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Summary Uniform proppant distribution in a cluster and a stage with multiple clusters is a primary objective to optimize fracturing parameters and improve the production from each cluster. Because fracturing slurry is typically pumped at high pressure and rate in fields, it is a big challenge to study proppant transport behavior and distribution characteristics through laboratory experiments. There is still a lack of an effective model to quantitatively evaluate proppant distribution based on an actual wellbore configuration. The objective is to propose a novel method to accurately evaluate the distribution uniformity and quickly optimize fracturing parameters based on field conditions. This paper conducts particle transport experiments in a horizontal pipe with six holes at the helical distribution. A 3D numerical model coupling of the computational fluid dynamics (CFD) and discrete element method (DEM) is used to study proppant distribution. Proppant distribution is quantitatively evaluated by the proppant transport efficiency (E) and normalized standard deviation (NSD). The effects of 10 parameters are investigated. An artificial neural network (ANN) model is developed to predict proppant distribution in a cluster. The results identify that proppant distribution among perforations is generally toe-biased in a horizontal wellbore due to a high pumping rate. Proppants with large inertia easily miss the heel-side holes and are suspended to the toe side. The complex vorticity flow carries them to the toe-side perforation regardless of hole orientation. Fluid distribution can significantly change proppant distribution regardless of fluid velocity. The heel-biased fluid distribution leads to the same bias of proppant, and the downward perforations receive more proppants. Proppant transport reaches equilibrium quickly, and the distribution is hard to change unless the injection condition varies. It is a good choice to increase fluid viscosity, add perforation sealers, and inject small mesh proppant, especially for the low density. The ANN model trained by extensive experimental and numerical samples can accurately evaluate proppant distribution uniformity. The study provides an efficient way to optimize injection parameter design and achieve real-time optimization coupled with the fiber-optic downhole diagnostic. It can be a crucial part of artificial intelligence hydraulic fracturing.

Summary Understanding proppant transport and distribution in hydraulic fractures is crucial to designing and optimizing hydraulic fracturing treatments in the field. The actual fracture surfaces are typically rough and form a tortuous pathway, significantly affecting proppant migration. However, many rough models are very small in size, and some have only one rough surface. Thus, it is inadequate to display proppant transport behaviors and placement laws. This study proposed a novel method to develop large-scale rough panels reproduced from actual hydraulic fractures. A large transparent slot (2×0.3 m) was successfully constructed to simulate a shear fracture with 5 mm relative displacement of two matched surfaces. Six kinds of proppants were selected to study the effects of particle density and size. Four types of slickwater were prepared to achieve viscous diversity. A high-resolution particle image velocimetry (PIV) system detected the instantaneous velocity and vector fields in the rough pathway to understand particle transport behaviors. The specific parametric study includes a quantitative analysis of the proppant bed profile, equilibrium height, coverage area, injection pressure, and volumes of proppant settled in the slot and outlet tank. Also, five tests are carried out in the smooth slot, which has the same size as the rough slot. The test results demonstrate that the narrow rough fracture would significantly hinder particle transport, especially in the horizontal direction. The proppant bed is higher and closer to the inlet than that in the smooth model. Particles mixed with highly viscous slickwater easily aggregate in the two-sided rough model and gradually form finger-like regions at the lower part of the inlet. The unstable flow and vortices can disperse aggregated particles and avoid particle clogging. Proppants injected at the high volume fraction are prone to settle quickly and build up a higher bed contact with the inlet, leading to more considerable injection pressure. Perforation blockage often occurred in the rough model, and the near-wellbore screenout was induced as the bed blocked all perforations. Enhancing the fluid carrying capacity and using smaller proppant help avoid perforation blockage and improve far-field fracture conductivity. Two correlations were developed to predict the equilibrium height and coverage area of the proppant bed. The experimental results and laws provide novel understandings that can help optimize hydraulic fracturing design and treatment by rationally selecting proppant and fracturing fluid to improve the productivity in tight reservoirs.

