Accurate Assessment and Tracking the Process of Liver-Specific Injury by the Residual Tissue Activity of Carboxylesterase 1 and Dipeptidyl Peptidase 4

Accurately assessing and tracking the progression of liver-specific injury remains a major challenge in the field of biomarker research. Here, we took a retrospective validation approach built on the mutuality between serum and tissue biomarkers to characterize the liver-specific damage of bile duct cells caused by a-naphthyl isothiocyanate (ANIT). We found that carboxylesterase 1 (CES1), as an intrahepatic marker, and dipeptidyl peptidase 4 (DPP-IV), as an extrahepatic marker, can reflect the different pathophysiolo-gies of liver injury. Levels of CES1 and DPP-IV can be used to identify liver damage itself and the inflam-matory state, respectively. While the levels of the conventional serological biomarkers alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were all con-comitantly elevated in serum and tissues after ANIT-induced injury, the levels of bile acids decreased in bile, increased in serum, and ascended in intrahepatic tissue. Although the level of c-glutamyl transpeptidase (c-GT) changed in an opposite direction, the duration was much shorter than that of CES1 and was quickly restored to normal levels. Therefore, among the abovementioned biomarkers, only CES1 made it possible to specifically determine whether the liver cells were destroyed or damaged with-out interference from inflammation. CES1 also enabled accurate assessment of the anti-cholestasis effects of ursodeoxycholic acid (UDCA;single component) and Qing Fei Pai Du Decoction (QFPDD;multi-component). We found that both QFPDD and UDCA attenuated ANIT-induced liver damage. UDCA was more potent in promoting bile excretion but showed relatively weaker anti-injury and anti-inflammatory effects than QFPDD, whereas QFPDD was more effective in blocking liver inflammation and repairing liver damage. Our data highlights the potential of the combined use of CES1 (as an intra-hepatic marker of liver damage) and DPP-IV (as an extrahepatic marker of inflammation) for the accurate evaluation and tracking of liver-specific injury-an application that allows for the differentiation of liver damage and inflammatory liver injury.(c) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

SHEAR saliva collection device augments sample properties for improved analytical performance

Despite being a convenient clinical substrate for biomonitoring, saliva's widespread utilization has not yet been realized. The non-Newtonian, heterogenous, and highly viscous nature of saliva complicate the development of automated fluid handling processes that are vital for accurate diagnoses. Furthermore, conventional saliva processing methods are resource and/or time intensive precluding certain testing capabilities, with these challenges aggravated during a pandemic. The conventional approaches may also alter analyte structure, reducing application opportunities in point-of-care diagnostics. To overcome these challenges, we introduce the SHEAR saliva collection device that mechanically processes saliva, in a rapid and resource-efficient way. We demonstrate the device's impact on reducing saliva's viscosity, improving sample's uniformity, and increasing diagnostic performance of a COVID-19 rapid antigen test. Additionally, a formal user experience study revealed generally positive comments. SHEAR saliva collection device may support realization of the saliva's potential, particularly in large-scale and/or resource-limited settings for global and community diagnostics. Copyright © 2023 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.

Correlation between psychological status and social support in non-anti-epidemic clinical nurses during the COVID-19 pandemic: a cross-sectional observational study

Objectives: To explore the psychological status and perceived social support in non-anti-epidemic clinical nurses during the COVID-19 pandemic and the correlation between these two factors. Methods: Data of nonanti-epidemic clinical nurses from medical institutions in Nantong City of Jiangsu Province were collected using the Psychological Questionnaire for Emergent Events of Public Health (PQEEPH) and the Perceived Social Support Scale (PSSS) from February to March, 2020. Results: A total of 1,187 non-anti-epidemic clinical nurses were included into this study. The scores of the following dimensions in PQEEPH ranked from highest to lowest: depression (0.52±0.02) points, neurasthenia (0.37±0.01) points, fear (0.87±0.02) points, obsession-anxiety (0.24±0.01) points, and hypochondriasis (0.25±0.01) points. The total PSSS score was 63.46 points, of which, the scores of family support, friend support and other support were (21.89±4.27), (21.25±4.16) and (20.32±4.18) points respectively, indicating that these three factors had a negative correlation with emotional response. Conclusions: Non-anti-epidemic clinical nurses experience a negative psychological state during the COVID-19 pandemic and experience great support from family and friends.

