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Objective Low-magnitude high-frequency vibration (LMHFV) has been reported to be capable of promoting osteoblast proliferation and differentiation. Reduced osteoblast activity and impaired bone formation were related to diabetic bone loss. We investigated the potential protective effects of LMHFV on high-glucose (HG)-induced osteoblasts in this study. In addition, the assessment of LMHFV treatment for bone loss attributed to diabetes was also performed in vivo. Method MC3T3-E1 cells induced by HG only or treated with LMHFV were treated in vitro. The experiments performed in this study included the detection of cell proliferation, migration and differentiation, as well as protein expression. Diabetic bone loss induced by streptozotocin (STZ) in rats was established. Combined with bone morphometric, microstructure, biomechanical properties and matrix composition tests, the potential of LMHFV in treating diabetes bone loss was explored. Results After the application of LMHFV, the inhibiting effects of HG on the proliferation, migration and differentiation of osteoblasts were alleviated. The GSK3β/β-catenin pathway was involved in the protective effect of LMHFV. Impaired microstructure and biomechanical properties attributed to diabetes were ameliorated by LMHFV treatment. The improvement of femur biomechanical properties might be associated with the alteration of the matrix composition by the LMHFV. Conclusion LMHFV exhibited a protective effect on osteoblasts against HG by regulating the proliferation, migration and differentiation of osteoblasts. The function of promoting bone formation and reinforcing bone strength made it possible for LMHFV to alleviate diabetic bone loss.

Acute myeloid leukaemia (AML) is a haematological cancer with poor outcomes due to a lack of efficacious targeted therapies. The Nuclear Factor of Activated T Cells (NFAT) family of transcription factors is well characterised as a regulator of the cell cycle and differentiation in the myeloid lineage. Recent evidence has demonstrated that NFAT family members may have roles in regulating AML leukemogenesis and resistance to targeted therapy in myeloid leukaemia. Furthermore, gene expression data from patient samples show that some NFATs are more highly expressed in poorly differentiated AML and after disease relapse, implying that the NFAT family may have roles in specific types of AML. This review outlines the evidence for the role of NFAT in healthy myeloid tissue and explores how NFAT might regulate AML pathogenesis, highlighting the potential to target specific NFAT proteins therapeutically in AML.

Summary Sustainably recycling photovoltaic (PV) products at the end of their life cycle is essential for reducing environmental impact and saving costs on raw materials. Halide perovskite PVs currently suffer from a short lifespan, and recycling and remaking could equivalently extend their life cycle. Here, we report an effective and rapid “one-key reset” recycling paradigm to collect all the functional layer components from a used PV device at a high material extraction efficiency. A remade device incorporating all the layers, including recycled indium tin oxide/tin oxide substrate, perovskite, spiro-OMeTAD, and gold, exhibits an average power conversion efficiency of over 20%. This finding that the recycled device exhibits identical performance compared with the original, while providing enormous savings in cost, time, and energy, could lead to a recycling route that will dramatically lower the levelized cost of energy of perovskite PVs and enhance their competitiveness in future energy markets.

Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.

OBJECTIVE The published studies regarding the relationships between zinc finger 365 (ZNF365) polymorphisms and inflammatory bowel disease (IBD) risk in Caucasians have yielded conflicting results. Therefore, we performed a meta-analysis to clarify this issue. METHODS The Electronic databases of PubMed, Web of Science, Wiley Online Library, and EMBASE were searched for eligible studies up to 31 November 2020. The quality of eligible studies was evaluated using the Newcastle-Ottawa Scale. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) under different genetic models were calculated to assess the strength of associations. RESULTS A total of 22 relevant case-control studies with 9542 ulcerative colitis (UC) patients and 13,886 controls, as well as 13,651 Crohn's disease (CD) patients and 15,256 controls, were involved in our meta-analysis. rs10761659 polymorphism significantly decreased CD and UC risk (except for the heterozygous model and the dominant model in UC), and rs10995271 polymorphism was significantly associated with UC (except for the heterozygous model and dominant model) rather than CD. CONCLUSIONS The meta-analysis demonstrated that the rs10761659 polymorphism might be a protective factor for both UC and CD in Caucasians, while the rs10995271 polymorphism might be a risk factor for UC rather than CD in Caucasians.

Bone metastasis is one of the leading causes of cancer-related death and remains incurable in spite of great efforts. Bone-targeted nanoparticle-based drug carriers can overcome the difficulties in delivering therapeutic agents to metastatic bone and endowing them with a stimuli-responsive feature for controllable drug release can further maximize their therapeutic outcome. In light of hypoxic microenvironment of bone metastasis, we herein reported a bone-targeted and hypoxia-responsive polymeric micelle system for effective treatment of bone metastatic prostate cancer. The micelles were self-assembled from a polyethylene glycol and poly-l-lysine based copolymer using alendronate as a bone-targeted moiety and azobenzene as a hypoxia-responsive linker, showing a high affinity to metastatic bone and a high sensitivity in responding to hypoxia in vitro. In vivo studies further showed that after a selective accumulation in metastatic bone, the micelles could respond to hypoxic bone metastasis for rapid drug release to an effective therapeutic dosage. As a result, the micelles could suppress tumor growth in bone and inhibit bone destruction by inhibiting osteoclast activity and promoting osteoblast activity, achieving an enhanced therapeutic outcome with relieved bone pain and prolonged survival time. Bone-targeted and hypoxia-responsive nanocarriers therefore represent a promising advancement for treating bone metastasis. To our best knowledge, it might be the first example of the application of hypoxia-responsive nanocarriers in treating bone metastasis.

An electrostatically charged spacecraft is subject to the Lorentz force and torque when moving in the geomagnetic field. By active modulation of the surface charge, the induced Lorentz force and to...

