Search

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. To solve this problem, we propose an edge computing based privacy-preserving truth discovery mechanism, PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers reliabilities. A truth-dependent budget recycle mechanism is designed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude based on worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy w-event (,)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.

In this study, we elaborate on a design and realization of fuzzy rule-based model in the horizontal federated learning framework. Traditional machine learning in distributed environment often involves sharing sensitive information with other sites or transferring data to a central server on which a global model is trained. These situations increase the communication overhead and pose serious threats to the privacy of sensitive data. Federated learning opens up the possibility for collaboratively training a global model on a basis of distributed on-site data without sacrificing data privacy. While fuzzy rule-based models have been used in system modeling due to their strong modeling abilities and good interpretability, the implementation of fuzzy rule-based models in a distributed environment without compromising data privacy still requires careful consideration. This work proposes a two-step federated learning approach to train a global model on a basis of private data located across different sites without their centralization. The first step concerns the determination of the structure of the data through federated collaborative clustering. Subsequently, a shared global model is trained jointly by all the participating clients. An advantage of the propose method is that it achieves high accuracy without violating data privacy. A series of experimental studies are conducted to gain a detailed insight into the realization steps and demonstrate the effectiveness of the proposed method.

It is well-known that the HVAC (heating, ventilation and air conditioning) dominates electricity consumption in commercial buildings. In this paper, we focus on one of the core problems in building operation, namely chiller sequencing to reduce HVAC electricity consumption. Our contributions are threefold. First, we make a case for why it is important to quantify the performance profile of a chiller, namely coefficient of performance (COP), at run-time, by developing a data-driven COP estimation methodology. Second, we show that predicting COP accurately is a non-trivial problem, requiring considerable computation time. To overcome this barrier, we develop a data-driven COP prediction model and an edge-based chiller sequencing framework integrating the COP predictions, and show that they strike a good balance between electricity saving and ease of use for real-world deployment. Finally, we evaluate the performance of our scheme by applying it to real-world data, spanning 4 years, obtained from multiple chillers across 3 large commercial buildings in Hong Kong. The results show an electricity saving of over 30% compared to baselines. We offer our edge based data-driven chiller sequencing framework as a cost-effective and practical mechanism to reduce electricity consumption associated with HVAC operation in commercial buildings.

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affect efficiency. Aiming to cumbersome the aforementioned scarcities, this letter is concerned with a novel approach to the controller design for modular multilevel converter, which makes use of event-triggered neural predictor-based finite control-set model predictive control methodology under low switching frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field.

In 2012, SEC mandated all corporate filings for any company doing business in US be entered into the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. In this work we are investigating ways to analyze the data available through EDGAR database. This may serve portfolio managers (pension funds, mutual funds, insurance, hedge funds) to get automated insights into companies they invest in, to better manage their portfolios. The analysis is based on Artificial Neural Networks applied to the data. In particular, one of the most popular machine learning methods, the Convolutional Neural Network (CNN) architecture, originally developed to interpret and classify images, is now being used to interpret financial data. This work investigatesthe best way to input data collected from the SEC filings into a CNN architecture. We incorporate accounting principles and mathematical methods into the design of three image encoding methods. Specifically, two methods are derived from accounting principles (Sequential Arrangement, Category Chunk Arrangement) and one is using a purely mathematical technique (Hilbert Vector Arrangement). In this work we analyze fundamental financial data as well as financial ratio data and study companies from the financial, healthcare and IT sectors in the United States. We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1D input directly for fundamental data. We do not find the Hilbert Vector Arrangement technique to be significantly better than other imaging techniques.

Past research has produced contradictory insights into how prior collaboration between organizations—their relational embeddedness—impacts collective collaborative performance. We theorize that the...

In this article, we propose a novel predictive control framework for modular multilevel converter, which makes use of neural predictor-based low switching frequency finite control-set model predictive control methodology with respect to online weighting factors tuning subject to robustness characteristics. The main objectives of this article are to maintain a low switching frequency operation and to enhance the system robustness. Firstly, a modified cost function together with an online weighting factors auto-tuning technique, which has a good potential to fulfill the low frequency operation, is presented under different operating conditions. Next, we provide a framework to explicitly address parameter mismatches and unknown nonlinear system dynamics in this proposal by introducing the neural network predictor control technique. The novelty of contribution of this work lies not only in incorporating the proposed cost function with a predictor-based neural network design solution for satisfying the requirement of the low switching frequency and robustness, but also in the utilization of the auto-tuning technique for online determining the suitable weighting factors. Finally, we demonstrate the applicability of the proposed predictive control framework in comparison to state-of-the-art finite control-set model predictive control approaches under various operating conditions.

OBJECTIVE: This study aimed to explore the clinical detection and prognosis of coagulation function in patients with liver failure and sepsis. METHODS: The plasma fibrinogen (FIB), factor II, factor VII, factor V, factor IV, antithrombin III (ATIII), platelet (PLT), mean PLT volume (MPV), D-dimer, prothrombin activity (PTA), and fibrin degradation product (FDP) levels and thromboelastogram values were detected in patients with liver failure complicated with sepsis and compared with those in the liver failure and liver cirrhosis groups. The patients with liver failure complicated with sepsis were analyzed by univariate and multivariate logistic regression, and the regression equation was established. RESULTS: The levels of FIB, factor II, factor VII, factor V, ATIII, PLT, MPV, D-dimer, and FDP in the patients with liver failure complicated with sepsis were compared with those in the control group patients, and the differences were statistically significant (p

The local model poisoning attack is an attack to manipulate the shared local models during the process of distributed learning. Existing defense methods are passive in the sense that they try to mitigate the negative impact of the poisoned local models instead of eliminating them. In this paper, we leverage the new federated analytics paradigm, to develop a proactive defense method. More specifically, federated analytics is to collectively carry out analytics tasks without disclosing local data of the edge devices. We propose a Federated Anomaly Analytics enhanced Distributed Learning (FAA-DL) framework, where the clients and the server collaboratively analyze the anomalies. FAA-DL firstly detects all the uploaded local models and splits out the potential malicious ones. Then, it verifies each potential malicious local model with functional encryption. Finally, it removes the verified anomalies and aggregates the remaining to produce the global model. We analyze the FAA-DL framework and show that it is accurate, robust, and efficient. We evaluate FAA-DL by training classifiers on MNIST and Fashion-MNIST under various local model poisoning attacks. Our experiment results show FAA-DL improves the accuracy of the learned global model under strong attacks up to 6.90 times and outperforms the state-of-the-art defense methods with a robustness guarantee.

