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Purposes: Postoperative rehabilitation of upper limb function is crucial for breast cancer. Therefore, we developed a rehabilitation management platform using virtual reality to improve rehabilitation compliance and effect. The purpose of this research was to understand the user usability experience of breast cancer patients about the postoperative rehabilitation management of upper limb function using virtual reality. Methods: A qualitative descriptive research was designed. We used a maximum difference purpose sampling method. According to the inclusion and exclusion criteria, a 3-armor hospital in Changchun was selected for the recruitment. A one-on-one semi-structured interviews were conducted with patients after breast cancer operation. The Colaizzi seven-step analysis method was used to classify data under summarized themes. Results: Twenty patients participated in this semi-structured interview. User experience could be summarized into four themes as follows: 1) experience and feeling after using the virtual reality rehabilitation management platform; 2) factors influencing the use of the virtual reality rehabilitation management platform; 3) willingness to recommend the virtual reality rehabilitation management platform to peers; and 4) suggestions to improve the virtual reality rehabilitation management platform. Conclusions: Breast cancer patients who used the rehabilitation management platform had a good experience, and their recognition and satisfaction were high. The use of the platform is influenced by many factors, and most patients are willing to recommend this platform to their peers. Future studies should be conducted according to patients’ feedback and suggestions on how to further optimize and improve the platform. [ABSTRACT FROM AUTHOR]

Biofilms adhering to surfaces have severe impacts on both public health and industry. Environmentally friendly strategies for combating biofilms are needed due to the biosafety issues brought by traditional commercial anti‐biofilm additives. Enzyme‐based strategies for biofilm treatment have attracted great research interest, but there is still a major challenge ahead due to the high economic cost and limited stability of natural enzymes. Inspired by the antifouling mechanism of natural haloperoxidase (HPO) secreted by marine algae, which catalyzes the oxidative bromination of bacterial quorum sensing signaling molecules, its artificial enzyme is proposed here in response to the situation mentioned above. A cerium‐based metal–organic framework (Ce‐MOF) is verified to possess HPO‐like activity. Based on its favorable enzyme‐like activity, the Ce‐MOF exhibits remarkable antibacterial and biofilm formation suppression abilities. This study not only expands the variety of HPO‐like artificial enzymes but also paves the way for the application prospect of Ce‐MOFs in water pipe cleaning and biofouling treatment. [ABSTRACT FROM AUTHOR]

With the increasing prevalence and mortality, chronic kidney disease (CKD) has become a world public health problem. As the primary pathological manifestation in CKD, renal fibrosis is often used as a critical target for the treatment of CKD and inhibits the progression of CKD to end-stage renal disease (ESRD). As a potential drug, natural products have been confirmed to have the potential as a routine or supplementary therapy for chronic kidney disease, which may target renal fibrosis and act through various pharmacological activities such as anti-inflammatory and anti-oxidation of natural products. This article briefly introduces the pathological mechanism of renal fibrosis and systematically summarizes the latest research on the treatment of renal fibrosis with natural products of Chinese herbal medicines. [ABSTRACT FROM AUTHOR]

We explore the association of hope and quality of life in breast cancer chemotherapy women. Their quality of life is related to treatment effects and disease outcomes. This cross-sectional study was conducted in City, China, in 2017. In a convenience sampling, 450 women who underwent breast cancer chemotherapy were selected from two hospitals. Descriptive statistics, single-factor analysis, Spearman correlation, linear regression, and structural equation modeling were used to analyze data. The mean quality of life score was 65.65. In linear regression analysis, we found patients' quality of life was significantly related to age, marital status, education level, chemotherapy cycle, and hope. Structural equation results showed the "temporality and future" and "interconnectedness" subscales of the HHI explained 43% of the variance in quality of life. We found hope is an important aspect in quality of life, and further research is needed to determine if nurses can influence this aspect of care. [ABSTRACT FROM AUTHOR]

At present, the number of diabetes patients has exceeded 537 million worldwide and this number continues to increase. Stem cell therapy represents a new direction for the treatment of diabetes; the use of stem cells overcomes some shortcomings associated with traditional therapies. Functional β cells play an important role in the pathogenesis of diabetes. As therapeutic targets, functional β cells are restored by a variety of stem cells, including pluripotent stem cells, mesenchymal cells, and urine-derived stem cells. Although all types of stem cells have their own characteristics, they mainly promote the repair and regeneration of β cells through directional differentiation, immunomodulation, and paracrine signaling after homing to the injured site. However, stem cell therapy still faces many obstacles, such as low long-term cell survival rate after transplantation, low maintenance time of blood glucose homeostasis, immune rejection, and tumorigenesis. Recently, genetically edited pluripotent stem cells and the cotransplantation of mesenchymal stem cells and islet cells have made significant progress in improving the efficacy of stem cell transplantation processes, also providing powerful tools for the study of the mechanisms underlying diabetes and disease modeling. In this review, we first focused on: (1) stem cells as a pool for the differentiation of insulin-producing cells; (2) stem cells as a source for regenerative repair of damaged islets and as a potential cotransplanted population with islets; (3) the potential of combining gene editing with stem cell therapy; and (4) selection of the stem cell transplantation approach. Based on these topics, we discuss the challenges within the field of adapting stem cell-supported and stem cell-derived transplantations and the promising routes for overcoming these problems. [ABSTRACT FROM AUTHOR]

Background: Functional exercise is crucial for breast cancer patients after surgery, and the use of virtual reality technology to assist patients with postoperative upper limb functional rehabilitation has gradually attracted the attention of researchers. However, the usability of the developed rehabilitation system is still unknown to a large extent. The purpose of this study was to develop a virtual reality upper limb rehabilitation system for patients after breast cancer surgery and to explore its usability. Methods: We built a multidisciplinary team based on virtual reality and human-computer interaction technology and designed and developed an upper limb function rehabilitation system for breast cancer patients after surgery. Breast cancer patients were recruited from a grade III-a general hospital in Changchun city for the experiment. We used the System Usability Scale to evaluate the system availability, the Presence Questionnaire scale to measure the immersive virtual reality scene, and the Simulator Sickness Questionnaire subjective measurement scale for simulator sickness symptoms. Results: This upper limb rehabilitation system hardware consisted of Head-mounted Display, a control handle and notebook computers. The software consisted of rehabilitation exercises and game modules. A total of 15 patients were tested on this system, all of whom were female. The mean age was 54.73±7.78 years, and no patients were excluded from the experiment because of adverse reactions such as dizziness and vomiting. The System Usability Scale score was 90.50±5.69, the Presence Questionnaire score was 113.40±9.58, the Simulator Sickness Questionnaire-nausea score was 0.93±1.16, the Simulator Sickness Questionnaire-oculomotor score was 0.80±1.27, the Simulator Sickness Questionnaire-disorientation score was 0.80±1.27, and the Simulator Sickness Questionnaire total score was 2.53±3.40. Conclusions: This study fills in the blanks regarding the upper limb rehabilitation of breast cancer patients based on virtual reality technology system usability research. As the starting point of research in the future, we will improve the system's function and design strictly randomized controlled trials, using larger samples in the promotion, to evaluate its application in breast cancer patients with upper limbs and other physiological functions and the feasibility and effects of rehabilitation. [ABSTRACT FROM AUTHOR]

