognostic indicators of RFA therapy are not known, and there are few strategies for RFA sensitization. The transcription factor sterol regulatory element binding protein 1 (SREBP)-1 regulates fatty-acid synthesis but also promotes the proliferation or metastasis of HCC cells. Here, the clinical importance of SREBP-1 and potential application of knockdown of SREBP-1 expression in RFA of advanced HCC was elucidated. In patients with advanced HCC receiving RFA, a high level of endogenous SREBP-1 expression correlated to poor survival. Inhibition of SREBP-1 activation using a novel small-molecule inhibitor, SI-1, not only inhibited the aerobic glycolysis of HCC cells, it also enhanced the antitumor effects of RFA on xenograft tumors. Overall, our results: (i) revealed the correlation between SREBP-1 and HCC severity; (ii) indicated that inhibition of SREBP-1 activation could be a promising approach for treatment of advanced HCC.

Background Few studies have exclusively investigated the value of pathological complete response (pCR), in esophageal squamous cell carcinoma (ESCC) patients, although it is a clinically significant parameter to evaluate the impact of neoadjuvant chemoradiotherapy (nCRT) on treatment outcome after surgery. The aim of our study was to explore the relationship between pCR after nCRT and survival among patients with local ESCC. Methods All patients receiving nCRT followed by surgery in NEOCRTEC5010-trial (NCT01216527) were included. Non-pCR patients were classified into three subgroups: ypTanyN0M0, ypT0NanyM0 and ypTanyNanyM0. The Kaplan-Meier method with log-rank test was employed to evaluate disease-free survival (DFS) and overall survival (OS). Multivariate regression analysis was performed using a Cox proportional hazards model to identify clinicopathological parameters associated with pCR. Results Among the 185 patients included, 80 (43.2%) achieved pCR after nCRT. The mean survival time of the pCR group was significantly longer than that of the non-pCR group (92.6 vs. 69.2 months; HR, 2.70; 95% CI: 1.48-4.92; P=0.001). The 5-year OS and DFS of the pCR group were 79.3% and 77% respectively, compared to 54.8% and 51.2%, respectively, in the non-pCR group. The results showed that the OS and DFS of the ypTanyN0M0 group were better than those of the ypT0NanyM0 group and the ypTanyNanyM0 group. We also found that the number of dissected lymph nodes and pCR were independent risk factors for DFS and OS rates. Conclusions pCR after nCRT is an important prognostic indicator of OS and DFS in patients with ESCC. In addition, lymph-node status could represent an important parameter in the prognostic evaluation of esophageal cancer patients.

Summary The inappropriate use of antibiotics is a severe public health problem worldwide, contributing to the emergence of multidrug-resistant (MDR) bacteria. To explore the possible impacts of the inappropriate use of antibiotics on the immune system, we use Klebsiella pneumoniae (K. pneumoniae) infection as an example and show that imipenem increases the mortality of mice infected by MDR K. pneumoniae. Further studies demonstrate that imipenem enhances the secretion of outer membrane vesicles (OMVs) with significantly elevated presentation of GroEL, which promotes the phagocytosis of OMVs by macrophages that depends on the interaction between GroEL and its receptor, lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1). OMVs cause the pyroptosis of macrophages and the release of proinflammatory cytokines, which contribute to exacerbated inflammatory responses. We propose that the inappropriate use of antibiotics in the cases of infection by MDR bacteria such as K. pneumoniae might cause damaging inflammatory responses, which underlines the pernicious effects of inappropriate use of antibiotics.

