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Abstract Background Pancreatic ductal adenocarcinoma tumors exhibit resistance to chemotherapy, targeted therapies, and even immunotherapy. Dendritic cells use glucose to support their effector functions and play a key role in anti-tumor immunity by promoting cytotoxic CD8+ T cell activity. However, the effects of glucose and lactate levels on dendritic cells in pancreatic ductal adenocarcinoma are unclear. In this study, we aimed to clarify how glucose and lactate can impact the dendritic cell antigen-presenting function and elucidate the relevant mechanisms. Methods Glycolytic activity and immune cell infiltration in pancreatic ductal adenocarcinoma were evaluated using patient-derived organoids and resected specimens. Cell lines with increased or decreased glycolysis were established from KPC mice. Flow cytometry and single-cell RNA sequencing were used to evaluate the impacts on the tumor microenvironment. The effects of glucose and lactate on the bone marrow-derived dendritic cell antigen-presenting function were detected by flow cytometry. Results The pancreatic ductal adenocarcinoma tumor microenvironment exhibited low glucose and high lactate concentrations from varying levels of glycolytic activity in cancer cells. In mouse transplantation models, tumors with increased glycolysis showed enhanced myeloid-derived suppressor cell infiltration and reduced dendritic cell and CD8+ T cell infiltration, whereas tumors with decreased glycolysis displayed the opposite trends. In three-dimensional co-culture, increased glycolysis in cancer cells suppressed the antigen-presenting function of bone marrow-derived dendritic cells. In addition, low-glucose and high-lactate media inhibited the antigen-presenting and mitochondrial functions of bone marrow-derived dendritic cells. Conclusions Our study demonstrates the impact of dynamic glycolytic reprogramming on the composition of immune cells in the tumor microenvironment of pancreatic ductal adenocarcinoma, especially on the antigen-presenting function of dendritic cells.

Abstract The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body. Among the 8 nodes, node number 1 is located in the center of the grid (the center of the human body). The number of rounds is 9000 and the nodes are adopted with the initial energy of the nodes of 0.5 J and the radio range of 10 m. MATLAB software was used to simulate the WBAN network, which consists of IoT sensors embedded in the human body. The eight-item health assessment tool takes the following into account: pulse rate, blood pressure (mm Hg), serum cholesterol (mg/dl), temperature (°C), exercise-induced angina, and exercise-induced ST-wave depression, major blood vessels are counted using a medical procedure called endoscopy that involves examining the alveoli, which are small air sacs in the lungs where gas exchange occurs. We compared the number of major vessels at rest with the maximal heart rate during activity. The sensors were responsible for sending this data to the health center (base station). The data collected from the installation of these 8 sensors on 303 patients were collected and evaluated by machine learning method using MLP neural network method. Finally, it can be claimed that the present study has provided an automated method of determining the health of people using the IoT in a way that provides a state of health with an accuracy of over 99% and can be used in medical centers.

Ingestion of breast milk represents the primary exposure pathway for endocrine-disrupting chemicals (EDCs) in newborns. To elucidate the associated risks, it is essential to quantify EDC levels in both breast milk and infant urine. This study measured the concentrations of 13 EDCs, including parabens (methyl paraben (MP), ethyl paraben (EP), propyl paraben (PP), iso-propyl paraben, butyl paraben, and iso-butyl paraben), bisphenols (bisphenol A (BPA), bisphenol F, bisphenol S, bisphenol AF, and bisphenol Z), triclosan (TCS), and triclocarban, in breast milk and infant urine to assess their potential health effects and endocrine disruption risks. In total, 1 014 breast milk samples were collected from 20 cities across China, along with 144 breast milk samples and 134 urine samples from a mother-infant cohort in Hangzhou. The EDCs were detected using ultra-high-performance liquid chromatography-triple quadrupole mass spectrometry. Endocrine-disrupting potency was evaluated using a predictive method based on EDC affinity for 15 hormone receptor proteins. The toxicological priority index (ToxPi), incorporating population exposure data, was employed to assess health risks associated with exposure to multiple EDCs. Among the 13 EDCs, MP, EP, PP, BPA, and TCS were detected in over 50 % of breast milk samples, with the highest median concentrations observed for MP (0.37 ng/mL), EP (0.29 ng/mL), and BPA (0.17 ng/mL). Across the 20 cities, 0 %–40 % of infants had a hazard index (HI) exceeding 1. Based on affinity prediction analysis and estimated exposure, cumulative endocrine disruption risk intensity was ranked as MP > TCS > BPA > EP > PP. This research highlights the extensive exposure of Chinese infants to EDCs, offering a detailed analysis of their varying endocrine disruption potencies and underscoring the significant health risks associated with EDCs in breast milk.

