Inclusive Smart Textile Design for Healthy Ageing

Population ageing is a global phenomenon. This trend poses healthcare ser-vices, social care, and political challenges, yet implies a growing demand for ageing-related products and services. Smart textile technology has been increas-ingly applied in healthcare applications to support healthy ageing from many aspects. This research indicated the challenges for older people to stay in their own house in later life from the previous literature and reviewed smart home healthcare products and smart textiles for healthy ageing. We found that the current development of elderly textile products neglects the real needs of older people in healthcare products in the home environment from their perspectives. Thus, this research aims to discover the health and well-being needs of people aged 60+ living independently at home in the UK, especially during COVID-19. This research conducted interviews with 12 individuals and questionnaires with 43 individuals for questionnaires. Results highlighted the current unmet healthcare-related needs at home and participants' experiences and attitudes towards healthcare products. Finally, it indicated the potential opportunity for inclusive smart textile design for healthy ageing in the future. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

COVID-19: How Effective Are the Repurposed Drugs and Novel Agents in Treating the Infection?

Coronavirus disease 2019 (COVID-19) induced by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has impacted the lives and wellbeing of many people. This globally widespread disease poses a significant public health concern that urges to discover an effective treatment. This review paper discusses the effectiveness of repurposed drugs used to treat COVID-19 and potential novel therapies for COVID-19. Among the various repurposed drugs, remdesivir is the only agent approved by the Food and Drug Administration (FDA) to treat COVID-19. On the other hand, several drugs have been listed in the Emergency Use Authorization (EUA) by the FDA to treat COVID-19, including casirivimab and imdevimab, baricitinib (in combination with remdesivir), bamlanivimab, tocilizumab, and IL-6 inhibitors. In addition, in vitro and clinical studies have suggested cepharanthine, sotrovimab, and XAV-19 as potential treatments to manage COVID-19. Due to inadequate understanding of COVID- 19 and the rapid mutation of SARS-CoV-2, COVID-19 remains a threat to global public health, with vaccination considered the most effective method to decrease COVID-19 transmission currently. Nevertheless, with the intense efforts of clinical researchers globally, more promising treatments for COVID-19 will be established in the future.

Towards Explainable AI on Chest X-Ray Diagnosis Using Image Segmentation and CAM Visualization

Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image-to-label result provide insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. Class activation maps are a method of providing insight into a convolutional neural network's feature maps that lead to its classification but in the case of lung diseases, the region of concern is only the lungs. Therefore, the proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray's class activation map to provide a visualization that improves the explainability and trust of an AI's diagnosis by focusing on a model's weights within the region of concern. The proposed U-Net model achieves 97.72% accuracy and a dice coefficient of 0.9691 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent Pneumonia Detection Model of Focal Transformer and Multi-scale-input SPD

The clinical diagnosis results based on lung X-rays provide important evidence in the COVID-19 pneumonia diagnosis process and for some other disease. However, due to the similarity of the lesions among many types of pneumonia displayed by X-rays, and due to the huge amount of X-ray readings of a doctor's daily work, traditional reading and identification method purely by human have problems of diagnosis mistakes, missed diagnosis and huge time consumption. Therefore, an intelligent detection model of pneumonia with multi-scale-input Focal Transformer integrated with SPD module is proposed to automatically identify various types of pneumonia including COVID-19 pneumonia. The method can pay attention to the multi-scale characteristic features of pneumonia lesions, and then make improved classification among COVID-19 pneumonia, cases with lung opacity, viral pneumonia and normal cases, providing stronger support for radiologists in medical diagnosis. The experiment results show that the proposed model has advantages in comparison to the traditional network models ResNet-50 and Swin Transformer in aspects of accuracy, recall, F1-Measure and other indicators. © 2022 IEEE.

Experts proposal and frequently asked questions of rapid screening and prevention of novel coronavirus pneumonia in children.

The outbreak of novel coronavirus pneumonia (NCP) has become the most severe public health issue at the moment, threatening people's lives. Pediatricians in Shanghai have recently launched a discussion on the focused questions of NCP, including the incidence situation, epidemiological features, essentials of early screening, treatment and nosocomial infection prevention of children's novel coronavirus infection (2019-nCoV), and further put forward the experts proposal upon the patterns of disease occurrence, development, diagnosis and control, for the reference of frontline pediatricians.Copyright © 2020 by the Chinese Medical Association.