Multisensory sensitivity (MSS), observed in some chronic pain patients, may reflect a generalized central nervous system sensitivity. While several surveys measure aspects of MSS, there remains no gold standard. We explored the underlying constructs of four MSS-related surveys (80 items in total) using factor analyses using REDCap surveys (N = 614, 58.7% with pain). Four core- and six associated-MSS factors were identified from the items assessed. None of these surveys addressed all major sensory systems and most included additional related constructs. A revised version of the Somatosensory Amplification Scale was developed, encompassing five core MSS systems: vision, hearing, smell, tactile, and internal bodily sensations: the 12-item Multisensory Amplification Scale (MSAS). The MSAS demonstrated good internal consistency (alpha = 0.82), test-retest reliability (ICC3,1 = 0.90), and construct validity in the original and in a new, separate cohort (R = 0.54 - 0.79, p < 0.0001). Further, the odds of having pain were 2 - 3.5 times higher in the highest sex-specific MSAS quartile relative to the lowest MSAS quartile, after adjusting for age, sex, BMI, and pain schema (p < 0.03). The MSAS provides a psychometrically comprehensive, brief, and promising tool for measuring the core-dimensions of MSS. PERSPECTIVE: Multiple multisensory sensitivity (MSS) tools are used, but without exploration of their underlying domains. We found several measures lacking core MSS domains, thus we modified an existing scale to encompass five core MSS domains: light, smell, sound, tactile, and internal bodily sensations using only 12 items, with good psychometric properties.

‘Co-control’, or synergistically reducing CO2 and local air pollutant emissions, is an important strategy for cities to achieve ‘low carbon’ and ‘blue sky’ simultaneously. However, there were few studies to evaluate and compare the level of co-control of CO2 and local air pollutants in cities yet. The present study proposed qualitative and quantitative methods to evaluate the level of co-control of CO2 and three local air pollutant (SO2, NOx, and particulate matter PM) emissions in key environmental protection cities in China over two periods (2012–2015 and 2015–2018). Statistical analysis found that, though three local air pollutant emissions positively correlated with CO2 emission, no significantly positive correlation was found between local air pollutants emission reductions and CO2 emission reductions, indicating that co-control effects were poor in general. By using the co-control effect coordinate system, qualitative evaluation showed that less than half of the sample cities could achieve co-control of the total amount of CO2 and local air pollutants. By employing the indicator of emission reduction equivalence (EReq), quantitative evaluation showed that the co-control level of the sample cities improved in 2015–2018 compared with 2012–2015. Further regression analysis showed that, reducing coal consumption and economic development significantly enhanced the co-control performance of the observed cities. The present case study proved that the proposed methods for evaluation and comparison of the city co-control performance works well, and can be used in other countries and regions to promote global cities racing to carbon and local air pollutants co-control.

Introduction Autonomic symptoms are common in patients with dementia with Lewy bodies (DLB). Although autonomic dysfunction is considered as one of the primary early markers in patients with DLB, there are few studies that examine the timing of the onset of autonomic symptoms in these patients. The purpose of the current study was to evaluate the time of onset of autonomic symptoms in patients with DLB using a questionnaire. Methods Autonomic symptoms were evaluated in patients with DLB using the Scales for Outcomes in Parkinson's disease - autonomic (SCOPA-Aut) questionnaire. Time of the onset of autonomic symptoms of patients with DLB was also assessed relative to that of memory loss. Results A total of 106 patients with DLB were included. The most frequent symptom of dysautonomia in patients with DLB was constipation (60.4%), followed by orthostatic dizziness (33%), urinary frequency (30.2%), and daytime hyperhidrosis (22.6%). The gastrointestinal system was the most prominent system of autonomic dysfunction in patients with DLB. Orthostatic dizziness, daytime hyperhidrosis, and constipation all preceded the onset of memory loss by 0.2 ± 4.9, 3.3 ± 8.6, and 3.7 ± 9.2 years, respectively, while urinary incontinence occurred 1.7 ± 1.5 years following the onset of memory loss. Conclusion Many autonomic symptoms precede the onset of memory loss in patients with DLB, especially constipation and daytime hyperhidrosis. These symptoms can facilitate an early diagnosis of patients with DLB.

The aim of this Phase 1/2, 2-part, multicenter trial was to report clinical safety and efficacy of long-term golodirsen treatment among ambulatory patients with exon 53 skip-amenable Duchenne muscular dystrophy (DMD). Part 1 was a 12-week, randomized, double-blind, placebo-controlled, dose-titration study followed by 9-week safety review. Part 2 was a 168-week, open-label evaluation of golodirsen 30 mg/kg. Part 1 primary endpoint was safety. Part 2 primary endpoints were dystrophin protein expression and 6-minute walk test (6MWT); secondary endpoints were percent predicted forced vital capacity (FVC%p) and safety. Post hoc ambulation analyses used mutation-matched external natural history controls. All patients from Part 1 (golodirsen, n = 8; placebo, n = 4) plus 13 additional patients entered Part 2; 23 completed the study. Adverse events were generally mild, nonserious, and unrelated to golodirsen, with no safety-related discontinuations or deaths. Golodirsen increased dystrophin protein (16.0-fold; P

In this article, we present federated analytics, a new distributed computing paradigm for data analytics applications with privacy concerns. With the advances of sensing, communication, and edge computing technologies, data are massively generated, transmitted and analyzed in an edge-cloud computing environment. In many applications, the edge devices and the data generated in the edge belong to heterogeneous owners. Data privacy and confidentiality have become increasing concerns to these owners. The current edge-cloud computing paradigm for data analytics, where data are sent to a central server for analytics, can no longer match the application requirements. Federated analytics is a newly proposed computing paradigm where raw data are kept local with local analytics and only the insights generated from local analytics are sent to a server for result aggregation. Federated analytics differs from the recent federated learning paradigm in the sense that federated learning emphasizes collaborative model training, whereas federated analytics emphasizes drawing conclusions from data. In this article, we first clarify what federated analytics is and its position in the research literature. We then present why we need federated analytics, that is, the motivation and application case studies. Finally, we discuss the opportunities and challenges of federated analytics.

Single-component organic solar cells (SCOSCs) with high stability and simplified fabrication process are supposed to accelerate the commercialization of organic photovoltaics. However, the types of photo-active materials and photovoltaic performance of SCOSCs are still far lagging behind the bulk-heterojunction type organic solar cells (BHJ OSCs). It is still an arduous task to introduce new photo-active materials into SCOSCs, aiming to improve the efficiencies of SCOSCs. One feasible way is to construct double-cable polymers with new structures and tune conformation, morphology and mobility for the improvement in power conversion efficiencies (PCEs). Hence, in this work, we constructed a new double-cable polymer PBTT-BPTI by introducing fused core 5,7-dibromo-2,3-bis(2-ethylhexyl)benzo[此处b,c斜体么? 正文中斜了]dithiophene-4,8-dione (TTDO) into the main backbone and benzo[ghi]-perylene triimide (BPTI) unit into the side chain. Both of the two units show strong electron-withdrawing property, which are expected to broaden absorption spectra and enhance intermolecular interaction. The double-cable polymer exhibited a broad absorption in the range of 300-700 nm with an optical band gap (Eg) of 1.79 eV. The PCE of PBTT-BPTI-based SCOSCs was 2.15%, which may be limited by the unconstructed efficient electron transporting channels.

