During the COVID-19 pandemic, many people devoted longer time to screen viewing due to the need for study, work, and online social activities, instead of outdoor activities, which may have led to an ...increase in dry eye symptoms. This study aimed to evaluate the prevalence of dry eye during the COVID-19 pandemic.
PubMed, Cochrane Library, Embase, and Web of Science were searched from January 1, 2020 to October 20, 2022. Cross-sectional surveys on dry eye prevalence conducted after January 1, 2020 were included. Two review authors independently performed data extraction and assessed study quality. The random-effects model was used to analyze the prevalence of dry eye, and the odds ratio was used to assess the strength of the association between variables. Subgroup analysis was performed to detect heterogeneity, the leave-one-out method for sensitivity analysis, and the Egger test for publication bias.
A total of eleven studies with 15692 individuals met the eligibility criteria. The prevalence of dry eye during the COVID-19 pandemic was 61.0% (95%CI: 51.8%-70.2%) globally and 56.7% (95%CI: 45.3%-68.1%) in Asia. The prevalence of dry eye had significant differences in sex and visual display time, with higher prevalence among females and visual display time of more than 4 hours per day. Subgroup analysis was performed based on diagnostic tools, study population, and average age. A significant difference was found in diagnostic tools, but no significant change in heterogeneity (P<0.05). The leave-one-out method showed stable results, and the Egger test identified no significant publication bias.
The prevalence of dry eye during the COVID-19 pandemic is significantly higher than before, and a higher prevalence is found among females and those having a visual display time of more than 4 hours per day.
We present here a time‐dependent three‐dimensional magnetohydrodynamic (MHD) solar wind simulation from the solar surface to the Earth's orbit driven by time‐varying line‐of‐sight solar magnetic ...field data. The simulation is based on the three‐dimensional (3‐D) solar‐interplanetary (SIP) adaptive mesh refinement (AMR) space‐time conservation element and solution element (CESE) MHD (SIP‐AMR‐CESE MHD) model. In this simulation, we first achieve the initial solar wind background with the time‐relaxation method by inputting a potential field obtained from the synoptic photospheric magnetic field and then generate the time‐evolving solar wind by advancing the initial 3‐D solar wind background with continuously varying photospheric magnetic field. The model updates the inner boundary conditions by using the projected normal characteristic method, inputting the high‐cadence photospheric magnetic field data corrected by solar differential rotation, and limiting the mass flux escaping from the solar photosphere. We investigate the solar wind evolution from 1 July to 11 August 2008 with the model driven by the consecutive synoptic maps from the Global Oscillation Network Group. We compare the numerical results with the previous studies on the solar wind, the solar coronal observations from the Extreme ultraviolet Imaging Telescope board on Solar and Heliospheric Observatory, and the measurements from OMNI at 1 astronomical unit (AU). Comparisons show that the present data‐driven MHD model's results have overall good agreement with the large‐scale dynamical coronal and interplanetary structures, including the sizes and distributions of the coronal holes, the positions and shapes of the streamer belts, the heliocentric distances of the Alfvénic surface, and the transitions of the solar wind speeds. However, the model fails to capture the small‐sized equatorial holes, and the modeled solar wind near 1 AU has a somewhat higher density and weaker magnetic field strength than observed. Perhaps better preprocessing of high‐cadence observed photospheric magnetic field (particularly 3‐D global measurements), combined with plasma measurements and higher resolution grids, will enable the data‐driven model to more accurately capture the time‐dependent changes of the ambient solar wind for further improvements. In addition, other measures may also be needed when the model is employed in the period of high solar activity.
Key Points
Use the consecutive high‐cadence GONG's magnetogram synoptic maps as input
Continuously drive the model at the solar surface by the characteristic method
Trace the evolution of the global coronal response to variations of the photospheric magnetic field
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide, causing a global pandemic. Bat-origin RaTG13 is currently the most phylogenetically related virus. Here we ...obtained the complex structure of the RaTG13 receptor binding domain (RBD) with human ACE2 (hACE2) and evaluated binding of RaTG13 RBD to 24 additional ACE2 orthologs. By substituting residues in the RaTG13 RBD with their counterparts in the SARS-CoV-2 RBD, we found that residue 501, the major position found in variants of concern (VOCs) 501Y.V1/V2/V3, plays a key role in determining the potential host range of RaTG13. We also found that SARS-CoV-2 could induce strong cross-reactive antibodies to RaTG13 and identified a SARS-CoV-2 monoclonal antibody (mAb), CB6, that could cross-neutralize RaTG13 pseudovirus. These results elucidate the receptor binding and host adaption mechanisms of RaTG13 and emphasize the importance of continuous surveillance of coronaviruses (CoVs) carried by animal reservoirs to prevent another spillover of CoVs.