Especially in the case of persistent EBV, a virus that is already able to trigger HLH, APDS patients are theoretically more prone to develop hyperinflammation and thus HLH [[8]]. Appropriate treatment of APDS patients developing HLH is a great therapeutic challenge. Both patients were children at the time of APDS diagnosis, which suggests that genetic diagnosis is more often considered in pediatric HLH. Although chronically active EBV and EBV-associated lymphomas are common in APDS, HLH has only been reported in two APDS patients, who are summarized in Table 3 [[9]]. [Extracted from the article]

• Denitrification and DNRA contributed more to N transformation than anammox. • Salinity increase shifted nitrate reduction regime from denitrification toward DNRA. • Both C/N ratio and sulfide content are the important factors partitioning the shift. Salinization in estuarine wetlands significantly alters the balance between their nitrogen (N) removal and retention abilities but these processes have not yet been characterized effectively. In the present study, the potential rates of sediment denitrification, anaerobic ammonium oxidation (anammox), and dissimilatory nitrate reduction to ammonium (DNRA) were mapped using N isotope tracing methods along salinity gradients across the Yellow River Delta wetland (YRDW) in China. The contribution of anammox to total dissimilatory N transformations in YRDW was merely 6.8%, whereas denitrification and DNRA contributed 52.3% and 40.9%, respectively. The potential rate of denitrification (5.82 μmol/kg/h) decreased significantly along salinity gradients and markedly exceeded DNRA potential rate (2.7 μmol/kg/h) in fresh wetlands, but was lower than that of DNRA in oligohaline wetlands (3.06 and 3.18 μmol/kg/h, respectively). Moreover, a significantly positive relationship between salinity and DNRA/denitrification was obeserved, indicating that increased salinity may favor DNRA over denitrification. Furthermore, total sulfur (TS) content and ratio of total organic carbon to total nitrogen (C/N) increased with the salinity gradient and showed evident positive relationships with the DNRA/denitrification ratio. In this study, we proved that increased salinization resulted in the dominance of DNRA over denitrification, possible through the addition of S and alteration of the C/N in estuarine wetlands, leading to increased N retention in estuarine wetlands during salinization, which would enhance the eutrophication potential within wetlands and in downstream ecosystems. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods: In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information. Results: Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated. Conclusions: Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation. [ABSTRACT FROM AUTHOR]

A readily available catalyst consisting of iron dichloride in combination with 1,10‐phenanthroline catalyzes the ring‐closing C−H amination of N‐benzoyloxyurea to form imidazolidin‐2‐ones in high yields. The C−H amination reaction is very general and applicable to benzylic, allylic, propargylic, and completely non‐activated aliphatic C(sp3)−H bonds, and it also works for C(sp2)−H bonds. The surprisingly simple method can be performed under open flask conditions. [ABSTRACT FROM AUTHOR]

A readily available catalyst consisting of iron dichloride in combination with 1,10‐phenanthroline catalyzes the ring‐closing C−H amination of N‐benzoyloxyurea to form imidazolidin‐2‐ones in high yields. The C−H amination reaction is very general and applicable to benzylic, allylic, propargylic, and completely non‐activated aliphatic C(sp3)−H bonds, and it also works for C(sp2)−H bonds. The surprisingly simple method can be performed under open flask conditions. [ABSTRACT FROM AUTHOR]

Paeonia lactiflora Pall. (PL) has been commonly used to de-stressing the liver and relieve depression in traditional Chinese medicine for over a thousand years. Recently, it has been widely used in studies on anti-depressant, anti-inflammatory and regulation of intestinal flora. However, the polysaccharide component has received less attention than the saponin component of PL. This study aimed to elucidate the effects of Paeonia lactiflora polysaccharide (PLP) on depressive behavior in mice in a chronic unpredictable mild stress (CUMS) model and its possible action mechanisms. A model of chronic depression induced by the CUMS approach. Behavioral experiments were used to assess the success of the CUMS model and the therapeutic impact of PLP. Then the extent of damage to the colonic mucosa was assessed by H&E staining; the extent of neuronal damage was assessed by Nissler staining. Inflammatory factor expression was assessed at different sites in the mouse by enzyme-linked immunoassay (Elisa). The alterations of faecal microflora were detected by 16S rRNA gene sequencing. In the colonic tissues, NLRP3, ASC and Caspase-1 mRNA and protein levels detected by quantitative real-time PCR (qRT-PCR) and Western blot(WB). PLP can improve depressive behavior in CUMS mice, and colonic mucosal and neuronal damage. Elisa assay showed that PLP could reduce interleukin-1β (IL-1β), interleukin-6 (IL-6), tumour necrosis factor-α (TNF-α) levels, and increase 5-Hydroxytryptamine(5-HT) levels in CUMS mice. 16S sequencing analysis showed that PLP could regulate the intestinal flora of CUMS mice and increase their species richness. In addition, PLP significantly inhibited NLRP3/ASC/Caspase-1 signalling pathways activation in the colonic tissues of CUMS mice. PLP modulates depression-related intestinal ecological dysregulation, increases species richness, and inhibits inflammatory factors levels and NLRP3 inflammasome activation to reduce colonic mucosal and neurons damage, thereby improving depression-like behavior and neurotransmitter release in CUMS mice. [Display omitted] [ABSTRACT FROM AUTHOR]

