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Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregation model consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines.

Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling, which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec, which introduces two graphs in modeling: (1) a knowledge graph connecting diseases, medicines, symptoms, and examinations; (2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines, which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other.

Glioblastoma (GBM) is the most devastating brain tumor and highly resistant to conventional chemotherapy. Herein, we introduce biomimetic nanosonosensitizer systems (MDNPs) combined with noninvasive ultrasound (US) actuation for orthotopic GBM-targeted delivery and sonodynamic-enhanced chemotherapy. MDNPs were fabricated with biodegradable and pH-sensitive polyglutamic acid (PGA) and the chemotherapeutic agent and sonosensitizer doxorubicin (DOX), camouflaged with human GBM U87 cell membranes. MDNPs presented homologous targeting accumulation and in vivo long-term circulation ability. They effectively passed through the blood-brain barrier (BBB) under US assistance and reached the orthotopic GBM site. MDNPs exhibited controllable US-elicited sonodynamic effect by generation of reactive oxygen species (ROS). ROS not only induced cancer cell apoptosis but also downregulated drug-resistance-related factors to disrupt chemoresistance and increase sensitivity to chemotherapy. The in vivo study of orthotopic GBM treatments further proved that MDNPs exhibited US-augmented synergistic antitumor efficacy and strongly prolonged the survival rate of mice. The use of low-dose DOX and the safety of US enabled repeated treatment (4 times) without obvious cardiotoxicity. This effective and safe US-enhanced chemotherapy strategy with the advantages of noninvasive brain delivery and high drug sensitivity holds great promise for deep-seated and drug-resistant tumors.

The gut microbiota is known to be associated with the regulation of many neurological diseases and behaviors, including chronic pain. However, it is unclear whether the gut microbiota is critical to the itch sensation. In this study, we investigated the effects of gut microbiota depletion on acute itch.First, an antibiotic cocktail was orally administered to deplete the gut microbiota in male C57BL/6 mice. Then, pruritogens were intradermally injected to induce acute itch behavior. In addition, antibiotic-treated mice received transplantation of fecal microbiota from untreated mice, followed by tests for acute itch. The changes in c-Fos expression in trigeminal ganglia (TG) neurons were also investigated by immunofluorescence staining.Our results indicated that chronic antibiotic treatment significantly reduced the diversity and richness of the gut microbiota of mice. Compared to vehicle-treated mice, antibiotic-treated mice showed reductions in acute itch behavior induced by compound 48/80, chloroquine (CQ), and serotonin (5-HT), respectively. Moreover, repositioning of microbiota reversed the reductions in acute itch behavior in antibiotic-treated mice. In addition, immunofluorescence staining revealed that antibiotic-treated mice displayed decreased c-Fos expression in ipsilateral TG compared to controls.Our study, for the first time, discovered that antibiotic-induced gut microbiota depletion could reduce acute itch behavior, which may be connected with decreased TG neuronal activity.

Mounting evidence indicates that the neuropeptide FF (NPFF) system is involved in the side effects of opioid usage, including antinociceptive tolerance, hyperalgesia, abuse, constipation, and respiratory depression. Our group recently discovered that the multitarget opioid/NPFF receptor agonist DN-9 exhibits peripheral antinociceptive activity. To improve its metabolic stability, antinociceptive potency, and duration, in this study, we designed and synthesized a novel cyclic disulfide analogue of DN-9, OFP011, and examined its bioactivity through

In patients with nonvalvular atrial fibrillation (NVAF), the impact of left ventricular diastolic function on the risk of left atrial appendage (LAA) thrombus has rarely been studied. This prospective study aimed to investigate the relationship between diastolic function and the risk of LAA thrombus in patients with NVAF. Seventy-six patients with NVAF admitted to receive radiofrequency catheter ablation were prospectively enrolled. All the patients underwent transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) in 24 h before ablation. Diastolic function was estimated by TTE including left atrial volume index (LAVI) and E/e', as well as invasive left atrial pressure (LAP) measured during the ablation procedure. LAA peak emptying velocity (LAA-PEV) and the intensity of spontaneous echo contrast (SEC) were determined by TEE. Average E/e', LAVI and mean LAP had a significant positive correlation with the intensity of SEC, the coefficient of correlation were 0.344 (p = 0.002), 0.416 (p 0.001) and 0.402 (p 0.001), respectively. After adjustment for CHA

