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The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

Room-temperature phosphorescence and organic afterglow materials exhibit significant applications in diverse fields. Among them, aqueous-phase organic afterglow materials display interesting biomedical and other applications, whereas afterglow material fabrication in aqueous medium remains less explored when compared to those in solid states. In view of the excellent afterglow performance in difluoroboron β-diketonate containing systems, here we report supramolecular inclusion complexation of γ-cyclodextrin (γCD) with difluoroboron β-diketonate (BF2bdk) phosphors for devising aqueous-phase room-temperature afterglow crystalline micro/nanostructures. The γCD-BF2bdk host-guest supramolecular interactions induce crystallization of the two-component system into micro/nanostructures where BF2bdk’s triplet excited states can be well protected from nonradiative decay and oxygen quenching, leading to the emergence of aqueous-phase afterglow with phosphorescence lifetimes around 1 s and high photoluminescence quantum yields (PLQY) > 50 %. The aqueous afterglow materials possess visible-light-excitable property and can serve as donor of energy transfer for constructing long-wavelength and color-tunable afterglow systems. Their potential applications for bioimaging were further demonstrated. The present study provides a simple method but a new avenue for the preparation of high-performance aqueous-phase afterglow materials.

The kidney-brain axis is a bidirectional communication network connecting the kidneys and the brain, potentially affected by inflammation, uremic toxin, vascular injury, neuronal degeneration, and so on, leading to a range of diseases. Numerous studies emphasize the disruptions of the kidney-brain axis may contribute to the high morbidity of neurological disorders, such as cognitive impairment (CI) in the natural course of chronic kidney disease (CKD). Although the pathophysiology of the kidney-brain axis has not been fully elucidated, epidemiological data indicate that patients at all stages of CKD have a higher risk of developing CI compared with the general population. In contrast to other reviews, we mentioned some commonly used medicines in CKD that may play a pivotal role in the pathogenesis of CI. Revealing the pathophysiology interactions between kidney damage and brain function can reduce the potential risk of future CI. This review will deeply explore the characteristics, indicators, and potential pathophysiological mechanisms of CKD-related CI. It will provide a theoretical basis for identifying CI that progresses during CKD and ultimately prevents and treats CKD-related CI.

Abstract Aim Cannabinoid receptors are components of the endocannabinoid system that affect various physiological functions. We aim to investigate the effect of cannabinoid receptor modulation on kidney disease. Methods PubMed, Web of Science databases, and EMBASE were searched. Articles selection, data extraction and quality assessment were independently performed by two investigators. The SYRCLE’s RoB tool was used to assess the risk of study bias, and pooled SMD using a random-effect model and 95% CIs were calculated. Subgroup analyses were conducted in preselected subgroups, and publication bias was evaluated. We compared the effects of CB1 and CB2 antagonists and/or knockout and agonists and/or genetic regulation on renal function, blood glucose levels, body weight, and pathological damage-related indicators in different models of chronic and acute kidney injury. Results The blockade or knockout of CB1 could significantly reduce blood urea nitrogen [SMD,− 1.67 (95% CI − 2.27 to − 1.07)], serum creatinine [SMD, − 1.88 (95% CI − 2.91 to − 0.85)], and albuminuria [SMD, − 1.60 (95% CI − 2.16 to − 1.04)] in renal dysfunction animals compared with the control group. The activation of CB2 group could significantly reduce serum creatinine [SMD, − 0.97 (95% CI − 1.83 to − 0.11)] and albuminuria [SMD, − 2.43 (95% CI − 4.63 to − 0.23)] in renal dysfunction animals compared with the control group. Conclusions The results suggest that targeting cannabinoid receptors, particularly CB1 antagonists and CB2 agonists, can improve kidney function and reduce inflammatory responses, exerting a renal protective effect and maintaining therapeutic potential in various types of kidney disease.