A Contrastive Graph Convolutional Network for Toe-Tapping Assessment in Parkinson's Disease

One of the common motor symptoms of Parkinson’s disease (PD) is bradykinesia. Automated bradykinesia assessment is critically needed for helping neurologists achieve objective clinical diagnosis and hence provide timely and appropriate medical services. This need has become especially urgent after the outbreak of the coronavirus pandemic in late 2019. Currently, the main factor limiting the accurate assessment is the difficulty of mining the fine-grained discriminative motion features. Therefore, we propose a novel contrastive graph convolutional network for automated and objective toe-tapping assessment, which is one of the most important tests of lower-extremity bradykinesia. Specifically, based on joint sequences extracted from videos, a supervised contrastive learning strategy was followed to cluster together the features of each class, thereby enhancing the specificity of the learnt class-specific features. Subsequently, a multi-stream joint sparse learning mechanism was designed to eliminate potentially similar redundant features of joint position and motion, hence strengthening the discriminability of features extracted from different streams. Finally, a spatial-temporal interaction graph convolutional module was developed to explicitly model remote dependencies across time and space, and hence boost the mining of fine-grained motion features. Comprehensive experimental results demonstrate that this method achieved remarkable classification performance on a clinical video dataset, with an accuracy of 70.04% and an acceptable accuracy of 98.70%. These results obviously outperformed other existing sensor- and video-based methods. The proposed video-based scheme provides a reliable and objective tool for automated quantitative toe-tapping assessment, and is expected to be a viable method for remote medical assessment and diagnosis. IEEE

Graph-BAS(3)Net: Boundary-Aware Semi-Supervised Segmentation Network with Bilateral Graph Convolution

Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples. However, most of the existing SSLs neglected the geometric shape constraint in object, leading to unsatisfactory boundary and non-smooth of object. In this paper, we propose a novel boundary-aware semi-supervised medical image segmentation network, named Graph-BAS(3)Net, which incorporates the boundary information and learns duality constraints between semantics and geometrics in the graph domain. Specifically, the proposed method consists of two components: a multi-task learning framework BAS(3)Net and a graph-based cross-task module BGCM. The BAS(3)Net improves the existing GAN-based SSL by adding a boundary detection task, which encodes richer features of object shape and surface. Moreover, the BGCM further explores the co-occurrence relations between the semantics segmentation and boundary detection task, so that the network learns stronger semantic and geometric correspondences from both labeled and unlabeled data. Experimental results on the LiTS dataset and COVID-19 dataset confirm that our proposed Graph-BAS(3) Net outperforms the state-of-the-art methods in semi-supervised segmentation task.

Impact of COVID-19 on paratransit operators and riders: A case of central Alabama

Paratransit plays an important role in offering mobility and accessibility in local communities, especially for mobility disadvantaged group such as seniors, persons with disabilities, and persons with medical conditions. This study comprehensively evaluates the impacts of COVID-19 on paratransit services from paratransit operator and individual rider perspectives. In particular, we mine a paratransit dataset that covers trip logs of more than 800 unique riders over a seven month period in order to understand how the pandemic impacted service and influenced trip purposes of individual riders. For service providers, our analyses show that a significant loss in paratransit ridership was accompanied by drastic changes in travel behavior among paratransit riders. Results indicate that the operator was able to deliver safe and efficient mobility services during COVID-19, but at a 60% higher cost per rider than under pre-pandemic conditions. The results also reveal a varying level of impacts for individual riders given heterogeneity among trip purposes and demographic profiles. Moreover, similarities are identified across a range of individual riders, depending on specific trip purposes and the availability of alternative travel options. This study is among the first to investigate paratransit operations during COVID-19 in terms of impacts to both operators and individual riders. The lessons learned and policy insights should be useful to other paratransit operators and policymakers in preparing for current and future pandemics.