Abstract Background Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. Results To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. Conclusions The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

Abstract In China, there has been a persistent upward trend in the incidence and mortality rates of colorectal cancer (CRC), with CRC ranking second in incidence and fifth in mortality among all malignant tumors. Although circular RNAs (circRNAs) have been implicated in the progression of various cancers, their specific role in CRC progression remains largely unexplored. The objective of this study was to elucidate the role and underlying mechanisms of circXRN2 in CRC. Differential expression of circXRN2 was identified through whole transcriptome sequencing. The expression levels of circXRN2 and miR-149-5p were quantified in CRC tissues, corresponding adjacent normal tissues, and CRC cell lines using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The stability of circXRN2 was confirmed through RNase R and actinomycin D experiments. The binding interaction between circXRN2 and miR-149-5p was validated through RNA pull-down, RNA immunoprecipitation, and dual-luciferase assays. The biological functions of circXRN2 were assessed through a battery of in vitro experiments, including the CCK-8 assay, EdU assay, scratch assay, Transwell assay, and flow cytometry assay. Additionally, in vivo experiments involving a tumor transplantation model and a liver-lung metastasis model were conducted. The influence of circXRN2 on the expression of epithelial–mesenchymal transition (EMT)-related genes was determined via Western blotting analysis. In CRC tissues and cells, there was an upregulation in the expression levels of both circXRN2 and ENC1, while miR-149-5p exhibited a downregulation in its expression. The overexpression of circXRN2 was found to enhance tumor proliferation and metastasis, as evidenced by results from both in vitro and in vivo experiments. Functionally, circXRN2 exerted its antitumor effect by suppressing cell proliferation, migration, and invasion while also promoting apoptosis. Mechanistically, the dysregulated expression of circXRN2 had an impact on the expression of proteins within the EMT signaling pathway. Our results demonstrated that circXRN2 promoted the proliferation and metastasis of CRC cells through the miR-149-5p/ENC1/EMT axis, suggesting that circXRN2 might serve as a potential therapeutic target and novel biomarker in the progression of CRC.

Intrahepatic cholangiocarcinoma (ICC) is a highly heterogeneous malignancy. The reasons behind the global rise in the incidence of ICC remain unclear, and there exists limited knowledge regarding the immune cells within the tumor microenvironment (TME). In this study, a more comprehensive analysis of multi-omics data was performed using machine learning methods. The study found that the immunoactivity of B cells, macrophages, and T cells in the infiltrating immune cells of ICC exhibits a significantly higher level of immunoactivity in comparison to other immune cells. During the immune sensing and response, the effect of antigen-presenting cells (APCs) such as B cells and macrophages on activating NK cells was weakened, while the effect of activating T cells became stronger. Simultaneously, four distinct subpopulations, namely BLp, MacrophagesLp, BHn, and THn, have been identified from the infiltrating immune cells, and their corresponding immune-related marker genes have been identified. The immune sensing and response model of ICC has been revised and constructed based on our current comprehension. This study not only helps to deepen the understanding the heterogeneity of infiltrating immune cells in ICC, but also may provide valuable insights into the diagnosis, evaluation, treatment, and prognosis of ICC.

With the phase-out of perfluorooctanoic acid (PFOA) and its replacement by perfluoroalkyl ether carboxylic acids (PFECAs), there is a potential for increased exposure to various new PFECAs among the general population in China. While there are existing studies on dietary exposure to legacy perfluoroalkyl and polyfluoroalkyl substances (PFASs), research on dietary exposure to PFECAs, especially among the general Chinese populace, remains scarce. In the present study, we investigated the distribution of PFECAs in dietary sources from 33 cities across five major regions in China, along with the associated dietary intake. Analysis indicated that aquatic animal samples contained higher concentrations of legacy PFASs compared to those from terrestrial animals and plants. In contrast, PFECAs were found in higher concentrations in plant and terrestrial animal samples. Notably, hexafluoropropylene oxide dimer (HFPO-DA) was identified as the dominant compound in vegetables, cereals, pork, and mutton across the five regions, suggesting widespread dietary exposure. PFECAs constituted the majority of PFAS intake (57 %), with the estimated daily intake (EDI) of HFPO-DA ranging from 2.33 to 3.96 ng/kg bw/day, which corresponds to 0.78–1.32 times the reference dose (RfD) (3.0 ng/kg bw/day) set by the United States Environmental Protection Agency. Given the ubiquity of HFPO-DA and many other PFECAs in the nationwide diet of China, there is an urgent need for further research into these chemicals to establish relevant safety benchmarks or consumption advisory values for the diet.

Most of microbe cells spend the majority of their times in quiescence due to unfavorable environmental conditions. The study of this dominant state is crucial for understanding the basic cell physiology. Retained recovery ability is a critical property of quiescent cells, which consists of two features: how long the cells can survive (the survivability) and how fast they can recover (the recovery activity). While the survivability has been extensively studied under the background of chronological aging, how the recovery activity depends on the quiescent time and what factors influence its dynamics have not been addressed quantitatively. In this work, we systematically quantified both the survivability and the recovery activity of long-lived quiescent fission yeast cells at the single cell level under various nutrient conditions. It provides the most profound evolutionary dynamics of quiescent cell regeneration ability described to date. We found that the single cell recovery time linearly increased with the starvation time before the survivability significantly declined. This linearity was robust under various nutrient conditions and the recovery speed was predetermined by the initial nutrient condition. Transcriptome profiling further revealed that quiescence states under different nutrient conditions evolve in a common trajectory but with different speed. Our results demonstrated that cellular quiescence has a continuous spectrum of depths and its physiology is greatly influenced by environmental conditions.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