Introduction The aim of this study was to understand the global incidence and disability-adjusted life years (DALY) of oral disorders by age, gender, region, and Sociodemographic Index (SDI) from 1990 to 2019. Material and methods The estimated annual percentage change (EAPC) and 95% confidence intervals (CIs) were used to assess the trends in age-standardized incidence and DALY rates from 1990 to 2019. Results The global age-standardized incidence rate (EAPC = 0.01) of oral disorders increased slightly from 1990 to 2019. From 1990 to 2019, the age-standardized DALY rate decreased in high-SDI (EAPC = –0.43) and high-middle-SDI (EAPC = –0.14) regions, but it showed increasing trends in low-SDI (EAPC = 0.22), low-middle-SDI (EAPC = 0.36), and middle-SDI (EAPC = 0.17) regions. The EAPC in the age-standardized DALY rate was negatively correlated with the regional SDI value ( = –0.402, p = 0.001). From 1990 to 2019, the region with the largest increase in age-standardized DALY rate was South Asia (EAPC = 0.67), while the country with the greatest increase in age-standardized DALY rate was India (EAPC = 0.82). Conclusions From 1990 to 2019, the global age-standardized incidence rate of oral disorders showed a slight increasing trend. It is necessary to control the increase in DALY and the disease burden associated with oral disorders in low-, low-middle-, and middle-SDI regions, such as South Asia, particularly in India.

Abstract Three-dimensional (3D) covalent organic frameworks (COFs) possess higher surface areas, more abundant pore channels, and lower density compared to their two-dimensional counterparts which makes the development of 3D COFs interesting from a fundamental and practical point of view. However, the construction of highly crystalline 3D COF remains challenging. At the same time, the choice of topologies in 3D COFs is limited by the crystallization problem, the lack of availability of suitable building blocks with appropriate reactivity and symmetries, and the difficulties in crystalline structure determination. Herein, we report two highly crystalline 3D COFs with pto and mhq-z topologies designed by rationally selecting rectangular-planar and trigonal-planar building blocks with appropriate conformational strains. The pto 3D COFs show a large pore size of 46 Å with an extremely low calculated density. The mhq-z net topology is solely constructed from totally face-enclosed organic polyhedra displaying a precise uniform micropore size of 1.0 nm. The 3D COFs show a high CO2 adsorption capacity at room temperature and can potentially serve as promising carbon capture adsorbents. This work expands the choice of accessible 3D COF topologies, enriching the structural versatility of COFs.

The effective capture and recovery of radioiodine species associated with nuclear fuel reprocessing is of significant importance in nuclear power plants. Porous materials have been proven to be one of the most effective adsorbents for the capture of radioiodine. In this work, we design and synthesize a series of conjugated microporous polymers (CMPs), namely, TPDA–TFPB CMP, TPDA–TATBA CMP, and TPDA–TECHO CMP, which are constructed based on a planar rectangular 4-connected organic monomer and three triangular 3-connected organic monomers, respectively. The resultant CMPs are characterized using various characterization techniques and used as effective adsorbents for iodine capture. Our experiments indicated that the CMPs exhibit excellent iodine adsorption capacities as high as 6.48, 6.25, and 6.37 g g−1 at 348 K and ambient pressure. The adsorption mechanism was further investigated and the strong chemical adsorption between the iodine and the imine/tertiary ammonia of the CMPs, 3D network structure with accessible hierarchical pores, uniform micromorphology, wide π-conjugated structure, and high-density Lewis-base sites synergistically contribute to their excellent iodine adsorption performance. Moreover, the CMPs demonstrated good recyclability. This work provides guidance for the construction of novel iodine adsorbent materials with high efficiency in the nuclear power field.

Narrowband afterglow materials display interesting functions in high-quality anti-counterfeiting and multiplexed bioimaging. However, there is still a limited exploration of these afterglow materials, especially for those with a full width at half maxima (FWHM) around 30 nm. Here, we report the fabrication of narrowband organic/inorganic hybrid afterglow materials via energy transfer technology. Coronene (Cor) with a long phosphorescence feature and broad phosphorescence band is selected as the donor for energy transfer, and inorganic quantum dots (QDs) of CdSe/ZnS with a narrowband emission are used as acceptors. Upon doping into the organic matrix, the resultant three-component materials exhibit a narrowband afterglow with an afterglow lifetime of approximately 3.4 s and an FWHM of 31 nm. The afterglow wavelength of the afterglow materials can be controlled by the QDs. This work based on organic/inorganic hybrids provides a facile approach for developing multicolor and narrowband afterglow materials, as well as opens a new way for expanding the features of organic afterglow for multifunctional applications. It is expected to rely on narrowband afterglow emitters to solve the “spectrum congestion” problem of high-density information storage in optical anti-counterfeiting and information encryption.

Abstract Background RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear. Methods The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro. Results We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose. Conclusion Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.