INTRODUCTION Limited information exists about the relationship between body mass index (BMI) and the outcome of acute respiratory distress syndrome (ARDS). OBJECTIVE To evaluate the hospital mortality of patients with ARDS in relation to BMI. MATERIALS AND METHODS We conducted a retrospective, multicenter study including patients with ARDS. ARDS was defined according to the Berlin criteria. Body weight and height were obtained to calculate BMI. The primary outcome was in-hospital mortality. Secondary outcomes were intensive care unit (ICU) mortality, invasive positive pressure ventilation (IPPV) free days within 28 days and length of stays in the ICU and hospital. RESULTS Among 523 patients, 28 (5%) were underweight (BMI

Duplex kidney is a common congenital malformation appeared as duplication of pelvis and ureter. However, renal duplication within sinus renalis is an extremely rare variation of the renal collecting system. In this study, we report a case of an asymptomatic kidney disease in a 33-year-old man, who demonstrates abnormal echo of renal sinus anomaly discovered incidentally in ultrasound examination. Computed tomography urography (CTU) exhibited the other small duplex kidney located in renal sinus. In the excretory phase images, the contrast medium within its small renal pelvis could be seen to flow into the right major renal calices. This case exhibited a very rare anatomical variation of duplicated renal malformation.

This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

Background Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. Methods ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. Results 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P Conclusions In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Colloidal quantum dots (QDs) containing toxic lead and cadmium compounds, i.e., Pb/Cd(S,Se,Te), have been widely studied for hydrogen generation because of their promising optoelectronic properties and efficient photocatalytic activities. However, it is still desirable to construct heavy-metal-free QDs with high photoconversion efficiencies through careful design and doping with more environmentally benign alternatives. Here, we design and synthesize Cu-doped AgIn5S8 (Cu–AIS) QDs that contain Cu T2 state sites between the Ag 4d and S 3p orbitals, producing adjustable bandgaps and slowing photogenerated charge recombination compared to pristine AIS QDs. We demonstrate that this Cu-doping configuration not only lowers the activation energy barrier with a more negative potential, but it also leads to the efficient separation and transportation of photogenerated electrons and holes. As proof-of-concept, we fabricate a photoelectrochemical (PEC) cell based on a Cu–AIS QD-sensitized TiO2 photoanode and achieve a maximum saturated photocurrent density of ∼9.8 mA cm−2 at 0.75 V versus a reversible hydrogen electrode (RHE). Furthermore, photostability experiments demonstrate that the operational long-term stability of the Cu–AIS QDs is much better than that of an AIS QD-based PEC cell. Overall, heavy-metal-free Cu–AIS QDs pave the way to the design and synthesis of environmentally friendly, high-efficiency, and cost-effective PEC systems for solar-driven hydrogen generation, and other optoelectronic devices, such as photovoltaics and photodetectors.

Different remote sensing image applications require different quality remote sensing images, and selecting the best quality remote sensing image from multiple remote sensing images with similar quality directly determines the application effect of the image. According to the different applications of remote sensing images, the Rough Set Theory, Fuzzy Set Theory and Back Propagation Neural Network theory (BP Neural Network) are used to establish a remote sensing image quality evaluation model. Combining the basic influencing factors of remote sensing image quality and the influencing factors of specific applications as evaluation indicators, the remote sensing image quality evaluation model is imported, and the best quality remote sensing images are selected in specific applications for remote sensing images of special quality. Finally, this paper proves the feasibility of the model in the selection of the best image quality for specific applications of three different experiments.

Granulomatous amoebic encephalitis is a rare progressive infectious disease of the central nervous system with a high mortality rate. It usually presents as multiple ring-enhancing lesions in the brain with surrounding edema evident, while the granulomatous amoeba encephalitis reported in this case is manifested as a solitary mass in the right frontal lobe mimicking intracranial tumors. For the same disease with different imaging manifestations, clinical diagnosis of granulomatous amoeba encephalitis is difficult and may delay the diagnosis and treatment, resulting in increased mortality. The purpose of this article is to raise awareness of this disease and avoid misdiagnosis, given that early diagnosis is crucial for the correct treatment of patients.

In this paper, we coin the term “crypto place” to describe an emerging type of virtual place on the blockchain. Using an encrypted monument that was built to memorialize Dr. Wenliang Li, one of the whistleblowers of China’s coronavirus outbreak, we extensively investigate three definitive dimensions of crypto place in terms of decentralized location, immutable locales and transaction-based sense of place. We then reflect upon the complicated social implications of blockchain technology much beyond purely serving as an alternative cryptocurrency, and further examine how place information is stored, disseminated, and incentivized on blockchain. Through this paper, we investigate the relevance of blockchain to geography studies and discuss how it may enrich the concept of place in today’s data-intensive and decentralized world.

Purpose: We analyzed and compared the imaging characteristics of the vessel wall of the middle cerebral artery (MCA) in symptomatic and asymptomatic patients using a 3.0-T high-resolution magnetic resonance imaging (HR-MRI) protocol, including a 3-dimensional T1-sampling perfection with application-optimized contrasts using different flip angle evolutions sequence. Methods: Fifty-three patients with atherosclerotic stenosis of the MCA underwent 3.0-T HR-MRI examinations. The characteristics of atherosclerotic plaques in 53 patients (28 symptomatic, 25 asymptomatic) were analyzed, including plaque distribution and signal intensity. Plaque burden (PB), stenosis degree, and the remodeling index were measured and compared between symptomatic and asymptomatic patients. Results: The PB of the symptomatic group was significantly higher than that of the asymptomatic group ( P = .006), and moderate−severe stenosis was more common ( P = .01). The remodeling index of the symptomatic group was also lower ( P = .015) and negative remodeling (NR) was more common ( P = .043). Binary logistic regression analysis showed that stenosis degree was a risk factor in symptomatic patients (odds ratio = 135, P = .023). Conclusion: There is a trend that some characteristics of plaques and vessels, including the moderate-severe stenosis, larger PB, and NR, were observed more frequently among patients with symptomatic atherosclerotic stenosis of the MCA than among asymptomatic patients.

