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BackgroundThe medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. ObjectiveThis study aims to propose an electronic health record (EHR)–oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. MethodsThe study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. ResultsThe system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. ConclusionsThe proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Dual layer cement-based absorber is synthesized by mixing with graphene nanosheets and manganese-zinc ferrite, to study the effect of absorbing filler content on the mechanical properties, microstructure, electrical resistivity and reflectivity of the paste. The microstructure of the absorber is seen by Scanning Electron Microscope (SEM) images, Fourier Transform Infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD) curves of the absorber. The results show that graphene nanosheets significantly reduce the electrical resistivity of paste, increasing its mechanical properties by improving its pore structure. SEM images indicate that graphene nanosheets promote the increase and coarsening of cement hydration products and produce a large number of dense bulk crystals. Furthermore, reflectivity measurements show that the minimum reflectivity of – 14.1 dB is obtained in the range of 2 ~ 18 GHz and the effective bandwidth of 16 GHz is obtained when reflectivity is less than – 7 dB. This study provides a new method for the preparation of dual layer cement-based absorber.

Uncertainty is mushrooming throughout COVID-19, and intolerance of uncertainty (IoU) nudges people into mental health difficulties involving fear, depression, and anxiety. The objective of this study was to investigate the role of depression and fear of COVID-19 (FoC) in the association between IoU and future career anxiety (FCA) among Chinese university students during the COVID-19 pandemic. This study involved 1,919 Chinese undergraduate students from 11 universities in eight Chinese cities with an online self-administered survey that included demographic information, IoU, FoC, depression, and FCA completed by all participants. Our study demonstrated a positive relationship between IoU and FCA and the chain mediation effect of FoC and depression. Thus, understanding how FoC affects FCA not only informs university career professionals and assists students in preparing for employment, but also motivates schools to offer career opportunities workshops and, most importantly, provides mental health support to help students effectively cope with uncertainty and overcome COVID-19-related stress.

The input of chemical fertilizers to save land and the input of agricultural mechanization to save labor have been important driving forces in China’s grain production since the reform and opening-up. In recent years, the existing literature discussed the relationship between the scale of agricultural land and the application of chemical fertilizers but ignored the role of mechanization in agricultural production and management. We used the intermediary effect model to examine how the cropland use scale affected farmers’ fertilization behavior through agricultural mechanization investment. Our dataset contained data from the Fixed Observation Points of the Ministry of Agriculture and Rural Affairs in China conducted between 2015 and 2017. The results showed that the expansion of the cropland scale significantly reduced the fertilizer input per mu and indirectly reduced the fertilizer input of farmers by increasing the input of agricultural mechanization. In a word, agricultural mechanization played a mediation effect. Further heterogeneity results showed that because grain crops are easier to operate mechanically, the mediation effect was more significant; similarly, under the patterns of “south prefer to service, north prefer to self-purchase” and “south grow rice and north grow wheat,” southern areas received a more significant reduction effect in mainland China. These findings defined the role of agricultural mechanization and provided a new perspective for realizing the reduction and efficiency of chemical fertilizers and reducing agricultural non-point source pollution.

BackgroundImmunoglobulin G4-related disease (IgG4-RD) is a newly defined disease entity, with great heterogeneity among IgG4-RD subgroups with different organ involvement patterns. Identification of the proteomic characteristics of IgG4-RD subgroups will be critical for the understanding of the pathogenic mechanisms of IgG4-RD.MethodIn this study, we performed proteomic analysis using Tandem Mass Tags (TMT) technology with “high field” mass analyzer with improved resolution and sequencing speed to investigate the proteomic profile of saliva and plasma samples from ten untreated IgG4-RD patients and five healthy controls (HCs). Differentially expressed proteins (DEPs) were identified by “t test” function in R package. Functional enrichment analysis was used to investigate pathways enriched in IgG4-RD samples.ResultsMost salivary DEPs identified in IgG4-RD patients compared with HCs were mainly enriched in neutrophil mediated GO bioprocess. Within the comparisons between four IgG4-RD subgroups, more DEPs were identified in the comparison of Mikulicz group and Head and neck group. Among four subgroups of IgG4-RD, Head and neck group showed the most distinctive proteomic expression pattern when compared with HCs. Moreover, “Neutrophil mediated process” related GO bioprocess was commonly identified between comparisons of Mikulicz group and Head and neck group, Head and neck group and Retroperitoneal aorta group, Head and neck group and HCs, IgG4-RD patients with saliva gland involvement and those without saliva gland involvement. Key DEPs that involved in this GO bioprocess were identified. Besides, we performed proteomic analysis for plasma samples between ten IgG4-RD and five HCs and there were several DEPs identified overlapped in saliva and plasma.ConclusionWe identified multiple processes/factors and several signaling pathways in saliva that may be involved in the IgG4-RD pathogenesis.

