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The novel coronavirus disease 2019 (COVID-19) has infected 1.9% of the world population by May 2, 2021. Since most previous studies that examined risk factors for mortality and severity were based on hospitalized individuals, population-based cohort studies are called for to provide evidence that can be extrapolated to the general population. Therefore, we aimed to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19 diagnosed in 2020 in Ontario, Canada. We conducted a retrospective cohort study of all individuals with COVID-19 in Ontario, Canada diagnosed between January 15 and December 31, 2020. Cases were linked to health administrative databases maintained in the ICES which covers all residents in Ontario. The primary outcome is all-cause 30-day mortality after the first COVID-19 diagnosis, and the secondary outcome is a composite severity index containing death and hospitalization. To examine the risk factors for the outcomes, we employed Cox proportional hazards regression models and logistic regression models to adjust for demographic, socio-economic variables and comorbidities. Results were also stratified by age groups. A total of 167,500 individuals were diagnosed of COVID-19 in 2020 and included in the study. About half (43.8%, n = 73,378) had at least one comorbidity. The median follow-up period were 30 days. The most common comorbidities were hypertension (24%, n = 40,154), asthma (16%, n = 26,814), and diabetes (14.7%, n = 24,662). Individuals with comorbidity had higher risk of mortality compared to those without (HR = 2.80, 95%CI 2.35-3.34; p<0.001), and the risk substantially was elevated from 2.14 (95%CI 1.76-2.60) to 4.81 (95%CI 3.95-5.85) times as the number of comorbidities increased from one to five or more. Significant predictors for mortality included comorbidities such as solid organ transplant (HR = 3.06, 95%CI 2.03-4.63; p<0.001), dementia (HR = 1.46, 95%CI 1.35-1.58; p<0.001), chronic kidney disease (HR = 1.45, 95%CI 1.34-1.57; p<0.001), severe mental illness (HR = 1.42, 95%CI%, 1.12-1.80; p<0.001), cardiovascular disease (CVD) (HR = 1.22, 95%CI, 1.15-1.30), diabetes (HR = 1.19, 95%, 1.12-1.26; p<0.001), chronic obstructive pulmonary disease (COPD) (HR = 1.19, 95%CI 1.12-1.26; p<0.001), cancer (HR = 1.17, 95%CI, 1.09-1.27; p<0.001), hypertension (HR = 1.16, 95%CI, 1.07-1.26; p<0.001). Compared to their effect in older age groups, comorbidities were associated with higher risk of mortality and severity in individuals under 50 years old. Individuals with five or more comorbidities in the below 50 years age group had 395.44 (95%CI, 57.93-2699.44, p<0.001) times higher risk of mortality compared to those without. Limitations include that data were collected during 2020 when the new variants of concern were not predominant, and that the ICES databases do not contain detailed individual-level socioeconomic and racial variables. We found that solid organ transplant, dementia, chronic kidney disease, severe mental illness, CVD, hypertension, COPD, cancer, diabetes, rheumatoid arthritis, HIV, and asthma were associated with mortality or severity. Our study highlights that the number of comorbidities was a strong risk factor for deaths and severe outcomes among younger individuals with COVID-19. Our findings suggest that in addition of prioritizing by age, vaccination priority groups should also include younger population with multiple comorbidities.

Sialic acids are a family of negatively charged monosaccharides which are commonly presented as the terminal residues in glycans of the glycoconjugates on eukaryotic cell surface or as components of capsular polysaccharides or lipooligosaccharides of some pathogenic bacteria. Due to their important biological and pathological functions, the biosynthesis, activation, transfer, breaking down, and recycle of sialic acids are attracting increasing attention. The understanding of the sialic acid metabolism in eukaryotes and bacteria leads to the development of metabolic engineering approaches for elucidating the important functions of sialic acid in mammalian systems and for large-scale production of sialosides using engineered bacterial cells. As the key enzymes in biosynthesis of sialylated structures, sialyltransferases have been continuously identified from various sources and characterized. Protein crystal structures of seven sialyltransferases have been reported. Wild-type sialyltransferases and their mutants have been applied with or without other sialoside biosynthetic enzymes for producing complex sialic acid-containing oligosaccharides and glycoconjugates. This mini-review focuses on current understanding and applications of sialic acid metabolism and sialyltransferases.

