To summarize currently available evidence on maternal, fetal, and neonatal outcomes of pregnant women infected with Coronavirus Disease 2019 (COVID-19).
PubMed, Google Scholar, CNKI, Wanfang Data, ...VIP, and CBMdisc were searched for studies reporting maternal, fetal, and neonatal outcomes of women infected with COVID-19 published from 1 January 2020 to 26 March 2020. The protocol was registered with the Open Science Framework (DOI: 10.17605/OSF.IO/34ZAV).
In total, 18 studies comprising 114 pregnant women were included in the review. Fever (87.5%) and cough (53.8%) were the most commonly reported symptoms, followed by fatigue (22.5%), diarrhea (8.8%), dyspnea (11.3%), sore throat (7.5%), and myalgia (16.3%). The majority of patients (91%) had cesarean delivery due to various indications. In terms of fetal and neonatal outcomes, stillbirth (1.2%), neonatal death (1.2%), preterm birth (21.3%), low birth weight (<2500 g, 5.3%), fetal distress (10.7%), and neonatal asphyxia (1.2%) were reported. There are reports of neonatal infection, but no direct evidence of intrauterine vertical transmission has been found.
The clinical characteristics of pregnant women with COVID-19 are similar to those of non-pregnant adults. Fetal and neonatal outcomes appear good in most cases, but available data only include pregnant women infected in their third trimesters. Further studies are needed to ascertain long-term outcomes and potential intrauterine vertical transmission.
The building sector is one of the largest energy user and carbon emitter globally. To achieve China’s national carbon target, the building sector in China needs to achieve carbon peaking and ...neutrality targets by 2030 and 2060, respectively. However, data deficiency on building energy and emissions become barriers for tracking the status of building energy and emissions, and identify potential opportunities for achieving dual carbon targets. To address these shortcomings, this study established an integrated China Building Energy and Emission Model (CBEEM). With CBEEM, this study evaluated the building-construction and building-operation energy and emissions in China, and revealed the status quo and potential challenge and opportunities. According to modelling results, building operation energy use of China was 1.06 billion tce in 2020, accounting for 21% of China’s total primary energy consumption. Building construction energy consumption was 0.52 billion tce in 2020, accounting for another 10% of total primary energy consumption. Key messages found on building carbon emissions are: building construction embodied emissions were 1.5 billion tCO
2
in 2020 and are declining slowly, building operational carbon emissions were 2.2 billion tCO
2
in 2020 and are still increasing. International comparisons between China and other countries on building stock, energy use intensity and carbon emission intensity were conducted as well, and help shed a light on the challenges for decarbonization of China’s building sector. Finally, technology perspectives to achieve carbon neutrality target were discussed and related policy suggestions were provided.
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. ...Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
The constructed CTF/TNS heterostructures show superior Li-S battery performance as a sulfur host due to their multiple-in-one advantages of 3D spatial sulfur confinement, dual-site chemical ...polysulfides anchoring and efficient electron/ion transport.
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•2D heterostructures of layered CTF in situ-grown on Ti3C2 nanosheets were fabricated.•Covalent Ti-N interaction between TNS and CTF components enabled a stable interface.•The 2D heterostructures held multiple-in-one advantages for superior Li-S batteries.•Li-S batteries based on the 2D heterostructures showed outstanding cycling stability.
The development of sulfur host materials with simultaneous suppressed shuttle effect, improved electrical/ionic conductivity and high sulfur loading is highly desired for lithium-sulfur batteries. Herein, we proposed that two-dimensional heterostructures made of layered covalent triazine framework on Ti3C2 MXene nanosheets (CTF/TNS) as a sulfur host show multiple-in-one advantages for lithium-sulfur batteries. The integrity of organic CTF with ordered pore structure and inorganic TNSs with high conductivity imparts the heterostructures three-dimensional spatial confinement for high sulfur loading and efficient electron/ion transport for improved reaction kinetics. In addition, lithiophilic N sites in CTF and sulfurophilic Ti sites in TNSs enable dual-site chemical anchoring of polysulfides to effectively suppress shuttle effect. With a high sulfur loading of 76 wt%, the S@CTF/TNS cathode shows high reversible capacity (1441 mA h g−1 at 0.2 C), outstanding cycling stability (up to 1000 cycles at 1 C with a 0.014 % capacity decay rate per cycle) and excellent rate capability. Notably, even with a high areal sulfur loading of 5.6 mg cm−2, a high capacity retention of 94 % is still obtained after 100 cycles.
Ecological risk assessment is the basis for sustainable land use and ecological protection and management in coastal areas. The research scale of ecological risk assessment is usually determined by ...empirical methods, and existing research focused on coastal areas is relatively limited. This paper aims to explore the spatiotemporal pattern of ecological risk in coastal areas, taking the Shandong Peninsula as the study area, and to determine the optimal scale of ecological risk assessment based on semivariogram analysis. The results show that the waterbodies and unused land had a relatively high landscape loss index, while the cropland had the least value. The optimal scale for landscape risk assessment in the Shandong Peninsula was 4 km. The ecological risk was generally low in the Shandong Peninsula, with the lowest risk regions and lower risk regions accounting for more than 70% of the total area. During 1990–2018, the average ecological risk dropped from 0.0819 to 0.0698, indicating a lower probability of adverse ecological effects caused by landscape changes. The spatial agglomeration pattern of the ecological risk showed low–low agglomeration in the central and northeast areas and high–high agglomeration in the northwest along the sea. Scale effects had a significant influence on the uncertainty of the assessment of ecological risks, including the study extent, size of risk units, risk level classification, and data resolutions. It is suggested that protecting cropland was important for both food safety and the stability of the ecosystem in the Shandong Peninsula. Waterbodies in this area are vulnerable due to their fragmentation and close distribution with built-up land and agricultural land. This study could offer useful information for coastal ecological risk control, and provide new perspectives regarding optimal scale selection when conducting local ecological risk assessment.