The berm revetment with a Seabee slope has been widely used in coastal defense structures. It is necessary to understand the distribution of wave-induced uplift pressure as it may cause serious damage to the cover layer of the revetment. Therefore, a series of physical experiments are carried out to quantify the uplift pressure on the revetment under various wave conditions. It is found that the freeboard of the berm is a key parameter which categorizes the laboratory results into two groups. Under negative freeboard, the distribution of uplift pressure on the upper slope is similar to that on the lower slope. Additionally, the maximum uplift pressure on both upper and lower slopes is strongly related to the surf-similarity parameter ξp. Under positive freeboard, the uplift pressure on the lower slope is significantly larger than that of the upper slope. The maximum uplift pressure on the lower slope is similar to that of the conventional rubble mound revetment. At last, Empirical equations are proposed to estimate the maximum relative uplift pressure under positive and negative freeboards. [ABSTRACT FROM AUTHOR]

The first example of a chiral osmium catalyst is reported in which the overall chirality originates exclusively from a stereogenic metal center (metal-centered chirality) with all coordinating ligands being achiral. The non-C2-symmetric chiral-at-metal complex contains two cyclometalated 7-methyl-1,7-phenanthrolinium heterocycles which can be described as two chelating pyridylidene remote N-heterocyclic carbene (rNHC) ligands. The octahedral coordination sphere is completed with one CO and one acetonitrile ligand. A monodentate chiral oxazoline ligand is used as a chiral auxiliary ligand to obtain enantiomerically pure chiral-at-osmium complexes (>99 : 1 e.r.). Finally, it is demonstrated that the developed chiral-at-osmium complex is suitable for ring-closing enantioselective C(sp3)–H aminations, including the first example of catalytic enantioselective cyclizations of azidoformates to chiral 2-oxazolidinones. [ABSTRACT FROM AUTHOR]

Background. Breast cancer is the common malignancy with high morbidity and mortality in women. S-phase kinase-associated protein 2 (Skp2) has been characterized to play an oncogenic role in the breast carcinogenesis and progression. Therefore, inactivation of Skp2 in breast cancer might be a novel approach for fighting breast malignancy. A natural compound diosgenin has been reported to exert anticancer activity in a variety of human cancers. However, the underlying mechanism has not been fully determined. Methods. In this study, we aim to explore whether diosgenin performed antitumor activity via inhibition of Skp2 in breast cancer cells using several methods including MTT, Transwell invasion assay, RT-PCR, western blotting, and transfection. Results. We found that diosgenin inhibited cell viability and stimulated apoptosis. Moreover, we found that diosgenin reduced cell invasion in breast cancer cells. Furthermore, diosgenin inhibited the expression of Skp2 in breast cancer cells. Notably, diosgenin reduced cell viability and motility and induced apoptosis via suppression of Skp2 in breast cancer cells. Conclusion. Our findings revealed that diosgenin could be a potential inhibitor of Skp2 for treating breast cancer. [ABSTRACT FROM AUTHOR]

Nitrite misuse seriously threatens food safety, therefore, it is of significance to develop a facile, sensitive and selective methodology for trace detection of nitrite in food samples for ensuring people's health and safety. Herein, a novel fluorescent sensor was successfully designed for selective, sensitive and visual detection of nitrite. This sensing mechanism involves the inner-filter effect between synthetic green carbon dots and the S-nitrosothiol compound. Green carbon dots were synthesized using a one-step hydrothermal method and used as fluorescent probes, and S-nitrosothiol was generated through the π-conjugation triggered nitrite–thiol reaction. Selective and sensitive nitrite detection was realized using a fluorescence spectrophotometer in the detection range of 0.4–20 μg mL−1. The detection limit of the developed method was calculated to be 0.23 μg mL−1 with satisfactory recoveries ranging from 86.61% to 103.22%. Upon illumination with the help of a portable ultraviolet lamp, the brightness and color of the green fluorescent probe changed with the addition of nitrite at different concentrations. Visual changes were directly observed with the naked eye and used for the qualitative and semiquantitative analysis of nitrite contents. The developed fluorescent sensor offers an effective, simple, rapid and low cost analytical method for sensitive and visual nitrite detection in meat samples to monitor and reduce the risk of nitrite overuse in food processing. [ABSTRACT FROM AUTHOR]

Bayesian analysis provides a framework for the inverse uncertainty quantification (UQ) of combustion kinetic models. As the workhorse of the Bayesian approach, the Markov chain Monte Carlo (MCMC) methods, however, incur a substantial computational cost. In this work, a surrogate model is employed to improve the traditional MCMC algorithm. Specifically, the test errors of three typical surrogate models are compared, namely Polynomial Chaos Expansion (PCE), High Dimensional Model Representation (HDMR) and Artificial Neural Network (ANN); and ANN is shown to be a relatively more efficient surrogate model for the approximation of combustion reaction systems under extensive conditions. An inverse UQ method, which is the combination of the ANN and traditional MCMC method, and as such termed ANN–MCMC, is adopted. The calculation is performed on the methanol oxidation system and a series of ignition delay data are employed to optimize the rate coefficients of the kinetic model. The estimated posterior distributions of the rate coefficients and the model predictions using the ANN–MCMC are compared with the traditional MCMC methods, with the results showing that the ANN–MCMC can significantly reduce the computational cost needed for the MCMC algorithm, especially on the estimation of the posterior distributions of the input parameters. The rejection rate of the samples in a Markov chain is very high, especially for the calculation of the posterior distribution of less sensitive parameters, thus a large number of samples are needed to reach a desired accuracy for traditional MCMC process. While no samples are rejected when training the ANN surrogate model. Therefore, fewer original samples are needed to get a converged ANN surrogate, which can then generate a large number of low-cost ANN samples for a better accuracy of the MCMC process. The errors for the estimated posterior distributions using ANN–MCMC depend on the accuracy of converged ANN surrogates and more accurate results are obtained with improved settings of ANN. The ANN–MCMC is especially suitable to the computational systems when the computational ability is limited. [ABSTRACT FROM AUTHOR]