ABSTRACTObjectives Multiple myeloma (MM) is a malignant disease characterized by a single clonal proliferation of B cell-derived plasma cells in the bone marrow. It is the second most common haematologic malignancy in adults. The objective of this retrospective study is to evaluate the diagnostic and prognostic value of haematologic parameters in MM.Methods The difference of NLR/ALB ratio (NAR) and NLR/HDL-C ratio (NHR) between the 151 newly diagnosed MM patients and 153 healthy controls was compared. According to NAR and NHR cutoff values obtained from the ROC curve, MM patients were divided into low group and high group. The differences in hematological parameters and survival time between the two groups were compared. Independent prognostic analysis was performed using Cox proportional hazard regression model.Results The NAR and NHR values in MM group were significantly higher than those in control group (P

Abstract Background With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. Results In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. Conclusions PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.

Various perfluoroethercarboxylic acids (PFECA) have emerged as next-generation replacements of legacy per- and polyfluoroalkyl substances (PFAS). However, there is a paucity of information regarding their bioaccumulation ability and hazard characterization. Here, we explored the accumulation and hepatotoxicity of four PFECA compounds (HFPO-DA, HFPO-TA, PFO4DA, and PFO5DoDA) in comparison to perfluorooctanoic acid (PFOA) after chronic low-dose exposure in mice. Except for HFPO-DA, the levels of all tested PFAS in the liver exceeded that in serum. High molecular weight PFECA compounds (PFO5DoDA and HFPO-TA) showed stronger accumulation capacity and longer half-lives (t1/2) than low molecular weight PFECA compounds (HFPO-DA and PFO4DA) and even legacy PFOA. Although hepatomegaly is a common apical end point of PFAS exposure, the differentially expressed gene (DEG) profiles in the liver suggested significant differences between PFOA and the four PFECA compounds. Gene enrichment analysis supported a considerable inhibitory effect of PFECA, but not PFOA, on the glucocorticoid receptor (GR) signaling pathway. Both HFPO-TA and PFO5DoDA demonstrated a more pronounced ability to perturb RNA expression profiles in vivo and to suppress GR signaling in vitro compared to HFPO-DA and PFO4DA. Calculated reference doses (RfDs) emphasized the potential hazard of PFECA to human health. Overall, our findings indicate that PFECA alternatives do not ease the concerns raised from legacy PFAS pollution.

Graphical AbstractTo recycle the propylene safely from a gas mixture consisting of propylene and oxygen, alkali metal-modified Pd catalysts were used to remove oxygen from the gas with the help of hydrogen.

MicroRNAs (miRNAs) play an important role in various biological processes and their abnormal expression could lead to the occurrence of diseases. Exploring the potential relationships between miRNAs and diseases can contribute to the diagnosis and treatment of complex diseases. The increasing databases storing miRNA and disease information provide opportunities to develop computational methods for discovering unobserved disease-related miRNAs, but there are still some challenges in how to effectively learn and fuse information from multi-source data. In this study, we propose a multi-view information fusion based method for miRNA-disease association (MDA)prediction, named MVIFMDA. Firstly, multiple heterogeneous networks are constructed by combining the known MDAs and different similarities of miRNAs and diseases based on multi-source information. Secondly, the topology features of miRNAs and diseases are obtained by using the graph convolutional network to each heterogeneous network view, respectively. Moreover, we design the attention strategy at the topology representation level to adaptively fuse representations including different structural information. Meanwhile, we learn the attribute representations of miRNAs and diseases from their similarity attribute views with convolutional neural networks, respectively. Finally, the complicated associations between miRNAs and diseases are reconstructed by applying a bilinear decoder to the combined features, which combine topology and attribute representations. Experimental results on the public dataset demonstrate that our proposed model consistently outperforms baseline methods. The case studies further show the ability of the MVIFMDA model for inferring underlying associations between miRNAs and diseases.