Flameless combustion, also called MILD combustion (Moderate or Intense Low Oxygen Dilution), is a technology that reduces NOx emissions and improves combustion efficiency. Appropriate turbulence-chemistry interaction models are needed to address this combustion regime via computational modelling. Following a similar analysis to that used in the Extended EDC model (E-EDC), the purpose of the present work is to develop and test a Novel Extended Eddy Dissipation Concept model (NE-EDC) to be better able to predict flameless combustion. In the E-EDC and NE-EDC models, in order to consider the influence of the dilution on the reaction rate and temperature, the coefficients are considered to be space dependent as a function of the local Reynolds and Damköhler numbers. A comparative study of four models is carried out: the E-EDC and NE-EDC models, the EDC model with specific, fixed values of the model coefficients optimized for the current application, and the Flamelet Generated Manifold (FGM) model with pure fuel and air as boundary conditions for flamelet generation. The models are validated using experimental data of the Delft Lab Scale furnace (9 kW) burning Natural Gas (T = 446 K) and preheated air (T = 886 K) injected via separate jets, at an overall equivalence ratio of 0.8. among the considered models, the NE-EDC results show the best agreement with experimental data, with a slight improvement over the E-EDC model and a significant improvement over the EDC model with tuned constant coefficients and the FGM model.

Multi-exposure image fusion is an effective method for depicting high dynamic range of the target scene in a single image. However, there are still some problems remaining: the preserving of global contrast, the preserving of the local details in saturated regions, and the existence of halo artifacts. To solve these problems, this paper proposes a new multi-exposure image fusion algorithm with detail enhancement. Firstly, the well-exposedness evaluation function, the chromatic information evaluation function and the local detail preserved function are used to measure weight maps. Then, an improved multi-exposure fusion framework based on pyramid decomposition is proposed to further enhance the details. The experimental results demonstrate that the proposed algorithm can preserve more details than the state-of-the-art. In the view of appreciation, our approach could produce a more realistic brightness distribution of target scene as well as avoid halo artifacts.

Background The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatography (ERCP) treatment of common bile duct (CBD) stones. Methods 1954 cholangiograms were collected from three hospitals for training and testing the DSAS. The D-LinkNet34 and U-Net were adopted to segment the CBD, stones, and duodenoscope. Based on the segmentation results, the stone size, distal CBD diameter, distal CBD arm, and distal CBD angulation were estimated. The performance of segmentation and estimation was assessed by mean intersection over union (mIoU) and average relative error. A technical difficulty scoring scale, which was used for assessing the technical difficulty of CBD stone removal, was developed and validated. We also analyzed the relationship between scores evaluated by the DSAS and clinical indicators including stone clearance rate and need for endoscopic papillary large-balloon dilation (EPLBD) and lithotripsy. Results The mIoU values of the stone, CBD, and duodenoscope segmentation were 68.35 %, 86.42 %, and 95.85 %, respectively. The estimation performance of the DSAS was superior to nonexpert endoscopists. In addition, the technical difficulty scoring performance of the DSAS was more consistent with expert endoscopists than two nonexpert endoscopists. A DSAS assessment score ≥ 2 was correlated with lower stone clearance rates and more frequent EPLBD. Conclusions An intelligent DSAS based on deep learning was developed. The DSAS could assist endoscopists by automatically scoring the technical difficulty of CBD stone extraction, and guiding the choice of therapeutic approach and appropriate accessories during ERCP.

Objective Thermal injury repair is a complex process during which maintaining the proliferation of human dermis fibroblasts (HDFs) and synthesis of extracellular matrix (ECM) plays a critical role. In the present study, we analyzed potential molecular markers and the probable association between differentially-expressed lncRNAs and protein-coding genes within denatured dermis following thermal injury, attempting to provide further insights to thermal injury repair pathogenesis. Methods and main results We found that the expression of 3940 lncRNAs was increased, while that of 1438 lncRNAs was reduced in the denatured dermis following thermal injury when compared to normal tissue. Of them, 338 were upregulated and 154 were downregulated by more than 128 times. Via cross-check with another microarray profile analysis on differentially-expressed lncRNAs after thermal injury, LINC00302 was found to be downregulated after thermal injury; more importantly, this skin-specially expressed lncRNA is located near a series of genes related to multiple skin inflammation and skin barrier-associated genomes. LINC00302 overexpression promoted the cell viability and the protein levels of α-SMA and Collagen I in HDFs. Conclusions In conclusion, mRNAs and lncRNAs could be differentially expressed in the denatured dermis following thermal injury. mRNA and lncRNA regulatory signaling pathways could participate in thermal injury repair pathogenesis. More importantly, LINC00302 may play a critical role in thermal injury repair.

Introduction: Severe acute respiratory viral infections are frequency accompanied by multiple organ dysfunction, including acute kidney injury (AKI). In December 2019, the coronavirus disease 2019 (COVID-19) outbreak began in Wuhan, Hubei Province, China, and rapidly spread worldwide. While diffuse alveolar damage and acute respiratory failure are the main features of COVID-19, other organs may be involved, and the incidence of AKI is not well described. We assessed the incidence and clinical characteristics of AKI in patients with laboratory-confirmed COVID-19 and its effects on clinical outcomes. Methods: We conducted a multicenter, retrospective, observational study of patients with COVID-19 admitted to two general hospitals in Wuhan from 5 January 2020 to 21 March 2020. Demographic data and information on organ dysfunction were collected daily. AKI was defined according to the KDIGO clinical practice guidelines. Early and late AKI were defined as AKI occurring within 72 h after admission or after 72 h, respectively. Results: Of the 116 patients, AKI developed in 21 (18.1%) patients. Among them, early and late AKI were found in 13 (11.2%) and 8 (6.9%) patients, respectively. Compared with patients without AKI, patients with AKI had more severe organ dysfunction, as indicated by a higher level of disease severity status, higher sequential organ failure assessment (SOFA) score on admission, an increased prevalence of shock, and a higher level of respiratory support. Patients with AKI had a higher SOFA score on admission (4.5 ± 2.1 vs. 2.8 ± 1.4, OR 1.498, 95% CI 1.047–2.143 ) and greater hospital mortality (57.1% vs. 12.6%, OR 3.998, 95% CI 1.088–14.613) than patients without AKI in both the univariate and multivariate analyses. Patients with late AKI, but not those with early AKI, had a significantly prolonged length of stay (19.6 vs. 9.6 days, p = 0.015). Conclusion: Our findings show that admission SOFA score was an independent risk factor for AKI in COVID-19 patients, and patients with AKI had higher in-hospital mortality. Moreover, AKI development after 72 h of admission was related to prolonged hospitalization time.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Data-driven technologies have been changing every aspect of human life and the fast-developing banking sector with its data-rich nature has become the implementation field of these fast-evolving technologies. Deep learning, as one of the emerging technologies in recent years, has also been inevitably adopted for various improvements in banking. To the best of our knowledge, there is no comprehensive literature review, which focuses on specifically deep learning and its implementations in banking. Therefore, this paper investigates the deep learning technology in-depth and summarizes the relevant applications in banking so to contribute to the existing literature. Moreover, by providing a reliable and up-to-date review, it is also aimed to serve as the one-stop repository for banks and researchers who are interested in embracing deep learning, whilst bringing insights for the directions of future research and implementation.