An enantioselective ring‐closing C(sp3)−H amination of 2‐azidoacetamides is catalyzed by a chiral‐at‐metal ruthenium complex and provides chiral imidazolidin‐4‐ones in 31–95 % yield, with enantioselectivities of up to 95 % ee, and at catalyst loadings down to 0.1 mol % (turnover number (TON)=740). To our knowledge, this is the first example of a highly enantioselective C(sp3)−H amination with aliphatic azides. Mechanistic experiments reveal the importance of the amide group, which presumably enables initial bidentate coordination of the 2‐azidoacetamides to the catalyst. DFT calculations show that the transition state leading to the major enantiomer features a better steric fit and favorable π–π stacking between the substrate and the catalyst framework. Achiral ligands, chiral catalyst: By utilizing a newly prepared chiral‐at‐metal ruthenium catalyst for chelate activation of 2‐azidoacetamides, the challenging catalytic enantioselective ring‐closing C(sp3)−H amination of these substrates was achieved in high yields and enantioselectivities. [ABSTRACT FROM AUTHOR]

An enantioselective ring‐closing C(sp3)−H amination of 2‐azidoacetamides is catalyzed by a chiral‐at‐metal ruthenium complex and provides chiral imidazolidin‐4‐ones in 31–95 % yield, with enantioselectivities of up to 95 % ee, and at catalyst loadings down to 0.1 mol % (turnover number (TON)=740). To our knowledge, this is the first example of a highly enantioselective C(sp3)−H amination with aliphatic azides. Mechanistic experiments reveal the importance of the amide group, which presumably enables initial bidentate coordination of the 2‐azidoacetamides to the catalyst. DFT calculations show that the transition state leading to the major enantiomer features a better steric fit and favorable π–π stacking between the substrate and the catalyst framework. [ABSTRACT FROM AUTHOR]

We study the relative orbifold Donaldson-Thomas theory of [C2/Zn+1]×P1. A correspondence is established between the DT theory relative to disjoint union of vertical fibers to quantum multiplication by divisors for the Hilbert scheme of points on [C2/Zn+1]. This determines a correspondence between the whole theories if a further nondegeneracy condition is assumed. The result can also be viewed as a crepant resolution correspondence to the DT theory of An×P1. [ABSTRACT FROM AUTHOR]

• Mixed-methods investigation of the sense of joint agency in joint action. • Musical duets elicit stronger joint agency than synchronized tones. • Relations between partners' actions strengthen joint agency, beyond synchrony achieved. • Multiple cues contribute to multifaceted joint agency. When people perform joint actions together, they often experience a sense of joint agency ("we did that together"). The current study investigated whether relations between partners' actions within joint actions that require precise interpersonal synchrony influence joint agency, above and beyond the degree of synchrony partners achieve. We employed a mixed-methods approach that combined a quantitative experiment with a qualitative analysis of post-experiment interviews. Partners produced synchronized tone sequences that comprised either constant pitch sequences (simple temporal alignment between partners' actions) or musical duets (complex metrical and harmonic relations between partners' actions). Participants reported stronger joint agency for duets than constant pitches, when comparing trials with equally good synchronization. Post-experiment interviews revealed that joint agency was also influenced by participants' knowledge of the music and their perceptions of task performance, difficulty, and enjoyability. These findings further our understanding of joint agency for joint actions that require precise interpersonal synchrony. [ABSTRACT FROM AUTHOR]

Scavenger receptor class B type 1 (SR-B1) is over-expressed in tumour cells where it mediates the uptake of drug payload of reconstituted high density lipoprotein (rHDL) via the process of reverse cholesterol transport. In this study, rHDL was prepared to determine its function as a drug delivery carrier for targeting hepatocellular carcinoma by incorporating the anti-tumour drug garcinia glycosides (GG) into rHDL to yield rHDL/GG nano-complexes. Structural analysis indicated that the rHDL/GG nano-complex was similar to HDL in size. HepG2 cells treated with fluorescent-labelled rHDL/GG exhibited a time-dependent increase in cell death. Further experiment in which HepG2 cells were treated with rHDL/GG plus plasma-derived HDL showed reduction in cell death compared to treatment with rHDL/GG alone, suggesting that plasma-derived HDL compete with rHDL/GG for binding to the SR-B1 on the cell. We concluded that rHDL could not only incorporate GG but could also serve as a carrier, targeting the drug to HepG2 cells via SR-B1. [ABSTRACT FROM AUTHOR]

The catalytic asymmetric alkylation of the remote, unactivated δ-position of N-alkyl amides was enabled by the combination of visible-light-induced proton-coupled electron transfer, 1,5-hydrogen atom transfer, and chiral Lewis acid catalysis in up to 82% yield and up to 97% ee. [ABSTRACT FROM AUTHOR]

• FeCoNiCrNb 0.5 Mo x high-entropy alloy coatings are prepared by laser cladding. • Mo element promotes the formation of Laves phase and eutectic microstructure. • The appropriate addition of Mo improves corrosion resistance of the coatings. • The passive region of FeCoNiCrNb 0.5 Mo x high-entropy alloy coatings are wider than that of most high-entropy alloys. In order to improve the properties of the surface corrosion resistance of the load bearing parts of marine equipment in the high salt and high humid environment, novel FeCoNiCrNb 0.5 Mo x (x = 0.00, 0.25, 0.50, 0.75, 1.00) high-entropy alloy coatings were prepared on the 42CrMo steel by laser cladding. The testing results show that the FeCoNiCrNb 0.5 Mo x coatings consist of FCC phase and Laves phase, and the increase of Mo content promotes the formation of the Laves phase. FeCoNiCrNb 0.5 Mo 0.50 , FeCoNiCrNb 0.5 Mo 0.75 and FeCoNiCrNb 0.5 Mo 1.00 coatings have complete eutectic structure. The appropriate addition of Mo improves corrosion resistance by promoting the formation of Laves phase. Mo 0.75 coating possesses the best corrosion resistance property. Compared with the substrate of the 42CrMo steel, the FeCoNiCrNb 0.5 Mo x coatings possess much better corrosion resistance, which means that they are promising materials for surface protection of marine engineering equipment. [ABSTRACT FROM AUTHOR]

Stromwater biofilter technology was greatly improved through adding iron-rich soil, plant detritus and eutrophic lake sediment. Significant ammonium and phosphate removal efficiencies (over 95%) in treatments with iron-rich soil were attributed to strong adsorption capability resulting in high available phosphorus (P) in media, supporting the abundance and activity of nitrifiers and denitrifiers as well as shaping compositions, which facilitated nitrogen (N) removal. Aquatic and terrestrial plant detritus was more beneficial to nitrification and denitrification by stimulating the abundance and activity of nitrifiers and denitrifiers respectively, which increased total nitrogen (TN) removal efficiencies by 17.6% and 22.5%. In addition, bioaugmentation of nitrifiers and denitrifiers from eutrophic sediment was helpful to nutrient removal. Above all, combined application of these materials could reach simultaneously maximum effects (removal efficiencies of P, ammonium and TN were 97–99%, 95–97% and 60–63% respectively), suggesting reasonable selection of materials has important contribution and application prospect in stormwater biofilters. [ABSTRACT FROM AUTHOR]