Propane catalytic oxidation is an important industrial chemical process. However, poor activity is frequently observed for stable C–H bonds, especially for non-noble catalysts in low temperature. Herein, we reported a controlled synthesis of catalyst Co3O4@CeO2–IE via inverse loading and proposed a strategy of oxygen vacancy for its high catalytic oxidation activity, achieving better performance than traditional supported catalyst Co3O4/CeO2–IM, i.e., the T50 (temperature at 50% propane conversion) of 217 °C vs. 235 °C and T90 (temperature at 90% propane conversion) of 268 °C vs. 348 °C at the propane space velocity of 60,000 mL g−1 h−1. Further investigations indicate that there are more enriched oxygen vacancies in Co3O4@CeO2–IE due to the unique preparation method. This work provides an element doping strategy to effectively boost the propane catalytic oxidation performance as well as a bright outlook for efficient environmental catalysts.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that greatly threatens public health. This study aimed to examine a convenient early-warning biomarker of fatal outcomes in patients with SFTS to reduce mortality.MethodsA retrospective cohort study was performed, and patients with confirmed SFTS were enrolled in the top two hospitals in Anhui Province, China from 1 May 2016 to 31 October 2019. The clinical symptoms, laboratory indicators, and treatment data of patients with SFTS were evaluated. All patients with SFTS were followed up till 28 days from the start of admission. The laboratory indicators that could be used to predict the fatal outcome were identified.ResultsA total of 228 patients with SFTS were enrolled, 177 patients were enrolled in the survival group, and 51 patients in the death group. The median age of all 228 patients with SFTS was 63 years. Five laboratory indicators (SFTSV viral load, neutrophil to lymphocyte ratio (NLR), aspartate transaminase (AST)/alanine aminotransferase (ALT), ALT, and blood urea nitrogen (BUN)) were identified as the predicting factors of the fatal outcome of patients with SFTS. The area under the receiver operating characteristic (ROC) curve (AUC) of SFTSV viral load was the highest (0.919), then NLR (0.849), followed by AST/ALT (0.758), AST (0.738), and BUN (0.709). The efficacy of SFTVS viral load and NLR in predicting fatal outcomes was significantly higher than AST/ALT, AST, and BUN. The Kaplan–Meier survival curves show that the case fatality rate was significantly increased in patients whose SFTSV viral load was higher than 500,000 or NLR higher than 2.0. Gamma-globulin treatment showed a significant difference between the survival group and the death group, and the duration of gamma-globulin that had been proposed should not be

Abstract Background Pancreatic stellate cells (PSCs) occupy the majority of the pancreatic cancer microenvironment, contributing to aggressive behavior of pancreatic cancer cells (PCCs). Recently, anti-fibrotic agents have proven to be an effective strategy against cancer, but clinical trials have shown little efficacy, and the driving mechanism remains unknown. N-acetyl-cysteine (NAC) is often used for pulmonary cystic fibrosis. Pioglitazone, an agonist of peroxisome proliferator-activated receptor gamma, was habitually used for type II diabetes, but recently reported to inhibit metastasis of PCCs. However, few studies have focused on the effects of these two agents on cancer-stromal interactions. Method We evaluated the expression of α-smooth muscle actin (α-SMA) and the number of lipid droplets in PSCs cultured with or without NAC. We also evaluated changes in invasiveness, viability, and oxidative level in PSCs and PCCs after NAC treatment. Using an indirect co-culture system, we investigated changes in viability, invasiveness, and migration of PSCs and PCCs. Combined treatment effects of NAC and Pioglitazone were evaluated in PSCs and PCCs. In vivo, we co-transplanted KPC-derived organoids and PSCs to evaluate the effects of NAC and Pioglitazone’s combination therapy on subcutaneous tumor formation and splenic xenografted mouse models. Results In vitro, NAC inhibited the viability, invasiveness, and migration of PSCs at a low concentration, but not those of PCCs. NAC treatment significantly reduced oxidative stress level and expression of α-SMA, collagen type I in PSCs, which apparently present a quiescent-like state with a high number of lipid droplets. Co-cultured PSCs and PCCs mutually promoted the viability, invasiveness, and migration of each other. However, these promotion effects were attenuated by NAC treatment. Pioglitazone maintained the NAC-induced quiescent-like state of PSCs, which were reactivated by PCC-supernatant, and enhanced chemosensitivity of PCCs. In vivo, NAC and Pioglitazone’s combination suppressed tumor growth and liver metastasis with fewer stromal components and oxidative stress level. Conclusion NAC suppressed activated PSCs and attenuated cancer-stromal interactions. NAC induces quiescent-like PSCs that were maintained in this state by pioglitazone treatment.

Abstract To explore the effects of urea dissociation on reducing false-positive results of the Elecsys HIV combi PT assay. A retrospective analysis was used to evaluate the false-positive rate of the Elecsys HIV combi PT assay. Six false-positive sera, six positive sera and six sera from patients with early HIV infection were collected. Dissociation was performed using 1 mol/L, 2 mol/L, 4 mol/L, 6 mol/L, or 8 mol/L urea, and HIV screening assay were then detected to select the appropriate concentration of urea dissociation. Next, 55 false-positive sera and 15 sera from early HIV infection were used to verify the best concentration of urea to achieve dissociation. Retrospective analysis showed that the COI of the Elecsys HIV combi PT assay in false-positive sera ranged from 1.0 to 200.0, and approximately 97.01%(227/234) of false-positive sera were in the range of 1.0–15.0. The avidity index (AI) in positive and false-positive sera decreased as the urea dissociation concentration increased. When the dissociation concentration was 6 mol/L, the AI of false-positive serum was between 0.0234 and 0.2567, and the AI of early HIV infection sera was between 0.4325 and 0.5017. The difference in AI between false-positive and positive samples was significant. When negativity was defined as an AI of less than 0.3970, the sensitivity and specificity were 100.0% and 100.0%, respectively. Urea-mediated dissociation could significantly reduce the false-positive rate of the Elecsys HIV combi PT assay with a low COI. Our findings provided a reference for distinguishing positive and false-positive of the Elecsys HIV combi PT assay.