The design and synthesis of organic photocatalysts remain a great challenge due to their strict structural constraints. However, this could be mitigated by achieving structural flexibility by constructing permanent porosity into the materials. Conjugated microporous polymers (CMPs) are an emerging class of porous materials with an amorphous, three-dimensional network structure, which makes it possible to integrate the elaborate functional groups to enhance photocatalytic performance. Here, we report the synthesis of a novel CMP, named TAPFc-TFPPy-CMP, constructed by 1,1′3,3′-tetra(4-aminophenyl)ferrocene (TAPFc) and 1,3,6,8-tetrakis(4-formylphenyl)pyrene (TFPPy) monomers. The integration of the p-type dopant 7,7,8,8-tetracyanoquinodimethane (TCNQ) into the TAPFc-TFPPy-CMP improved the light adsorption performance, leading to a decrease in the optical bandgap from 2.00 to 1.43 eV. The doped CMP (TCNQ@TAPFc-TFPPy-CMP) exhibited promising catalytic activity in photocatalytic CO2 reduction under visible light, yielding 546.8 μmol g−1 h−1 of CO with a selectivity of 96% and 5.2 μmol g−1 h−1 of CH4. This represented an 80% increase in the CO yield compared to the maternal TAPFc-TFPPy-CMP. The steady-state photoluminescence (PL) and fluorescence lifetime (FL) measurements reveal faster carrier separation and transport after the doping. This study provides guidance for the development of organic photocatalysts for the utilization of renewable energy.

This study aimed to explore whether stroke prevention strategies differ for men and women. Data used were from China Kadoorie Biobank. According to the China-PAR Project model, a predicted 10-year stroke risk of ≥7% is defined as a high stroke risk. The effects of risk factor control and medication use as primary and secondary stroke prevention strategies were assessed, respectively. Logistic regression models were used to assess the sex-specific differences in the primary and secondary stroke prevention practices. Of the 512,715 participants (59.0% women), 218,972 (57.4% women) had a high risk of stroke and 8884 (44.7% women) had an established stroke. Of high-risk participants, women were considerably less likely than men to receive antiplatelets (odds ratio [OR], 0.80; [95% confidence interval, CI, 0.72–0.89]), antihypertensives (0.46[0.44–0.48]), and antidiabetics (0.65[0.60–0.70]). Meanwhile, stroke women were significantly less likely to receive antiplatelets (0.75[0.65–0.85]) but more likely to receive antidiabetics (1.56 [1.34–1.82]) than their male counterparts. Besides, differences were found in risk factor control between women and men. Sex-specific differences in stroke prevention strategies are prevalent in China. Effective prevention requires the implementation of better overall nationwide strategies and special emphasis on women.

BackgroundPeritonitis is considered as one of the most serious complications that cause hospitalization in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). There is limited evidence on the impact of the parathyroid hormone (PTH) on the first peritoneal dialysis (PD)-associated peritonitis episode. We aimed to investigate the influence of serum intact parathyroid hormone (iPTH) on peritonitis in patients undergoing PD.MethodsThis was a retrospective cohort study. Patients undergoing initial CAPD from a single center in China were enrolled. The baseline characteristics and clinical information were recorded. The primary outcome of interest was the occurrence of the first PD-associated peritonitis episode. Five Cox proportional hazard models were constructed in each group set. In group set 1, all participants were divided into three subgroups by tertiles of the serum concentration of iPTH; in group set 2, all participants were divided into three subgroups based on the serum concentration of iPTH with 150 pg/ml interval (300 pg/ml). Hazard ratios and 95% confidence intervals (CIs) were calculated for each model. The multivariate linear regression analysis elimination procedure assessed the association between the clinical characteristics at baseline and the iPTH levels. Restricted cubic spline models were constructed, and stratified analyses were also conducted.ResultsA total of 582 patients undergoing initial PD (40% women; mean age, 45.1 ± 11.5 years) from a single center in China were recruited. The median follow-up duration was 25.3 months. Multivariate Cox regression analysis showed that, in the fully adjusted model, a higher serum iPTH level (tertile 3, iPTH >300 pg/ml) was significantly associated with a higher risk of PD-associated peritonitis at 3 years [tertile 3: hazard ratio (HR) = 1.53, 95%CI = 1.03–2.55, p = 0.03; iPTH > 300 pg/ml: HR = 1.57, 95%CI = 1.08–2.27, p = 0.02]. The hazard ratio for every 100 pg/ml increase in serum iPTH level was 1.12 (95%CI = 1.05–1.20, p