BACKGROUND AND AIMS Stroke is currently the leading cause of death in China; however, the past decade has produced no new epidemiological studies of stroke. Therefore, the current study aimed to compare the prevalence and risk factors of stroke between 2010 and 2019. METHODS A comparative study was used to analyze the prevalence of risk factors for stroke in a population aged 65 years or older between 2010 and 2019. Demographic characteristics, risk factors, medical history, and other clinical characteristics were collected for all participants via door-to-door interviews and inpatient hospital records. RESULTS The standardized prevalence of stroke was 7.9% in 2010 and 14.2% in 2019 (p

Earth current generated by the operation of urban rail transit can enter into the neutral point of grounding transformer, which will result in the deterioration of transformer operation performance and cause electrical safety problems. In this article, the neutral current for 220 kV transformers has been measured in the real power system, the characteristics of dc and harmonic current are analyzed, and a novel suppression method for the dc and harmonic current simultaneously combining an active converter with a dc blocking capacitor is proposed, which solves not only the traditional dc bias problem but also the intrusion of the harmonic current from urban rail transit. The active converter is designed to compensate harmonic voltage to suppress harmonic current, and the capacitor provides blocking for the dc current. The protection scheme in case of the high fault current of the neutral point is presented. The improved control method is designed to control the neutral current and adjust the fundamental impedance for the requirements of relay protection. The effectiveness of the proposed method is verified by the digital simulations and the laboratory experiments based on the field working conditions, and the results demonstrate the satisfying performances.

The aim of this paper is to establish the notion of the creative city, show its essential characteristics, and critically evaluate and analyse what elements impact creativity and innovation in cities using real case studies and related literature. Finally, in the essay, the topic of whether or not China will have creative cities in the future is asked, and the feasibility of constructing creative cities in China is discussed, prompting a new way of thinking about China’s future creative city plan.

Autonomous driving approaches today are mainly based on perception-planning-action modular pipelines and the End2End paradigm respectively. The End2End paradigm is a strategy that directly maps raw sensor data to vehicle control actions. This strategy is very promising and appealing because complex module design and cumbersome data labeling are avoided. Since this approach lacks a degree of interpretability, safety and practicability. we propose an autonomous driving approach based on trajectory learning using deep neural networks in this paper. In comparison to End2End algorithm, it is found that the trajectory learning algorithm performs better in autonomous driving. As for trajectory learning algorithm, the CNN_Raw-RNN network structure is established, which is verified to be more effective than the original CNN_LSTM network structure. Besides, we propose an autonomous driving architecture of a pilot and copilot combination. The pilot is responsible for trajectory prediction via imitation learning with labeled driving trajectories, while the copilot is a safety module that is employed to verify the effectiveness of the vehicle trajectory by the results of the semantic segmentation auxiliary task. The proposed autonomous driving architecture is verified with a real car on urban roads without manual intervention within 40 km.

We are now in an information era and the volume of data is growing explosively. However, due to privacy issues, it is very common that data cannot be freely shared among the data generating. Federated analytics was recently proposed aiming at deriving analytical insights among data-generating devices without exposing the raw data, but the intermediate analytics results. Note that the computing resources at the data generating devices are limited, thus making on-device execution of computing-intensive tasks challenging. We thus propose to apply the digital twin technique, which emulates the resource-limited physical/end side, while utilizing the rich resource at the virtual/computing side. Nevertheless, how to use the digital twin technique to assist federated analytics while preserving distributed data privacy is challenging. To address such a challenge, this work first formulates a problem on digital twin-assisted federated distribution discovery. Then, we propose a federated Markov chain Monte Carlo with a delayed rejection (FMCMC-DR) method to estimate the representative parameters of the global distribution. We combine a rejection–acceptance sampling technique and a delayed rejection technique, allowing our method to be able to explore the full state space. Finally, we evaluate FMCMC-DR against the Metropolis–Hastings (MH) algorithm and random walk Markov chain Monte Carlo method (RW-MCMC) using numerical experiments. The results show our algorithm outperforms the other two methods by 50% and 95% contour accuracy, respectively, and has a better convergence rate.

激光显示、医疗设备及量子通信迫切需求高性能GaN基绿光激光二极管(LD). 研制绿光LD最大的挑战是生长高势能均匀性的InGaN/GaN多量子阱. 我们采用各种光学测量手段对绿光LD结构和芯片进行了表征. 在激发功率密度为7 W cm−2时, 300 K下光致发光半高宽为108 meV, 电流密度为20 A cm−2时, 电致发光半高宽为114 meV, 这些研究结果表明势能均匀性得到了显著改善. 同时, 由变温光致发光测试得到的表征局域态分布宽度的σ值和由时间分辨光致发光测试得到的表征激子局域带尾态的E0值都很小, 进一步表明势能均匀性很好. 由于势能均匀性的极大改善, 实现了斜率效率0.8W A −1, 输出光功率可以达到1.7W的绿光LD芯片.

Background: Parkinson’s disease (PD) is a common neurodegenerative disease. With the aging of the Chinese population, it is important to understand the prevalence of PD in the elderly. Objective: Little data are available on the current prevalence of PD in China. The aim of this study was to determine the prevalence of PD in Chinese individuals aged 65 years and older and to analyze associated risk factors. Methods: We performed a population-based cross-sectional survey using a multistage cluster sampling design. Residents aged 65 years and older were drawn from 11 urban districts and 10 rural counties across China. Data were entered into spreadsheets and analyzed using SPSS 24. Results: We identified 151 patients with PD among 8,124 residents aged 65 years and over, including 75 men and 76 women. The overall prevalence of PD in the study population was 1.86%, and the standardized prevalence of PD was 1.60%. The crude prevalence in men (2.12%) was higher than that in women (1.66%) and the standardized prevalence in urban areas (1.98%) was higher than that in rural areas (1.48%). Logistic regression analysis showed that independent risk factors for PD were older age, heavy metal or pesticide exposure, urban residence, rapid eye movement sleep behavior disorder, and heart disease. Conclusions: The prevalence of PD among individuals aged 65 years and older in China has remained constant. The prevalence of PD is higher in men than in women and higher in urban areas than in rural areas.

Graphdiyne(GDY) has the unique feature in the topological ordered arranged sp- and sp2-hybridized carbon atoms, thus deriving a series of 2D allotropes. Due to inhomogeneous π-bonding and carbon orbital overlap between different hybrid carbon atoms, GDY possesses a natural band gap with a Dirac cones structure. And GDY exhibits semiconductor property with a conductivity of 2.516×l0−4 S/m at room temperature. The topological distribution of alkyne and benzene bonds of GDY makes its surface charge distribution extremely uneven, which produces high intrinsic activity for further modification. Its unique molecular structure endows the specific interaction with various species, such as ions, atoms, molecules and nanoparticles, showing excellent charge transport capability and unique advantages in mass transfer and energy conversion. From the view of the interaction principle between GDY and different compositions, we summarized the application of GDY-based materials in the fields of catalysis, energy conversion and storage, biological detection and so on.