Towards Efficient Masked-Face Alignment Via Cascaded Regression

Practical and efficient face alignment has been highly required and widely focused in recent years, especially under the trend of edge computation and real-Time operation. And it is a critical need to deal with masked faces in the context of COVID-19 epidemic. In this paper, we propose a novel cascaded facial landmark detector towards efficient masked face alignment, which we call QCN (Quantized Cascaded Network). QCN consists of three stages: Alignment, estimation and refinement. The alignment stage help to pre-Align the faces to alleviate extreme poses. And the next two stages localize facial landmarks in a coarse-To-fine manner. Thanks to the Network Architecture Search and Quantization techniques, the networks of QCN are designed as efficient as possible. Specifically, QCN occupies 1.75 Mb storage and runs in 84.18 MFLOPs only. Despite costs little computations, the proposed method yields 62.62% AUC (@0.08) on test set of JD-landmark-mask, which achieves 2nd place in the Grand Challenge of 106-point Facial Landmark Localization in ICME2021. © 2021 IEEE.

Construction of Information Bank of Chemical Components in Isatidis Radix

OBJECTIVE: Isatidis Radix is the dried root of Isatis indigotica Fortune of Cruciferae. As a representative traditional Chinese medicine for heat-clearing and detoxification, Isatidis Radix and its preparations are widely used in the prevention and treatment of all kinds of colds and have played an active role in the prevention and treatment of SARS, H1N1 and COVID-19. Although the chemical ingredient of Isatidis Radix has been studied deeply, there is no information bank, website or literature that can comprehensively query the information of all compounds at home and abroad, which is not conducive to the development of related research. So establishment of the chemical composition information bank is in need. METHODS: According to the category of chemical ingredients, the Chinese and English names, molecular formulas, exact molecular weights, structural formulas and references of nearly 400 chemical components in Isatidis Radix were comprehensively sorted out, and the chemical composition information bank of Isatidis Radix was constructed. RESULTS: By September 2020, a total of 392 compounds in 17 categories had been extracted, isolated and identified from Isatidis Radix. CONCLUSIONT: The established chemical composition information bank can provide the basis for the separation and identification of chemical components, quality control, material basis mining, network pharmacology research and so on. Copyright 2022 by the Chinese Pharmaceutical Association.

A Self-Supervised Metric Learning Framework for the Arising-from-Chair Assessment of Parkinsonians with Graph Convolutional Networks

The onset and progression of Parkinson’s disease (PD) gradually affect the patient’s motor functions and quality of life. The PD motor symptoms are usually assessed using the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Automated MDS-UPDRS assessment has been recently required as an invaluable tool for PD diagnosis and telemedicine, especially with the recent novel coronavirus pandemic outbreak. This paper proposes a novel vision-based method for automated assessment of the arising-from-chair task, which is one of the key MDS-UPDRS components. The proposed method is based on a self-supervised metric learning scheme with a graph convolutional network (SSM-GCN). Specifically, for human skeleton sequences extracted from videos, a self-supervised intra-video quadruplet learning strategy is proposed to construct a metric learning formulation with prior knowledge, for improving the spatial-temporal representations. Afterwards, a vertex-specific convolution operation is designed to achieve effective aggregation of all skeletal joint features, where each joint or feature is weighted differently based on its relative factor of importance. Finally, a graph representation supervised mechanism is developed to maximize the potential consistency between the joint and bone information streams. Experimental results on a clinical dataset demonstrate the superiority of the proposed method over the existing sensor-based methods, with an accuracy of 70.60% and an acceptable accuracy of 98.65%. The analysis of discriminative spatial connections makes our predictions more clinically interpretable. This method can achieve reliable automated PD assessment using only easily-obtainable videos, thus providing an effective tool for real-time PD diagnosis or remote continuous monitoring. IEEE