Non-thermal plasma (NTP) technology has great potential to treat volatile organic compounds (VOCs) at low concentrations and large volumes. In this work, toluene, one of the most stable VOCs, was employed as a model pollutant to study its degradation by double dielectric barrier discharge (DDBD) at ambient temperature and atmospheric pressure. The performance of the DDBD reactor was evaluated in terms of discharge power, removal efficiency, COx selectivity, and byproduct formation. Online and continuous measurements by PTR-TOFMS showed that toluene degradation resulted in the formation of 12 major volatile organic byproducts as well as 7 minor ones with concentrations ranging from ppbv to ppmv. Moreover, the particle size distribution and number concentration of particles formed during toluene degradation were measured using a scanning mobility particle sizer, and the effects of discharge power, initial toluene concentration, gas flow rate and O2 content on particle formation were systematically investigated. The results show that both NTP-induced polymerization and oxidation contribute to aerosol formation, and the total concentration of organic byproducts in the aerosol phase is about 107 cm−3 with diameters in the range of 10 and 100 nm. This pioneered investigation on gas- and particle-phase organic byproducts from toluene degradation by NTP would deepen the understanding of plasma degradation of VOCs and accelerate the practical application of NTP for VOCs treatment.

BackgroundGrowing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN.MethodsGene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein–protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed.ResultsFour gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases.ConclusionsThis study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.

Summary: D-amino acids are of biological significance yet are not clearly understood due to the lack of powerful analytical tools for their identification. Thus, the specific detection of a single enantiomer of a particular amino acid remains a great challenge due to their structural similarity. Here, we report a strategy to incorporate multiple reaction sites on a chiral 1,1′-bi-2,2′-naphthol-based fluorescent probe. It can respond specifically to D-arginine, while producing no response when in contact with all other amino acids. The probe can report arginine’s concentration, and enantiomeric configuration and colorimetric studies enable its qualitative determination.

BackgroundLong noncoding RNA (lncRNA) in plasma exosomes is a potential non-invasive diagnostic biomarker for diabetic retinopathy (DR). However, the changes in plasma exosomal lncRNAs and diagnostic relevance in patients with DR patients remain unclear.MethodsA case–control study with type 2 diabetes mellitus (T2DM) and patients with comorbid DR were enrolled, and their clinical information and blood samples were collected. Plasma exosomes were extracted, and the relative expression levels of representative differentially expressed exosomal lncRNAs were determined. A logistic regression model was used to analyze the relationships of DR with relative lncRNA expression and DR-related factors, and receiver operating characteristic (ROC) curve analysis was used to evaluate the value of exosomal lncRNAs for DR diagnosis.ResultsSixty-two patients with T2DM and sixty-two patients with DR were matched by age, sex, and disease duration. The fasting blood glucose concentration, glycosylated hemoglobin level (HbA1c), and relative expression of the plasma exosomal lncRNA DLX6-AS1 were significantly higher in the DR group than in the T2DM group, whereas the 2-h C-peptide concentration and relative expression of the lncRNAs PRINS and FAM190A-3 were lower in the DR group. After adjusting for relevant confounders, the fasting blood glucose concentration, HbA1c level, 2-h C-peptide concentration, and relative expression of lncRNA DLX6-AS1, PRINS, and FAM190A-3 were found to be associated with DR. Both DLX6-AS1 [area under the curve (AUC): 0.658 (0.562–0.754)], PRINS [AUC: 0.798 (0.722–0.873)], and FAM190A-3 [AUC: 0.603 (0.503-0.702)] expression had predictive value for DR diagnosis. The combination of DLX6-AS1 and PRINS yielded an AUC of 0.813 (0.740–0.886). In males, the combination of DLX6-AS1 and PRINS yielded an AUC of 0.860 (0.780–0.940).ConclusionThe fasting blood glucose concentration, HbA1c level, and exosomal DLX6-AS1 expression were identified as risk factors for DR, whereas the 2-h C-peptide concentration and exosomal PRINS and FAM190A-3 were identified as protective against DR. The combination of exosomal DLX6-AS1 and PRINS had good diagnostic value for DR in the general population and males. More attention should be paid to the role of exosomal PRINS expression as a predictive and diagnostic DR biomarker in females.