ICT-Facilitated Coproduction during COVID-19: Intelligence Innovation of Community Governance in Guangzhouĝ

Since the COVID-19 outbreak in 2020, ICT-based technology application platforms have played a prominent role in promoting cooperative governance of community epidemic prevention, realizing cooperative supply of public services, and promoting resident participation. Starting from the definition, background and prospect of cooperative production, the study explores how public services can effectively promote collaborative governance through ICTs, combined with the popularization of ICT platforms and applications to promote citizens' ability to access information, participate in public affairs and participate in the development of ways. The practice of community cooperative governance during the COVID-19 pandemic in Guangzhou demonstrated how the city can ensure the development of community public management and services while coordinating the prevention and control of COVID-19 based on ICT-related information systems and technology platforms. Based on the application of ICT, the ability of citizens to participate in community public governance has been improved, and the mode of public service supply has been changed, and the pressure on community governance has been reduced through scientific and technological governance tools, so as to promote the cooperative production and participation of public governance to achieve the sharing of results and responsibilities, providing a new way for public governance in the future intelligent society. © 2022 ACM.

Structure and Toughness Optimization of Multi-layer Weighted Regional Logistics Network: Taking the Central Region of China as an Example

The operation of the regional logistics network is often interrupted by emergencies such as rainstorms and earthquakes, especially the COVID-19 pandemic in recent years. Therefore, it is particularly important to improve the toughness of the regional logistics network to resist the risk of emergencies. This paper firstly constructed a multi-layered weighted regional logistics network of highways and railways in the central region of China based on the gravity model, analyzed its network structure characteristics by using dominant flow and social network analysis methods, then simulated the evolution trend of network toughness under different strategies. Finally, the optimization model of logistics network structural toughness under fixed cost was proposed to explore the optimization path of network structural toughness. The results show that: (1) The economically developed cities are located in the core area of the regional logistics network, on the contrary, they are located in the edge area of the regional logistics network. (2) The network as a whole has formed a "two main and four auxiliary” distribution pattern with Zhengzhou and Wuhan as the two main cores in the north and south, and Taiyuan, Hefei, Changsha, and Nanchang as the four auxiliary cores. (3) The network has higher toughness under the node random order failure strategy than under the node specified order failure strategy, and the optimization plans improve the structural toughness of the regional logistics network by 11.68%. © 2022 SPIE.

Transferable Discriminative Learning for Medical Open-Set Domain Adaptation: Application to Pneumonia Classification

Previous pneumonia classification algorithms have succeeded in the clinic under closed and static environments. However, in the real world, the emergence of new categories (e.g., COVID-19) and changes in data distribution will cause the existing methods to lose their robustness. In this paper, we formalize this problem as medical open-set domain adaptation under open and dynamic environments. The critical challenge of this problem is to accurately detect the open class samples with subtle differences from the common class. To achieve that, we propose transferable discriminative learning that remarkably achieves robust pneumonia classification with distribution shift and open class emerging. First, we propose the transferable high-density clustering module to detect open class samples and obtain reliable common class samples by considering the density degree. Secondly, we present the transferable triplet loss to enlarge the semantic feature difference between common class and open class samples. Finally, we design the transferable scoring function to detect open class samples effectively. A series of empirical studies show that our algorithm remarkably outperforms state-of-the-art methods. This result demonstrates its potential as a clinical tool for medical open-set domain adaptation. © 2022 IEEE.

Computational Screening of Repurposed Drugs Targeting Sars-Cov-2 Main Protease By Molecular Docking