GRAPH-BASED PYRAMID GLOBAL CONTEXT REASONING WITH A SALIENCY-AWARE PROJECTION FOR COVID-19 LUNG INFECTIONS SEGMENTATION

Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed for this issue, it is a challenging task because of infections of various size appearing in different lobe zones. To tackle these issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR) module, which is capable of modeling long-range dependencies among disjoint infections as well as adapt size variation. We first incorporate graph convolution to exploit long-term contextual information from multiple lobe zones. Different from previous average pooling or maximum object probability, we propose a saliency-aware projection mechanism to pick up infection-related pixels as a set of graph nodes. After graph reasoning, the relation-aware features are reversed back to the original coordinate space for the down-stream tasks. We further construct multiple graphs with different sampling rates to handle the size variation problem. To this end, distinct multi-scale long-range contextual patterns can be captured. Our Graph-PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance. Experiments demonstrated that the proposed method consistently boost the performance of state-of-the-art backbone architectures on both of public and our private COVID-19 datasets.

The critical period of myopia, insight from the myopic shift in school age children after COVID-19 home confinement

Purpose : Time spent in outdoor activities is decreased due to home confinement for the Covid-19 epidemic. Concerns have been raised about whether it may have worsened the burden of myopia due to substantially decreased time spent outdoors and increased screen time at home. The purpose of this study is to investigate the refractive change and prevalence of myopia for school-aged children during the Covid-19 home confinement. Methods : In this school-based cross-sectional study in 10 elementary schools in Feicheng, China, and a total of 123,535 children aged 6 to 13 years were screened during 6 consecutive years (2015-2020). The Non-cycloplegic photorefraction was examined by Spot photoscreener. The Spherical Equivalent Refraction (SER) was recorded for each child and the prevalence of myopia for each age group in each year was calculated. The mean SER and prevalence of myopia were compared between the year of 2020 (after home confinement) and the previous 5 years for each age group. Results : A total of 194,904 test results (389,808 eyes) from 123,535 children were included in the analysis. A substantial myopic shift (around-0.3D) was found in the 2020 schoolbased photoscreenings when compared with previous years (2015-2019) for younger school-aged children aged 6 (-0.32D), 7 (-0.28D), and 8 years (-0.29D). The prevalence of myopia in the 2020 photoscreenings was much higher than the highest prevalence of myopia within years of 2015-2019 for children at age of 6 (21.5% vs 5.7%), 7 (26.2% vs 16.2%), and 8 (37.2% vs 27.7%). The differences in SER and prevalence of myopia between 2020 and previous years were minimal in children aged 9-13 years. Conclusions : Covid-19 home confinement was associated with a significant myopic shift for younger children (aged 6-8 years) according to the 2020 school-based photoscreenings. Younger children's refractive status may be more sensitive to environmental changes than older ages, given they are in a critical period for the development of myopia.

[Genomic analysis of a 2019-novel coronavirus (2019-nCoV) strain in the first COVID-19 patient found in Hangzhou].

Objective: To understand the viral genomic characteristics of a 2019-novel coronavirus (2019-nCoV) strain in the first COVID-19 patient found in Hangzhou, China. Methods: Viral RNA was extracted in throat swab and sputum sample of the patient and was performed real-time reverse transcription PCR detection and obtained viral genome by high-throughput sequencing method. Phylogenetic analysis was conducted using 29 2019-nCoV genomes and 30 ß-coronavirus genomes deposited in NCBI GenBank. Fifteen genomes from Wuhan were grouped by mutation sites and others were identified by Wuhan's or specific mutation sites. Results: A 29 833 bp length genome of the first 2019-nCoV strain in Hangzhou was obtained, covering full length of the coding regions of coronavirus. Phylogenetic analysis showed that the genome was closest to the genome of a bat SARS-like coronavirus strain RaTG13 with an identity of 96.11% (28 666/29 826). Among the genes between two genomes, E genes were highly conserved (99.56%), while S genes had lowest identity (92.87%). The genome sequence similarities among 29 strains from China (Hangzhou, Wuhan, and Shenzhen), Japan, USA, and Finland, were all more than 99.9%; however, some single nucleotide polymorphisms were identified in some strains. Conclusion: The genome of Hangzhou 2019-nCoV strain was very close to the genomes of strains from other cities in China and overseas collected at early epidemic phase. The 2019-nCoV genome sequencing method used in this paper provides an useful tool for monitoring variation of viral genes.