ObjectivesHuman leucocyte antigen B27 (HLA-B27) is an important biomarker for ankylosing spondylitis (AS). However, delay in the diagnosis of AS is still common in clinical practice. Several single nucleotide polymorphisms (SNPs) in the coding gene of tumor necrosis factor alpha (TNFα) have been reported to be AS susceptibility loci. Our aim was to explore whether SNPs in TNFα could be used to improve the performance of HLA-B27 for predicting AS.MethodsFive SNPs (rs1799964, rs1800630, rs1799724, rs1800629, and rs361525) spanning TNFα were genotyped by qPCR-Invader assay in 93 AS patients and 107 healthy controls for association analysis and linkage disequilibrium (LD) analysis. Random forest algorithm was utilized to construct the predictive classifiers for AS. HLA-B was genotyped by PCR-sequence-based typing in a subset of the HLA-B27-positive subjects (38 AS patients and 5 healthy controls).ResultsThe T allele of rs1799724 was verified to significantly increase the risk of AS (OR = 4.583, p < 0.0001), while the A allele of rs361525 showed an association with the reduced AS risk (OR = 0.168, p = 0.009). In addition, the rs1799964T-rs1800630C-rs1799724T-rs1800629G-rs361525G haplotype was significantly associated with a higher risk of AS (p < 0.0001). The optimal set of variables for classifiers to predict AS only consisted of HLA-B27. Strong associations with HLA-B27 status were found in both rs1799724 (p < 0.0001) and rs361525 (p = 0.001), and all the analyzed HLA-B27-positive subjects carried HLA-B*27:04 or HLA-B*27:05.ConclusionIn the Chinese Han population, the minor allele T of rs1799724 could increase the risk of AS, while the minor allele A of rs361525 protects individuals from AS. However, the contributions of rs1799724 and rs361525 to AS risk were dependent on HLA-B27 status, suggesting the importance of taking the independence and specificity into consideration in AS susceptibility loci studies.

Engineering unique electronic structure of catalyst to boost catalytic performance is of prime scientific and industrial importance. Herein, the identification of intrinsic electronic sensitivity for direct propene epoxidation was first achieved over highly stable Au/wormhole-like TS-1 catalyst. Results show that the electron transfer of Au species can be regulated by manipulating the dynamic evolutions and contents of Au valence states, thus resulting in different catalytic performance in 100 h time-on-stream. By DFT calculations, kinetic analysis and multi-characterizations, it is found that the Au0 species with higher electronic population can easily transfer more electrons to activate surface O2 compared with Au1+ and Au3+ species. Moreover, there is a positive correlation between Au0 content and activity. Based on this correlation, a facile strategy is further proposed to boost Au0 percentage, resulting in the reported highest PO formation rate without adding promoters. This work harbors tremendous guiding significance to the design of highly efficient Au/Ti-containing catalyst for propene epoxidation with H2 and O2.

The advocacy of green chemical industry has led to the development of highly efficient catalysts for direct gas-phase propene epoxidation with green, sustainable and simple essence. The S-1/TS-1@dendritic-SiO2 material with three-layer core–shell structure was developed and used as the support for Au catalysts, which showed simultaneously fantastic PO formation rate, PO selectivity and stability (over 100 h) for propene epoxidation with H2 and O2. It is found that silicalite-1 (S-1) core and the middle thin layer of TS-1 offer great mass transfer ability, which could be responsible for the excellent stability. The designed dendritic SiO2 shell covers part of the acid sites on the external surface of TS-1, inhibiting the side reactions and improving the PO selectivity. Furthermore, three kinds of SiO2 shell morphologies (i.e., dendritic, net, mesoporous shell) were designed, and relationship between shell morphology and catalytic performance was elucidated. The results in this paper harbour tremendous guiding significance for the design of highly efficient epoxidation catalysts.

Objective To conduct a systematic review of the efficacy and safety of Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis. Method We searched PubMed, Embase, Cochrane Library, CNKI, WanFang Data, and WeiPu for comparative studies on Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis. The search period runs from the establishment of the database until September 2021. Data extraction and quality evaluation were carried out on the documents that met the inclusion criteria, and a meta-analysis of the included literature was conducted using the RevMan5.3 software. Results A total of 17 randomized controlled trials that met the inclusion criteria were included, with a total sample size of 1680 patients (841 patients in the study group and 839 in the control group). The effective rate was significantly higher in the study group than in the control group [RR = 1.22, 95% CI (1.16, 1.27), P < 0.00001]. In addition, 24-hour urine protein levels [SMD = -1.11, 95% CI (-1.40, -0.83), P < 0.00001], urine NAG enzyme [SMD = -0.99, 95% CI (-1.27, -0.72), P < 0.00001], leukotactin-1 [SMD = -2.43, 95% CI (-3.50, -1.35), P < 0.00001], and the incidence of adverse reactions [RR = 0.43, 95% CI (0.28, 0.66), P < 0.00001] were lower in the study group when compared to the control group. Conclusion It is safer to treat chronic glomerulonephritis with Shyenyankangfu tablets in combination with losartan potassium. At the same time, it alleviates disease-related symptoms, reduces the influence of cytokine levels, and has fewer adverse reactions, making it more conducive to disease recovery. However, additional multi-center, randomized, control trials with large sample sizes must be conducted to confirm the findings.