• A novel NPP prediction model was developed. • The model took into account both the effects of temperature and land use change. • The model has a high accuracy and a good applicability for prediction of the NPP in the study area. • The changes of land use and NPP in Sichuan-Chongqing region under different scenarios were simulated. The net primary productivity (NPP) is a direct reflection of the productivity of vegetation communities; predicting changes in the NPP is important for assessing the sustainability of ecosystems. However, few studies have been conducted on the prediction of the NPP, and it is necessary to develop more prediction models. Therefore, in this study, a model based on temperature and land use/cover changes (LUCCs) was developed and applied to the Sichuan-Chongqing region, and the prediction results were obtained under the development scenarios composed of a shared socioeconomic pathway (SSP) and a representative concentration pathway (RCP). The results show that: (1) the NPP in the Sichuan-Chongqing region in 2020 simulated using the newly developed model to the actual NPP in 2020, and the kappa coefficient was 75.03%, which indicates that this model has a good applicability. (2) The total NPP increased in all five SSP-RCP scenarios (from high to low, SSP5-8.5, SSP4-6.0, SSP1-2.6, SSP2-4.5, and SSP3-7.0), but the SSP3-7.0 and SSP5-8.5 scenarios include uncontrolled urban expansion, leading to destruction of ecological areas. The SSP2-4.5 scenario continues the existing development pattern, with more moderate land use and NPP changes. The urban land use in the SSP1-2.6 and SSP4-6.0 scenarios is more intensive and the level of ecological protection is better. Simulating the LUCCs under different SSP-RCP scenarios and predicting the NPP changes can provide a theoretical basis for coping with future ecological changes and improving ecosystem stability. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) simulation in the design of combustion devices imposes increased demand on combustion kinetic models with acceptable uncertainties. Model optimization is often utilized to constrain the model parameters with experimental data to reduce the prediction uncertainties. Since it is unaffordable to conduct experiments under all the concerned conditions, experimental design approaches are proposed to find the most valuable experiments to be conducted. An integrated computational framework, OptEx (Optimal Experiments), is proposed to facilitate applications of experimental design, data clustering, and model optimization with optimal experimental data. Specifically, this framework integrates the functions of dimension reduction, global sensitivity analysis, forward uncertainty quantification, model-analysis-based experimental design, and model optimization. The share of data and surrogate models between different modules significantly improves the computational efficiencies of model analysis, experimental design and model optimization. Two case studies of a methanol system are utilized to demonstrate the functionalities of OptEx. First, experimental designs are performed based on sensitivity entropy and surrogate model similarity analysis to find out informative while independent experiments. Second, experimental data clustering with OptEx is demonstrated by grouping massive experiments and selecting optimal experiments for each group. For both cases, a small size of optimal experimental datasets are utilized for model optimization, yielding an optimized model with smaller uncertainty bounds. Meanwhile, the input parameter uncertainties are significantly reduced and parameter correlations are identified via the joint probability distributions of optimized parameters. [ABSTRACT FROM AUTHOR]

Synchrotron-based molecular-beam mass spectrometry (MBMS) measurements can yield the concentrations of various species during combustion experiments. These quantitative data are helpful for combustion kinetic model development, validation, and optimization. The uncertainties of stable species can be limited within 30%, but for free radicals, the uncertainties can be large with a factor of 2–4 owing to the unknown photoionization cross sections. Therefore, measurements of free radicals are often used qualitatively for model validation, and how to utilize these measurements quantitatively for model optimization is still a challenge. In the present work, we propose to use derived targets, which are the ratios of the mole fractions of the same free radicals under two different experimental conditions, to optimize kinetic models. These derived targets have much smaller uncertainties compared to the original mole fractions because the unknown photoionization cross sections of the free radicals are canceled out, leading to more effective optimization results. To demonstrate this method, a dimethyl ether (DME) system and a methanol system are chosen as case studies. A global-sensitivity-based model analysis is first conducted to select informative targets for model optimization, i.e., derived targets of C 2 H 5 in the DME system and the derived targets of CH 3 in the methanol system. Model optimization is then performed using the ANN-MCMC method with these selected derived targets. The results of the global sensitivity analysis show that some important reactions can be highlighted when the derived targets are considered, while these reactions may not be important when considering only the original targets. The results of the comparison of two kinds of the optimized models show that better posterior models can be achieved by the model optimization based on derived targets than based on the original targets. [ABSTRACT FROM AUTHOR]

The transition state theory (TST) and RRKM/master equation (ME) method have been well acknowledged in developing combustion models. Uncertainties in input parameters, such as energies and vibrational frequencies which are produced from quantum chemical computations, largely contribute to uncertainties of the theoretically predicted rate coefficients. The potential correlations among these parameters and their effects on the uncertainty of rate coefficients have been investigated in the present work. The correlations in quantum chemical computations on H abstraction, H addition and thermal decomposition reactions, regarding radical sites, molecular types and reaction types are investigated and quantified using Pearson correlation coefficients. The results show that notable correlations exist in energies and imaginary frequencies. The correlation factors are then incorporated into the global uncertainty analysis for two typical TST and RRKM/ME computation systems, i.e., H abstraction reactions of acetaldehyde by the HO 2 radical (TST) and the multi-well multi-channel reactions on the C 4 H 7 potential energy surface (RRKM/ME) to unravel the effects of correlation on the uncertainties of rate coefficients and branching ratios. Comparing with the random independent sampling for input parameters, to include correlations among input parameters largely reduces the predicted uncertainties, with the largest reduction of ∼30% for absolute rates and ∼45% for the branching ratio in TST calculations and ∼33% for absolute rates and ∼50% for branching ratios in RRKM/ME calculations. The uncertainty propagation behavior with and without considering the correlation in the input parameters was further uncovered by the sensitivity analysis. In the TST calculation, the uncertainty reduction for absolute rate coefficients solely originates from the reduction of the sampling space, and the uncertainty reduction for the branching ratio originates from both the reduced parameter space and the cancelation of sensitive parameters. In the RRKM/ME calculation, the uncertainty reduction in rate coefficients arises only from reduced sampling space, but the uncertainty reduction in branching ratios is due to both the parameter cancelling effect and correlation. [ABSTRACT FROM AUTHOR]