OBJECTIVE: The prognostic value of interleukin-6 (IL-6) in patients with atrial fibrillation (AF) has not been fully elucidated. Therefore, we conducted a cohort study and a meta-analysis to assess the predictive value of IL-6 for stroke and mortality in patients with AF. METHODS: A cohort study was performed in newly diagnosed non-valvular patients with AF. A total of 217 patients with AF were followed up for a mean of 27 months. A multivariate Cox regression analysis was used to evaluate the association between IL-6 and stroke/all-cause mortality. The incremental value was also assessed by adding IL-6 to the CHA2DS2-VASc score. Besides, a meta-analysis of all reported cohort studies and our cohort study was conducted to validate the association of circulating IL-6 and stroke/mortality in patients with AF. RESULTS: Our cohort study showed that elevated plasma level of IL-6 was an independent risk factor for predicting stroke [hazard ratio (HR)=3.81; 95% confidence interval (CI), 1.11–13.05; p=0.033] and all-cause mortality (HR=3.11; 95% CI, 1.25–7.72; p=0.015) in patients with AF. Adding IL-6 levels to CHA2DS2-VASc score showed limited improvement of the predictive power for stroke [area under curve (AUC) from 0.81 to 0.88, p=0.006]. Meta-analysis confirmed that increased circulating level of IL-6 was significantly associated with increased risk of stroke (pooled HR=1.97; 95% CI, 1.22–3.17; p=0.006) and all-cause mortality (pooled HR=2.73; 95% CI, 2.29–3.25; p

The CHA 2 DS 2 -VASc score was initially applied to stratify stroke risk in patients with atrial fibrillation (AF) and was found to be effective in predicting all-cause mortality outcomes. To date, it is still unclear whether circulating long non-coding RNAs (lncRNAs) as emerging biomarkers, can improve the predictive power of the CHA 2 DS 2 -VASc score in stroke and all-cause mortality.Candidate lncRNAs were screened by searching the literature and analyzing previous RNA sequencing results. After preliminary verification in 29 patients with AF, the final selected lncRNAs were evaluated by Cox proportional hazards regression in 192 patients to determine whether their relative expression levels were associated with stroke and all-cause mortality. The c-statistic, net reclassification improvement (NRI), and integrated discrimination improvement of the patients were calculated to evaluate the discrimination and reclassification power for stroke and all-cause mortality when adding lncRNA expression levels to the CHA 2 DS 2 -VASc score model.Five plasma lncRNAs associated with stroke and all-cause mortality in AF patients were selected in our screening process. Patients with elevated H19 levels were found to have a higher risk of stroke (hazard ratio [HR] 3.264, 95% confidence interval [CI]: 1.364-7.813, P = 0.008). Adding the H19 expression level to the CHA 2 DS 2 -VASc score significantly improved the discrimination and reclassification power of the CHA 2 DS 2 -VASc score for stroke in AF patients. In addition, the H19 level showed a marginally significant association with all-cause mortality (HR 2.263, 95% CI: 0.889-5.760, P = 0.087), although it appeared to have no significant improvement for the CHA 2 DS 2 -VASc model for predicting all-cause mortality.Plasma expression of H19 was associated with stroke risk in AF patients and improved the discriminatory power of the CHA 2 DS 2 -VASc score. Therefore, lncRNA H19 served as an emerging non-invasive biomarker for stroke risk prediction in patients with AF.

Objectives The optimization of the dual-parameter Fano resonance (FR) sensor based on the surface plasmon resonance (SPR) relies on the conventional parametric sweep method, which is time-consuming and difficult to find the optimized design. This paper aims to investigate the approaches for finding the best design automatically. Methods A particle swarm optimization (PSO) algorithm combining the linear weighted sum method (LWSM) and the technique for order preference by similarity to an ideal solution (TOPSIS) are proposed to optimize the structure parameters efficiently. Then the multi-objective particle swarm optimization (MOPSO) algorithm is used to compare the optimization results with the proposed algorithm. Results After several optimization iterations, the PSO-LWSM and PSO-TOPSIS optimize the sensitivity to 795 nm/RIU and 0.024 nm/°C with 30 iterations, and the MOPSO algorithm optimizes the sensitivity to 790 nm/RIU and 0.023 nm/°C with 100 iterations. The structures obtained through these two methods are similar and give the results within a shorter time than the parametric sweep method. The MOPSO algorithm can find more combinations of optimized solutions than the PSO-LWSM and PSO-TOPSIS, but it takes more time. Conclusion In summary, the proposed algorithms are better and faster optimization approaches than the traditional parametric scanning method for optimizing FR-based sensors.