This paper reveals some relations between fuzzy logic and quantum logic on partial residuated implications (PRIs) induced by partial t-norms as well as proposes partial residuated monoids (PRMs) and partial residuated lattices (PRLs) by defining partial adjoint pairs. First of all, we introduce the connection between lattice effect algebra and partial t-norms according to the concept of partial t-norms given by Borzooei, together with the proof that partial operation in any commutative quasiresiduated lattice is partial t-norm. Then, we offer the general form of PRI and the definition of partial fuzzy implication (PFI), give the condition that partial residuated implication is a fuzzy implication, and prove that each PRI is a PFI. Next, we propose PRLs, study their basic characteristics, discuss the correspondence between PRLs and lattice effect algebras (LEAs), and point out the relationship between LEAs and residuated partial algebras. In addition, like the definition of partial t-norms, we provide the notions of partial triangular conorms (partial t-conorms) and corresponding partial co-residuated lattices (PcRLs). Lastly, based on partial residuated lattices, we define well partial residuated lattices (wPRLs), study the filter of well partial residuated lattices, and then construct quotient structure of PRMs.

The self-adaptive nature of smart textiles to the ambient environment has made them an indispensable part of emerging wearable technologies. However, current advances generally suffer from complex material preparation, uncomfortable fitting feeling, possible toxicity, and high cost in fabrication, which hinder the real-world application of smart materials in textiles. Herein, humidity-response torsional and tensile yarn actuators from twisted and coiled structures are developed using commercially available, cost-effective, and biodegradable viscose fibers based on yarn-spinning and weaving technologies. The twisted yarn shows a reversible torsional stroke of 1400° cm−1 in 5 s when stimulated by water fog with a spraying speed of 0.05 g s−1; the coiled yarn exhibits a peak tensile stroke of 900% upon enhancing the relative humidity. Further, textile manufacturing allows for the scalable fabrication to create fabric artificial muscles with high-dimensional actuation deformations and human-touch comfort, which can boost the potential applications of the humidly adaptive yarns in smart textile and advanced textile materials.

Background: Post-stroke insomnia (PSI) affects the quality of life for stroke patients, reduces the likelihood of successful rehabilitation, and produces additional complications following stroke. Previous reports have provided some information regarding PSI risk factors, but little is known concerning protective factors for PSI. This study analyzed the relationship between acupuncture and insomnia in stroke patients and explored the use of acupuncture as a preventive treatment.Methods: Patients diagnosed with stroke from 2010 to 2019 were identified in the case database of the First Affiliated Hospital of Guangzhou University of Chinese These patients followed until 2020, and numerous factors were examined, including gender, age, stroke type, stroke location, and baseline comorbidities. A 1:1 propensity score was used to match an equal number of patients receiving acupuncture with stroke patients who did not receive acupuncture (N = 1,680 for each group). The purpose of the study was to compare the incidence of insomnia in these two stroke cohorts. We used the Cox regression model and Kaplan-Meier method to estimate the risk of insomnia as the outcome event.Results: Compared with the non-acupuncture cohort in general, stroke patients who received acupuncture treatment exhibited a lower risk of insomnia after adjusting for age, gender, stroke type, stroke location, and comorbidities (adjusted hazard ratio HR = 0.27, 95% confidential interval = 0.23 to 0.32). Acupuncture also reduced the risk of PSI for both genders. The respective risks were HR = 0.28 (adjusted) for males and HR = 0.26 (adjusted) for females. Acupuncture also lowered the risk for PSI for different age groups. The risks were HR = 0.22 (adjusted) for individuals 18 to 39 years of age, HR = 0.31 (adjusted) for individuals 40 to 59 years of age, HR = 0.28 (adjusted) for those 60 to 79 years of age, and HR = 0.18 (adjusted) for individuals 80 years of age and older. Concerning the stroke type, regardless of whether the stroke was ischemic, hemorrhagic, or a combination of the two stroke types, patients who received acupuncture exhibited lower risk (adjusted HR = 0.28, 0.17, and 0.49, respectively). Concerning stroke location, except for the cerebral hemispheres (adjusted HR = 1.10, 95% confidential interval = 0.12 to 1.01), the risk of PSI after receiving acupuncture was lower for the frontal lobe (adjusted HR = 0.42), the basal ganglia (adjusted HR = 0.22), the radiation crown (adjusted HR = 0.42), the diencephalon (adjusted HR = 0.20), or multiple partial strokes (adjusted HR = 0.26), the risk of PSI after receiving acupuncture was lower. For all baseline complications, acupuncture reduced the risk of insomnia. The cumulative incidence of insomnia in the acupuncture cohort was significantly lower than the non-acupuncture cohort (log-rank test, P = 0.000).Limitations: First, our research only included patients from a single center. Second, we did not classify the post-stroke insomnia severity. Second, the information was extracted manually. Overall, the sample size was small, and we needed to increase the sample size to strengthen the conclusions.Conclusion: Acupuncture treatment reduced the risk of insomnia in stroke patients. Future research be conducted with increased sample sizes and further elaboration on the specific acupuncture protocols that were used.