Following the idea of Aganagic–Okounkov [2] , we study vertex functions for hypertoric varieties, defined by K -theoretic counting of quasimaps from P 1. We prove the 3d mirror symmetry statement that the two sets of q -difference equations of a 3d hypertoric mirror pairs are equivalent to each other, with Kähler and equivariant parameters exchanged, and the opposite choice of polarization. Vertex functions of a 3d mirror pair, as solutions to the q -difference equations, satisfying particular asymptotic conditions, are related by the elliptic stable envelopes. Various notions of quantum K -theory for hypertoric varieties are also discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Luminescent fusiform Al-MOF nanosheets were synthesized by a one-step hydrothermal method. • The prepared nanosheets were used for sensitive and rapid detection of nitrofuran. • Paper-based analytical device was designed for visual semi-quantitative detection of nitrofuran. Developing highly sensitive and visual methods for rapid detection of antibiotics is significant to ensure food quality and safety. To meet the requirement of nitrofuran antibiotics detection, luminescent fusiform Al(III)-containing metal–organic frameworks (Al-MOF) nanosheets were successfully synthesized by one-step hydrothermal method. And then, the nanosheet served as a fluorescent probe to detect nitrofuran via the inner-filter effect mechanism. The developed sensor allowed sensitive and selective detection of nitrofuran with good linear relationships. And, the detection limit (LOD) values were estimated to be 0.53, 0.838 and 0.583 μM for nitrofurazone, nitrofurantoin and furazolidone detection, respectively. The practical application of the proposed system was verified by HPLC in spiked milk samples with satisfying recoveries ranging from 88.14 to 126.21% and low relative standard deviations of 2.85 ~ 8.13%. Moreover, we designed fluorescent test papers for semi-quantitative detection of nitrofuran via naked-eye colorimetric assay. The established method provides an alternative strategy for semiquantitative detection of nitrofuran. [ABSTRACT FROM AUTHOR]

Background and Aims. Arterial stiffness and sarcopenia are commonly seen in patients with type 2 diabetes mellitus (T2DM), and both are age-related diseases. However, few studies have addressed the causal relationship between age, arterial stiffness, and sarcopenia, especially in patients with T2DM. This study is aimed at investigating the relationship among age, arterial stiffness, and sarcopenia in patients with T2DM. Methods and Results. This cross-sectional study enrolled 557 inpatients with diabetes at the First Affiliated Hospital of Wenzhou Medical University, China, between June 2020 and July 2021. Patients who were diagnosed with T2DM and underwent examination of dual-energy X-ray absorptiometry, handgrip strength, 6-meter walk speed, and brachial-ankle pulse wave velocity (baPWV, a recognized indicator of arterial stiffness) were enrolled. A total of 447 patients were included. A dose-dependent relationship was found between age and sarcopenia. We also found a dose-dependent relationship between age and baPWV. Similarly, significant dose-dependent relationships were found across baPWV tertiles with higher prevalence of sarcopenia. Then, a mediation analysis was performed to explore the mediation effect of arterial stiffness on age-associated sarcopenia. We found that the prevalence of sarcopenia increased by 0.0115 (95% CI, 0.0028–0.0239) per 1 year increase in age by the mediation effect of baPWV and that the direct effect of aging on sarcopenia was 0.0441 (95% CI, 0.0101–0.0909) per 1 year older. baPWV mediated 20.5% of the positive relationship between increased age and the prevalence of sarcopenia. Conclusions. Elevated baPWV partially mediates the association of age and sarcopenia among patients with T2DM. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis of combustion kinetic models is time consuming, because numerous samples generated by kinetic model simulations within the uncertainty space of the input parameters are needed. To alleviate this problem, surrogate models are often adopted to replace the kinetic model to generate samples. Directly constructing surrogate models is difficult in high-dimensional systems, therefore, local sensitivity analysis is usually first performed to exclude unimportant parameters. However, local sensitivity analysis may cause inaccurate screening when the model is nonlinear and the uncertainties of the input parameters are large. To overcome this difficulty, we propose herein the active subspace-based surrogate model (ASSM) method, which uses the active subspace to transform the high-dimensional input to low-dimensional features and then maps these features to the model target. The entire parameters are included in the ASSM while the mapping relationship is fitted with low-dimensional features. The accuracy and the computational cost of the ASSM are compared with those of the local sensitivity-based surrogate model (LSSM) in the hydrogen and methanol combustion systems. The results show that the ASSM can improve the data utilization and lead to a lower computational cost compared to the LSSM when several targets are considered, because one kinetic model simulation can yield original samples for many model targets simultaneously for the ASSM. The global sensitivity analysis with the ASSM and the LSSM is compared in the hydrogen, methanol and n-decane combustion systems, and the results show that the ASSM could lead to more reliable sensitivity indices than the LSSM because the active subspace-based dimensionality reduction can prevent the possibility of missing important parameters by transforming the entire parameters to new features instead of excluding some parameters. [ABSTRACT FROM AUTHOR]

Based on the brief introduction of the dangers of antibiotic residues and the current status of antibiotic detection technology, the fluorescence sensing methods based on carbon quantum dots, semiconductor quantum dots, metal-organic framework materials and upconversion nanomaterials for antibiotic detection application were reviewed. It pointed out that compared with traditional detection methods, fluorescence sensor detection method had the advantages of speediness, high sensitivity and visualization. However, due to the food matrix effect, complex sample pretreatment steps were often required to purify and enrich the target substance. Therefore, appropriate pre-treatment technologies or matrix purification methods, new novel fluorescent nanomaterials, portable sensing detection equipment suitable for on-site detection, and the specificity of detection by combining aptamer, antibody and molecular imprinting techniques were the technological trend. It was expected that this review was able to provide a reference and theoretical basis for the practical applications of fluorescence sensing technology in antibiotic detection in the future. [ABSTRACT FROM AUTHOR]