High level of uric acid (UA) is the major origin of gout, and is highly associated with various pregnant complications, such as preeclampsia and gestational diabetes. However, UA’s level and role in the very early stage of pregnancy has not been uncovered. This study aims to investigate the relevance of serum UA and decidualization, an essential process for the establishment and maintenance of pregnancy in women and mice during the early stage of pregnancy. In this study, we first proved that expression level of UA synthase xanthine dehydrogenase (XDH) is highly increased along with decidualization of endometrial stromal cells in both in vitro and in vivo models. Furthermore, serum and endometrial levels of UA are higher in mice with decidualized uterin horn and in vitro decidualized stromal cells. The existence of monosodium urate (MSU) crystal was also confirmed by immunostaining. Next, the roles of MSU on decidualization were explored by both in vitro and in vivo models. Our data shows MSU crystal but not UA enhances the decidualization response of endometrial stromal cells, via the upregulation of inflammatory genes such Ptgs2 and Il11. inhibiting of Cox-2 activity abolishes MSU crystal induced higher expression of decidualization marker Prl8a2. At last, in women, we observed enriched expression of XDH in decidua compare to non-decidualized endometrium, the serum level of UA is significantly increased in women in very early stage of pregnancy, and drop down after elective abortion. In summary, we observed an increased serum UA level in the early stage of women’s pregnancy, and proved that the increased level of UA results from the expressed XDH in decidualizing endometrium of both human and mouse, leading to the formation of MSU crystal. MSU crystal can enhance the decidualization response via inflammatory pathways. Our study has uncovered the association between UA, MSU, and decidualization during the early stage of pregnancy.

IMPORTANCE: Most older patients with esophageal cancer cannot complete the standard concurrent chemoradiotherapy (CCRT). An effective and tolerable chemoradiotherapy regimen for older patients is needed. OBJECTIVE: To evaluate the efficacy and toxic effects of CCRT with S-1 vs radiotherapy (RT) alone in older patients with esophageal cancer. DESIGN, SETTING, AND PARTICIPANTS: A randomized, open-label, phase 3 clinical trial was conducted at 23 Chinese centers between June 1, 2016, and August 31, 2018. The study enrolled 298 patients aged 70 to 85 years. Eligible participants had histologically confirmed esophageal cancer, stage IB to IVB disease based on the 6th edition of the American Joint Committee on Cancer (stage IVB: only metastasis to the supraclavicular/celiac lymph nodes) and an Eastern Cooperative Oncology Group performance status of 0 to 1. Data analysis was performed from August 1, 2020, to March 10, 2021. INTERVENTIONS: Patients were stratified according to age (

When the size information of flows is unknown, how to set the priorities of flows to reduce the latency of small flows is a challenging problem. Most of the existing works exploit multi-level feedback queue (MLFQ) to assist the priority setting of the different sizes of flows with the global data center traffic distribution. However, this can lead to a mismatch between link-level flows and MLFQ threshold settings. In this paper, we propose a dynamic MLFQ based host-to-host packet scheduling scheme, which can effectively reduce the link-level flows mismatch. First, we deploy an MLFQ at the sender to ensure small flows have higher priority than larger flows in general. Second, we design a clustering algorithm to calculate the MLFQ threshold based on the link flows information and adjust priority dynamically at the receiver. The experimental results show that our scheme can reduce the tail delay of small flows by about 13% and does not affect the performance of large flows.

The clinical outcomes of triple-negative breast cancer (TNBC) chemotherapy are unsatisfactory. Water solubility and biosafety of chemo drugs are also major barriers for achieving satisfactory treatment effect. In this study, we have reported a combinational strategy by self-assembly engineering nanodrugs PC NDs, which were composed of paclitaxel (PTX) and curcumin (Cur), for effective and safe TNBC chemotherapy. PC NDs were prepared through reprecipitation method without using any additional carriers. The PC NDs were preferentially taken up by TNBC cells and we also observed pH-related drug release. Compared with free PTX and simple PTX/Cur mixture, PC NDs have shown higher therapeutic efficiency and better prognosis while the metastasis rate was significantly lower than that of either PTX or PTX/Cur mix group. Therefore, the self-assembly engineered PC NDs might be a promising nanodrugs for efficient and safe TNBC chemotherapy.