Purpose This study aimed to assess the relationship between the cardiac rhythm response to ibutilide infusion after pulmonary vein isolation and the recurrence of long-term atrial arrhythmias. Methods One hundred and thirty-eight patients with non-paroxysmal atrial fibrillation who had had their first catheter ablation were retrospectively included. All patients whose atrial fibrillation did not terminate after pulmonary vein isolation were administered intravenous ibutilide (1.0 mg). Those with termination of atrial fibrillation after ibutilide administration were defined as responders (n = 86); those without termination of atrial fibrillation, as non-responders ( n = 52). The primary endpoint was any documented recurrence of atrial arrhythmia lasting more than 30 seconds after the initial catheter ablation. Results Conversion of atrial fibrillation to sinus rhythm, directly or via atrial flutter, with ibutilide administration was achieved in 62.3% of patients. A longer duration of atrial fibrillation was associated with failed termination of atrial fibrillation (odds ratio 1.009, 95% confidence interval 1.002-1.017, p = 0.011). During a median follow-up period of 610 days (interquartile range 475-1 106) post ablation, non-responders (n = 24, 46.2%) had a higher recurrence rate of atrial arrhythmia than the responders (n = 26, 30.2%; log-rank, p = 0.011) after the initial catheter ablation. Multivariate Cox regression analysis revealed that non-responders (hazard ratio 1.994, 95% confidence interval 1.117-3.561, p = 0.020) was significantly correlated with recurrence of atrial arrhythmias. Conclusions In patients whose atrial fibrillation persisted after pulmonary vein isolation, the response to ibutilide administration could predict the recurrence of atrial arrhythmias after catheter ablation, which may be useful for risk stratification for recurrence of atrial fibrillation and individualised management of atrial fibrillation.

Objective. This study promotes the comparison and research of Chinese literature and Japanese literature from the perspective of the ecological environment of sustainable development. Methods. This study analyzes and compares the differences in the literary cultures of different countries. After the analysis, it is found that the literary culture has never been solid and conservative. We should look at the national literary culture and national differences from the perspective of development. Results. The results show that in the research of Chinese and Japanese comparative literature from the perspective of a sustainable ecological environment, it is found that through the comparison and analysis of concept differences, thinking differences, behavior differences, and language differences in Chinese and Japanese comparative literature, it can be seen that Chinese literature has a more far-reaching impact on national quality education than Japanese literature, which is more progressiveness and has a reference value. Conclusion. The analysis and research of Chinese and Japanese comparative literature cannot be underestimated in promoting the development and progress of the country, and it can provide a good foundation for promoting the establishment of a bridge of friendly exchange between Chinese and Japanese nationalities so as to shorten the distance between each other.