Objective This study was designed to evaluate the efficacy of analgesia and incidence of postoperative nausea and vomiting (PONV) of several widely used clinical treatments for postoperative analgesia following abdominal surgery through network meta-analysis (NMA) based on published randomized controlled trials (RCTs). Methods This NMA was registered on PROSPERO as CRD 42020169606. Primary outcomes were pain scores (visual analog scale) and accumulative opioid consumption, and secondary outcomes assessed the incidence of PONV at 24 hours after surgery. Results A total of 215 RCTs and 15114 patients were identified in this NMA. In comparison to placebo, use of a preoperative paravertebral block (mean: -12.63, 95% CI: -21.12 to -4.13), continuous wound infiltration (mean: -9.68, 95%CI: -13.15 to -6.22), and postoperative wound infiltration (mean: -6.34, 95%CI: -10.59 to -2.08) had significantly lower pain scores, less opioid consumption (mean: -2.00, 95%CI: -3.52 to -0.48; mean: -1.34, 95%CI: -1.87 to -0.81; mean: -1.41, 95%CI: -2.07 to -0.74, respectively) and lower incidence of PONV (OR: 0.30, 95%CI :0.13 to 0.67; OR: 0.49, 95%CI: 0.24 to 0.98; OR: 0.55, 95%CI: 0.34 to 0.89, respectively). Conclusions The findings from our work provide evidence that preoperative paravertebral block was superior to continuous or postoperative wound infiltration to provide postoperative analgesia, nausea and vomiting after abdominal surgery.

New Equations for Estimating GFR without Race Equations for estimating GFR with serum creatinine overestimate measured GFR in Blacks. The authors report new equations, without race as an inflation factor, using cystatin C and creatinine that reduced errors in estimation between Black participants and non-Black participants.Background Current equations for estimated glomerular filtration rate (eGFR) that use serum creatinine or cystatin C incorporate age, sex, and race to estimate measured GFR. However, race in eGFR equations is a social and not a biologic construct. Methods We developed new eGFR equations without race using data from two development data sets: 10 studies (8254 participants, 31.5% Black) for serum creatinine and 13 studies (5352 participants, 39.7% Black) for both serum creatinine and cystatin C. In a validation data set of 12 studies (4050 participants, 14.3% Black), we compared the accuracy of new eGFR equations to measured GFR. We projected the prevalence of chronic kidney disease (CKD) and GFR stages in a sample of U.S. adults, using current and new equations. Results In the validation data set, the current creatinine equation that uses age, sex, and race overestimated measured GFR in Blacks (median, 3.7 ml per minute per 1.73 m(2) of body-surface area; 95% confidence interval [CI], 1.8 to 5.4) and to a lesser degree in non-Blacks (median, 0.5 ml per minute per 1.73 m(2); 95% CI, 0.0 to 0.9). When the adjustment for Black race was omitted from the current eGFR equation, measured GFR in Blacks was underestimated (median, 7.1 ml per minute per 1.73 m(2); 95% CI, 5.9 to 8.8). A new equation using age and sex and omitting race underestimated measured GFR in Blacks (median, 3.6 ml per minute per 1.73 m(2); 95% CI, 1.8 to 5.5) and overestimated measured GFR in non-Blacks (median, 3.9 ml per minute per 1.73 m(2); 95% CI, 3.4 to 4.4). For all equations, 85% or more of the eGFRs for Blacks and non-Blacks were within 30% of measured GFR. New creatinine-cystatin C equations without race were more accurate than new creatinine equations, with smaller differences between race groups. As compared with the current creatinine equation, the new creatinine equations, but not the new creatinine-cystatin C equations, increased population estimates of CKD prevalence among Blacks and yielded similar or lower prevalence among non-Blacks. Conclusions New eGFR equations that incorporate creatinine and cystatin C but omit race are more accurate and led to smaller differences between Black participants and non-Black participants than new equations without race with either creatinine or cystatin C alone. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases.)

The improvement of reservoir operation optimization (ROO) can lead to comprehensive economic benefits as well as sustainable development of water resources. To achieve this goal, an algorithm named wind-driven optimization (WDO) is first employed for ROO in this paper. An improved WDO(IWDO) is developed by using a dynamic adaptive random mutation mechanism, which can avoid the algorithm stagnation at local optima. Moreover, an adaptive search space reduction (ASSR) strategy that aims at improving the search efficiency of all evolutionary algorithms is proposed. The application results of the Goupitan hydropower station show that IWDO is an effective and viable algorithm for ROO and is capable of obtaining greater power generation compared to the classic WDO. Moreover, it is shown that the ASSR strategy can improve the search efficiency and the quality of scheduling results when coupled with various optimization algorithms such as IWDO, WDO and particle swarm optimization. HIGHLIGHTS A new algorithm named wind-driven optimization algorithm (WDO) is first introduced to reservoir operation optimization.; WDO is improved by two novel strategies.; One of the strategies mentioned in point 2 is also suitable for other algorithms to improve the efficiency of algorithms.

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal.

This study introduced new MRI techniques such as neurite orientation dispersion and density imaging (NODDI); NODDI applies a three-compartment tissue model to multishell DWI data that allows the examination of both the intra- and extracellular properties of white matter tissue. This, in turn, enables us to distinguish the two key aspects of axonal pathology—the packing density of axons in the white matter and the spatial organization of axons (orientation dispersion (OD)). NODDI is used to detect possible abnormalities of posttraumatic encephalomalacia fluid-attenuated inversion recovery (FLAIR) hyperintense lesions in neurite density and dispersion. Methods. 26 epilepsy patients associated with FLAIR hyperintensity around the trauma encephalomalacia region were in the epilepsy group. 18 posttraumatic patients with a FLAIR hyperintense encephalomalacia region were in the nonepilepsy group. Neurite density and dispersion affection in FLAIR hyperintense lesions around encephalomalacia were measured by NODDI using intracellular volume fraction (ICVF), and we compare these findings with conventional diffusion MRI parameters, namely, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Differences were compared between the epilepsy and nonepilepsy groups, as well as in the FLAIR hyperintense part and in the FLAIR hypointense part to try to find neurite density and dispersion differences in these parts. Results. ICVF of FLAIR hyperintense lesions in the epilepsy group was significantly higher than that in the nonepilepsy group ( P < 0.001 ). ICVF reveals more information of FLAIR(+) and FLAIR(-) parts of encephalomalacia than OD and FA and ADC. Conclusion. The FLAIR hyperintense part around encephalomalacia in the epilepsy group showed higher ICVF, indicating that this part may have more neurite density and dispersion and may be contributing to epilepsy. NODDI indicated high neurite density with the intensity of myelin in the FLAIR hyperintense lesion. Therefore, NODDI likely shows that neurite density may be a more sensitive marker of pathology than FA.