Traditional facial recognition methods depend on a large number of training samples due to the massive turning of synaptic weights for low-level feature extractions. In prior work, a brain-inspired model of visual recognition memory suggested that grid cells encode translation saccadic eye movement vectors between salient stimulus features. With a small training set for each recognition type, the relative positions among the selected features for each image were represented using grid and feature label cells in Hebbian learning. However, this model is suitable only for the recognition of familiar faces, objects, and scenes. The model's performance for a given face with unfamiliar facial expressions was unsatisfactory. In this study, an improved computational model via grid cells for facial recognition was proposed. Here, the initial hypothesis about stimulus identity was obtained using the histograms of oriented gradients (HOG) algorithm. The HOG descriptors effectively captured the sample edge or gradient structure features. Thus, most test samples were successfully recognized within three saccades. Moreover, the probability of a false hypothesis and the average fixations for successful recognition were reduced. Compared with other neural network models, such as convolutional neural networks and deep belief networks, the proposed method shows the best performance with only one training sample for each face. Moreover, it is robust against image occlusion and size variance or scaling. Our results may give insight for efficient recognition with small training samples based on neural networks.

Gut microbial changes have been causally associated with metabolic diseases, triggering huge interest in designing gut microbiome-based therapy. Here we discuss the open questions and challenges of translating ongoing gut microbiome-based intervention strategies to safe and efficacious clinical therapies. We also consider how the accumulating insights into host–microbe interactions in metabolic regulation could be harnessed as new dimensions to strengthen the translational prospect.

Background As China’s aging trend intensifies and the proportion of empty nests increases, the health-related quality of life of the elderly is the focus of social attention. Previous studies focused on the quality of life of the elderly, rather than empty nesters, and lacked the discussion of the mechanism of influencing factors. Thus, this study aimed to explore the influencing factors of the quality of life (QOL) and interaction mechanisms among empty nesters in Shanxi Province, China, so as to provide practical reference for improving the QOL of empty nesters. Methods A total of 4901 empty nesters in Shanxi Province, China, were selected using multi-stage random cluster sampling method in this cross-sectional study. The quality of life was assessed with the Short Form 36 Health Survey (SF-36). Multiple linear regression analysis (stepwise) was performed to examine the factor associated with QOL. Structural equation model (SEM) approach was conducted to test the direct and indirect association between QOL influencing factors with QOL. Results The average QOL score of empty nesters was 63.34 (SD = 17.23). The multiple linear regression revealed that gender, age, education, monthly income, drinking status, exercise frequency, physical examination frequency, attention to daily nutritional health, relationship with a spouse and relationship with children were significant predictors of the empty nester’s QOL (P R2 = 0.128). SEM showed that behavioral lifestyle had a direct effect on QOL (β = 0.446, P β = 0.288, P β = 0.115, P Conclusion Behavioral lifestyle was the strongest influencing factor in the quality of life among empty nesters, followed by socio-economic status and family relationships. Thus, maintaining a healthy behavioral lifestyle was important to improve the QOL of the empty nesters. Our findings provide a concrete and strong reference for the formulation of targeted intervention strategies.

sBackgroundIn China, more and more older people have encountered a situation called “empty nest.” Meanwhile, the health status of empty-nest older adults is an increasing public health concern. This research aims to examine the effectiveness of Self-Mutual-Group (SMG) model in improving quality of life of the empty-nest older adults to provide a scientific evidence for improving their health.MethodsA prospective intervention study was conducted among empty-nest older adults in Taiyuan, Shanxi. Multi-stage stratified random cluster sampling was employed to selected participants. A total of 396 empty nesters were enrolled as participants, of which 204 and 192 were in the intervention and control group, respectively. The intervention group received a seven-month SMG-based intervention. A participant’s quality of life was measured at the baseline and seven months after using the Short Form 36-Item Health Survey (SF-36).ResultsNo significant difference was found between the intervention and control groups in terms of participant characteristics at baseline (P > 0.05). After the intervention, participants’ scores on Mental Component Summary (MCS), Physical Component Summary (PCS), role emotional (RE), vitality (VT), social function (SF), mental health (MH) and general health (GH) increased significantly in the intervention group. Additionally, these scores differed significantly from those in the control group (P ConclusionThis study showed that the SMG-based health management is effective in improving quality of life among empty-nest older adults after seven months.Trial registrationStudy on the ‘SMG’ Health Management Model Based on Community Organization Theory among empty-nest older adults (ChiCTR1800015884). Registration date: 26-04-2018. Retrospectively registered.