Objectives Early acute kidney injury (AKI) after burn contributes to disastrous prognoses for severely burned patients. Burn-induced renal oxidative stress and secondary proinflammatory mediator release contribute to early AKI development, and Toll-like receptor (TLR) 4 regulates inflammation. Heme oxygenase-1 (HO-1) is a stress-responsive enzyme that plays a vital role in protecting against ischemia-induced organ injury via its antioxidant properties and regulation of inflammation. We investigated the potential effect of HO-1 induction in preventing burn-induced early AKI and its related mechanism. Methods A classic major-burn rat model was established using a 100 °C water bath, and hemin was injected intraperitoneally immediately after the injury to induce HO-1. Histological staining and blood tests were used to assess AKI progression based on structural changes and function. Renal levels of HO-1, oxidative stress, proinflammatory mediators and TLR4-related signals were detected using ELISA, immunostaining, qRT-PCR, and western blotting. The selective TLR4 inhibitor TAK242 and TLR4 inducer LPS were introduced to determine the roles of HO-1 in burn-related renal inflammation and the TLR4 pathway. Results Hemin improved burn-induced renal histological damage and dysfunction, and this beneficial effect was related to reduced renal oxidative stress and the release of proinflammatory mediators, such as tumor necrosis factor-alpha (TNF-α), interleukin (IL)-1β, IL-6 and intracellular adhesion molecule-1 (ICAM-1). Hemin downregulated the expression of TLR4 and the subsequent phosphorylation of IKKα/β, IκBα, and NF-κB p65;. TAK242 exerted an effect similar to but weaker than hemin; and LPS reversed the antiinflammatory effect of hemin and the regulation of TLR4 signals. These results suggested that the TLR4 signaling pathway mediated the HO-1-facilitated regulation of renal inflammation after burn. Conclusion The present study demonstrated that HO-1 induction prevented burn-induced early AKI by targeting renal inflammation, which was mediated via regulation of the TLR4/NF-κB signaling pathway.

With the rapid development of social media platforms and the increasing scale of the social media data, the rumor detection task has become vitally important since the authenticity of posts cannot be guaranteed. To date, many approaches have been proposed to facilitate the rumor detection process by utilizing the multi-task learning mechanism, which aims to improve the performance of rumor detection task by leveraging the useful information contained in stance detection task. However, most of the existing approaches suffer from three limitations: (1) only focus on the textual content and ignore the multi-modal information which is key component contained in social media data; (2) ignore the difference of feature space between the stance detection task and rumor detection task, resulting in the unsatisfactory usage of stance information; (3) largely neglect the semantic information hidden in the finegrained stance labels. Therefore, in this paper, we design a Multi-modal Meta Multi-Task Learning (MM-MTL) framework for social media rumor detection task. To make use of multiple modalities, we design a multi-modal post embedding layer which considers both textual and visual content. To overcome the feature sharing problem of the stance detection task and rumor detection task, we propose a meta knowledge-sharing scheme to share some higher meta network layers and capture the metaknowledge behind the multi-modal post. To better utilize the semantic information hidden in the fine-grained stance labels, we employ the attention mechanism to estimate the weight of each reply. Extensive experiments on two Twitter benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of microvideo sharing platforms. Traditional approaches consider microvideo recommendation as a matching task and ignore the rich relationships among users and micro-videos from various modalities (e.g., visual, acoustic, and textual). Recently, GNNbased approaches show promising performance for the microvideo recommendation task. However, they mainly focus on the homogeneous graph which includes only one type of nodes or relations, and cannot be applied to the heterogeneous graph which consists of users, micro-videos, and related multi-modal information. In this paper, we propose a novel Heterogeneous Hierarchical Feature Aggregation Network (HHFAN) for personalized micro-video recommendation. Our goal is to explore the highly complicated relationship information among users, micro-videos and related multi-modal information from a modality-aware Heterogeneous Information Graph (M-HIG), and thus generate highquality user and micro-video embeddings for recommendation. The proposed model consists of two key components: (1) In data structure level, we build a heterogeneous graph and utilize a random walk based sampling strategy to sample neighbors for users and micro-videos. (2) In representation learning level, we design a hierarchical feature aggregation network including the intra- and inter-type feature aggregation networks to better capture the complex structure and rich semantic information in the heterogeneous graph. We evaluate our method on two realworld datasets and the results demonstrate that the proposed model outperforms the baseline methods.