A new species of the genus Tipulodina Enderlein, 1912, Tipulodina bifurcata Xue & Men, sp. nov. (Guangxi, South China) is described and illustrated. A key to the known species in China is provided. The morphological description of the female internal reproductive system of the new species is provided, which represents the first description for this genus.

To express wider uncertainty, Běhounek and Daňková studied fuzzy partial logic and partial function. At the same time, Borzooei generalized t-norms and put forward the concept of partial t-norms when studying lattice valued quantum effect algebras. Based on partial t-norms, Zhang et al. studied partial residuated implications (PRIs) and proposed the concept of partial residuated lattices (PRLs). In this paper, we mainly study the related algebraic structure of fuzzy partial logic. First, we provide the definitions of regular partial t-norms and regular partial residuated implication (rPRI) through the general forms of partial t-norms and partial residuated implication. Second, we define regular partial residuated lattices (rPRLs) and study their corresponding properties. Third, we study the relations among commutative quasi-residuated lattices, commutative Q-residuated lattices, partial residuated lattices, and regular partial residuated lattices. Last, in order to obtain the filter theory of regular partial residuated lattices, we restrict certain conditions and then propose special regular partial residuated lattices (srPRLs) in order to finally construct the quotient structure of regular partial residuated lattices.

Abstract is missing (Short communication)

Perfluoropolyether carboxylic acids (PFECAs, CF3(OCF2)nCOO−, n = 2–5) are novel alternatives to perfluorooctanoic acid (PFOA) and are widely used in industrial production. However, although they have been detected in surface water and human blood, their toxicities on aquatic organisms remain unknown. We used zebrafish embryos to compare the developmental toxicities of various PFECAs (e.g., perfluoro (3,5,7-trioxaoctanoic) acid (PFO3OA), perfluoro (3,5,7,9-tetraoxadecanoic) acid (PFO4DA), and perfluoro (3,5,7,9,11-pentaoxadodecanoic) acid (PFO5DoDA)) with that of PFOA and to further reveal the key events related to toxicity caused by these chemicals. Results showed that, based on half maximal effective concentrations (EC50), toxicity increased in the order: PFO5DoDA > PFO4DA > PFOA > PFO3OA, with uninflated posterior swim bladders the most frequently observed malformation. Similar to PFOA, PFECA exposure significantly lowered thyroid hormone (TH) levels (e.g., T3 (3,5,3′-L-triiodothyronine) and T4 (L-thyroxine)) in the whole body of larvae at 5 d post-fertilization following disrupted TH metabolism. In addition, the transcription of UDP glucuronosyltransferase 1 family a, b (ugt1ab), a gene related to TH metabolism, increased dose-dependently. Exogeneous T3 or T4 supplementation partly rescued PFECA-induced posterior swim bladder malformation. Our results further suggested that PFECAs primarily damaged the swim bladder mesothelium during early development. This study is the first to report on novel emerging PFECAs as thyroid disruptors causing swim bladder malformation. Furthermore, given that PFECA toxicity increased with backbone OCF2 moieties, they may not be safer alternatives to PFOA. Keywords: Perfluoropolyether carboxylic acids, Swim bladders, Thyroid disruptors, Developmental toxicity, Zebrafish, Perfluorooctanoic acid

Background/Aims: This study aimed to investigate the expression and prognostic value of kinesin family member 2A (KIF2A) and the suppression effects of microRNA-206 (miR-206) on KIF2A in ovarian cancer. Methods: Ovarian cancer tissues from patients and ovarian cancer cell lines (A2780 and SKOV3) were used in this study. miR-206 mimics and control were transiently transfected into cells. RT-qPCR was performed to detect KIF2A mRNA and miR-206 expression levels, Western blot was performed to detect KIF2A protein levels, Dual-Luciferase Reporter Assay was used to examine the inhibition effects of miR-206 on KIF2A mRNA, immunohistochemical staining was used to examine the expression of KIF2A in tissue sections. CCK-8, transwell and Annexin-V-FITC/Propidium Iodide staining with flow cytometry were used to detect the cell proliferation, migration/invasion, and apoptosis respectively. Results: Our study explored the expression profiles of KIF2A and miR-206 in the patients with ovarian cancer. We found that overexpression of KIF2A was associated with a poor prognosis in ovarian cancer. We also found that KIF2A mRNA contains two target sites for miR-206 binding and confirmed that miR-206 directly suppresses KIF2A; inhibits ovarian cancer cell proliferation, migration, and invasion; and induces apoptosis. Conclusion: The results suggest KIF2A could serve a valuable prognostic indicator in ovarian cancer and provide a rationale for treatment of ovarian cancer by targeting KIF2A via miR-206.