Background During outbreak of Coronavirus Disease 2019 (COVID-19), healthcare providers are facing critical clinical decisions based on the prognosis of patients. Decision support tools of risk stratification are needed to predict outcomes in patients with different clinical types of COVID-19. Methods This retrospective cohort study recruited 2425 patients with moderate or severe COVID-19. A logistic regression model was used to select and estimate the factors independently associated with outcomes. Simplified risk stratification score systems were constructed to predict outcomes in moderate and severe patients with COVID-19, and their performances were evaluated by discrimination and calibration. Results We constructed two risk stratification score systems, named as STPCAL (including significant factors in the prediction model: number of clinical symptoms, the maximum body temperature during hospitalization, platelet count, C-reactive protein, albumin and lactate dehydrogenase) and TRPNCLP (including maximum body temperature during hospitalization, history of respiratory diseases, platelet count, neutrophil-to-lymphocyte ratio, creatinine, lactate dehydrogenase, and prothrombin time), to predict hospitalization duration for moderate patients and disease progression for severe patients, respectively. According to STPCAL score, moderate patients were classified into three risk categories for a longer hospital duration: low (Score 0–1, median = 8 days, with less than 20.0% probabilities), intermediate (Score 2–6, median = 13 days, with 30.0–78.9% probabilities), high (Score 7–9, median = 19 days, with more than 86.5% probabilities). Severe patients were stratified into three risk categories for disease progression: low risk (Score 0–5, with less than 12.7% probabilities), intermediate risk (Score 6–11, with 18.6–69.1% probabilities), and high risk (Score 12–16, with more than 77.9% probabilities) by TRPNCLP score. The two risk scores performed well with good discrimination and calibration. Conclusions Two easy-to-use risk stratification score systems were built to predict the outcomes in COVID-19 patients with different clinical types. Identifying high risk patients with longer stay or poor prognosis could assist healthcare providers in triaging patients when allocating limited healthcare during COVID-19 outbreak.

City health examination and evaluation of territorial spatial planning is a new policy tool in China. However, research on city health examination and evaluation of territorial spatial planning is still in the exploratory stage in China. Guided by sustainable cities and communities (SDG11), a reasonable city health examination and evaluation index system for Xining City in Qinghai Province is constructed in this paper. The improved technique for order preference by similarity to ideal solution (TOPSIS) was used to quantify the evaluation results, and the city health index was visualized using the city health examination signals and warning panel. The results show that the city health index of Xining City continuously rose from 35.76 in 2018 to 69.76 in 2020. However, it is still necessary to address the shortcomings in innovation, coordination, openness and sharing and to improve the level of city space governance in a holistic way. This study is an exploration of the methodology used in city health examination and the evaluation of territorial spatial planning in China, which can provide a foundation for the sustainable development of Xining City and also provide a case reference for other cities seeking to carry out city health examinations and evaluations of territorial spatial planning in China.

Coronavirus disease 2019 (COVID-19) is highly infectious and has spread worldwide. Medical staff may be exposed to the infection under circumstances of medical supply shortages and improper protection. How to better protect medical staff has become the focus of all walks of life. Therefore, during the COVID-19 outbreak, timely adjustments and optimization of nursing management strategies in isolation wards are important for improving care quality and reducing the infection of medical staff. Based on a literature review and clinical nursing management practices, this article summarizes the nursing management strategies in isolation wards during the COVID-19 outbreak, aiming to provide references for clinical nursing managers.

Background Patients with atrial fibrillation are at increased risk of stroke and mortality. It is not clear if inflammatory biomarkers are associated with stroke and mortality in patients with atrial fibrillation. We aimed to evaluate the predictive value of three inflammatory biomarkers (interleukin [IL]-9, IL-10, and interferon [IFN]-γ) for stroke and mortality in atrial fibrillation. Method A total of 232 patients with new-onset atrial fibrillation were enrolled and 217 patients were completely followed-up. Peripheral plasma concentrations of cytokines (IL-9, IL-10, and IFN-γ) were measured using Luminex xMAP assays. The association between dichotomous groups of cytokines and outcomes were evaluated by a Cox proportional hazards model. The incremental value of inflammatory biomarkers, in addition to the CHA2DS2-VASc score, was also assessed. Results Patients were followed-up for a median duration of 27 (interquartile range [IQR], 23-30) months. The elevated plasma level of IFN-γ was an independent risk factor for stroke (hazard ratio [HR], 4.02 [IQR, 1.06-15.34]; p=0.042) and all-cause mortality (HR, 3.93 [IQR, 1.43-10.78]; p=0.008) in patients with atrial fibrillation. Adding high IFN-γ to the CHA2DS2-VASc score showed improvement in discrimination and reclassification prediction for stroke and mortality. However, IL-9 and IL-10 had no statistically significant association with stroke and all-cause mortality in patients with atrial fibrillation. Conclusions In this “real-world” cohort of patients with atrial fibrillation, we have shown for the first time that plasma levels of IFN-γ could provide incremental prognostic value supplementary to that obtained from the CHA2DS2-VASc scores for predicting of stroke and all-cause mortality.