Background Low voltage zones (LVZ) are associated with poor outcomes in patients with atrial fibrillation (AF). The APPLE and DR-FLASH scores predict LVZ in patients undergoing catheter ablation. This study aimed to assess the relationship of mitral valve regurgitation (MR) and LVZ after adjusting for APPLE or DR-FLASH scores. Methods This was a retrospective study on patients with AF who underwent their first catheter ablation. All patients underwent a transthoracic echocardiographic examination before ablation. The APPLE and DR-FLASH scores were calculated at baseline. LVZ determined by high-density mapping was defined as bipolar voltage amplitude 5% of the left atrial surface area. Results Altogether, 152 patients (mean age 62.0 ± 10.8 years, 65.8% men, and 36.2% with persistent AF) were included. Of the 152 patients, 47 (30.9%) had LVZ. The patients with LVZ had more moderate-to-severe MR (17.0% vs. 3.8%, P = 0.014) and higher APPLE scores (1.7 ± 1.1 vs. 1.2 ± 1.1, P = 0.009) and DR-FLASH scores (3.0 ± 1.5 vs. 2.4 ± 1.4, P = 0.010). Using multivariate logistic regression analysis, we found moderate-to-severe MR was related to LVZ presence after adjusting for the APPLE (OR 4.040, P = 0.034) or DR-FLASH (OR 4.487, P = 0.020) scores. Furthermore, moderate-to-severe MR had an incremental predictive value for LVZ presence in addition to the APPLE (P = 0.03) or DR-FLASH (P = 0.02) scores. Conclusion In patients with AF, MR severity was related to LVZ after adjusting the APPLE score or DR-FLASH score.

With the growing amount of multimodal data, cross-modal retrieval has attracted more and more attention and become a hot research topic. To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities. However, these approaches may suffer from the following limitations: 1) They overcome the modality gap by introducing loss in the common representation space, which may not be sufficient to eliminate the heterogeneity of various modalities; 2) They treat labels as independent entities and ignore label relationships, which is not conducive to establishing semantic connections across multimodal data; 3) They ignore the non-binary values of label similarity in multi-label scenarios, which may lead to inefficient alignment of representation similarity with label similarity. To tackle these problems, in this paper, we propose two models to learn discriminative and modality-invariant representations for cross-modal retrieval. Firstly, the dual generative adversarial networks are built to project multimodal data into a common representation space. Secondly, to model label relation dependencies and develop inter-dependent classifiers, we employ multi-hop graph neural networks (consisting of Probabilistic GNN and Iterative GNN), where the layer aggregation mechanism is suggested for using propagation information of various hops. Thirdly, we propose a novel soft multi-label contrastive loss for cross-modal retrieval, with the soft positive sampling probability, which can align the representation similarity and the label similarity. Additionally, to adapt to incomplete-modal learning, which can have wider applications, we propose a modal reconstruction mechanism to generate missing features. Extensive experiments on three widely used benchmark datasets, i.e., NUS-WIDE, MIRFlickr, and MS-COCO, show the superiority of our proposed method.

Supplementary Material and Methods. The file includes descriptive methodology and contains supplementary table 1 with primer sequences used throughout the paper.

Supplementary Figure 3. Rapamycin-preconditioned CD8+ T cells upregulate the levels of antioxidant molecules

Supplementary Figure 1. Differential apoptosis in CD62Llo and CD62Lhi CD8+ T cells

Supplementary Figure 4. Schematic presentation summarizing anti-tumor potential of c-SHhi cells.

IntroductionPsoriasis is a common skin disease that seriously affects patients' quality of life. The association of air pollutants with psoriasis, and the extent of their effects remains unclear.MethodsBased on a distributed lag non-linear model, this study explored the short-term effects of air pollutants on outpatients with psoriasis in Hefei, China, between 2015 and 2019 by analyzing the exposure–lag–response relationship, after controlling for confounding influences such as meteorological factors, long-term trends, day of the week, and holidays. Stratified analyses were performed for patients of different ages and genders.ResultsThe maximum relative risks of psoriasis outpatients' exposure to SO2, NO2, and O3 were 1.023 (95% confidence intervals (CI): 1.004–1.043), 1.170 (95% CI: 1.046–1.307), and 1.059 (95% CI: 1.030–1.090), respectively. An increase of 10 μg/m3 of NO2 was associated with a 2.1% (95% CI: 0.7–3.5%) increase in outpatients with psoriasis, and a decrease of 10 μg/m3 of O3 was associated with an 0.8% (95% CI: 0.4–1.2%) increase in outpatients with psoriasis. Stratified analyses showed that male subjects were more sensitive to a change in meteorological factors, while female subjects and outpatients with psoriasis aged 0–17 years old were more sensitive to a change in air pollutants.DiscussionShort-term air pollutant exposures were associated with outpatients having psoriasis, suggesting that patients and high-risk people with psoriasis should reduce their time spent outside and improve their skin protection gear when air quality is poor.