BackgroundRyanodine receptor 2 (RYR2) encodes a component of a calcium channel. RYR2 variants were well-reported to be associated with catecholaminergic polymorphic ventricular tachycardia (CPVT), but rarely reported in epilepsy cases. Here, we present a novel heterozygous mutation of RYR2 in a child with focal epilepsy.MethodsAt the age of 2 years and 7 months, the patient experienced seizures, such as eye closure, tooth clenching, clonic jerking and hemifacial spasm, as well as abnormal electroencephalogram (EEG). Then, he was analyzed by whole-exome sequencing (WES). The mutations of both the proband and his parents were further confirmed by Sanger sequencing. The pathogenicity of the variant was further assessed by population-based variant frequency screening, evolutionary conservation comparison, and American Association for Medical Genetics and Genomics (ACMG) scoring.ResultsWES sequencing revealed a novel heterozygous truncating mutation [c.12670G > T, p.(Glu4224*), NM_001035.3] in RYR2 gene of the proband. Sanger sequencing confirmed that this mutation was inherited from his mother. This novel variant was predicted to be damaging by different bioinformatics methods. Cardiac investigation showed that the proband had no structural abnormalities, but sinus tachycardia.ConclusionWe proposed that RYR2 is a potential candidate gene for focal epilepsy, and epilepsy patients carried with RYR2 variants should be given more attention, even if they do not show cardiac abnormalities

Abstract Background With the development of wearable health-monitoring technologies, a variety of textile electrodes have been produced and applied by researchers. However, there are no universal and effective methods even testing platforms for evaluating the skin–electrode electrochemical interface for textile electrodes because different human bodies have different skin characteristics. Methods An electrochemical modeling and evaluation for textile electrodes to skin was proposed, and two electrochemical evaluation platforms (EEP) were set up based on two simulated skin models (SSM). First, skin–electrode electrochemical interface (SEEI) models for traditional wet electrodes and textile electrodes were analyzed. Based on the SEEI models and YY/T 0196-2005 (Chinese YY/T pharmaceutical industry standard for disposable ECG electrode), three skin–electrode electrochemical characteristics (SEEC), including skin–electrode static impedance (SESI), skin–electrode alternating current impedance (SEAI), and skin–electrode polarization voltage (SEPV), were proposed. Then, three electrochemical evaluation methods for textile electrodes to skin were proposed and analyzed, which were the correlation between SEEC and skin–electrode contact pressure (SECP), skin–electrode relative movement (SERM), and conduction loss of active signals (CLAS). Finally, an electrochemical evaluation platform was set up based on an active simulated skin model (ASSM) and passive simulated skin model (PSSM). Results 9 feature parameters based on the passive electrochemical evaluation platform (PEEP) and 11 feature parameters based on the active electrochemical evaluation platform (AEEP) were obtained for evaluating textile electrodes. And four kinds of textile electrode characteristics including SEEC, SECP, SERM, and CLAS were quantitatively measured based on the electrochemical evaluation platform, and the testing accuracy and range for these characteristics were measured separately. Finally, correlation between SEEC and SECP for 10 kinds of textile electrode samples was studied, and 14 electrochemical characteristics and four skin–electrode contact pressure characteristics were extracted. Experimental results showed that significant correlations were found between six SEEC characteristics and SECP characteristics, and the correlation coefficient between ACI_3 and USECP was the highest. And the polarization voltages of most dry electrode samples showed a downward trend with the increase of contact pressure. Conclusions The electrochemical evaluation platform yielded effective experimental data and could provide strong support for the evaluation and application of textile electrodes, which was also effective in evaluating other bioelectric electrodes such as 3M electrode, stainless steel electrode, dry electrode and microneedle electrode.