Long non-coding RNA (lncRNA) and circular RNA (circRNA) have both been found to play important roles in cardiovascular diseases, including myocardial infarction, heart failure, and atherosclerosis. However, the role of lncRNA and circRNA in atrial fibrillation (AF) has rarely been investigated. This study aimed to identify lncRNA and circRNA expression profiles in AF patients.Atrial tissues from seven patients with AF and seven matched controls were collected. The lncRNA and circRNA expression profiles of atrial tissues were identified using Hiseq/Proton RNA sequencing. Validation was performed by reverse transcription quantitative real-time PCR (qRT-PCR) on 35 pairs of AF patients and controls. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A competing endogenous RNA (ceRNA) network was constructed.A total of 557 lncRNAs and 280 circRNAs were significantly differentially expressed with fold change1.5 (p0.05). An lncRNA Voltage Dependent Anion Channel 2 Pseudogene 2 (VDAC2P2) and two circRNAs chr13_41887361_41865736_-21625 and chr13_100368574_100301460_-67114 were validated, using qRT-PCR, to have significantly different expression levels. GO and KEGG pathway analysis showed that some pathways such as ribosome and chromatin modification, Rap1 signalling and cardiac muscle contraction were involved in the pathogenesis of AF. Competing endogenous RNAs were predicted based on constructional network analysis. The LncRNA-miRNA-mRNA and circRNA-miRNA-mRNA networks were constructed by co-expressing lncRNA/circRNA and mRNAs, which were competitively combined with miRNAs.This study characterised lncRNA and circRNA expression and their interaction with mRNA and miRNA in AF.

BackgroundThe receptor of severe respiratory syndrome coronavirus 2 (SARS-CoV-2), angiotensin-converting enzyme 2, is more abundant in kidney than in lung tissue, suggesting that kidney might be another important target organ for SARS-CoV-2. However, our understanding of kidney injury caused by Coronavirus Disease 2019 (COVID-19) is limited. This study aimed to explore the association between kidney injury and disease progression in patients with COVID-19.MethodsA retrospective cohort study was designed by including 2630 patients with confirmed COVID-19 from Huoshenshan Hospital (Wuhan, China) from 1 February to 13 April 2020. Kidney function indexes and other clinical information were extracted from the electronic medical record system. Associations between kidney function indexes and disease progression were analyzed using Cox proportional-hazards regression and generalized linear mixed model.ResultsWe found that estimated glomerular filtration rate (eGFR) and creatinine clearance (Ccr) decreased in 22.0% and 24.0% of patients with COVID-19, respectively. Proteinuria was detected in 15.0% patients and hematuria was detected in 8.1% of patients. Hematuria (HR 2.38, 95% CI 1.50–3.78), proteinuria (HR 2.16, 95% CI 1.33–3.51), elevated baseline serum creatinine (HR 2.84, 95% CI 1.92–4.21) and blood urea nitrogen (HR 3.54, 95% CI 2.36–5.31), and decrease baseline eGFR (HR 1.58, 95% CI 1.07–2.34) were found to be independent risk factors for disease progression after adjusted confounders. Generalized linear mixed model analysis showed that the dynamic trajectories of uric acid was significantly related to disease progression.ConclusionThere was a high proportion of early kidney function injury in COVID-19 patients on admission. Early kidney injury could help clinicians to identify patients with poor prognosis at an early stage.Graphic abstract