The optimal treatment for vitreoretinal lymphoma (VRL) remains a challenge, as central nervous system (CNS) relapse occurs frequently, leading to the worst impact on survival. We previously proposed combined intravitreal methotrexate and systemic high-dose methotrexate therapy for this disease. This study aimed to report the long-term outcomes of patients with VRL using this combination treatment.We conducted a retrospective cohort study on patients with VRL at a tertiary referral center between 2003 and 2018.Thirty-two patients were included, of whom 23 had primary VRL (PVRL) and nine had concurrent intraocular and CNS diseases. The treatment was well tolerated. Twenty-six (81.3%) patients achieved complete response (CR). After a median follow-up time of 103.5 months, the 5-year survival rate was 73.3%, whereas the 5-year progression-free survival (PFS) rate was 29.9%. Twenty-four (75%) patients relapsed, including 12 with isolated intraocular relapses at first relapse and a total of 17 with CNS/systemic relapses. The development of CNS/systemic relapse negatively affected survival, but intraocular relapse did not. The median CNS/systemic PFS was 69.5 months, but the risk of CNS/systemic relapse increased steadily with a cumulative incidence rate at 2, 5, and 10 years being 22.6%, 44.2%, and 65%, respectively. Multivariate analysis identified concurrent CNS disease at diagnosis as the only poor-risk factor for CNS/systemic relapse.This study confirms good efficacy and acceptable toxicities of the combination approach. However, incorporation of further intensive consolidation strategies into the treatment protocol to effectively prevent subsequent CNS/systemic relapse deserves to be considered.

Background SARS-CoV-2 subtypes Alpha, Delta, Omicron BA.1, Omicron BA.2, Omicron BA.2.12.1, and Omicron BA.4/BA.5 have significant differences in transmission and immune escape ability. Currently, no effective detection methods are available for these subtypes. Routine detection methods are prone to missed detection. Methods In this study, a rapid detection method based on ARMS-PCR and molecular beacon probes was developed for the identification of SARS-CoV-2 subtypes Alpha, Delta, Omicron BA.1, Omicron BA.2, Omicron BA.2.12.1, and Omicron BA.4/5. Specific primers and probes were designed and validated using gel electrophoresis, real-time fluorescence quantitative PCR, and molecular hybridization. Results ARMS-PCR and molecular beacon probe-based assays can be applied in RT-PCR and fluorescence assays to differentiate SARS-CoV-2 subtypes Alpha, Delta, Omicron BA.1, Omicron BA.2, Omicron BA.2.12.1, and Omicron BA.4/5. Conclusions In the present study, we developed a simple, rapid and accurate detection method based on ARMS-PCR and molecular beacon probes for rapid genotyping of SARS-CoV-2 subtypes Alpha, Delta, Omicron BA.1, Omicron BA.2, Omicron BA.2.12.1, and Omicron BA.4/5. It can be used in real-time fluorescence quantitative PCR and molecular hybridization to identify subtypes of COVID-19, effectively improving the detection rate to provide guidance for disease prevention and treatment.