Background High levels of circulating neutrophil extracellular traps (NETs) are associated with a poor prognosis in influenza A infection. It remains unclear whether NETs in the plasma or bronchoalveolar lavage fluid (BALF) can predict clinical outcomes in influenza. Methods One hundred eighteen patients who were diagnosed with H1N1 influenza in 2017–2018 were recruited. The NETs were assessed in plasma and BALF samples by quantifying cell-free deoxyribonucleic acid (cfDNA) and protein-DNA complexes. Predictions of severe illness and 60-day mortality were analyzed with receiver operating characteristic curves. Results The NET levels were significantly elevated in the BALF and contributed to the pathology of lungs, yet it was not associated with disease severity or mortality in patients severely infected with H1N1. Plasma NET levels were significantly increased in the patients with severe influenza and positively correlated with the oxygen index and sequential organ failure assessment scores. High levels of plasma cfDNA (>286.6 ng/mL) or histone-bound DNA (>9.4 ng/mL) discriminated severe influenza from mild, and even higher levels of cfDNA (>306.3 ng/mL) or histone-bound DNA (>23.1 ng/mL) predicted fatal outcomes in severely ill patients. Conclusions The cfDNA and histone-bound DNA in plasma represent early predictive biomarkers for the prognosis of influenza.

Accurate delineation of cancer margins is critical for endoscopic curative resection. This study aimed to train and validate real-time fully convolutional networks for delineating the resection margin of early gastric cancer (EGC) under indigo carmine chromoendoscopy (CE) or white light endoscopy (WLE), and evaluated its performance and that of magnifying endoscopy with narrow-band imaging (ME-NBI). We collected CE and WLE images of EGC lesions to train fully convolutional networks ENDOANGEL. ENDOANGEL was tested both on stationary images and endoscopic submucosal dissection (ESD) videos. The accuracy and reliability of ENDOANGEL and NBI-dependent delineation were further evaluated by a novel endoscopy–pathology point-to-point marking. ENDOANGEL had an accuracy of 85.7% in the CE images and 88.9% in the WLE images under an overlap ratio threshold of 0.60 in comparison with the manual markers labeled by the experts. In the ESD videos, the resection margins predicted by ENDOANGEL covered all areas of high-grade intraepithelial neoplasia and cancers. The minimum distance between the margins predicted by ENDOANGEL and the histological cancer boundary was 3.44 ± 1.45 mm which outperformed the resection margin based on ME-NBI. ENDOANGEL has the potential to assist endoscopists in delineating the resection extent of EGC under CE or WLE during ESD.

Summary Background Colonoscopy performance varies among endoscopists, impairing the discovery of colorectal cancers and precursor lesions. We aimed to construct a real-time quality improvement system (ENDOANGEL) to monitor real-time withdrawal speed and colonoscopy withdrawal time and to remind endoscopists of blind spots caused by endoscope slipping. We also aimed to evaluate the effectiveness of this system for improving adenoma yield of everyday colonoscopy. Methods The ENDOANGEL system was developed using deep neural networks and perceptual hash algorithms. We recruited consecutive patients aged 18–75 years from Renmin Hospital of Wuhan University in China who provided written informed consent. We randomly assigned patients (1:1) using computer-generated random numbers and block randomisation (block size of four) to either colonoscopy with the ENDOANGEL system or unassisted colonoscopy (control). Endoscopists were not masked to the random assignment but analysts and patients were unaware of random assignments. The primary endpoint was the adenoma detection rate (ADR), which is the proportion of patients having one or more adenomas detected at colonoscopy. The primary analysis was done per protocol (ie, in all patients having colonoscopy done in accordance with the assigned intervention) and by intention to treat (ie, in all randomised patients). This trial is registered with http://www.chictr.org.cn , ChiCTR1900021984. Findings Between June 18, 2019, and Sept 6, 2019, 704 patients were randomly allocated colonoscopy with the ENDOANGEL system (n=355) or unassisted (control) colonoscopy (n=349). In the intention-to-treat population, ADR was significantly greater in the ENDOANGEL group than in the control group, with 58 (16%) of 355 patients allocated ENDOANGEL-assisted colonoscopy having one or more adenomas detected, compared with 27 (8%) of 349 allocated control colonoscopy (odds ratio [OR] 2·30, 95% CI 1·40–3·77; p=0·0010). In the per-protocol analysis, findings were similar, with 54 (17%) of 324 patients assigned ENDOANGEL-assisted colonoscopy and 26 (8%) of 318 patients assigned control colonoscopy having one or more adenomas detected (OR 2·18, 95% CI 1·31–3·62; p=0·0026). No adverse events were reported. Interpretation The ENDOANGEL system significantly improved the adenoma yield during colonoscopy and seems to be effective and safe for use during routine colonoscopy. Funding Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Hubei Province Major Science and Technology Innovation Project, and the National Natural Science Foundation of China.

Stereo dense image matching normally refers to per-pixel correspondence search between stereo pairs. However, their matching results actually depend on matching window sizes. Large windows usually ...