Background: COVID-19 (Coronavirus disease 2019) is caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses significant global health and economic crisis that urges effective treatment.Methods: A total of 11 molecules (baricitinib, danoprevir, dexamethasone, hydrox-ychloroquine, ivermectin, lopinavir, methylprednisolone, remdesivir, ritonavir and saridegib, ascorbic acid, and cepharanthine) were selected for molecular docking studies using AutoDock VINA to study their antiviral activities via targeting SARS-CoV's main protease (Mpro), a cysteine protease that mediates the maturation cleavage of polyproteins during virus replication.Results: Three drugs showed stronger binding affinity toward Mpro than N3 (active Mpro inhibitor as control): danoprevir (-7.7 kcal/mol), remdesivir (-8.1 kcal/mol), and saridegib (-7.8 kcal/mol). Two primary conventional hydrogen bonds were identified in the danoprevir-Mpro complex at GlyA:143 and GlnA:189, whereas the residue GluA:166 formed a carbon-hydrogen bond. Seven main conventional hydrogen bonds were identified in the remdesivir at AsnA:142, SerA:144, CysA:145, HisA:163, GluA:166, and GlnA:189, whereas two carbon-hydrogen bonds were formed by the residues HisA:41 and MetA:165. Cepharanthine showed a better binding affinity toward Mpro (-7.9 kcal/mol) than ascorbic acid (-5.4 kcal/mol). Four carbon-hydrogen bonds were formed in the cepharanthine-Mpro complex at HisA:164, ProA;168, GlnA;189, and ThrA:190.Conclusion: The findings of this study propose that these drugs are potentially inhibiting the SAR-CoV-2 virus by targeting the Mpro protein.

Nexus between uncertainty, remittances, and households consumption: Evidence from dynamic SUR application

Household consumption induces aggregated economic activities by pushing market demand, capital accumulation and financial growth in the economy;on the other hand, instability in household consumption adversely affects the overall economic progress. Thus, exploring the key determinants responsible for household consumption instability is essential. The motivation of the study is to gauge the role of pandemic uncertainties and remittance inflow on household consumption in lower, Lower-middle, and Upper-Middle-income Countries for the period 1996 to 2020. The study employed several econometrical tools, including a panel cointegration test with the error correction term, dynamic SUR. The panel unit root test following CADF and CIPS documented variables are stationary after the first difference, and long-run associations are confirmed with the panel cointegration test. The coefficient of Dynamic Seemingly Unrelated Regression exposed pandemic uncertainties and has a negative impact on household consumption in all three-panel estimations;however, the coefficient of PUI is more prominent with COVID-19 effects. Remittances’ role in household consumption was positive and statistically significant, suggesting migrant remittances encourage additional consumption among households. On the policy aspect, the study proposed that the government should undertake macro policies to manage policy uncertainties so that the normal course of consumption level should not be interrupted because household consumption volatility creates discomfort in aggregated development. Moreover, efficient reallocation and remittance channels should be ensured in the economy;therefore, efficient institutional development has to be confirmed. Copyright © 2022 Yin, Qamruzzaman, Xiao, Mehta, Naqvi and Baig.

Study on electron beam radiation sterilization process for disposable medical protective clothing

The outbreak of COVID-19 has led to a sharp increase in the demand for disposable medical protective clothing in the short term. In order to shorten the marketing cycle, a large number of domestic disposable medical protective clothing products have been sterilized by electron beam irradiation, which is more efficient than ethylene oxide sterilization. However, the performance of such clothing must adhere to strict requirements and the process parameters of this sterilization method still lack systematic data support. In order to ensure the reliability of electron beam sterilization of disposable medical protective clothing, research on a corresponding process was carried out. Typical disposable medical protective clothing available on the market made of polypropylene (PP) and coated with polyethylene (PE) was selected as the material studied. An appropriate method was selected to establish the corresponding sterilization dose with reference to the standard methods—“Disposable medical protective clothing irradiation sterilization emergency specification (temporary)”and“ISO 11137-2:2013 Sterilization of health care products—Radiation—Part 2: Establishing the sterilization dose.”The change in material properties after irradiation sterilization with different absorbed doses was studied. Based on the obtained parameters, an algorithm for the average absorbed dose on irradiation by an irradiation electron linac was proposed. Results showed that absorbed doses of 20.3 kGy and 31.5 kGy allowed the products to achieve sterility assurance levels of 10−3 and 10−6, respectively. The material performance of the products after irradiation at 25.0 kGy, 30.0 kGy and 35.0 kGy were able to meet national standard requirements such as elongation at break, breaking strength, impermeability, and filtration efficiency. Thus, a satisfactory electron beam irradiation sterilization process for medical disposable protective clothing has been established. © 2021 The authors.