Autosomal dominant polycystic kidney disease is a genetic kidney disease caused by mutations in the genes PKD1 or PKD2. Its course is characterized by the formation of progressively enlarged cysts in the renal tubules bilaterally. The basic genetic explanation for autosomal dominant polycystic kidney disease is the double-hit theory, and many of its mechanistic issues can be explained by the cilia doctrine. However, the precise molecular mechanisms underpinning this condition's occurrence are still not completely understood. Experimental evidence suggests that aquaporins, a class of transmembrane channel proteins, including aquaporin-1, aquaporin-2, aquaporin-3, and aquaporin-11, are involved in the mechanism of autosomal dominant polycystic kidney disease. Aquaporins are either a potential new target for the treatment of autosomal dominant polycystic kidney disease, and further study into the physiopathological role of aquaporins in autosomal dominant polycystic kidney disease will assist to clarify the disease's pathophysiology and increase the pool of potential treatment options. We primarily cover pertinent findings on aquaporins in autosomal dominant polycystic kidney disease in this review. [ABSTRACT FROM AUTHOR]

• The abnormally high expression of Tspan5 mRNA in the tubal may be related to TP. • The expression level of Tspan5 mRNA may reflect degree of endometrial decidualization. [ABSTRACT FROM AUTHOR]

Focuses on prediction relating to the racial and ethnic minority population parity of the physician workforce in the United States. Growth in the physician-to-population ratio; Statistical information on race and ethnicity of all first-year resident physicians and first-year resident physicians who are American citizens during the academic year 1993 to 1994; Future challenges in the physician workforce policy.

We consider a pair of quiver varieties |$(X;X^{\prime})$| related by 3D mirror symmetry, where |$X =T^*{Gr}(k,n)$| is the cotangent bundle of the Grassmannian of |$k$| -planes of |$n$| -dimensional space. We give formulas for the elliptic stable envelopes on both sides. We show an existence of an equivariant elliptic cohomology class on |$X \times X^{\prime} $| (the mother function) whose restrictions to |$X$| and |$X^{\prime} $| are the elliptic stable envelopes of those varieties. This implies that the restriction matrices of the elliptic stable envelopes for |$X$| and |$X^{\prime}$| are equal after transposition and identification of the equivariant parameters on one side with the Kähler parameters on the dual side. [ABSTRACT FROM AUTHOR]

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians' lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT. [ABSTRACT FROM AUTHOR]

Syndecan-binding protein (SDCBP) was reported to stimulate the advancement of esophageal squamous cell carcinoma (ESCC) and could potentially be a target for ESCC treatment. There is a growing corpus of research on the anti-tumor effects of iron chelators; however, very few studies have addressed the involvement of dexrazoxane in cancer. In this study, structure-based virtual screening was employed to select drugs targeting SDCBP from the Food and Drug Administration (FDA)-approved drug databases. The sepharose 4B beads pull-down assay revealed that dexrazoxane targeted SDCBP by interacting with its PDZ1 domain. Additionally, dexrazoxane inhibited ESCC cell proliferation and anchorage-independent colony formation via SDCBP. ESCC cell apoptosis and G2 phase arrest were induced as measured by the flow cytometry assay. Subsequent research revealed that dexrazoxane attenuated the binding ability between SDCBP and EGFR in an immunoprecipitation assay. Furthermore, dexrazoxane impaired EGFR membrane localization and inactivated the EGFR/PI3K/Akt pathway. In vivo, xenograft mouse experiments indicated that dexrazoxane suppressed ESCC tumor growth. These data indicate that dexrazoxane might be established as a potential anti-cancer agent in ESCC by targeting SDCBP. [ABSTRACT FROM AUTHOR]

Type 2 diabetic kidney disease (T2DKD) is a common microvascular complication of type 2 diabetes mellitus (T2DM), and its incidence is significantly increasing. Microinflammation plays an important role in the development of T2DKD. Based on this, this study investigated the value of inflammatory markers including neutrophil–lymphocyte ratio (NLR), high-sensitivity C-reactive protein (hs-CRP), monocyte chemoattractant protein-1 (MCP-1) in the prediction of T2DKD. This was a cross-sectional survey study. A total of 90 patients with T2DM, who were hospitalized in the nephrology and endocrinology departments of the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from June 2021 to January 2022, were included and divided into three groups (A1, A2, A3) according to the urinary albumin-to-creatinine ratio (UACR). Observe and compare the basic information, clinical and laboratory data, and the inflammatory markers NLR, hs-CRP, MCP-1. Results revealed that high levels of NLR (OR = 6.562, 95% CI 2.060–20.902, P = 0.001) and MCP-1 (OR = 1.060, 95% CI 1.026–1.095, P < 0.001) were risk factors in the development of T2DKD. Receiver operating characteristic curve analysis showed that the area under curve of NLR and MCP-1 in diagnosing T2DKD were 0.760 (95% CI 0.6577–0.863, P < 0.001) and 0.862 (95% CI 0.7787–0.937, P < 0.001). Therefore, the inflammatory markers NLR and MCP-1 are risk factors affecting the development of T2DKD, which of clinical value may be used as novel markers of T2DKD. [ABSTRACT FROM AUTHOR]

Aim: This research aimed to explore the level of hope and symptom burden of breast cancer women undergoing chemotherapy, and predictive factors of hope were also investigated. Background: Chemotherapy brings physical and psychological stress to breast cancer patients. As an effective coping strategy, hope gives them the courage to overcome difficulties and improve prognosis and survival. Therefore, efforts are needed to raise hope. Design/Methods: A total of 450 women who were undergoing breast cancer chemotherapy participated in this cross‐sectional study. Sociodemographic data, disease characteristics, and measures of hope and symptom burden were collected using questionnaires. Hope was assessed using the validated Herth Hope Index, and the previously validated Memorial Symptom Assessment Scale was used to assess symptom burden. This paper adhered to the STROBE guidelines. Results: Chinese breast cancer chemotherapy women hope average scores of 30.15 ± 4.82 were in the medium range of the Hearth Hope Index as specified by Herth to be 24–35. Patients with age ≤45, religious beliefs and lighter symptom burden have a higher level of hope. These variables explained a total of 22.9% of the variation in hope. Conclusions: The level of hope for women undergoing breast cancer chemotherapy still needs to be further improved. Symptom burden can negatively predict hope. Relevance to clinical practice: If nurses can decrease breast cancer chemotherapy women symptom burden, there is an impact on increasing levels of hope. [ABSTRACT FROM AUTHOR]