Adipose browning leads to increased energy expenditure and reduced adiposity and has, therefore, become an attractive therapeutic strategy for obesity. In this study, we elucidated the effect of green tea aqueous extract (GTAE) on the browning of inguinal white adipose tissue (Ing‐WAT) and brown adipose tissue (BAT) in high‐fat diet (HFD)‐fed mice. The main phytochemical components identified in GTAE through high‐performance liquid chromatography (HPLC) included (−)‐gallocatechin, (−)‐epigallocatechin, (−)‐catechin, (−)‐epigallocatechin‐3‐gallate, caffeine, (−)‐epicatechin, (−)‐gallocatechin gallate, and (−)‐epicatechin‐3‐gallate. Daily supplementation with 1% GTAE for 12 weeks markedly reduced bodyweight gain, systemic inflammation, oxidative stress, and improved insulin resistance. Additionally, histological analysis revealed that dietary supplementation with 1% GTAE reversed HFD‐induced adipocyte size and hepatic steatosis. These effects were associated with activation of browning in the Ing‐WAT and BAT, which mediate systemic metabolic dysfunction in HFD‐fed mice. Taken together, our data support the use of GTAE, a natural product, for the attenuation of obesity through the activation of fat browning., The present study found that mice fed a high‐fat diet showed significantly lower weight gain upon GTAE administration than their control counterparts. In addition, GTAE ameliorated the high‐fat diet‐induced hepatic steatosis, insulin resistance, and low‐systematic inflammatory. These effects were associated with browning activation in the Ing‐WAT and BAT.

Docetaxel resistance developed in half of castration-resistant prostate cancer (CRPC) patients hinders its long-term clinical application. The current study was designed to investigate the effects of Chinese medicine Zhoushi Qi Ling decoction on the docetaxel resistance of prostate cancer as well as elucidate the underlying molecular mechanism. In our study, Qi Ling significantly decreased viability and colony formation as well as increased apoptosis of docetaxel-resistant (DR) CRPC cells. Qi Ling-treated DR cells exhibited decreased glucose consumption, lactate release and pyruvate production. Moreover, lncRNA SNHG10 was upregulated in DR tissues of CRPC patients and was negatively correlated with the progression-free survival. Bioinformatics analysis indicated miR-1271-5p as the associated miRNA possibly binding with SNHG10. miR-1271-5p up-regulation dramatically decreased the luciferase activity of SNHG10 in DR cells. SNHG10 knockdown sharply increased the expression of miR1271-5p in DR cells. Targetscan predicted TRIM66 as one of the downstream targets of miR-1271-5p. miR-1271-5p up-regulation drastically reduced luciferase activity as well as TRIM66 expression in DR cells. Also, the knockdown of SNHG10 remarkably suppressed the expression of TRIM66 in DR cells. Additionally, Qi Ling treatment reduced SNHG10 and TRIM66, while increased miR1271-5p, in DR cells. In summary, Qi Ling inhibited docetaxel resistance and glycolysis of CRPC possibly via SNHG10/miR-1271-5p/TRIM66 pathway.

Faced with increasing governance tensions to cope with conflicts and risks associated with megaprojects, relationship management is seen as an essential aspect for improving relationship quality and achieving project success. Drawing upon transaction cost theory, this study investigated the effects of two types of ties (relational ties and structural ties) on relationship quality. Data from 212 megaproject practitioners were analyzed by structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM revealed the indirect effects of two types of ties on relationship quality and the mediating role of contractual functions in the management of relationships. Through the lens of symbiosis, the fsQCA revealed three types of configurations that were sufficient for shaping relationship quality. This study provides a systematic and nuanced distinction between multi-dimensional strengths of ties and contractual functions in project management. Such knowledge would allow megaproject managers to develop effective interventions aimed at enhancing relationship quality.

Hui Zhang,1,&ast; Dandan Chen,1,&ast; Jing Shao,1 Ping Zou,2 Nianqi Cui,3 Leiwen Tang,1 Xiyi Wang,4 Dan Wang,1 Jingjie Wu,1 Zhihong Ye1 1Department of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Scholar Practitioner Program, School of Nursing, Nipissing University, Toronto, Ontario, Canada; 3Department of Nursing, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 4Department of Nursing, School of Nursing, Shanghai JiaoTong University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhihong YeDepartment of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, 3# Qingchun Dong Road, Jianggan District, Hangzhou, Zhejiang, People’s Republic of ChinaTel +86 13606612119Email 3192005@zju.edu.cnPurpose: Machine learning (ML) techniques have emerged as a promising tool to predict risk and make decisions in different medical domains. We aimed to compare the predictive performance of machine learning-based methods for 4-year risk of metabolic syndrome in adults with the previous model using logistic regression.Patients and Methods: This was a retrospective cohort study that employed a temporal validation strategy. Three popular ML techniques were selected to build the prognostic models. These techniques were artificial neural networks, classification and regression tree, and support vector machine. The logistic regression algorithm and ML techniques used the same five predictors. Discrimination, calibration, Brier score, and decision curve analysis were compared for model performance.Results: Discrimination was above 0.7 for all models except classification and regression tree model in internal validation, while the logistic regression model showed the highest discrimination in external validation (0.782) and the smallest discrimination differences. The logistic regression model had the best calibration performance, and ANN also showed satisfactory calibration in internal validation and external validation. For overall performance, logistic regression had the smallest Brier score differences in internal validation and external validation, and it also had the largest net benefit in external validation.Conclusion: Overall, this study indicated that the logistic regression model performed as well as the flexible ML-based prediction models at internal validation, while the logistic regression model had the best performance at external validation. For clinical use, when the performance of the logistic regression model is similar to ML-based prediction models, the simplest and more interpretable model should be chosen.Keywords: prognosis model, metabolic syndrome, calibration, discrimination, machine learning

Using the plantar pressure imaging analysis method to realize the optimization design of shoe last is still relatively preliminary. The analysis and utilization of imaging data still have problems such as single processing, incomplete information acquisition, and poor processing model robustness. A deep self-organizing map neural network based on Marr-Hildreth filter (dSOM-wh) is developed in this research. The structure and learning algorithms were optimized by learning vector quantization (LVQ) and count propagation (CP). As a kind of Marr-Hildreth filter, Laplacian of Gaussian (LoG) was developed for the preprocessing. The proposed method performed high effectiveness in accuracy (AC) (92.88%), sensitive (SE) (0.8941), and f-measurement (F1) (0.8720) by comparing with ANN, CNN, SegNet, ResNet, and pre-trained inception-v neural networks. The classification-based plantar pressure biomedical functional zoning technologies have potential applications in the comfort shoe production industry.