Stem cell-derived trophoblast organoids model human placental development, X chromosome inactivation, and susceptibility to emerging pathogens

Problem: Trophoblast organoids derived from human placental villi provide a powerful 3D model system of placental development, but access to first-trimester tissues is limited due to ethical and legal restrictions. Here we sought to establish a methodology for establishing 3D trophoblast organoids from naïve human pluripotent stem cells (hPSCs), which have an expanded potential for extraembryonic differentiation. Method of Study: We previously demonstrated that naïve hPSCs readily give rise to self-renewing human trophoblast cells (hTSCs) that resemble post-implantation cytotrophoblast (CTB) progenitors and can further differentiate into specialized trophoblast lineages. Here we examined whether hTSCs derived from three distinct sources (naïve hPSCs, human blastocysts, and first-trimester placental tissues) have the potential to self-organize into 3D trophoblast organoids by transfer to Matrigel droplets in the presence of trophoblast organoid medium. The expression of protein markers in the resulting stem cellderived trophoblast organoids (SC-TOs) was examined by immunofluorescence and light-sheet microscopy, while their single cell transcriptome was analyzed using the 10X Genomics platform. We also investigated the X chromosome inactivation (XCI) status of organoids derived from female naïve hPSCs and their ability to differentiate into invasive extravillous trophoblast (EVT) organoids. Finally, we evaluated whether SC-TOs are susceptible to infection by various emerging pathogens (SARS-CoV-2 and Zika virus), as a basis for establishing a stem cell-based model system of placental infections during the first trimester. Results: Trophoblast organoids generated from naïve and primary hTSCs displayed comparable tissue architecture, placental hormone secretion, microRNA expression, and capacity for long-term selfrenewal. In-depth single cell transcriptome profiling revealed that SCTOs encompass a variety of trophoblast identities that closely correspond to CTB progenitor, syncytiotrophoblast (STB) and EVT cell types found in human post-implantation embryos. Interestingly, the cellular composition in trophoblast organoids derived from naïve and primary hTSCs was highly similar, which suggests that trophoblast organoid culture represents a powerful attractor state in which the influence of subtle epigenetic differences between naïve and primary hTSCs is mitigated. These organoid cultures displayed clonal XCI patterns previously described in the human placenta.Upon differentiation into specialized EVT organoids, extensive trophoblast invasion was observed in co-culture assays with human endometrial cells. We further demonstrated that SC-TOs display selective vulnerability to infection by SARS-CoV-2 and Zika virus, which correlated with the expression levels of their respective entry factors. Conclusions: The generation of trophoblast organoids from naïve hPSCs provides an accessible and patient-specific 3D model system of the developing placenta and its susceptibility to emerging pathogens. The ability to genetically manipulate naïve hPSCs prior to differentiation into SC-TOs enables functional interrogation of regulatory factors implicated in placental organogenesis.

The Basic Reproduction Number and Delayed Action of T Cells for Patients Infected with SARS-CoV-2

COVID-19 has been prevalent for the last two years. The transmission capacity of SARS-CoV-2 differs under the influence of different epidemic prevention policies, making it difficult to measure the infectivity of the virus itself. In order to evaluate the infectivity of SARS-CoV-2 in patients with different diseases, we constructed a viral kinetic model by adding the effects of T cells and antibodies. To analyze and compare the delay time of T cell action in patients with different symptoms, we constructed a delay differential equation model. Through the first model, we found that the basic reproduction number of severe patients is greater than that of mild patients, and accordingly, we constructed classification criteria for severe and mild patients. Through the second model, we found that the delay time of T cell action in severe patients is much longer than that in mild patients, and accordingly, we present suggestions for the prevention, diagnosis, and treatment of different patients.

A Solution for Modularized-Curriculum-Oriented E-Learning Teaching Plan Intelligent Recommend System

E-learning is a very important way for busy modern people to obtain knowledge because of its convenience and efficiency. Especially it’s a key for most of the students to sustain learning during COVID-19 pandemic. And curriculum modularizing makes curriculum system flexible and easy to add new knowledge to train proper talents meeting the requirement of society conveniently. However, the complex relationships and constraints between modules, curriculum and curriculum system make the adjustment of teaching plan very difficult. This paper puts forward a solution for the modularized-curriculum-oriented E-learning teaching plan adjustment system. The solution can insure the curriculum system being overall optimized based on the idea of information system, knowledge management and data mining. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Ubiquitous Smartphone-Based Respiration Sensing With Wi-Fi Signal

Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3-4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

A Spatiotemporal Bidirectional Attention-Based Ride-Hailing Demand Prediction Model: A Case Study in Beijing During COVID-19