Background Circular RNA (circRNA) have been reported to play important roles in cardiovascular diseases including myocardial infarction and heart failure. However, the role of circRNA in atrial fibrillation (AF) has rarely been investigated. We recently found a circRNA hsa_circ_0099734 was significantly differentially expressed in the AF patients atrial tissues compared to paired control. We aim to investigate the functional role and molecular mechanisms of mmu_circ_0005019 which is the homologous circRNA in mice of hsa_circ_0099734 in AF. Methods In order to investigate the effect of mmu_circ_0005019 on the proliferation, migration, differentiation into myofibroblasts and expression of collagen of cardiac fibroblasts, and the effect of mmu_circ_0005019 on the apoptosis and expression of Ito, INA and SK3 of cardiomyocytes, gain- and loss-of-function of cell models were established in mice cardiac fibroblasts and HL-1 atrial myocytes. Dual-luciferase reporter assays and RIP were performed to verify the binding effects between mmu_circ_0005019 and its target microRNA (miRNA). Results In cardiac fibroblasts, mmu_circ_0005019 showed inhibitory effects on cell proliferation and migration. In cardiomyocytes, overexpression of mmu_circ_0005019 promoted Kcnd1, Scn5a and Kcnn3 expression. Knockdown of mmu_circ_0005019 inhibited the expression of Kcnd1, Kcnd3, Scn5a and Kcnn3. Mechanistically, mmu_circ_0005019 exerted biological functions by acting as a miR-499-5p sponge to regulate the expression of its target gene Kcnn3. Conclusions Our findings highlight mmu_circ_0005019 played a protective role in AF development and might serve as an attractive candidate target for AF treatment.

Dysregulation of long noncoding RNA (lncRNA) is known to be involved in numerous human diseases, including lung cancer. However, the precise biological functions of most lncRNA remain to be elucidated. Here, we identified a novel up-regulated lncRNA, LINC01436 (RefSeq: NR_110419.1), in non-small cell lung cancer (NSCLC). High expression of LINC01436 was significantly associated with poor overall survival. Notably, LINC01436 expression was transcriptionally repressed by E2F6 under normoxia, and the inhibitory effect was relieved in a hypoxic microenvironment. Gain- and loss-of-function studies revealed that LINC01436 acted as a proto-oncogene by promoting lung cancer cell growth, migration and invasion in vitro. Xenograft tumor assays in nude mice confirmed that LINC01436 promoted tumor growth and metastasis in vivo. Mechanistically, LINC01436 exerted biological functions by acting as a microRNA (miR)-30a-3p sponge to regulate the expression of its target gene EPAS1. Our findings characterize LINC01436 as a new hypoxia-sensitive lncRNA with oncogenic function in NSCLC, suggesting that LINC01436 may be a potential biomarker for prognosis and a potential target for treatment.

Additional file 1. Statistical analysis for indicators of COVID-19 hospitalization duration, progression and associated factors.

Low temperature is a major adverse environment that affects normal plant growth. Previous reports showed that the actin cytoskeleton plays an important role in the plant response to low-temperature stress, but the regulatory mechanism of the actin cytoskeleton in this process is not clear. C-repeat binding factors (CBFs) are the key molecular switches for plants to adapt to cold stress. However, whether CBFs are involved in the regulation of the actin cytoskeleton has not been reported. We found that Arabidopsis actin depolymerizing factor 5 (ADF5), an ADF that evolved F-actin bundling function, was up-regulated at low temperatures. We also demonstrated that CBFs bound to the ADF5 promoter directly in vivo and in vitro. The cold-induced expression of ADF5 was significantly inhibited in the cbfs triple mutant. The freezing resistance of the adf5 knockout mutant was weaker than that of wild type (WT) with or without cold acclimation. After low-temperature treatment, the actin cytoskeleton of WT was relatively stable, but the actin cytoskeletons of adf5, cbfs, and adf5 cbfs were disturbed to varying degrees. Compared to WT, the endocytosis rate of the amphiphilic styryl dye FM4-64 in adf5, cbfs, and adf5 cbfs at low temperature was significantly reduced. In conclusion, CBFs directly combine with the CRT/DRE DNA regulatory element of the ADF5 promoter after low-temperature stress to transcriptionally activate the expression of ADF5; ADF5 further regulates the actin cytoskeleton dynamics to participate in the regulation of plant adaptation to a low-temperature environment.