Identifying new treatments for existing drugs can help reduce drug development costs and explore novel indications of drugs. The prediction of associations between drugs and diseases is challenging because their similarities and relations are complicated and non-linear. We propose a HeteroDualNet model to address this issue. Firstly, three types of matrices are extracted to represent intra-drug similarities, intra-disease similarity and drug-disease associations. The intra-drug similarities consider three drug features and a newly introduced drug-related disease correlation. Secondly, an embedding mechanism is proposed to integrate these matrices in a heterogenous drug-disease association layer (hetero-layer). Further, a neighbouring heterogeneous layer (hetero-layer-N) is constructed to incorporate the biological premise that similar drugs can often treat related diseases. Finally, a dual convolutional neural network is built with hetero-layer and hetero-layer-N as two branches to learn from characteristics of drug-disease and the relations of their neighbours simultaneously. HeteroDualNet outperformed the other four methods in comparison over a public dataset of 763 drugs and 681 diseases in terms of Areas Under the Curves of Receiver Operating Characteristics and Precision-Recall, and recall rate at top k. Case study of five drugs further proved the capacity of HeteroDualNet in finding reliable disease candidates of drugs as validated by database records or literature. Our findings show that the embedded heterogenous layers of original and neighbouring drug-disease representations in a dual neural network improved the association prediction performance.

To address the drawback of low thermal conductivity of conventional organic phase change materials (PCMs), a paraffin-wax-based phase change composite (PCC) was assembled via a vacuum impregnation method, using a new type of carbon fiber network material as the supporting matrix. The carbon fiber sheet (CFS) material exhibited a network structure comprising high-thermal-conductivity carbon fibers, beneficial for enhancing the heat transfer properties of the PCC. The sheet-shaped carbon fiber material was stacked and compressed, and then impregnated with the liquid paraffin wax PCM to form the composite. The thermal conductivity, durability, shape stability, chemical stability, and heat storage characteristics of the PCC specimen were carefully analyzed. The maximum thermal conductivity of the PCC was 11.68 W·m−1·K−1 (4670% compared to that of pure paraffin) in the radial direction, and 0.93 W·m−1·K−1 in the axial direction of the sample, with 17.44 vol % of added CFS. The thermal conductivity retention rate after 200 thermal cycles was 78.6%. The PCC also displayed good stability in terms of chemical structure, shape, and heat storage ability. This study offers insights and a possible strategy for the development of anisotropic high-thermal-conductivity PCCs for potential applications in latent heat storage systems.

Several microRNAs (miRNAs) have been reported as oncogenes or tumor suppressors in many cancers, including gastric cancer (GC). However, the role and molecular mechanism of miR-3129 in GC is largely unknown. We aimed to explore the function and the underlying molecular mechanism of miR-3129 in GC. Cancer tissues and corresponding adjacent tissues were collected from 50 patients with GC, and the expression of miR-3129 was detected by RT-qPCR. The expression of miR-3129 and pRb in human GC cell line SCG7091 was altered by transient transfection. Thereafter, MTT and flow cytometry assays were used to analyze cell viability and cell cycle. The expression of cyclin E, CDK2, CDK2 inhibitors (p16 and 21), and pRb were detected by RT-qPCR and western blot. A significant up-regulation of miR-3129 was observed in GC tissues compared to adjacent tissues. Overexpression of miR-3129 significantly improved cell viability after 4 days of post-transfection. Flow cytometry assay results showed that the miR-3129 overexpression arrested more SGC7901 cells at S phase. Moreover, overexpression of miR-3129 down-regulated the expression of CDK2 inhibitors while it up-regulated the expression levels of cyclin E, CDK2, and pRb. Interestingly, we found that pRb inhibition reversed the effect of miR-3129 inhibitor on cell proliferation in SGC7901 cells, increased cell viability, reduced cells at G0/1 phase, and modulated the expression of proliferation-related factors. Our results revealed that miR-3129 functioned as an oncogene through positive regulation of pRb and may prove to be a promising option for molecular therapy of GC.

Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA−disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA−disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA−disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred’s ability to discover potential lncRNA−disease associations.

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA−disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA−disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

Agarwood is the fragrant resinous material mainly formed from species of Aquilaria. 2-(2-phenylethyl)chromones, especially the highly oxidized 5,6,7,8-tetrahydro-2-(2-phenylethyl)chromones, are the main representative compounds from agarwood. It is important to determine whether agarwood in trade is from cultivated trees or natural trees in the Convention on the International Trade in Endangered Species (CITES). We characterized the 2-(2-phenylethyl)chromones in agarwood by ultra-performance liquid chromatography coupled with electrospray ionization mass spectrometry (UPLC–ESI-QTOF-MS) and differentiated wild from cultivated agarwood by metabolomic analysis. A total of 141 chromones including 50 potentially new compounds were evaluated as belonging to four structural classes (unoxidized 2-(2-phenylethyl)chromones, 5,6,7,8-tetrahydro-2-(2-phenylethyl)-chromones, bi-2-(2-phenylethyl)chromones, and tri-2-(2-phenylethyl)chromones). The metabolic difference between wild and cultivated agarwood was analyzed by component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Fourteen markers of metabolisms in wild and cultivated agarwood were constructed (e.g., 6,7-dimethoxy-2-(2-phenylethyl)chromone, 6,8-dihydroxy-2-(2-phenylethyl)chromone, 6-methoxy-2-(2-phenylethyl)chromone, etc.). These results indicated that UPLC–ESI-QTOF-MS-based metabonomics analysis in agarwood may be useful for distinguishing wild agarwood from cultivated agarwood.