Carbon tetrachloride (CCl4), Concanavalin A (ConA), bile duct ligation (BDL), and liver resection (LR) are four types of commonly used mouse models of acute liver injury. However, these four models belong to different types of liver cell damage while their application situations are often confounded. In addition, the systematic changes of multiple extra-liver organs after acute liver injury and the crosstalk between liver and extra-liver organs remain unclear. Here, we aim to map the morphological, metabolomic and transcriptomic changes systematically after acute liver injury and search for the potential crosstalk between the liver and the extra-liver organs. Significant changes of transcriptome were observed in multiple extra-liver organs after different types of acute liver injury despite dramatic morphological damage only occurred in lung tissues of the ConA/BDL models and spleen tissues in the ConA model. Liver transcriptomic changes initiated the serum metabolomic alterations which correlated to transcriptomic variation in lung, kidney, and brain tissues of BDL and LR models. The potential crosstalk might lead to pulmonary damage and development of hepatorenal syndrome (HRS) and hepatic encephalopathy (HE) during liver injury. Serum derived from acute liver injury mice damaged alveolar epithelial cells and human podocytes in vitro. Our data indicated that different types of acute liver injury led to different transcriptomic changes within extra-liver organs. Integration of serum metabolomics and transcriptomics from multiple tissues can improve our understanding of acute liver injury and its effect on the other organs.

In this paper, the Eulerian Stochastic Field (ESF) model in the Transported Probability Density Function (TPDF) class model is combined with the Flamelet Generated Manifolds (FGM) model. This method solves the joint probability density function transport equation by ESF method that considers the interaction mechanism between flame and turbulence with high precision. At the same time, by making use of the advantage of the FGM model, this model is able to incorporate the detailed chemical reaction mechanism (GRI 3.0) with acceptable computational cost. The new model has been implemented in the open source CFD suite-OpenFOAM. Validation of the model has been carried out by simulating the Sandia flame series (three turbulent piloted methane jet flames) issued by the National Laboratory of the United States. The accuracy and advancement of the ESF/FGM turbulent combustion model are verified by comparing the LES results of the new model with the rich experimental data as well as the RANS results. The results demonstrate that the model has a strong ability in capturing combustion phenomena such as extinction and re-ignition in turbulent flame, which is essential in the accurate prediction of the combustion process in real combustion devices, for example, aircraft engines. Keywords: Eulerian Stochastic Field (ESF), Flamelet Generated Manifolds (FGM), Large Eddy Simulation (LES), OpenFOAM, Turbulent combustion

Colloidal quantum dot (CQD) based photoelectrochemical cells (PECs) for solar-driven hydrogen (H2) generation offer an avenue to convert solar energy into chemical fuel. So far, recent advances in CQD-based PEC performance have relied on the use of defect passivation strategies. Specifically, shell-isolated defect passivation in ternary I–III–VI CQDs has been shown to contribute to lowered trap state densities, strengthened effective charge separation, and improved photovoltaic performance. Here we present a kind of heavy metal-free Cu-doped AgIn5S8/ZnS (CAIS/ZnS) core/shell CQD that shows a remarkable photocatalytic efficiency with an engineered shell thickness. In particular, we add a capping ZnS layer to protect the CAIS/ZnS CQD surface in photoanodes and thus enable a dual defect passivation strategy based on the homojunction combination. This strategy passivates surface defects and enhances photogenerated charge transportation in order to maintain high catalytic efficiency, and the additional ZnS layer suppresses energy transfer and nonradiative recombination for better charge injection and greater stability performance of PEC devices. As a result, we achieved a saturated photocurrent density of 23.40 mA cm−2 for a CAIS/ZnS CQD-based PEC measured at 0.75 V versus RHE under 3 suns (that is 3 times the standard solar irradiance of 100 mW cm−2), as compared to 10.60 mA cm−2 under 1 sun, which is a record for heavy metal-free CQD-based photoelectrodes for solar driven hydrogen generation. Importantly, our PEC devices maintained over 50% of their original photocurrent density after 6800 s of aging under 1 sun at 0.5 V versus RHE. Our work reveals a new platform for the development of high-efficiency photocatalysts based on I–III–VI CQDs.

The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When the percentage of LiDAR points in the simulated datasets was 0.16%, the matching accuracy of our method achieved a subpixel level, while that of the original stereo matching algorithm was 3.4 pixels. The experimental results show that LGSM is suitable for indoor, street, aerial, and satellite image datasets and provides good transferability across semi-global matching and AD-Census. Furthermore, the qualitative and quantitative evaluations demonstrate that LGSM is superior to two state-of-the-art optimizing cost volume methods, especially in reducing mismatches in difficult matching areas and refining the boundaries of objects., Comment: we replace an article because of the addition of journal reference, DOI, and report number information

Using remote sensing techniques to detect trees at the individual level is crucial for forest management while finding the treetop is an initial and important first step. However, due to the large ...

Background: Venovenous extracorporeal membrane oxygenation (ECMO) has become the ultimate supporting technique for the rescue of severe acute respiratory distress syndrome (ARDS) patients. Although tracheostomy during ECMO has proven to be beneficial, the proper time point for performing the tracheostomy remains unclear. The purpose of our study was to demonstrate whether early tracheostomy (ET; within 7 days of ECMO initiation) outweighs delayed tracheostomy (DT; 8 days of more after ECMO initiation).Methods: A retrospective cohort study was established. All ARDS patients who underwent tracheostomy during V-V ECMO support in the intensive care unit (ICU) of a tertiary hospital from December 2013 to November 2020 were reviewed.Results: Of the 187 ARDS patients who received V-V ECMO support, 30 (16%) underwent tracheostomy—18 (60%) during ECMO support and the other 12 after ECMO decannulation. Among the 18 patients who underwent tracheostomy while receiving ECMO, 11 (61.1%) received ET, and 7 received DT. No significant difference was found between the ET and DT groups in terms of demographic data, medical history, disease severity (estimated based on the RESP, PRESERVE, APACHE Ⅱ, SOFA and Murray scores), ARDS risk factors or mechanical ventilation duration before ECMO. The ET group showed a decreased incidence of ventilator-associated pneumonia (VAP) during ECMO support (45.5% vs. 100%; P= 0.038) and shortened durations of ECMO (9.0 vs. 27.0 days; P= 0.011) and mechanical ventilation (16.0 vs. 56.0 days; P= 0.027). ET did not significantly alter the all-cause ICU mortality rate (54.5% vs. 28.6%; P= 0.367), all-cause hospital mortality rate (which was the same as the ICU mortality rate), length of ICU stay (336 vs. 627 hours; P= 0.085), or length of hospital stay (26 vs. 37 days; P= 0.285). Local bleeding at the tracheostomy wound did not differ between the two groups (27.3% vs. 42.9%, P= 0.627) .Conclusion: Compared with delayed tracheostomy, ET performed within 7 days of ECMO cannulation for severe ARDS patients could decrease the VAP incidence during ECMO support and shorten the durations of ECMO and mechanical ventilation; However, it may not improve the outcome. Prospective and multicenter studies are needed for further research.