Display omitted
•The complex structure of RaTG13 RBD with hACE2 was determined•Binding of RaTG13 RBD to 24 additional ACE2 orthologs was evaluated•Residue 501 plays a key role in determining the potential host range of RaTG13•SARS-CoV-2 induces strong cross-protective antibodies to RaTG13 RBD
Structural and molecular analysis of the receptor binding domain of RaTG13, a coronavirus phylogenetically closely related to SARS-CoV-2, bound to the human receptor ACE2 as well as ACE2 orthologs in 24 other species provides a framework to understand its host range as well as the basis of antibody cross-reactivity between the two viruses.
Fibers, with over 100 million tons produced each year, have been widely used in various areas. Recent efforts have focused on improving mechanical properties and chemical resistance of fibers via ...covalent cross-linking. However, the covalently cross-linked polymers are usually insoluble and infusible, and thus fiber fabrication is difficult. Those reported require complex multiple-step preparation processes. Herein, we present a facile and effective strategy to prepare adaptable covalently cross-linked fibers by direct melt spinning of covalent adaptable networks (CANs). At processing temperature, dynamic covalent bonds are reversibly dissociated/associated and the CANs are temporarily disconnected to enable melt spinning; at the service temperature, the dynamic covalent bonds are frozen, and the CANs exhibit favorable structural stability. We demonstrate the efficiency of this strategy via dynamic oxime-urethane based CANs, and successfully prepare adaptable covalently cross-linked fibers with robust mechanical properties (maximum elongation of 2639%, tensile strength of 87.68 MPa, almost complete recovery from an elongation of 800%) and solvent resistance. Application of this technology is demonstrated by an organic solvent resistant and stretchable conductive fiber.
An increasing number of clinical trials require biomarker-driven patient stratification, especially for revolutionary immune checkpoint blockade therapy. Due to the complicated interaction between a ...tumor and its microenvironment, single biomarkers, such as PDL1 protein level, tumor mutational burden (TMB), single gene mutation and expression, are far from satisfactory for response prediction or patient stratification. Recently, combinatorial biomarkers were reported to be more precise and powerful for predicting therapy response and identifying potential target populations with superior survival. However, there is a lack of dedicated tools for such combinatorial biomarker analysis.
Here, we present dualmarker, an R package designed to facilitate the data exploration for dual biomarker combinations. Given two biomarkers, dualmarker comprehensively visualizes their association with drug response and patient survival through 14 types of plots, such as boxplots, scatterplots, ROCs, and Kaplan-Meier plots. Using logistic regression and Cox regression models, dualmarker evaluated the superiority of dual markers over single markers by comparing the data fitness of dual-marker versus single-marker models, which was utilized for de novo searching for new biomarker pairs. We demonstrated this straightforward workflow and comprehensive capability by using public biomarker data from one bladder cancer patient cohort (IMvigor210 study); we confirmed the previously reported biomarker pair TMB/TGF-beta signature and CXCL13 expression/ARID1A mutation for response and survival analyses, respectively. In addition, dualmarker de novo identified new biomarker partners, for example, in overall survival modelling, the model with combination of HMGB1 expression and ARID1A mutation had statistically better goodness-of-fit than the model with either HMGB1 or ARID1A as single marker.
The dualmarker package is an open-source tool for the visualization and identification of combinatorial dual biomarkers. It streamlines the dual marker analysis flow into user-friendly functions and can be used for data exploration and hypothesis generation. Its code is freely available at GitHub at https://github.com/maxiaopeng/dualmarker under MIT license.
The Aurora A inhibitor alisertib shows encouraging activities in clinical trials against advanced breast cancer. However, it remains unclear whether and how the inflammatory microenvironment is ...involved in its efficacy. Here, we demonstrated that inhibition of Aurora A directly reshaped the immune microenvironment through removal of tumor-promoting myeloid cells and enrichment of anticancer T lymphocytes, which established a tumor-suppressive microenvironment and significantly contributed to the regression of murine mammary tumors. Mechanistically, alisertib treatment triggered apoptosis in myeloid-derived suppressor cells (MDSC) and macrophages, resulting in their elimination from tumors. Furthermore, alisertib treatment disrupted the immunosuppressive functions of MDSC by inhibiting Stat3-mediated ROS production. These alterations led to significant increases of active CD8
and CD4
T lymphocytes, which efficiently inhibited the proliferation of tumor cells. Intriguingly, alisertib combined with PD-L1 blockade showed synergistic efficacy in the treatment of mammary tumors. These results detail the effects of Aurora A inhibition on the immune microenvironment and provide a novel chemo-immunotherapy strategy for advanced breast cancers. SIGNIFICANCE: These findings show that inhibition of Aurora A facilitates an anticancer immune microenvironment, which can suppress tumor progression and enhance anti-PD-L1 therapy in breast cancer.