Single‐nucleotide polymorphisms (SNP) and long non‐coding RNAs (lncRNAs) have been involved in the process of lung cancer. Following clues given by lung cancer risk‐associated SNP, we aimed to find novel functional lncRNAs as candidate targets in lung cancer. We identified a lncRNA Oxidative Stress Responsive Serine Rich 1 Antisense RNA 1 (OSER1‐AS1) through a lung cancer risk‐associated SNP rs4142441. OSER1‐AS1 was down‐regulated in tumor tissue and its low expression was significantly associated with poor overall survival among non‐smokers in non‐small cell lung cancer (NSCLC) patients. Gain‐ and loss‐of‐function studies showed that OSER1‐AS1 acted as a tumor suppressor by inhibiting lung cancer cell growth, migration and invasion in vitro. Xenograft tumor assays and a metastasis mouse model confirmed that OSER1‐AS1 suppressed tumor growth and metastasis in vivo. The promoter of OSER1‐AS1 was repressed by MYC, and the 3′‐end of OSER1‐AS1 was competitively targeted by microRNA hsa‐miR‐17‐5p and RNA‐binding protein ELAVL1. Our results indicated that OSER1‐AS1 exerted tumor‐suppressive functions by acting as an ELAVL1 decoy to keep it away from its target mRNAs. Our findings characterized OSER1‐AS1 as a new tumor‐suppressive lncRNA in NSCLC, suggesting that OSER1‐AS1 may be suitable as a potential biomarker for prognosis, and a potential target for treatment., Our results demonstrated that long non‐coding RNA OSER1‐AS1 performed tumor suppressive function by forming an RNA‐protein complex with ELAVL1 and sequester it from binding and stabilizing its target mRNAs, and it may serve as a potential prognosis markers for patients with NSCLC.

Background: Single nucleotide polymorphisms (SNPs) and long non-coding RNAs (lncRNAs) have been involved in the process of lung cancer. Following clues given by lung cancer risk-associated SNPs, we aimed to find novel functional lncRNAs as candidate targets in non-small cell lung cancer (NSCLC). Methods: Case-control analyses were performed in 626 cases and 736 controls matched up on sex and age. The lncRNA OSER1-AS1 was identified near a lung cancer risk-associated SNP rs4142441. Kaplan–Meier survival analysis was performed to investigate the association between OSER1-AS1 expression and overall survival. The influence of rs4142441 on the expression level of OSER1-AS1 was confirmed using Luciferase assays. Subsequently, the biological function of OSER1-AS1 was assessed in vitro by cell proliferation, migration, and invasion experiments through gain- and loss-of-function approaches, and in vivo by subcutaneous tumor model and tail vein injection lung metastasis model. ChIP and RIP experiments were carried out to investigate the interaction between transcription factors, RNA-binding proteins, and OSER1-AS1.Results: OSER1-AS1 was down-regulated in tumor tissue and its low expression was significantly associated with poor overall survival among non-smokers in NSCLC patients. Gain- and loss-of-function studies revealed that OSER1-AS1 acted as a tumor suppressor by inhibiting lung cancer cell growth, migration and invasion in vitro. Xenograft tumor assays and metastasis mouse model confirmed that OSER1-AS1 suppressed tumor growth and metastasis in vivo. The promoter of OSER1-AS1 was repressed by MYC, and the 3’-end of OSER1-AS1 was competitively targeted by microRNA hsa-miR-17-5p and RNA-binding protein ELAVL1. Conclusion: Our results indicated that OSER1-AS1 exerted tumor-suppressive functions by acting as an ELAVL1 decoy to keep it away from its target mRNAs. Our findings characterized OSER1-AS1 as a new tumor suppressive lncRNA in NSCLC, suggesting that OSER1-AS1 may be suitable as a potential biomarker for prognosis, and a potential target for treatment.

Background Chemokines play an important role in inflammation and atherosclerosis. However, little is known about the relationship between chemokines and the prognosis of atrial fibrillation (AF). This “real-world” cohort study was designed to observe the prognostic value of plasma C C motif chemokine ligand (CCL) 18, CCL23, CCL28, C X C motif chemokine ligand (CXCL) 14, CXCL16 in newly diagnosed AF patients. Methods Baseline plasma levels of chemokines were measured in a cohort with 299 AF patients using Bio-plex Pro™ xMAP arrays. A Cox proportional hazard model was used to evaluate the associations of chemokines with AF outcomes. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the improvement of chemokines to CHA2DS2-VASc score. Results High CCL18 (hazard ratio [HR] 2.65, 95% confidence interval [CI] 1.18–5.98, P = 0.019) and CCL23 levels (HR 2.78, 95%CI 1.07–7.22, P = 0.036) were associated with stroke in AF patient. Patients with low CXCL14 (HR 0.39, 95%CI 0.15–0.97, P = 0.042) and high CXCL16 levels (HR 3.02, 95%CI 1.39–6.58, P = 0.005) have increased risk of all-cause mortality. High CCL16 levels (HR 5.41, 95%CI 2.32–12.63, P Conclusions Plasma levels of CCL18, CCL23, CXCL14, and CXCL16 were independently associated with AF outcomes. Chemokines added to CHA2DS2-VASc score significantly enhanced risk assessment for the outcomes. Incorporation of chemokines into clinical decisions may help the management of AF treatment.