Current treatment options for patients infected with hepatitis B virus (HBV) are suboptimal, because the approved drugs rarely induce cure due to the persistence of the viral DNA genome in the nucleus of infected hepatocytes, and are associated with either severe side effects (pegylated interferon-alpha) or require life-long administration (nucleos(t)ide analogs). We report here the evaluation of the safety and therapeutic efficacy of a novel, humanized antibody (hzVSF) in the woodchuck model of HBV infection. hzVSF has been shown to act as a viral entry inhibitor, most likely by suppressing vimentin-mediated endocytosis of virions. Targeting the increased vimentin expression on liver cells by hzVSF after infection with HBV or woodchuck hepatitis virus (WHV) was demonstrated initially. Thereafter, hzVSF safety was assessed in eight woodchucks naïve for WHV infection. Antiviral efficacy of hzVSF was evaluated subsequently in 24 chronic WHV carrier woodchucks by monotreatment with three ascending doses and in combination with tenofovir alafenamide fumarate (TAF). Consistent with the proposed blocking of WHV reinfection, intravenous hzVSF administration for 12 weeks resulted in a modest but transient reduction of viral replication and associated liver inflammation. In combination with oral TAF dosing, the antiviral effect of hzVSF was enhanced and sustained in half of the woodchucks with an antibody response to viral proteins. Thus, hzVSF safely but modestly alters chronic WHV infection in woodchucks, however, as a combination partner to TAF, its antiviral efficacy is markedly increased. The results of this preclinical study support future evaluation of this novel anti-HBV drug in patients.

Dissection of the heterogeneous and complex biological mechanisms of leukemia, elucidating the role of recurrent mutations and epigenetic modifications as well as aberrant activation of oncogenic signaling (1)(2)(3)(4), have advanced our understanding of leukemogenesis allowing precision medicine to transform approaches to diagnosis and therapy. In this Research Topic we focused on innovative technologies that will aid in the development of a multi-dimensional integrated molecular network, enabling the prediction of therapy responses and create novel treatments for those of most clinical need. We were delighted to receive ten exciting contributions, featuring a comprehensive update of the current potential and limitations of preclinical and clinical advancements.Risk stratification scores in AML are important tools for clinicians and scientists alike, due to the relative ease with which biomarker panels can assist with diagnosis, prediction of prognosis, overall survival (OS) and guide appropriate treatment choices. Our work has identified a gene signature associated with the histone demethylase KDM4A, the KDM4A-9 score, which is associated with poor OS and independent of age, cytogenetic risk and mutation status (2). KDM4A-9 is highly correlative with the LSC17 score, while having no overlap, showing the collective power of integrating risk scoring systems (2,5). In this Special Issue, Unlike most reviews on the same subject, it focused also on AML "persisters" highlighting that they are not necessarily pre-existing but arise by distinct mechanisms and may be induced by therapy. Understanding the plasticity of these mechanisms in different patients could lead to effective personalized therapy.Metabolic reprogramming is a key hallmark of cancer and gaining a fundamental understanding of disease biology in terms of metabolic profiling may enable personalized therapies that exploit the unique weaknesses in leukemic cells. As such, Zhang et al. analyzed publicly-available databases to investigate metabolism-related prognostic factors and identified four genes (PLA2G4A, HMOX2, AK1, SMPD3) that when highly expressed represented a poor prognostic predictor for AML patients. Dembitz and Gallipoli described recent evidence demonstrating the validity of developing therapies that target metabolic pathways in AML. The review highlights the differential metabolic pathways that are present in distinct AML subtypes and disease stages, which may enable truly personalized treatments for these patients, supported by FDA approval of the first metabolic inhibitors targeting IDH1/2 in AML. Further promising avenues for targeting metabolic pathways are discussed.The impact that the microenvironmental niche has on tumor persistence is now becoming Hematopoietic stem cell transplantation (SCT) represents a potential cure for acute leukemia, however as most hematological malignancies occur in older individuals, in which the myeloablative conditioning regimen prior to SCT is linked to a high risk of treatment-related mortality (TRM), this is not always possible. Incorporating reduced intensity conditioning (low dose fludarabine and cyclophosphamide) in patients with acute leukemia/MDS of ≥55 years old revealed that OS, leukemia free survival, TRM, and cumulative incidence of relapse were comparable to a historical, younger cohort of patients that underwent the normal conditioning regimen. This small study suggests that a reduction in the conditioning regimen prior to SCT may be a feasible option for older acute leukemia patients.In summary, although treatment of leukemia remains a challenge, by highlighting new research avenues, therapeutic targets and strategies, the articles in this Issue will inform scientists and clinicians, expanding the potential for development of non-canonical diagnostics or interventions.

Objectives: To evaluate the impact of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) on the inflammatory response and viral clearance in coronavirus disease 2019 (COVID-19) patients.Methods: We included 229 patients with confirmed COVID-19 in a multicenter, retrospective cohort study. Propensity score matching at a ratio of 1:3 was introduced to eliminate potential confounders. Patients were assigned to the ACEI/ARB group (n = 38) or control group (n = 114) according to whether they were current users of medication.Results: Compared to the control group, patients in the ACEI/ARB group had lower levels of plasma IL-1β [(6.20 ± 0.38) vs. (9.30 ± 0.31) pg/ml, P = 0.020], IL-6 [(31.86 ± 4.07) vs. (48.47 ± 3.11) pg/ml, P = 0.041], IL-8 [(34.66 ± 1.90) vs. (47.93 ± 1.21) pg/ml, P = 0.027], and TNF-α [(6.11 ± 0.88) vs. (12.73 ± 0.26) pg/ml, P < 0.01]. Current users of ACEIs/ARBs seemed to have a higher rate of vasoconstrictive agents (20 vs. 6%, P < 0.01) than the control group. Decreased lymphocyte counts [(0.76 ± 0.31) vs. (1.01 ± 0.45)*109/L, P = 0.027] and elevated plasma levels of IL-10 [(9.91 ± 0.42) vs. (5.26 ± 0.21) pg/ml, P = 0.012] were also important discoveries in the ACEI/ARB group. Patients in the ACEI/ARB group had a prolonged duration of viral shedding [(24 ± 5) vs. (18 ± 5) days, P = 0.034] and increased length of hospitalization [(24 ± 11) vs. (15 ± 7) days, P < 0.01]. These trends were similar in patients with hypertension.Conclusions: Our findings did not provide evidence for a significant association between ACEI/ARB treatment and COVID-19 mortality. ACEIs/ARBs might decrease proinflammatory cytokines, but antiviral treatment should be enforced, and hemodynamics should be monitored closely. Since the limited influence on the ACEI/ARB treatment, they should not be withdrawn if there was no formal contraindication.