miR-874 is located at 5q31.2, which is frequently deleted in cancer. miR-874 is downregulated in 22 types of cancers and aberrantly expressed in 18 types of non-cancer diseases. The dysfunction of miR-874 is not only closely related to the diagnosis and prognosis of tumor patients but also plays an important role in the efficacy of tumor chemotherapy drugs. miR-874 participates in the ceRNA network of long non-coding RNAs or circular RNAs, which is closely related to the occurrence and development of cancer and other non-cancer diseases. In addition, miR-874 is also involved in the regulation of multiple signaling pathways, including the Wnt/β-catenin signaling pathway, Hippo signaling pathway, PI3K/AKT signaling pathway, JAK/STAT signaling pathway, and Hedgehog signaling pathway. This review summarizes the molecular functions of miR-874 in the biological processes of tumor cell survival, apoptosis, differentiation, and tumorigenesis, and reveal the value of miR-874 as a cancer biomarker in tumor diagnosis and prognosis. Future work is necessary to explore the potential clinical application of miR-874 in chemotherapy resistance.

The removal of the adsorbed oil droplet is critical to deoiling treatment of oil-bearing solid waste. Ultrasonic cavitation is regarded as an extremely useful method to assist the oil droplets desorption in the deoiling treatment. In this paper, the effects of cavitation micro-jets on the oil droplets desorption were studied. The adsorbed states of oil droplets in the oil-contaminated sand were investigated using a microscope. Three representative absorbed states of the oil droplets can be summarized as: (1) the individual oil droplet adsorbed on the particle surface (2) the clustered oil droplets adsorbed on the particle surface; (3) the oil droplet adsorbed in a gap between particles. The micro-jet generation during the bubble collapse near a rigid wall under different acoustic pressure amplitudes at an ultrasonic frequency of 20 kHz was investigated numerically. The desorption processes of the oil droplets at the three representative absorbed states under micro-jets were also simulated subsequently. The results showed that the acoustic pressure has a great influence on the velocity of micro-jet, and the initial diameter of cavitation bubbles is significant for the cross-sectional area of micro-jets. The wall jet caused by a micro-jet impacting on the solid wall is the most important factor for the removal of the absorbed oil droplets. The oil droplet is broken by the jet impinging, and then it breaks away from the solid wall due to the shear force generated by the wall jet. In addition to a higher sound pressure, the cavitation bubble at a larger initial diameter is more important for the desorption of the clustered oil droplets. Conversely, the micro-jet generated by the cavitation bubble at a smaller initial diameter (0.1 mm) is more appropriate for the desorption of the oil droplet in a narrow or sharp-angled gap.

Transforming CO2 into value-added chemicals has been an important subject in recent years. The development of a novel heterogeneous catalyst for highly effective CO2 conversion still remains a great challenge. As an emerging class of porous organic polymers, covalent organic frameworks (COFs) have exhibited superior potential as catalysts for various chemical reactions, due to their unique structure and properties. In this study, a layered two-dimensional (2D) COF, IM4F-Py-COF, was prepared through a three-component condensation reaction. Benzimidazole moiety, as an ionic liquid precursor, was integrated onto the skeleton of the COF using a benzimidazole-containing building unit. Ionization of the benzimidazole framework was then achieved through quaternization with 1-bromobutane to produce an ionic liquid-immobilized COF, i.e., BMIM4F-Py-COF. The resulting ionic COF shows excellent catalytic activity in promoting the chemical fixation of CO2 via reaction with epoxides under solvent-free and co-catalyst-free conditions. High porosity, the one-dimensional (1D) open-channel structure of the COF and the high catalytic activity of ionic liquid may contribute to the excellent catalytic performance. Moreover, the COF catalyst could be reused at least five times without significant loss of its catalytic activity.

With the background of rapid development of intelligent city, intelligent traffic is also getting more and more attention and bus arrival time prediction has become one hotspot to the researchers in recent years. Accurate and real-time prediction of bus state cannot only help travelers to choose a better trip mode, but also provide some scientific advices for the traffic department to manage scientifically and make a reasonable scheduling. Considering the most study focus on the current reliability evaluation and few references about reliability prediction were written, this paper aims to firstly use a reliability evaluation method to get the reliability of bus line. Based on this, this paper proposes a reliability prediction method of further bus service using the random forest. Finally, the model of reliability prediction proposed in this paper is tested with the data of the bus line 23 in Dalian city of China. The result shows that the random forest with the reasonable parameters can predict the reliability of bus service accurately. Furthermore, the random forest method performs better than artificial neural network and support vector machine. It is feasible to predict the reliability of bus service.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