Ti3C2 MXene-based composites are emerging two-dimensional (2D) layered materials promising in electrochemical energy storage devices such as rechargeable ion batteries and supercapacitors. However, ...scalable preparation of Ti3C2 MXene-based composites, especially, integrated with one-dimensional (1D) materials through a facile low-temperature strategy remains a considerable challenge. Herein, novel sandwich-like Na0.23TiO2/Ti3C2 composites made of 1D amorphous Na0.23TiO2 nanobelts growing on 2D Ti3C2 nanosheets have been prepared through a one-step scalable transformation reaction of Ti3C2 MXene. The sandwich-like Na0.23TiO2/Ti3C2 composites comprising of 1D ultrathin nanobelts, 2D conductive nanosheets and 3D sandwich-like architecture with electrically connecting interfaces inside can effectively relieve strain of the electrode upon cycling, facilitate carrier transport dynamics and protect aggregation of the electrode material, favorable for high-performance rechargeable batteries. As a result, when employed as anodes in Li/Na-ion batteries, the Na0.23TiO2/Ti3C2 electrodes exhibit superior long cycling stability (up to 4000 cycles at the high rates with respective capacity retention of over or nearly 100%), and remarkable rate capability. This work may open a new way for scalable synthesis of 2D layered MXene-based composites with desired architectures and properties for practical energy applications.
The novel sandwich-like Na0.23TiO2/Ti3C2 composites made of 1D amorphous Na0.23TiO2 nanobelts growing on 2D Ti3C2 nanosheets were designed for long-life high-rate lithium/sodium-ion batteries. Display omitted
•Sandwich-like Ti3C2 MXene based composites were prepared by a scalable strategy.•The ratio of 1D nanobelts and 2D Ti3C2 nanosheets in the composites can be tuned.•Long-cycle and high-rate performance for Li/Na ion storage has been achieved.•The mechanism relies on the synergistic effect from the sandwich-like architecture.
The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas ...where data is unstructured, traditional machine learning has not been able to surpass the 'glass ceiling'; therefore, researchers have turned to neural networks as auxiliary tools to achieve significant breakthroughs or develop new research methods. An array of computational chemistry challenges can be addressed using neural networks, including virtual screening, quantitative structure-activity relationships, protein structure prediction, materials design, quantum chemistry, and property prediction, among others. This paper proposes a strategy for predicting the chemical properties of fruits by using graph neural networks, and it aims to provide some guidance to researchers and streamline the identification process.
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are ...essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test:
< 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2
fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval CI: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2
fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.
Postpartum depression (PPD) is a common psychiatric condition during the postnatal period that negatively impacts the well-being of both the mother and her infant. This study describes a systematic ...review and preliminary meta-analysis to assess the efficacy of mobile health (mHealth) interventions, which is defined as the use of portable electronic devices to support public health and medical practice, in addressing depressive symptoms among postpartum women.
Databases including PubMed, PsycINFO, the Cochrane Library, Embase and ClinicalTrials.gov were searched for randomized controlled trials (RCTs) assessing the effectiveness of mHealth interventions on PPD from database inception to December 2019.
The initial search identified 754 studies, of which, 11 studies fulfilled the inclusion criteria. These studies evaluated four types of distinct mHealth interventions and involved 2424 participants across six countries. Pooled results demonstrated that compared to the controls, the Edinburgh Postnatal Depression Scale score decreased in the mHealth intervention group (mean difference: -1.09, 95% confidence interval: -1.39 to -0.79).
Our study suggested that mHealth interventions may be a promising tool to complement routine clinical care in the prevention and treatment of PPD, but the clinical effectiveness of mHealth interventions needs to be better established. While most studies focused on telephone-based interventions, recent researches have also suggested the superiority and effectiveness of short messaging service (SMS) and smartphone applications, but the exact efficacy needs further evaluation. Therefore, more high-quality RCTs on app-based and SMS-based interventions are needed before the large-scale roll-out of these interventions in clinical practice.
The use of simultaneous wireless information and power transfer (SWIPT) is a key enabler of achieving convenience and prolonging the energy supply lifetime of wireless networks. To address the low ...efficiencies of far-field power transfer, a reconfigurable intelligent surface (RIS) is adopted to enhance the energy harvesting (EH) performance, which can construct a favorable wireless propagation environment. In this paper, we consider an RIS-assisted SWIPT system with wireless transfer from the access point (AP) to multiple-antenna receivers, which include information receivers (IRs) and energy receivers (ERs). First, we formulate the problem of maximizing the minimum rates of the IRs as a nonconvex constrained optimization problem. For ideal and nonideal channels, we propose two different solutions. Second, we simplify the objective function and decompose the problem into several subproblems by using sorting and iterative optimization algorithms. Moreover, under optimal boundary and Karush-Kuhn-Tucker (KKT) conditions, we successfully solve this problem. The simulation results illustrate a promising approach for wireless communication by comparing ideal RIS and unsatisfactory situations with no RIS cases under various conditions.
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