Long non-coding RNAs (lncRNAs) have been involved in the process of cancer occurrence, progression, and treatment. Lung cancer-related lncRNAs are still an emerging field, thus we sought to identify novel functional lncRNAs as candidate targets in lung cancer. Here, we identified one novel lncRNA, MUC5B-AS1 (Ensembl: ENST00000532061.2). MUC5B-AS1 was upregulated in lung adenocarcinoma tissues compared with normal lung tissues. Moreover, MUC5B-AS1 promoted lung cancer cell migration and invasion in vitro and promoted lung cancer cell metastasis in vivo. MUC5B-AS1 and its cognate sense transcript MUC5B were highly co-expressed and mutually regulated in lung adenocarcinoma. Mechanistically, MUC5B-AS1 promoted cell migration and invasion by forming an RNA–RNA duplex with MUC5B, thereby increasing MUC5B expression levels in lung adenocarcinoma. The high expression of MUC5B was significantly associated with poor outcomes in lung adenocarcinoma. Our findings highlight MUC5B-AS1 functions as an oncogenic lncRNA in tumor metastasis and implicate MUC5B-AS1 as an attractive candidate target for lung adenocarcinoma treatment.

Located on the southeast edge of the Qinghai-Tibetan plateau, Aba County is the core area of the Returning Grazing Land to Grassland Project (RGLGP) on the plateau. For the purpose of monitoring the grassland change before and after the grassland protection project in Aba County, Landsat images acquired in 1996, 2003 and 2009 were analyzed. Using Spectral Mixture Analysis (SMA) model, sub-pixel fractions of land cover components were obtained: bright vegetation (BV), dark vegetation (DV), bright soil (BS), dark soil (DS) and water. Fraction images present the distribution and proportions of typical land cover components in this study. Fractions BV and BS were chosen as two indicators for grassland degradation. Thereafter, Change Vector Analysis (CVA) model was applied on the two indicators. After the performance of the CVA model, change results which consisted of both grassland degradation and vegetation re-growth were obtained, showing the change patterns of grassland degradation and vegetation re-growing in Aba County between two gaps: from 1996 to 2003 (before the RGLGP) and from 2003 to 2009 (after the RGLGP). The change patterns of grassland degradation and vegetation re-growing can effectively assist in the development of environmental restoration measures and in the RGLGP plans for the Qinghai-Tibetan plateau.

Aims We aimed to investigate the role of Fasting Plasma Glucose (FPG) and glucose fluctuation in the prognosis of COVID-19 patients stratified by pre-existing diabetes. Methods The associations of FPG and glucose fluctuation indexes with prognosis of COVID-19 in 2,642 patients were investigated by multivariate Cox regression analysis. The primary outcome was in-hospital mortality; the secondary outcome was disease progression. The longitudinal changes of FPG over time were analyzed by the latent growth curve model in COVID-19 patients stratified by diabetes and severity of COVID-19. Results We found FPG as an independent prognostic factor of overall survival after adjustment for age, sex, diabetes and severity of COVID-19 at admission (HR: 1.15, 95% CI: 1.06–1.25, P = 1.02 × 10−3). Multivariate logistic regression analysis indicated that the standard deviation of blood glucose (SDBG) and largest amplitude of glycemic excursions (LAGE) were also independent risk factors of COVID-19 progression (P = 0.03 and 0.04, respectively). The growth trajectory of FPG over the first 3 days of hospitalization was steeper in patients with critical COVID-19 in comparison to moderate patients. Conclusions Hyperglycemia and glucose fluctuation were adverse prognostic factors of COVID-19 regardless of pre-existing diabetes. This stresses the importance of glycemic control in addition to other therapeutic management.

Electronic voltage capacitive transformers (ECVT) have become one of the most widely used transformers in power system due to its mature principle and good adaptability for high voltage power grid. However, with the increasing number of power equipment in substations in recent years, the electromagnetic environment around ECVT is becoming more and more complex. Nearby devices or conductors would interfere with ECVT to a certain extent, resulting in relatively large errors in measurement results and affecting the measurement accuracy of ECVT. In order to solve this problem, mathematical model of ECVT was built in this paper firstly, considering the influence of nearby devices based on the structure and working principle of ECVT. The ECVT with charged capacitances was modeled and simulated by Ansoft Maxwell finite element software, and the stray capacitance matrix was obtained. Then, the error data under the influence of adjacent conductors are calculated by putting stray capacitance matrix into the mathematical model of ECVT. Finally, the influence of adjacent conductors on the error characteristics of ECVT was analyzed by changing the voltage, distance and height of adjacent conductors.