This study investigated the presence of designer benzodiazepines in 35 urine specimens obtained from emergency department patients undergoing urine drug screening. All specimens showed apparent false-positive benzodiazepine screening results (i.e., confirmatory testing using a 19-component liquid chromatography–tandem mass spectrometry (LC–MS-MS) panel showed no prescribed benzodiazepines at detectable levels). The primary aims were to identify the possible presence of designer benzodiazepines, characterize the reactivity of commercially available screening immunoassays with designer benzodiazepines and evaluate the risk of inappropriately ruling out designer benzodiazepine use when utilizing common urine drug screening and confirmatory tests. Specimens were obtained from emergency departments of a single US Health system. Following clinically ordered drug screening using Abbott ARCHITECT c assays and laboratory-developed LC–MS-MS confirmatory testing, additional characterization was performed for investigative purposes. Specifically, urine specimens were screened using two additional assays (Roche cobas c502 and Siemens Dimension Vista) and LC–quadrupole time-of-flight mass spectrometry (LC–QTOF-MS) to identify presumptively positive species, including benzodiazepines and non-benzodiazepines. Finally, targeted, qualitative LC–MS-MS was performed to confirm the presence of 12 designer benzodiazepines. Following benzodiazepine detection using the Abbott ARCHITECT, benzodiazepines were subsequently detected in 28/35 and 35/35 urine specimens using Siemens and Roche assays, respectively. LC–QTOF-MS showed the presumptive presence of at least one non-Food and Drug Administration (FDA)-approved benzodiazepine in 30/35 specimens: flubromazolam (12/35), flualprazolam (11/35), flubromazepam (2/35), clonazolam (4/35), etizolam (9/35), metizolam (5/35), nitrazepam (1/35) and pyrazolam (1/35). Two or three designer benzodiazepines were detected concurrently in 13/35 specimens. Qualitative LC–MS-MS confirmed the presence of at least one designer benzodiazepine or metabolite in 23/35 specimens, with three specimens unavailable for confirmatory testing. Urine benzodiazepine screening assays from three manufacturers were cross-reactive with multiple non-US FDA-approved benzodiazepines. Clinical and forensic toxicology laboratories using traditionally designed LC–MS-MS panels may fail to confirm the presence of non-US FDA-approved benzodiazepines detected by screening assays, risking inappropriate interpretation of screening results as false positives.

Lead halide perovskite quantum dots (QDs) are known as prospective optoelectronic device materials because of their excellent luminescence, extraordinary photoelectric performance, and specific octahedron framework. Herein, we report a cost-effective approach for synthesizing highly stable CH3NH3PbBr3 QDs in low-polarity binary solvents without nitrogen protection. The CH3NH3PbBr3 QDs are tunable from 1.2 to 4.2 nm by adjusting the proportion of oleic acid and oleylamine as capping ligands. The photoluminescence quantum yield of CH3NH3PbBr3 QDs can reach 87.4%. The fluorescence can maintain over 80% of its earliest emission intensity under the atmosphere after 5 days, which is much better than that (∼10%) of QDs with ligand-assisted reprecipitation. The possible reaction mechanism of preparing CH3NH3PbBr3 QDs was also addressed. Notably, such a strategy can be applied extensively in the preparation of other lead halide perovskite QDs. Furthermore, the as-prepared thick PMMA-coated CH3NH3PbBr3 QDs were further conjoined with a red luminescence powder on a blue InGaN chip to obtain a powerful efficiency (45.4 lm W-1) warm white light-emitting diode.

Purpose Mobile instant messaging (IM) has been increasingly adopted by hotels to communicate with customers. This study aims to explore communication between hotels and customers and identifies the factors that affect hotel customers’ intention to use mobile IM to communicate with hotels. Design/methodology/approach A two-stage exploratory sequential mixed-method design, which combines semi-structured interview and policy-capturing method was applied. Findings The findings indicate that customers are more likely to use mobile IM to communicate with hotels for non-urgent matters; before and after a stay; and if customers are accustomed to using mobile IM for work and non-work purposes in daily life. Research limitations/implications This study goes beyond traditional theories to capturing communication-related factors that affect customers’ IM use in a hotel context. Practical implications The findings indicate why hotel managers should avoid relying on IM as the dominant communication channel. Originality/value This is among one of the first studies that explore customers’ communication needs and communication media choice in hotels.

To evaluate in a laboratory setting, the impact of three designs of endodontic access cavities on dentine removal and effectiveness of canal instrumentation in extracted maxillary first molars using micro-computed tomography (micro-CT).A total of 30 extracted intact maxillary first molars were selected and scanned by micro-CT with a voxel size of 24 µm and randomly distributed into three groups: the traditional endodontic cavity (TEC) group, the conservative endodontic cavity (CEC) group and the guided endodontic cavity (GEC) group. The pulp chambers of teeth in the groups were accessed accordingly. After root canal preparation, the teeth were rescanned. The volume of dentine removed after canal preparation, the noninstrumented canal areas, canal transportation and centring ratio were analysed. Data were analysed statistically using one-way analysis of variance. Tukey's post hoc test was used for multiple comparisons. The significance level was set at p .05.The total volume of dentine removed was significantly greater in the TEC group after root canal preparation (p .05). No significant differences in the volume of dentine removed occurred between the CEC and GEC groups (p .05). The volume of dentine removed in the crown, pericervical dentine and coronal third of the canal was significantly lower in CEC and GEC groups when compared to that in the TEC group (p .05), no difference was observed in the middle third of the canal and apical third of the canal amongst the three groups (p .05). There was no significant difference in noninstrumented canal area, canal transportation and centring ratio amongst the TEC, CEC and GEC groups (p .05).In extracted maxillary molars tested in a laboratory setting, CEC and GEC preserved more tooth tissue in the crown, pericervical dentine and coronal third of the canal compared with TEC after root canal preparation. The design of the endodontic access cavity did not impact on the effectiveness of canal instrumentation in terms of noninstrumented canal area, canal transportation and centring ratio.

What is known and objective Remifentanil can effectively decrease the sevoflurane concentration to block sympathetic adrenergic response to CO2 pneumoperitoneum stimulus,and liver dysfunction will significantly reduce the MACBAR (minimum alveolar concentration for blocking adrenergic response) of sevoflurane. However, the effects of different remifentanil concentrations on the MACBAR of sevoflurane in patients with liver dysfunction are unclear. The aim of this study was to observe the effects of different remifentanil concentrations by intravenous target-controlled infusion on the MACBAR of sevoflurane in patients with grade B liver dysfunction under carbon dioxide pneumoperitoneum stimulus. Methods Seventy-five patients with grade B liver dysfunction undergoing elective laparoscopic surgery were selected, and randomly divided into three groups with remifentanil plasma target concentrations of 0 (group R0 ), 1 (group R1 ) and 2 (group R2 ) ng/ml. Anaesthesia was induced by intravenous injection of propofol 2-3 mg/kg, remifentanil 2 μg/kg and cisatracurium 0.15 mg/kg. All groups were inhaled different concentrations of sevoflurane. The determination of sevoflurane MACBAR in each group was used a method of sequential-allocation technique, and venous blood samples were taken before and after the creation of carbon dioxide pneumoperitoneum to determine plasma adrenaline and noradrenaline concentrations. Results and discussions The MACBAR of sevoflurane in groups R0 , R1 and R2 was 4.83%, 3.00% and 2.10%, respectively. The MACBAR of sevoflurane was significantly difference among the three groups. When a similar effect of MACBAR had achieved in each group, no significant differences were found in the changes of plasma adrenaline and noradrenaline concentrations before and after the creation of pneumoperitoneum. What is new and conclusion Target-controlled infusion of different concentrations of remifentanil can reduce sevoflurane MACBAR during pneumoperitoneum stimulation in patients with liver dysfunction in some degree. However, the changes of plasma adrenaline and noradrenaline concentrations are consistent in the three groups when patient's stress response was inhibited at the same degree.