Abstract Background Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

Objective: The present study focused on examining fear of the coronavirus disease 2019 (COVID-19) is correlated with depression and explored the potential role of resilience and social support on the association between fear of the COVID-19 (FoC) and depression among Chinese outbound students studying online in China amid the COVID-19 pandemic period.Methods: A total of 476 Chinese outbound students from different universities worldwide, currently studying via online mode in China, completed an online survey including measures on FoC, resilience, social support, and depression.Results: (1) Fear of the COVID-19 was positively correlated with depression and negatively correlated with resilience and social support. Both resilience and social support were negatively correlated with depression. Social support showed a positive correlation with resilience. (2) The effect of FoC on depression mainly occurred through two paths: the mediating effect of resilience and the moderating effect of resilience. However, the moderating effect of social support on the association between FoC and depression was not sustained in this study.Conclusion: This study indicated the mediating and moderating effects of resilience on the association between FoC and depression among Chinese outbound students studying online in China during the COVID-19 pandemic period. The current findings confirmed that resilience has significant implications in preventing negative mental states under the COVID-19 context among this particular group.

Maintaining sustained attention during a prolonged cognitive task often comes at a cost: high levels of mental fatigue. Heuristically, mental fatigue refers to a feeling of tiredness or exhaustion, and a disengagement from the task at hand; it manifests as impaired cognitive and behavioral performance. In order to effectively reduce the undesirable yet preventable consequences of mental fatigue in many real-world workspaces, a better understanding of the underlying neural mechanisms is needed, and continuous efforts have been devoted to this topic. In comparison with conventional univariate approaches, which are widely utilized in fatigue studies, convergent evidence has shown that multivariate functional connectivity analysis may lead to richer information about mental fatigue. In fact, mental fatigue is increasingly thought to be related to the deviated reorganization of functional connectivity among brain regions in recent studies. In addition, graph theoretical analysis has shed new light on quantitatively assessing the reorganization of the brain functional networks that are modulated by mental fatigue. This review article begins with a brief introduction to neuroimaging studies on mental fatigue and the brain connectome, followed by a thorough overview of connectome studies on mental fatigue. Although only a limited number of studies have been published thus far, it is believed that the brain connectome can be a useful approach not only for the elucidation of underlying neural mechanisms in the nascent field of neuroergonomics, but also for the automatic detection and classification of mental fatigue in order to address the prevention of fatigue-related human error in the near future. Keywords: Mental fatigue, Functional connectivity, Graph theoretical analysis, Brain network

Abstract Background An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients. Methods Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions. Results Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]). Conclusions Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Evidences have suggested that Sjogren's syndrome (SS) is associated with viral infection. The aim of this study was to investigate the involvement of respiratory viral poly(I:C) in the pathogenesis of SS and potential mechanisms using a SS-like NOD/ShiLtJ (NOD) mouse model. 5-week female NOD mice were intratracheally administered poly(I:C) every other day for 5 times to mimic viral infection. Pilocarpine induced saliva secretion was determined every 8 days. Submandibular glands (SMG) and lungs were harvested for the detection of pathological changes. We found that intratracheal administration of poly(I:C) significantly advanced and enhanced the reduction of saliva flow rate in NOD mice. Furthermore, poly(I:C) treatment aggravated the histopathological lesions and inflammatory cells infiltration in SMG. Accompanied by elevated expression of IFN cytokines and IL-33, Th1 activation was enhanced in SMG of poly(I:C)-treated NOD mice, but Th17 cells activation was unchanged among the groups. In addition, intratracheal poly(I:C) exposure promoted the expression of IL-33 and increased T cells proportion in the lung, which were consistent with the change in SMG. Therefore, intratracheal poly(I:C) exposure aggravated the immunological and function disorder of SMG in NOD mice.

In this article, reduced graphene oxide/Ni/multi-walled carbon nanotubes/Fe 3 O 4 filled paste is synthesized with the aim of developing a novel shielding material. To do so, nano-dispersion presenting homogeneous distribution is made by ultrasonic dispersing technology. Next, the effects of nano-absorbent content on the fluidity, mechanical strength, pore structure, resistivity, and absorbing reflectivity of paste are studied. At the end, the microstructure of composite is uncovered by scanning electron microscopy, Fourier transformer infrared, X-ray diffraction images as well as the pore size distribution and absorbing reflectivity are revealed. The results indicate that a small load of reduced graphene oxide and other nano-absorbents can significantly reduce the fluidity and resistivity of paste, but its pore structure is improved so that its mechanical properties are increased. Scanning electron microscopy images indicate that reduced graphene oxide promotes the increasing and thickening of the cement hydration products as well as the growth of a large number of flower-like and compact bulk crystals. Furthermore, the minimum reflectivity of −10.6 dB is obtained in the range of 2–18 GHz while the effective bandwidth of 16 GHz is obtained when reflectivity is less than −5 dB. This research provides a new pathway for the preparation of monolayer cement–based absorber.