The COVID-19 pandemic has severely affected urban transport patterns, including the way residents travel. It is of great significance to predict the demand of urban ride-hailing for residents' healthy travel, rational platform operation, and traffic control during the epidemic period. In this paper, we propose a deep learning model, called MOS-BiAtten, based on multi-head spatial attention mechanism and bidirectional attention mechanism for ride-hailing demand prediction. The model follows the encoder-decoder framework with a multi-output strategy for multi-steps prediction. The pre-predicted result and the historical demand data are extracted as two aspects of bidirectional attention flow, so as to further explore the complicated spatiotemporal correlations between the historical, present and future information. The proposed model is evaluated on the real-world dataset during COVID-19 in Beijing, and the experimental results demonstrate that MOS-BiAtten achieves a better performance compared with the state-of-art methods. Meanwhile, another dataset is used to verify the generalization performance of the model. IEEE

Pharmacological research and clinical applications progress of Lianhua Qingwen capsules/granules in respiratory diseases

Lianhua Qingwen capsules/granules is an innovative Chinese medicine developed under the guidance of the TCM collateral disease theory. It has the efficacy of "clearing heat and removing toxin, ventilating the lungs and discharging heat". In 2003, it was approved as a new drug by the China National Medical Products Administration, through expedited approval during/SARS, and now, it has become a representative proprietary Chinese medicine for the treatment of infectious diseases of the respiratory system. Pharmacodynamic studies have revealed that LianhuaQingwen has broad-spectrum antiviral, antibacterial and anti-inflammatory, antipyretic, cough-relieving and immunoregulation effects. Clinically it has been used in the treatment of communicable and infectious respiratory diseases such as COVID-19, influenza, upper respiratory infection, pulmonary infections, acute exacerbation of chronic obstructive pulmonary disease, etc. and has achieved remarkable curative effects.

Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator

With the COVID-19 pandemic bringing about a severe global crisis, our health systems are under tremendous pressure. Automated screening plays a critical role in the fight against this pandemic, and much of the previous work has been very successful in designing effective screening models. However, they would lose effectiveness under the semi-supervised learning environment with only positive and unlabeled (PU) data, which is easy to collect clinically. In this paper, we report our attempt towards achieving semi-supervised screening of COVID-19 from PU data. We propose a new PU learning method called Constraint Non-Negative Positive Unlabeled Learning (cnPU). It suggests the constraint non-negative risk estimator, which is more robust against overfitting than previous PU learning methods when giving limited positive data. It also embodies a new and efficient optimization algorithm that can make the model learn well on positive data and avoid overfitting on unlabeled data. To the best of our knowledge, this is the first work that realizes PU learning of COVID-19. A series of empirical studies show that our algorithm remarkably outperforms state of the art in real datasets of two medical imaging modalities, including X-ray and computed tomography. These advantages endow our algorithm as a robust and useful computer-assisted tool in the semi-supervised screening of COVID-19. © 2021, Springer Nature Switzerland AG.

Cell-Type Apoptosis in Lung during SARS-CoV-2 Infection.

The SARS-CoV-2 pandemic has inspired renewed interest in understanding the fundamental pathology of acute respiratory distress syndrome (ARDS) following infection. However, the pathogenesis of ARDS following SRAS-CoV-2 infection remains largely unknown. In the present study, we examined apoptosis in postmortem lung sections from COVID-19 patients and in lung tissues from a non-human primate model of SARS-CoV-2 infection, in a cell-type manner, including type 1 and 2 alveolar cells and vascular endothelial cells (ECs), macrophages, and T cells. Multiple-target immunofluorescence assays and Western blotting suggest both intrinsic and extrinsic apoptotic pathways are activated during SARS-CoV-2 infection. Furthermore, we observed that SARS-CoV-2 fails to induce apoptosis in human bronchial epithelial cells (i.e., BEAS2B cells) and primary human umbilical vein endothelial cells (HUVECs), which are refractory to SARS-CoV-2 infection. However, infection of co-cultured Vero cells and HUVECs or Vero cells and BEAS2B cells with SARS-CoV-2 induced apoptosis in both Vero cells and HUVECs/BEAS2B cells but did not alter the permissiveness of HUVECs or BEAS2B cells to the virus. Post-exposure treatment of the co-culture of Vero cells and HUVECs with a novel non-cyclic nucleotide small molecule EPAC1-specific activator reduced apoptosis in HUVECs. These findings may help to delineate a novel insight into the pathogenesis of ARDS following SARS-CoV-2 infection.