Slit2 (slit guidance ligand 2), a ligand of the Roundabout1 (Robo1) transmembrane receptor, is often overexpressed in colorectal carcinomas (CRCs). In this study, we performed data mining in the Metabolic gEne RApid Visualizer (MERAV) database and found that Slit2 and TGF-β1 (Transforming growth factor-β1) are highly expressed in carcinomas relative to those in tumor-free tissues from healthy volunteers or wild type mice. Furthermore, expression of Slit2 and TGF-β1 in CRCs increases with pathological stages. Serum levels of Slit2 in patients with CRC and in ApcMin/+ mice with spontaneous intestinal adenoma were significantly increased compared with those in healthy controls. Specific blockage of Slit2 binding to Robo1 inactivated TGF-β/Smads signaling and inhibited tumor cell migration and metastasis, which can be partially restored by treatment with TGF-β1. However, specific inhibition of TGF-β1/Smads signaling reduced CRC tumor cell migration and invasion without affecting cell proliferation. This study suggests that activation of Slit2/Robo1 signaling in CRC induces tumor metastasis partially through activation of the TGF-β/Smads pathway. [ABSTRACT FROM AUTHOR]

To minimize the uncertainty of the parameters in combustion kinetics models, Bayesian methods are commonly used for uncertainty constraints based on experimental data. With the rapid and substantial growth of experimental data, using all the experimental data for optimization is not only redundant and time-consuming, but it could also lead to data consistency problems. In this work, the global sensitivity-based affinity propagation method (GSAP) is proposed to cluster experimental datasets and to select representative experimental conditions. Specifically, the global sensitivity coefficient is first obtained through an analysis to characterize the sources of uncertainty in the kinetic model under different experimental conditions. The similarity coefficient, which is defined based on the global sensitivity, measures the resemblance between two experimental conditions. By exchanging messages calculated from similarity, affinity propagation enables the experimental dataset to be automatically clustered into several classes without specifying the number of classes in advance. This method innovatively introduces the consideration of model and experimental uncertainty under different conditions to obtain better optimization results. The correctness and effectiveness of the method are validated through clustering and optimizing on a laminar flame speed dataset of common C 0 C 4 fuels. The dataset consisting of 288 experimental conditions has been automatically clustered into 27 categories, and an exemplar of each category is given. These exemplary conditions reflect the dominant chemistry behind their cluster. At the same time, these conditions have larger model prediction uncertainty and smaller experimental uncertainty to provide better Bayesian constraints. The uncertainty of the model parameters after Bayesian optimization is effectively constrained. The average uncertainty of model predictions across the dataset is reduced from 30 % to 10 % using only 27 exemplar conditions for optimization. While selecting experimental data for model optimization, the clustering strategies provided by this method also, in turn, help understand its underlying chemical essence. [ABSTRACT FROM AUTHOR]

Depressive symptoms is an important public health problem. The aim of the present study is to examine the association of dietary flavonoid intake with risk of depressive symptoms. In this study, we conducted an assessment to investigate the potential association between dietary flavonoid intake and the risk of depressive symptoms. Our analysis was based on a nationally representative sample of 9674 adults who participated in the 2007–2010 and 2017–2018 National Health and Nutrition Examination Surveys. Flavonoid intake was measured using a 24-hour dietary recall method, while depressive symptoms was evaluated using the Patient Health Questionnaire-9. To examine the relationship between dietary flavonoid intake and the risk of depressive symptoms, we employed logistic regression, subgroup and restricted cubic spline models. Following multivariate adjustment, the study found a negative association between total flavonoids, anthocyanidins, flavanones, flavones, isoflavones and the risk of depressive symptoms. In subgroup analysis, total flavonoid intake was inversely associated with risk of depressive symptoms among women whereas no association was found among man. Additionally, a non-linear relationship was observed between total flavonoid intake and depressive symptoms, with statistical significance (P for nonlinearity <0.001). The present study employed a cross-sectional design, which precludes the establishment of causality. Furthermore, the data relied on self-reported measures. In present study, moderate total flavonoids intake, but not high intake, was associated with lower odds of depressive symptoms suggesting a U-shaped association. • Following multivariate adjustment, the study found a negative association between anthocyanidins, flavanones, flavones, isoflavones, total flavonoids and the risk of depression symptom. • In subgroup analysis, total flavonoid intake was inversely associated with risk of depression symptom among women whereas no association was found among man. • a non-linear relationship was observed between total flavonoid intake and depression, with statistical significance (P for nonlinearity <0.001). • We included and adjusted the known and potential risk factors of depressive symptom. In addition, we conducted a doseresponse analysis to evaluate the correlation between total flavonoid intake and depression symptom. [ABSTRACT FROM AUTHOR]

Background: This meta-analysis aimed to explore the correlation between the different doses of remifentanil-based anaesthesia and postoperative pain in randomised trials. Methods: The electronic databases including PubMed, Cochrane, clinical trial registries, and Google Scholar were searched up to November 2022 for randomised controlled trials (RCTs) that assessed the dose dependent efficacy of remifentanil for postoperative pain intensity and hyperalgesia. Results: 31 studies involving 2019 patients were included for analysis. Compared with the high remifentanil dose administration, patients in low doses showed less postoperative pain intensity at 1-2 h (weighted mean differences (WMD): 0.60, 95% CI, 0.05 to 1.15), 3-8 h (WMD: 0.38, 95% CI, 0.00 to 0.75), 24 h (WMD: 0.26, 95% CI, 0.04 to 0.48) and 48 h (WMD: 0.32, 95% CI, 0.09 to 0.55). Remifentanil-free regimen failed to decrease the pain score at 24 h (WMD: 0.10, 95% CI, -0.10 to 0.30) and 48 h (WMD: 0.15, 95% CI, -0.22 to 0.52) in comparison with remifentanil-based anaesthesia. After excluding trials with high heterogeneity, the dose of the remifentanil regimen was closely correlated with the postoperative pain score (P=0.03). In addition, the dose of the remifentanil regimen was not associated with the incidence of postoperative nausea and vomiting (PONV) (P=0.37). Conclusions: Our meta-analysis reveals that the low dose of remifentanil infusion is recommendable for general anaesthesia maintenance. No evidence suggests that remifentanil-free regimen has superiority in reducing postoperative pain. Moreover, remifentanil doesn't have a dose dependent effect in initiating PONV. Trial registration: The protocol of present study was registered with PROSPERO (CRD42022378360). [ABSTRACT FROM AUTHOR]