In the present study on Device-to-Device (D2D) cache, the files in the file library are cached in the users’ space and a complete way. However, they all disregard the fact that most users do not watch the video in full, especially in the traffic accounting network, it is more obvious. Hence, this undoubtedly causes waste of storage space, and then segmenting video files can solve this problem well. Most existing articles about video file partitioning rely on helpers which the system deliberately selects to assist in caching file blocks, but here we increase the overall hit probability of the system by changing the cache probabilities of different blocks instead of helpers. In this paper, we propose a new caching strategy based on user leaving probability and video file library segmentation, and compare it with other traditional caching algorithms. Simulation and analytical results show that the proposed method HA is feasible, and are capable of offering better performance than other benchmarks in terms of hitting probability.

This paper investigates the effects of corrosion and scouring on the barge impact fragility of bridge structures. A typical four-span continuous reinforced concrete (RC) bridge is selected...

Background: Tumor status can affect patient prognosis. Prognostic nutritional index (PNI), as a nutritional indicator, is closely related to the prognosis of cancer. However, few studies have examined the combined prognostic value of CEA and PNI in patients. This study investigated the relationship between CEA/PNI and prognosis of colon cancer patients.Methods: A total of 513 patients with stage II–III colon cancer who underwent curative resection at two medical centers from 2009 to 2019 were included. Clinicopathological factors were assessed and overall survival (OS) was assessed in a cohort of 413 patients. Multivariate analysis was used to identify independent prognostic variables to construct histograms predicting 1-year and 3-year OS. Data from 100 independent patients in the validation group was used to validate the prognostic model.Results: The median OS time was 33.6 months, and mortality was observed in 54 patients. Multivariate analysis revealed that preoperative CEA/PNI, lymph node metastasis, peripheral nerve invasion, operation mode, and postoperative chemotherapy were independent factors for prognosis evaluation and thus were utilized to develop the nomogram. The C-index was 0.788 in the learning set and 0.836 in the validation set. The calibration curves reached favorable consensus among the 1-, 3-year OS prediction and actual observation.Conclusion: The combined use of CEA and PNI is an independent prognostic factor and thus can serve as a basis for a model to predict the prognosis of patients with stage II–III colon cancer.

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions, and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Facial diagnosis is a method of diagnosing diseases based on human facial features. It can be used to pre-diagnose diseases with facial features to improve diagnosis efficiency. In order to comprehensively analyze the research dynamics and development trends of facial diagnosis, a bibliometric analysis was conducted on 532 papers in the field of facial diagnosis from 1992 to 2021 in Web of Science. On the basis of the scientometrics theory, a series of knowledge mapping studies such as co-citation analysis and cluster analysis were carried out by using the visual analysis software CiteSpace to analyze and obtain the latest trends of facial diagnosis technology, such as publication, scientific research cooperation, research hotpots and frontier trends. The aim of this study are intended to provide guidance for future theoretical research and application in facial diagnosis.

open access article The ongoing COVID-19 pandemic has enhanced the impact of digitalisation as a driver of transformation and advancements across almost every aspect of human life. With the majority actively embracing smart technologies and their benefits, the journey of human digitalisation has begun. Will human beings continue to remain solitary unaffected beings in the middle of the whirlpool—a gateway to the completely digitalised future? This journey of human digitalisation probably started much earlier, before we even realised. This paper, in the format of an objective review and discussion, aims to investigate the journey of human digitalisation, explore the reality of domination between technology and humans, provide a better understanding of the human value and human vulnerability in this fast transforming digital era, so as to achieve valuable and insightful suggestion on the future direction of the human digitalisation journey.

open access article The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

Background: We performed a meta-analysis to systematically review the risk factors of mortality from non-HIV-related Pneumocystis pneumonia (PcP) and provide the theoretical basis for managing non-HIV-related PcP.Methods: PubMed, Embase, Web of Science, the Cochrane Library and CNKI databases were searched. A meta-analysis of the risk factors of mortality from non-HIV-related PcP was conducted.Results: A total of 19 studies and 1,310 subjects were retrieved and included in the meta-analysis, including 485 and 825 patients in the non-survivor and survivor groups, respectively. In the primary analysis, age, concomitant with other pulmonary diseases at diagnosis of PcP, solid tumors, cytomegalovirus(CMV) co-infection, lactate dehydrogenase (LDH), lymphocyte count, invasive ventilation during hospitalization, and pneumothorax were associated with mortality from non-HIV-related PcP, whereas sex, albumin, PcP prophylaxis, use of corticosteroids after admission, and time from onset of symptoms to treatment were not associated with mortality from non-HIV-related PcP.Conclusions: The mortality rate of non-HIV-infected patients with PcP was still high. Age, concomitant with other pulmonary diseases at diagnosis of PcP, solid tumors, CMV co-infection, LDH, lymphocyte count, invasive ventilation during hospitalization, and pneumothorax were risk factors of mortality from non-HIV-related PcP. Improved knowledge of prognostic factors is crucial to guide early treatment.