Background Hypertrophic scars can be caused by various injuries and lead to a decrease in quality of life of those affected. This study was performed retrospectively in our center to investigate the safety and effectiveness of fractional micro-plasma radio-frequency treatment combined with ablative fractional carbon dioxide (CO2) laser treatment in patients with hypertrophic scar. Methods This was a retrospective study performed in a single center between January 2019 and December 2020. All patients with hypertrophic scars receiving fractional micro-plasma radio-frequency treatment, ablative fractional CO2 laser treatment, or combined therapy of both were recruited to the study. The participants were then divided into a single therapy group or combined therapy group. The Vancouver scar scale was used to score and subsequently assess the effectiveness of scar treatment and the changes of scar thickness. Some adverse complications were also recorded to evaluate the safety of treatments. A subgroup analysis was then performed to investigate the differences of effectiveness and safety of combined therapy in scar patients at the early stage and late stage. Results A total of 64 patients with hypertrophic scars were enrolled in this study, including 45 receiving combined treatment, and 19 receiving single treatment. There was no significant difference in demographic data between the two groups. Notably, combined therapy could more effectively reduce the score of Vancouver scar scale (P=0.026) without significantly increasing the incidence of adverse complications. However, no significant difference was observed in scar thickness between the two groups. Moreover, multiple treatments could further increase the effectiveness of combined therapy according to either the score of Vancouver scar scale or the thickness of scars. Subgroup analysis revealed that combined therapy could reduce the score of Vancouver scar scale and scar thickness in patients much more at the early stage than at the late stage (P=0.032 and 0.042, respectively). Conclusions This study revealed that fractional micro-plasma radio-frequency treatment combined with ablative fractional CO2 laser treatment could be more effective in improving hypertrophic scars then single therapy. Also, multiple treatments could enhance the effectiveness of combined treatment, and patients should be encouraged to receive treatment as early as possible.

Background To investigate the effects of different plasma target concentrations of remifentanil on the minimum alveolar concentration (MAC) for blocking adrenergic response (BAR) of sevoflurane in children with laparoscopic herniorrhaphy. Methods Seventy-five children with 3-7 years old scheduled for laparoscopic herniorrhaphy were randomly divided into group R0, group R1, and group R2 according to different remifentanil plasma target concentration (0, 1, and 2 ngml-1), respectively. The MACBAR of sevoflurane was determined by the up-and-down and sequential method in each group. The concentrations of epinephrine and noradrenaline were also determined at corresponding time points. Results A total of 52 child patients were used among the anticipated 75 patients. In groups R0, R1, and R2, the MACBAR of sevoflurane was (3.29 ± 0.17) %, (2.12 ± 0.10) % and (1.29 ± 0.11) %, respectively, and a significant difference was found among the three groups (P Conclusion Remifentanil by target-controlled infusion can effectively reduce the MACBAR of sevoflurane during laparoscopic surgery in children. At a similar effect of MACBAR, both the changes of epinephrine and noradrenaline concentrations are not affected by the infusion of different remifentanil target concentrations. Trial registration The trial was registered at http://www.chictr.org.cn(ChiCTR1800019393, 8, Nov, 2018).

Epidemiological studies have suggested that fine particulate matter (PM2.5) and asthma have been independently associated with pulmonary fibrosis but rarely studied together. Furthermore, it is unknown whether airway fibrosis in asthma is more attributable to water-soluble ions of PM2.5. Our current study was to explore the potential mechanism of PM2.5 and water-soluble components on airway fibrosis in ovalbumin (OVA)-sensitized asthmatic rats. Rats were intratracheally instilled with PM2.5 and water-soluble components every 3 days for 4 times or 8 times. Histopathological examination demonstrated that lung inflammatory and airway fibrosis were induced after PM2.5 and water-soluble components exposure. Meanwhile, PM2.5, in particular water-soluble extracts, increased expression of collagen 1 (COL-1), connective tissue growth factor (CTGF), interleukin-6 (IL-6), transforming growth factor-β1 (TGF-β1), Smad family member 3 (Smad3), and p-Smad3, whereas decreased secretion of heme oxygenase-1 (HO-1). However, pretreating asthmatic rats with SB432542, the inhibitor of TGF-β1, and SIS3 HCl, the antagonist of Smad3, both reversed the activation of airway fibrosis induced by water-soluble extracts. Therefore, TGF-β1/Smad3 signaling pathway may be responsible for the pathological process of airway fibrosis in asthmatic rats following PM2.5 and water-soluble components exposure.

This article reports a case study on how government-sponsored native advertising influences everyday practices of journalism and journalists’ role identification in China. The process is the result...

Uromodulin, also named Tamm Horsfall protein, have been associated with renal function and sodium homeostasis regulation. The authors sought to examine the effects of salt intake on plasma and urinary uromodulin levels and the association of its genetic variants with salt sensitivity in Chinese adults. Eighty patients from our natural population cohort were maintained sequentially either on a usual diet for 3 days, a low‐salt diet (3.0 g) for 7 days, and a high‐salt diet (18.0 g) for an additional 7 days. In addition, the authors studied 514 patients of the Baoji Salt‐Sensitive Study, recruited from 124 families who received the same salt intake intervention, and investigated the association of genetic variations in uromodulin gene with salt sensitivity. Plasma uromodulin levels were significantly lower on a high‐salt diet than on a baseline diet (28.3 ± 4.5 vs. 54.9 ± 8.8 ng/ml). Daily urinary excretions of uromodulin were significantly decreased on a high‐salt diet than on a low‐salt diet (28.7 ± 6.7 vs. 157.2 ± 21.7 ng/ml). SNPs rs7193058 and rs4997081 were associated with the diastolic blood pressure (DBP), mean arterial pressure (MAP) responses to the high‐salt diet. In addition, several SNPs in the uromodulin gene were significantly associated with pulse pressure (PP) response to the low‐salt intervention. This study shows that dietary salt intake affects plasma and urinary uromodulin levels and that uromodulin may play a role in the pathophysiological process of salt sensitivity in the Chinese populations.