Classifying customers according to their characteristics can effectively meet the genuine needs of different customer groups. It also helps enterprises formulate reasonable marketing strategies and obtain considerable profits. Currently, there are many ways to classify customers. However, the procedures involved are complicated and cannot comprehensively and objectively reflect customer characteristics. Therefore, a customer group classification model is designed based on the deep cross network (DCN). The DCN algorithm can automatically learn simple data features, achieving data clustering. For the defects in this model, the deep weighted k -means clustering network (DWKCN) customer group classification method is constructed, improving the DCN algorithm. From the results, the algorithm has a high accuracy of 99.5%. Therefore, the proposed DWKCN algorithm can realize the customer group’s precise division and the marketing plan design, providing the references for different types of customers to formulate personalized needs.

The rapid development of Internet of Things and artificial intelligence brings increasing attention on the harvesting of distributed energy by using triboelectric nanogenerator (TENG), especially the direct current TENG (DC-TENG). It is essential to select appropriate triboelectric materials for obtaining a high performance TENG. In this work, we provide a set of rules for selecting the triboelectric materials for DC-TENG based on several basic parameters, including surface charge density, friction coefficient, polarization, utilization rate of charges, and stability. On the basis of the selection rules, polyvinyl chloride, used widely in industry rather than in TENG, is selected as the triboelectric layer. Its effective charge density can reach up to ~8.80 mC m −2 in a microstructure-designed DC-TENG, which is a new record for all kinds of TENGs. This work can offer a basic guideline for the triboelectric materials selection and promote the practical applications of DC-TENG. Appropriate triboelectric material selection is vital to for high performance direct current triboelectric nanogenerator (DC-TENG). The authors here provide effective selection rules as guideline to select triboelectric materials for DC-TENG to reduce the trial-and-error cost for DC-TENG’s research.

The success of pregnancy relies on the fine adjustment of the maternal immune system to tolerate the allogeneic fetus. Trophoblasts carrying paternal antigens are the only fetal-derived cells that come into direct contact with the maternal immune cells at the maternal–fetal interface. The crosstalk between trophoblasts and decidual immune cells (DICs) via cell–cell direct interaction and soluble factors such as chemokines and cytokines is a core event contributing to the unique immunotolerant microenvironment. Abnormal trophoblasts–DICs crosstalk can lead to dysregulated immune situations, which is well known to be a potential cause of a series of pregnancy complications including recurrent spontaneous abortion (RSA), which is the most common one. Immunotherapy has been applied to RSA. However, its development has been far less rapid or mature than that of cancer immunotherapy. Elucidating the mechanism of maternal–fetal immune tolerance, the theoretical basis for RSA immunotherapy, not only helps to understand the establishment and maintenance of normal pregnancy but also provides new therapeutic strategies and promotes the progress of immunotherapy against pregnancy-related diseases caused by disrupted immunotolerance. In this review, we focus on recent progress in the maternal–fetal immune tolerance mediated by trophoblasts–DICs crosstalk and clinical application of immunotherapy in RSA. Advancement in this area will further accelerate the basic research and clinical transformation of reproductive immunity and tumor immunity.

This study meta-analytically examined theoretically derived antecedents of both directions of work–family enrichment (sometimes labeled facilitation or positive spillover), namely, work–family enrichment and family–work enrichment. Contextual and personal characteristics specific to each domain were examined. Resource-providing (e.g., social support and work autonomy) and resource-depleting (e.g., role overload) contextual characteristics were considered. Domain-specific personal characteristics included the individuals' psychological involvement in each domain, the centrality of each domain, and work engagement. Results based on 767 correlations from 171 independent studies published between 1990 and 2016 indicate that several contextual and personal characteristics have significant relationships with enrichment. Although those associated with work tend to have stronger relationships with work–family enrichment and those associated with family tend to have stronger relationships with family–work enrichment, several antecedent variables have significant relationships with both directions of enrichment. Resource-providing contextual characteristics tend to have stronger relationships with enrichment than do resource-depleting characteristics. There was very little evidence of gender being a moderator of relationships between contextual characteristics and enrichment. Lastly, meta-analytic structural equation modeling provided evidence that a theoretical path model wherein work engagement mediates between several contextual characteristics and enrichment is largely generalizable across populations.