Angiogenesis is critical for the development, progression, and metastasis of hepatocellular carcinoma (HCC), but the roles of miR-3064-5p in HCC angiogenesis are still unknown. In this study, the roles of miR-3064-5p in HCC angiogenesis were studied in 192 HCC patients, xenograft mouse models, and HCC cell lines. The results showed that miR-3064-5p expression was significantly decreased in HCC tissues and cells, and downregulated miR-3064-5p was associated with upregulated angiogenic potential of HCC. MiR-3064-5p inhibited proangiogenic VEGFA and angiogenin expressions but induced antiangiogenic endostatin and MMP12 expressions, finally leading to suppression of HCC angiogenesis, as shown by the decline in intratumoral microvessel density (MVD). Moreover, miR-3064-5p was inversely correlated with lncRNA MALAT1 and FOXA1. FOXA1 bound to and interacted with CD24 and then regulated Src phosphorylation. MiR-3064-5p played an antiangiogenic role by inhibiting the FOXA1/CD24/Src pathway, whereas oncogenic MALAT1 functioned as a competing endogenous RNA (ceRNA) by sponging miR-3064-5p to alleviate the suppressive effect on the FOXA1 pathway. HCC patients with high miR-3064-5p, low MALAT1, or low FOXA1 expression had a better prognosis with longer overall survival and recurrence-free survival. In univariate and multivariate analyses, miR-3064-5p was identified as the independent prognostic predicator for HCC progression and patient survival. Taken together, miR-3064-5p exerts an antiangiogenic role by targeting the FOXA1/CD24/Src pathway but oncogenic lncRNA MALAT1 acts as a ceRNA to sponge miR-3064-5p. MiR-3064-5p is of great clinical significance and is a novel prognostic indicator and an attractive therapeutic target for HCC.

Background/Aims Interstitial cells play important roles in gastrointestinal (GI) neuro-smooth muscle transmission. The underlying mechanisms of colonic dysmotility have not been well illustrated. We established a partial colon obstruction (PCO) mouse model to investigate the changes of interstitial cells and the correlation with colonic motility. Methods Western blot technique was employed to observe the protein expressions of Kit, platelet-derived growth factor receptor-α (Pdgfra), Ca2+-activated Cl− (Ano1) channels, and small conductance Ca2+- activated K+ (SK) channels. Colonic migrating motor complexes (CMMCs) and isometric force measurements were employed in control mice and PCO mice. Results PCO mice showed distended abdomen and feces excretion was significantly reduced. Anatomically, the colon above the obstructive silicone ring was obviously dilated. Kit and Ano1 proteins in the colonic smooth muscle layer of the PCO colons were significantly decreased, while the expression of Pdgfra and SK3 proteins were significantly increased. The effects of a nitric oxide synthase inhibitor (L-NAME) and an Ano1 channel inhibitor (NPPB) on CMMC and colonic spontaneous contractions were decreased in the proximal and distal colons of PCO mice. The SK agonist, CyPPA and antagonist, apamin in PCO mice showed more effect to the CMMCs and colonic smooth muscle contractions. Conclusions Colonic transit disorder may be due to the downregulation of the Kit and Ano1 channels and the upregulation of SK3 channels in platelet-derived growth factor receptor-α positive (PDGFRα+) cells. The imbalance between interstitial cells of Cajal-Ano1 and PDGFRα-SK3 distribution might be a potential reason for the colonic dysmotility.

Detailed finite element (FE) models with 3-D solid elements are typically used to simulate impact behaviors of reinforced concrete (RC) beams and columns. However, the method usually requires a substantial amount of time and effort to model concrete and reinforcement and conduct nonlinear contact-impact analyses. Also, the accuracy of the method cannot be guaranteed due to the limitations in concrete material models implemented in general-purpose FE codes. In this paper, an efficient modeling method is proposed to capture both flexural and shear behaviors of RC beams and columns under low-velocity impact loading. A macroelement-based contact model was developed in the proposed method to capture interaction behaviors between impacting objects and RC members. In the contact model, a compression-only spring with an initial gap behavior was used to account for the stiffness and the kinematic response of an impacting object, and a combination of an elastic spring and a viscous damper in parallel was employed to simulate the contact stiffness and damping. By properly approximating the strain-rate effects, traditional fiber-section elements were demonstrated to be capable of predicting impact-induced flexural failures for RC members. On this basis, a general approach for both flexural- and shear-critical RC columns under impact loading was presented with the inclusion of additional shear springs in the fiber-section elements. Nearly fifty impact tests on RC beams and columns reported in the literature were employed to validate the proposed modeling method. Comparisons between the experimental and numerical results indicate that failure modes of the impacted members can be identified explicitly by the use of the proposed modeling method. Also, reasonable agreements were achieved for the impact forces and the impact-induced responses obtained from the impact tests and the analyses. The proposed method can be readily implemented without coding in any FE software as long as traditional fiber-section elements, and discrete macroelements are available. This feature would offer an advantage for the applications of the proposed method such as when assessing the structural response and vulnerability of a bridge structure subjected to vessel and vehicle collisions.

Current therapeutics for chronic infection with hepatitis B virus (HBV) rarely induce functional cure due to the immunotolerant status of patients. Small molecule agonists targeting toll‐like receptor 7 (TLR7) have been shown to elicit a functional cure in animal models of HBV but sometimes with poor tolerability due to immune‐related toxicities. In an effort to increase the therapeutic window of TLR7 agonists to treat chronic hepatitis B (CHB), we developed an oral TLR7 agonist, APR002, designed to act locally in the gastrointestinal tract and liver, thus minimizing systemic exposure and improving tolerability. Here, we describe the pharmacokinetic/pharmacodynamic (PK/PD) profile of APR002 in mice and uninfected woodchucks as well as the safety and antiviral efficacy in combination with entecavir (ETV) in woodchucks with CHB. Treatment of woodchucks chronically infected with woodchuck hepatitis virus (WHV) with weekly oral doses of APR002 was well‐tolerated. While APR002 and ETV single agents did not elicit sustained viral control, combination therapy resulted in durable immune‐mediated suppression of the chronic infection. These woodchucks also had detectable antibodies to viral antigens, enhanced interferon‐stimulated gene expression, and loss of WHV covalently closed circular DNA. Conclusion: APR002 is a novel TLR7 agonist exhibiting a distinct PK/PD profile that in combination with ETV can safely attain a functional cure in woodchucks with chronic WHV infection. Our results support further investigation of liver‐targeted TLR7 agonists in human CHB.