The most commonly used opioid analgesics are limited by their severe side-effects in the clinical treatment of pain. Preliminary reports indicate that the combination of classical opioids and N/OFQ receptor (NOP) ligands may be an effective strategy to reduce unwanted side-effects and improve antinociception. But the interaction of these two receptor ligands in pain regulation at the peripheral level remains unclear. In this study, the antinociception of a designed amide analogue of the mu opioid receptor (MOP) peptide agonist DAMGO, DAMGO-NH

The increased expression of programmed death-ligands 1 and 2 (PD-L1 and PD-L2, respectively) on tumour cells contributes to immune evasion, suggesting that these proteins are attractive therapeutic targets. This study aimed to evaluate the validity of cerebrospinal fluid (CSF) soluble PD-L1 (sPD-L1) and soluble PD-L2 (sPD-L2) as biomarkers for primary central nervous system lymphoma (PCNSL). We determined the CSF concentrations of sPD-L1 and sPD-L2 in 46 patients with PCNSL using enzyme-linked immunosorbent assays (ELISAs). A control group comprised 153 patients with other brain tumours, inflammatory/infectious status, or neurodegenerative diseases. Only CSF sPD-L1 levels were significantly higher in patients with PCNSL relative to the controls. CSF sPD-L1 also exhibited superior overall discrimination performance compared to CSF sPD-L2 in diagnosing PCNSL. Compared with patients with PCNSL with low CSF sPD-L1 levels, more patients with high levels had high serum lactate dehydrogenase levels, leptomeningeal involvement, and deep-brain involvement. Furthermore, CSF sPD-L1 could predict poor survival in PCNSL but CSF sPD-L2 could not. Intriguingly, CSF sPD-L1 levels were correlated with disease status and their dynamic changes post treatment could predict time to relapse. In conclusion, this study identified CSF sPD-L1 as a promising prognostic biomarker, indicating a therapeutic potential of PD-L1 blockade in PCNSL.

Background Breast reconstruction is an effective technique to rebuild the appearance of the breasts in patients after mastectomy and improves the prognosis. The current study aimed to compare and analyze willingness for breast reconstruction after breast cancer between populations in China and the United States, from the perspective of social concern, using big data analysis. We also aimed to explore factors affecting surgical selection and to identify methods that can improve social cognition and acceptance of breast reconstruction. Methods Using Baidu and Google, two representative Internet search engines in China and the United States as research tools, and using big data search volume as the benchmark, we compared and analyzed breast reconstruction willingness and attention characteristics between Chinese and American people, based on search heat, geographical distribution, age and sex, keyword distribution, ethnic group, and social development degree. Results In both the long-term and short-term, Chinese people paid more attention towards searching about breast cancer, but less attention to breast reconstruction after breast cancer surgery. However, in both the short-term and long-term, people from the United States paid more attention towards breast cancer and breast reconstruction with the help of the Internet, showing a synchronous change relationship. There was a large regional difference in the search volume for breast cancer among the Chinese population, while no significant regional differences were noted in the search volume for breast cancer in the United States. However, a large regional difference was observed in the search volume for breast reconstruction between the two countries; people in the coastal and economically developed areas paid more attention to it. Most people who paid attention to breast reconstruction in China were women aged 20–39 years, while the attention among men was low. Search keywords were also limited to breast cancer-related information. However, between Asians and European Americans, Americans paid more attention to breast cancer and were affected by regional development, religious beliefs, and health facilities. Conclusion Attention towards breast reconstruction after breast cancer was lower in the Chinese population than in the American population, and this difference was closely related to the level of regional development. There is insufficient information on breast reconstruction after breast cancer in recent Internet media. In addition to strengthening communication in clinics, media education is important to improve the cognitive level and social awareness of patients and their families, which is conducive to breast reconstruction.

We previously reported that a multifunctional opioid/neuropeptide FF receptor agonist, DN-9, achieved peripherally restricted analgesia with reduced side effects. To develop stable and orally bioavailable analogues of DN-9, eight lactam-bridged cyclic analogues of DN-9 between positions 2 and 5 were designed, synthesized, and biologically evaluated. In vitro cAMP assays revealed that these analogues, except 7, were multifunctional ligands that activated opioid and neuropeptide FF receptors. Analogue 1 exhibited improved potency for κ-opioid and NPFF2 receptors. All analogues exhibited potent, long-lasting, and peripherally restricted antinociception in the tail-flick test without tolerance development after subcutaneous administration and produced oral analgesia. Oral administration of the optimized compound analogue 1 exhibited powerful, peripherally restricted antinociceptive effects in mouse models of acute, inflammatory, and neuropathic pain. Remarkably, orally administered analogue 1 had no significant side effects, such as tolerance, dependence, constipation, or respiratory depression, at effective analgesic doses.