In this article, sulfonic acid-grafted reduced graphene oxide (S-rGO) were synthesized using a one-pot method under mild conditions, and used as Pt catalyst supports to prepare Pt/S-rGO electrocatalysts through a self-assembly route. The structure, morphologies and physicochemical properties of S-rGO were examined in detail by techniques such as atomic force microscope (AFM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The S-rGO nanosheets show excellent solubility and stability in water and the average particle size of Pt nanoparticles supported on S-rGO is ~3.8 nm with symmetrical and uniform distribution. The electrocatalytic properties of Pt/S-rGO were investigated for methanol oxidation reaction (MOR) in direct methanol fuel cells (DMFCs). In comparison to Pt supported on high surface area Vulcan XC-72 carbon (Pt/VC) and Pt/rGO, the Pt/S-rGO electrocatalyst exhibits a much higher electrocatalytic activity, faster reaction kinetics and a better stability. The results indicate that Pt/S-rGO is a promising and effective electrocatalyst for MOR of DMFCs.

Neutrophil extracellular traps (NETs), a web-like structures containing chromatin, have a significant role in assisting the capture and killing of microorganisms by neutrophils during infection. The specific engagement of cell-surface receptors by extracellular signaling molecules activates diverse intracellular signaling cascades and regulates neutrophil effector functions, including phagocytosis, reactive oxygen species release, degranulation, and NET formation. However, overproduction of NETs is closely related to the occurrence of inflammation, autoimmune disorders, non-canonical thrombosis and tumor metastasis. Therefore, it is necessary to understand neutrophil activation signals and the subsequent formation of NETs, as well as the related immune regulation. In this review, we provide an overview of the immunoreceptor-mediated regulation of NETosis. The pathways involved in the release of NETs during infection or stimulation by noninfectious substances are discussed in detail. The mechanisms by which neutrophils undergo NETosis help to refine our views on the roles of NETs in immune protection and autoimmune diseases, providing a theoretical basis for research on the immune regulation of NETs.

Diabetic retinopathy (DR) is a serious ocular complication that can pose a serious risk to a patient’s vision and overall health. Currently, the automatic grading of DR is mainly using deep learning techniques. However, the lesion information in DR images is complex, variable in shape and size, and randomly distributed in the images, which leads to some shortcomings of the current research methods, i.e., it is difficult to effectively extract the information of these various features, and it is difficult to establish the connection between the lesion information in different regions. To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose a multi-scale dynamic graph convolutional network (MDGNet) in this paper. MDGNet firstly uses convolution kernels with different sizes to extract features with different shapes and sizes in the lesion regions, and then automatically learns the corresponding weights for feature fusion according to the contribution of different features to model grading. Finally, the graph convolution operation is used to link the lesion features in different regions. As a result, our proposed method can effectively combine local and global features, which is beneficial for the correct DR grading. We evaluate the effectiveness of method on two publicly available datasets, namely APTOS and DDR. Extensive experiments demonstrate that our proposed MDGNet achieves the best grading results on APTOS and DDR, and is more accurate and diverse for the extraction of lesion information.

Dental Caries is a kind of chronic oral disease that greatly threaten human being’s health. Though dentists and researchers struggled for decades to combat this oral disease, the incidence and prevalence of dental caries remain quite high. Therefore, improving the disease management is a key issue for the whole population and life cycle management of dental caries. So clinical difficulty assessment system of caries prevention and management is established based on dental caries diagnosis and classification. Dentists should perform oral examination and establish dental records at each visit. When treatment plan is made on the base of caries risk assessment and carious lesion activity, we need to work out patient‑centered and personalized treatment planning to regain oral microecological balance, to control caries progression and to restore the structure and function of the carious teeth. And the follow-up visits are made based on personalized caries management. This expert consensus mainly discusses caries risk assessment, caries treatment difficulty assessment and dental caries treatment plan, which are the most important parts of caries management in the whole life cycle.