The super-hydrophobic copper surface was obtained by using a nanosecond pulsed laser. Different micro- and nano-structures were fabricated by changing the laser scanning interval and scanning speed, before heating in an electric heater at 150 &deg, C for two hours to explore the effect of laser parameters and heat treatment on the wettability of the copper surface. It was found that the laser-treated copper surface is super-hydrophilic, and then, after the heat treatment, the surface switches to hydrophobic or even super-hydrophobic. The best super-hydrophobic surface&rsquo, s apparent contact angle (APCA) was 155.6&deg, and the water sliding angle (WSA) was 4&deg, Super-hydrophobic copper is corrosion-resistant, self-cleaning, and dust-proof, and can be widely used in various mechanical devices.

Ibrutinib, an inhibitor of Bruton tyrosine kinase (BTK), has shown promising pharmacologic effects in acute myeloid leukemia (AML). In this study, we report that abivertinib or AC0010, a novel BTK inhibitor, inhibits cell proliferation, reduces colony-forming capacity, and induces apoptosis and cell cycle arrest in AML cells, especially those harboring FLT3-ITD mutations. Abivertinib was also found to be more sensitive than ibrutinib in treating AML. We demonstrate that in addition to targeting the phosphorylation of BTK, abivertinib also targeted the crucial PI3K survival pathway. Furthermore, abivertinib suppressed the expression of p-FLT3 and the downstream target p-STAT5 in AML cells harboring FLT3-ITD mutations. Moreover, in vitro and in vivo data revealed synergistic activity between abivertinib and homoharringtonine (HHT), a natural plant alkaloid commonly used in China, in treating AML cells with or without FLT3-ITD mutations. Collectively, these preclinical data suggest that abivertinib may be a promising novel agent for AML, with potential for combination treatment with HHT. Clinical studies on abivertinib-involved therapy are planned.

The Goos–Hänchen (GH) shift is one of the important aspects to evaluate the performance of multilayer surface plasmon resonance (SPR) sensors. However, the conventional SPR sensor design procedure based on the fixed parameter scanning (FPS) method is complex and time-consuming, and makes it difficult to gain the optimized design. In this paper, in order to design the optimal GH-shift-based SPR sensor, an improved differential evolution (IDE) algorithm based on chaos mapping, fitness elimination mechanism, nonlinear scale factor, and cross probability control strategy is proposed. By using such an IDE algorithm, the Ag‐ITO‐TMDCs‐graphene structure can be optimized. By the IDE algorithm, after 9 iterations, the maximum fitness value is obtained in the Ag‐ITO‐ M o S 2 -graphene structure, the maximum GH shift is 16591 λ , and the sensitivity is 3.3 × 10 8 λ / R I U . Compared with the FPS method, the GH shift is increased 192 times, and the sensitivity is increased 25,707.6 times. Compared with the DE algorithm, the number of iterations and the efficiency of the IDE algorithm are enormously improved as well. Such an algorithm provides a new, to the best of our knowledge, approach for designing a multilayer SPR sensor.

Qinghai Province is a very fragile area of ecological environment. While paying attention to economic development, we should pay more attention to the change trend of ecological environment. According to the urban statistical yearbook, the energy consumption of construction industry accounts for a large proportion of urban energy consumption. In the concept of sustainable development, we should pay attention to the problem of resource consumption and environmental pressure of urban construction industry. This paper takes Xining construction industry as the research object, based on the material flow analysis method, analyzes the material flow of newly built and demolished buildings in Xining city from 2011 to 2015, and reveals the material consumption status of Xining construction activities and the pressure on the environment.

At present, the implementation evaluation of urban and rural planning has some problems, such as poor standardization, insufficient subjective cognition, and lack of methodology, which make it difficult to carry out effectively. This paper discusses how to improve the effectiveness and operability of the implementation evaluation of urban and rural planning from three aspects: the evaluation content system, the evaluation organization and method, and the result judgment. Based on the basic concepts of urban and rural planning management and urban ecological environment, this paper analyzes the existing problems of urban ecological environment and urban and rural planning management, as well as the relationship between planning management and urban ecological environment. On this basis, the connotation of eco-city was discussed, and the idea of implementing steps for building eco-city and countryside planning was put forward, so as to promote the development of eco-city and countryside planning and eco-city and countryside construction in China.