The Knl1-Mis12-Ndc80 (KMN) network genes (including KNL, MIS12 and NDC80 complexes) encode a highly conserved network of protein complexes that act in cell mitosis. In recent years, multiple studies revealed that KMN network genes also play a vital role in tumor appearance and growth. However, the role of the KMN gene network in non-small cell lung cancer (NSCLC) remains unknown. In this study, we analyzed the effects of KMN genes expression and clinical phenotype in patients with lung adenocarcinoma (LUAD). The expression of KMN network genes and related clinical information was extracted from The Cancer Genome Atlas. The samples were classified into cluster I and II by consistent clustering. We analyzed the gene distribution by principal component analysis, and the potential risk characteristics were analyzed using the least absolute shrinkage and selection operator Cox regression algorithm. Univariate and multivariate Cox regression analyses were used to analyze the clinical information. The Database for Annotation, Visualization, and Integrated Discovery, Gene MANIA and gene set enrichment analysis were used to analyze function and correlation among genes of the KMN network. The expression levels of nine out of ten KMN genes were significantly up-regulated in LUAD and were associated with poor overall survival (OS). Higher expression of NDC80 and KNL1 was related to low OS in both univariate and multivariate analyses. According to two independent prognostic KMN network genes (KNL1 and NDC80), a risk signature was established to predict the prognosis of patients with LUAD. Additionally, the genes NDC80 and KNL1 were considerably enriched in pathways associated with signaling pathways, biological processes, and the cell cycle. The results indicate that KMN network genes are intimately related to lung adenocarcinoma. KMN network genes are involved in the malignant process of LUAD. Assessment of NDC80 and KNL1 might be helpful for prognostic stratification and treatment strategy development.

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

Studies have reported a positive association between hepatitis B surface antigen (HBsAg)-positive hepatitis B virus (HBV) infection and follicular lymphoma (FL). Nevertheless, clinical information concerning chronic HBV infection in FL is sparse. This retrospective cohort study investigated the prognostic impact of HBsAg in immunocompetent patients with FL treated with frontline rituximab-containing chemoimmunotherapy in an HBV-endemic area between 2006 and 2016. Among the 149 analyzed patients, 32 (21.5%) were HBsAg-positive. HBsAg positivity was positively associated with symptomatic splenomegaly, significant serous effusions, and peritreatment hepatic dysfunction. HBsAg-positive patients had a trend of lower complete remission rate (59.4% vs. 76.9%, P = 0.07), significantly poorer overall survival (hazard ratio for death, 2.68; 95% confidence interval, 1.21–5.92), and shorter progression-free survival than had HBsAg-negative patients. Multivariate analysis revealed that HBsAg is an independent adverse prognostic factor for overall survival. Intriguingly, HBsAg-positive patients had a higher incidence of progression of disease within 24 months (POD24) than had HBsAg-negative patients (cumulative incidence rate, 25.8% vs. 12.4%, P = 0.045). This study revealed that patients with FL and chronic HBV infection represent a distinct subgroup with a markedly poor prognosis. HBsAg was positively associated with POD24 and might serve as a new prognostic predictor of the survival of FL patients in endemic regions for HBV infection.