.
Neurodevelopmental cell communication plays a crucial role in neuroblastoma prognosis. However, determining the impact of these communication pathways on prognosis is challenging due to limited ...sample sizes and patchy clinical survival information of single cell RNA-seq data. To address this, we have developed the cell communication pathway prognostic model (CCPPM) in this study. CCPPM involves the identification of communication pathways through single-cell RNA-seq data, screening of prognosis-significant pathways using bulk RNA-seq data, conducting functional and attribute analysis of these pathways, and analyzing the post-effects of communication within these pathways. By employing the CCPPM, we have identified ten communication pathways significantly influencing neuroblastoma, all related to axongenesis and neural projection development, especially the BMP7-(BMPR1B-ACVR2B) communication pathway was found to promote tumor cell migration by activating the transcription factor SMAD1 and regulating UNK and MYCBP2. Notably, BMP7 expression was higher in neuroblastoma samples with distant metastases. In summary, CCPPM offers a novel approach to studying the influence of cell communication pathways on disease prognosis and identified detrimental communication pathways related to neurodevelopment.
To study the effects of water stress on the fluorescence parameters and photosynthetic characteristics of rice under drip irrigation and mulching, so as to determine the response mechanisms to water ...stress during the tillering stage. A two-year trial was carried out at Shihezi University, China. Three water gradients were investigated. The results showed that the chlorophyll content (a + b), photosynthetic rate (Pn), and leaf area index (LAI) decreased with decreasing soil moisture content at the tillering stage. The chlorophyll content (a + b) and Pn in the flooding irrigation (CK) treatment were significantly higher than those in the stress treatments, and the chlorophyll content (a + b) and Pn in the W1 and W2 treatments were significantly lower than those in the other treatments. The maximum LAI of the CK, W1, and W2 treatments were similar, while the W3 produced lower values; stress treatment improved the ability of tillering in the early and middle stages, while the decrease in soil water content in the tillering stage resulted in a decrease in the final tillering rate; drought stress in the tillering stage resulted in decreased rice yields. The yield of the W1 and W2 treatments were similar, while that of the W3 treatment was seriously reduced. The main reasons for the reduction in yield was the significant decrease in the number of effective panicles, the seed setting rate, and a decrease in the 1000-grains weight. Water consumption in the stress treatments decreased by 51.69%–58.78% compared to the CK treatment; water-use efficiency in the CK treatment was only 0.25 kg·m−3, and the water-use efficiency of the stress treatments increased by 40%–72%. We should make full use of the compensation effect of drought stress in the water regulation of drip irrigation in covered rice and adopt the water control measure of the W2 treatment in the tillering stage. These measures are conducive to improving water-use efficiency and achieving the goal of high quality, high yield, and high efficiency.
Inverse modeling can provide a reliable geological model for subsurface flow numerical simulation, which is a challenging issue that requires calibration of the uncertain parameters of the geological ...model to establish an acceptable match between simulation data and observation data. The general inverse modeling method needs to iteratively adjust the uncertain parameters, which is a difficult and time‐consuming high‐dimensional sampling problem. To address this problem, we propose a deep‐learning‐based inverse modeling method called pix2pixGAN‐DSI. In this method, the deep‐learning‐based image‐to‐image generative adversarial network (pix2pixGAN) is constructed to directly predict the posterior parameter fields from the posterior dynamic responses obtained by the data‐space inversion (DSI) method. This inverse modeling method does not need to iteratively adjust the uncertain parameters, which improves computational efficiency. The effectiveness of the proposed method is verified through a Gaussian model case and two non‐Gaussian channelized model cases. Through the analysis of posterior realizations, matching and forecast of production data, and uncertainty quantification, the results show that the proposed method can obtain reasonable estimates without iteration and parameterization.
Plain Language Summary
Numerical simulation is an effective means to characterize the flow of subsurface fluid and achieve efficient development of subsurface resources. Inverse modeling can calibrate the uncertain parameters of the numerical model and thus ensure simulation accuracy. However, most inverse modeling methods are based on iteratively adjusting the uncertain parameters, which requires performing a great deal of time‐consuming numerical simulations. In the last decades, significant progress has been made in the field of machine learning, especially in the field of deep learning. In this study, we use the generative adversarial network (a key achievement of deep learning) in combination with the data‐space inversion method to establish a novel inverse modeling workflow. The effectiveness of the proposed method is validated by three case analyses.
Key Points
A deep conditional generative adversarial network is developed to generate posterior parameter fields from the posterior dynamic responses
The data‐space inversion method is implemented to sample the posterior dynamic responses from prior model simulations and observed data
The proposed method is end‐to‐end, avoids parameterization, and can deal with complex geological parameter inversion problems
PDF