Abstract Background Coinfections in patients with coronavirus disease 2019 (COVID-19) affect patient prognosis. Patients with hematologic malignancies (HMs) are usually immunosuppressed and may be at high risk of coinfection, but few related data have been reported. Here, we conducted a retrospective study to explore coinfections in patients with HMs and COVID-19 by next-generation sequencing (NGS) of bronchoalveolar lavage fluid (BALF). Methods The data of hospitalized patients with pneumonia who underwent NGS analysis of BALF were reviewed. COVID-19 patients with HMs were enrolled in the HM group, and those without HMs were enrolled in the non-HM group. The coinfections of the two groups identified by NGS were analyzed. Results Fifteen patients were enrolled in the HM group, and 14 patients were enrolled in the non-HM group. The coinfection rates in the HM group and non-HM group were 80.0% and 85.7%, respectively. The percentage of coinfected bacteria in the HM group was significantly lower than that in the non-HM group (20.0% vs 71.4%, p = 0.005). The coinfection rates of fungi and viruses were 60.0% and 35.7%, respectively, in the HM group and 35.7% and 78.6%, respectively, in the non-HM group, with no significant differences. The most common coexisting pathogen in patients with HMs was Pneumocystis jirovecii (33.3%), and the most common coexisting pathogen in patients without HMs was human gammaherpesvirus 4 (50%). Coinfection with herpesviruses occurred frequently in both groups. Conclusions Our study showed that the majority of hospitalized patients with COVID-19 are likely to be co-infected with other pathogens. Pneumocystis jiroveci and herpesvirus are commonly coinfected pathogens in patients with HMs. Bacterial coinfection is rare in patients with HMs but is more common in patients without HMs.

Abstract Protein-energy wasting (PEW) is a major contributor to the high mortality among maintenance hemodialysis (MHD) patients. Cardiovascular disease (CVD) is the leading cause of death in dialysis patients, and PEW can significantly increase cardiovascular mortality in MHD patients. Previous studies have confirmed that PA may be a good objective indicator for determining the nutritional status and prognosis of MHD patients. Our study aimed to determine the predictive value of phase angle (PA) as detected by bioelectrical impedance analysis (BIA) on PEW and cardiovascular (CV) risk among MHD patients. Our retrospective observational study involved 161 adult patients with HD. The Cardiovascular risk score is a risk model based on the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS). We established LASSO logistic regression analysis model to identify key parameters related to body composition that can predict PEW in MHD patients. The area under the curve (AUC) values for PA, appendicular skeletal muscle mass index (ASMI), body cell mass (BCM), and mid-arm circumference (MAC) in predicting PEW in male MHD patients were relatively large, with 0.708, 0.674, 0.663, and 0.735, respectively. The predicted PEW values of these parameters were slightly lower in female patients than in men. We incorporated PA, ASMI, BCM, and MAC into a model that predicted the incidence of PEW in maintenance hemodialysis patients using LASSO technology. We discovered that the model predicted a greater AUC of PEW occurrence than any single factor, 0.877 for men and 0.76 for women. The results of the univariate logistic regression analysis showed that the low PA tertile array group had a greater incidence of PEW than the high PA group (P

Abstract The Generalized Feistel Structure ( $$\texttt{GFS}$$ GFS ) is one of the most widely used frameworks in symmetric cipher design. In FES 2010, Suzaki and Minematsu strengthened the cryptanalysis security of $$\texttt{GFS}$$ GFS by searching for shuffles with the best diffusion property. In ASIACRYPT 2018, Shi et al. suggested a set of shuffles, which makes $$\texttt{GFS}$$ GFS a better resistance against Demirci–Selcuk meet-in-the-middle cryptanalysis. Since these shuffles are different from the currently known good ones and also different from the shuffles used in $$\texttt{TWINE}$$ TWINE and $$\texttt{LBlock}$$ LBlock , our research focuses on a more comprehensive evaluation of $$\texttt{GFS}$$ GFS with different shuffles, including diffusion property of shuffle, differential, linear, impossible differential, zero-correlation linear, integral and Demirci–Selcuk meet-in-the-middle cryptanalysis, to find the best one. Such evaluations entail significant time consumption. Thus, we utilize Mixed Integral Linear Programming models and introduce an evaluate-and-filter strategy to achieve it efficiently. Our results verify that the shuffles discovered by Suzaki and Minematsu and those used in $$\texttt{TWINE}$$ TWINE and $$\texttt{LBlock}$$ LBlock are the best so far. We also find that the cryptanalysis resistances of $$\texttt{GFS}$$ GFS are not necessarily consistent. It is this finding that makes the necessity of our more comprehensive evaluation self-evident.