Background Macrophages are the major components of tumour microenvironment, which play critical roles in tumour development. Long noncoding RNAs (lncRNAs) also contribute to tumour progression. However, the potential roles of lncRNAs in modulating the interaction between cancer cells and macrophages in hepatocellular carcinoma (HCC) are poorly understood. Methods The expression of lncRNA ZNNT1 in tissues and cells was measured using qRT-PCR. The roles of ZNNT1 in HCC cells and macrophages were investigated using in vitro and in vivo assays. The molecular mechanisms of ZNNT1 were explored using qRT-PCR, RNA immunoprecipitation, RNA pull-down, chromatin immunoprecipitation, enzyme linked immunosorbent assay, and dual-luciferase reporter assays. Results ZNNT1 was identified as an HCC-related lncRNA, which was upregulated and associated with poor prognosis of HCC. ZNNT1 promoted HCC cellular growth, migration, and invasion, and suppressed apoptosis in vitro. ZNNT1 promoted HCC xenograft growth in vivo. Furthermore, ZNNT1 recruited and induced M2 polarization of macrophages. Mechanistically, ZNNT1 upregulated SPP1 expression and osteopontin (OPN) secretion via sponging miR-181a/b/c/d-5p and miR-33a/b-5p. Functional rescue assays identified OPN as the mediator of the oncogenic roles of ZNNT1 in HCC cells and also the effects of ZNNT1 on macrophages. M2 Macrophages-recruited by ZNNT1 enhanced malignant phenotypes of HCC cells, which was mediated by S100A9 secreted by M2 macrophages. Intriguing, S100A9 secreted by M2 macrophages also upregulated ZNNT1 expression in HCC cells via AGER/NF-κB signaling. Conclusions ZNNT1, OPN, and S100A9 formed a positive feedback loop, which promoted macrophages recruitment and M2 polarization, and enhanced malignant features of HCC cells. The ZNNT1/OPN/S100A9 feedback loop represents potential therapeutic target for HCC.

Several studies have indicated an association between serum copper and zinc and chronic kidney disease, while there was antagonism between serum copper and zinc, and the serum copper/zinc ratio was more accurate than detecting the two elements alone. This study aims to evaluate the associations of serum copper, zinc and copper/zinc rate with the risk of CKD using the 2011–2016 National Health and Nutritional Examination Surveys (NHANES). A total of 7764 participants were included in the analyses. Weighted multivariable logistic regression was applied to investigate the association between serum zinc and copper level and CKD after adjusting for potential confounders. A restricted cubic spline (RCS) to explore the relationships and trend between copper/zinc ratio and the risk of developing CKD. Machine learning of the XGBoost algorithm model was used to assess each variable’s contribution to the risk of having CKD. In this study, when the serum copper/zinc level decreased to less than 1.2, the risk of having CKD was reduced to 0.57 (95%CI, 0.39–0.84, p = 0.006). The RCS model showed a negative liner correlation between serum copper/zinc ratio and CKD risk (p

Syncope may have many different causes, requiring careful identification. Recurrent syncope is uncommon as an initial symptom of neck lymphoma. Head and neck tumors involving the carotid artery cause syncope associate with carotid sinus syndrome. We report the case of a 72-year-old man who suffered from recurrent syncope due to compression of the right carotid sinus by diffuse large B-cell lymphoma and was successfully treated with immunochemotherapy. Syncope may be an early or sole sign of a neck or head tumor. We should be aware of the possibility of an underlying malignancy in patients with unexplained syncope after initial evaluation.

Venom-derived Na

Cross-modal retrieval has become an active study field with the expanding scale of multimodal data. To date, most existing methods transform multimodal data into a common representation space where semantic similarities between items can be directly measured across different modalities. However, these methods typically suffer from following limitations: 1) They usually attempt to bridge the modality gap by designing losses in the common representation space which may not be sufficient to eliminate potential heterogeneity of different modalities in the common space. 2) They typically treat labels as independent individuals and ignore label relationships which are important for constructing semantic links between multimodal data. In this work, we propose a novel Dual Adversarial Graph Neural Networks (DAGNN) composed of the dual generative adversarial networks and the multi-hop graph neural networks, which learn modality-invariant and discriminative common representations for cross-modal retrieval. Firstly, we construct the dual generative adversarial networks to project multimodal data into a common representation space. Secondly, we leverage the multi-hop graph neural networks, in which a layer aggregation mechanism is proposed to exploit multi-hop propagation information, to capture the label correlation dependency and learn inter-dependent classifiers. Comprehensive experiments conducted on two cross-modal retrieval benchmark datasets, NUS-WIDE and MIRFlickr, indicate the superiority of DAGNN.