We aim to determine if visit‐to‐visit blood pressure variability (BPV) adds prognostic value for all‐cause mortality independently of the Framingham risk score (FRS) in the systolic blood pressure intervention trial (SPRINT). We defined BPV as variability independent of the mean (VIM) and the difference of maximum minus minimum (MMD) of the systolic blood pressure (SBP). Multivariable Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Based on FRS stratification, there were 1035, 2911, and 4050 participants in the low‐, intermediate‐, and high‐risk groups, respectively. During the trial, 230 deaths occurred since the 12th month with an average follow‐up of 2.5 years. In continuous analysis, 1‐SD increase of SBP VIM and MMD were significantly associated with all‐cause mortality (HR 1.18, 95% CI 1.05–1.32, p = .005; and HR 1.21, 95% CI 1.09–1.35, p < .001, respectively). In category analysis, the highest quintile of BPV compared with the lowest quintile had significantly higher risk of all‐cause mortality. Cross‐tabulation analysis showed that the 3rd tertile of SBP VIM in the high‐risk group had the highest HR of all‐cause mortality in total population (HR 4.99; 95% CI 1.57–15.90; p = .007), as well as in intensive‐therapy group (HR 7.48; 95% CI 1.01–55.45; p = .05) analyzed separately. Cross‐tabulation analysis of SBP MMD had the same pattern as VIM showed above. In conclusion, visit‐to‐visit BPV was an independent predictor of all‐cause mortality, when accounting for conventional risk factors or FRS. BPV combined with FRS conferred an increased risk for all‐cause mortality in the SPRINT trial.

Based on tissues from 34 patients, we investigated the performance of terahertz (THz) time-domain spectroscopy (TDS) to diagnose gastric tissues in surgical resected specimens. The results indicated that THz absorption spectra not only can clearly distinguish cancer tissues from normal tissues without hematoxylin-eosin staining (H&E staining) but also can identify carcinoma in situ and cancer tissues. Using THz-TDS to directly diagnose gastric cancer was also demonstrated with another 40 samples. Comparison of the data with the results of pathological examination indicated that the sensitivity and specificity were both 100%. Due to the ability to perform quantitative analysis and enable follow-up staining and traditional histopathological analysis, our study indicated considerable potential of THz-TDS for future automation, which is critical for the rapid and thorough prescreening of gastric cancer pathological examinations.

Background Small intestine cancers, as an extremely rare tumor type, account only for 3% of all gastrointestinal tumors. Small intestine adenocarcinoma (SIA), representing approximately one-third of all small bowel cancers, has received relatively little attention, both in research efforts and clinical cognizance. Owing to anatomical proximity and rarity, small bowel adenocarcinomas are frequently grouped with colorectal adenocarcinomas. Therefore, a large SIA patient cohort is needed to develop and validate new nomogram prognostic models specific to SIA patients. Methods Patients diagnosed with SIA between 2004 and 2016 were extracted from the Surveillance, Epidemiology, and Final Results (SEER) database. All patients were randomly assigned to the training cohort and the validation cohort (2:1). The basic clinical information, detailed pathological staging, and treatment information of the patients were included in the analysis. Nomograms were shaped following the evaluations of the Cox regression model and verified using the decision curve analysis (DCA), time-dependent receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves. Results The entire group comprised 6,947 patients with small intestine adenocarcinoma. According to the results of the multivariate Cox regression analysis, ten variables, including marital status, age, pathological grade, tumor location, T (tumor), N (nodes), M (metastasis) stage, surgery, chemotherapy, and regional nodes examined (RNE), were independent predictors of both of overall survival (OS) and cancer-specific survival (CSS). All significant variables were used to create the nomograms for OS and CSS. Various methods verified the reliability of the nomograms. The C-indexes of the OS and CSS nomogram were 0.756 (95% CI, 0.748-0.764) and 0.771 (95% CI, 0.761-0.781) in the training cohort and 0.748 (95% CI, 0.736-0.760) and 0.767 (95% CI, 0.752-0.781) in the validation cohort. The calibration curve showed good agreement between the nomogram prediction and actual survival. DCA indicated a clear net benefit of these new forecasting models. Conclusions This study built and verified nomograms to predict OS and CSS for rare SIA, which appear to be excellent tools to augment the clinically available evidence to facilitate the discussion between SIA patients and clinicians regarding therapeutic choice.

Background Inflammation is key in the pathogenesis of Nonalcoholic Fatty Liver Disease (NAFLD) – a common progressive liver disease. The soluble receptor for advanced glycation end products (sRAGE) attenuates inflammatory signaling; low levels of sRAGE are correlated with increased inflammation. Aim We sought to describe associations between sRAGE and NAFLD. Methods We conducted a cross-sectional analysis of 1088 Atherosclerosis Risk in Communities (ARIC) Study participants and used logistic regression to investigate the associations between sRAGE and NAFLD defined by elevated liver enzymes and fibrosis score. Results In this community-based sample (n = 1,088, mean age 56 years, 61% female, 78% Caucasian), persons in the lowest vs. highest quartile of sRAGE had significantly higher odds of elevated ALT (OR 2.82, 95% CI 1.18-6.76) but not elevated AST (OR 1.16, 95% CI 0.45-2.99); persons in the lowest vs. highest quartile had significantly lower odds of elevated FIB-4 index (OR 0.56, 95% CI 0.37-0.84). Conclusions We found an inverse cross-sectional association between sRAGE and liver inflammation; this is consistent with prior studies linking low sRAGE to inflammatory states. However, we observed a direct association between sRAGE and fibrosis. Our findings suggest that sRAGE is dynamic in NAFLD and patterns may vary with different stages of disease.

Hui Zhang,1 Dandan Chen,1 Jin Shao,1 Ping Zou,2 Nianqi Cui,3 Leiwen Tang,1 Xiyi Wang,4 Dan Wang,1 Zhihong Ye1 1Department of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, People’s Republic of China; 2School of Nursing, Nipissing University, Toronto, Ontario, Canada; 3Department of Nursing, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 4School of Nursing, Shanghai JiaoTong University, Shanghai, People’s Republic of ChinaCorrespondence: Zhihong YeDepartment of Nursing, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Qingchun Dong Road, Jianggan District, Hangzhou, Zhejiang, People’s Republic of ChinaTel +86 13606612119Email 3192005@zju.edu.cnPurpose: A prediction model for 4-year risk of metabolic syndrome in adults was previously developed and internally validated. However, external validity or generalizability for this model was not assessed so it is not appropriate for clinical application. We aimed to externally validate this model based on a retrospective cohort.Patients and Methods: A retrospective cohort design and a temporal validation strategy were used in this study based on a dataset from 1 January 2015 to 31 December 2018. Multiple imputation was used for missing values. Model performance was evaluated by using discrimination, calibration (calibration plot, calibration slope, and calibration intercept), overall performance (Brier score), and decision curve analysis.Results: In external validation, the C-statistic was 0.782 (95% CI, 0.771– 0.793). The calibration plot shows good calibration, calibration slope was 1.006 (95% CI, − 0.011– 1.063), and calibration intercept was − 0.045 (95% CI, − 0.113– 0.022). Brier score was 0.164.The discrimination and calibration of the prediction model were good in temporal external validation.Conclusion: The discrimination and calibration of the prediction model were satisfactory in the temporal external validation. However, clinicians should be aware that this prediction model was developed and validated in a tertiary setting. It is strongly recommended that further studies validate this model in international cohorts and large, prospective cohorts in different institutions.Keywords: prediction model, prognosis, metabolic syndrome, algorithms, calibration, discrimination