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1.
Cell Discov ; 8, 2022.
Article in English | PMC | ID: covidwho-2008267

ABSTRACT

The ongoing COVID-19 pandemic has continued to affect millions of lives worldwide, leading to the urgent need for novel therapeutic strategies. G-quadruplexes (G4s) have been demonstrated to regulate life cycle of multiple viruses. Here, we identify several highly conservative and stable G4s in SARS-CoV-2 and clarify their dual-function of inhibition of the viral replication and translation processes. Furthermore, the cationic porphyrin compound 5,10,15,20-tetrakis-(N-methyl-4-pyridyl)porphine (TMPyP4) targeting SARS-CoV-2 G4s shows excellent antiviral activity, while its N-methyl-2-pyridyl positional isomer TMPyP2 with low affinity for G4 has no effects on SARS-CoV-2 infection, suggesting that the antiviral activity of TMPyP4 attributes to targeting SARS-CoV-2 G4s. In the Syrian hamster and transgenic mouse models of SARS-CoV-2 infection, administration of TMPyP4 at nontoxic doses significantly suppresses SARS-CoV-2 infection, resulting in reduced viral loads and lung lesions. Worth to note, the anti-COVID-19 activity of TMPyP4 is more potent than remdesivir evidenced by both in vitro and in vivo studies. Our findings highlight SARS-CoV-2 G4s as a novel druggable target and the compelling potential of TMPyP4 for COVID-19 therapy. Different from the existing anti-SARS-CoV-2 therapeutic strategies, our work provides another alternative therapeutic tactic for SARS-CoV-2 infection focusing on targeting the secondary structures within SARS-CoV-2 genome, and would open a new avenue for design and synthesis of drug candidates with high selectivity toward the new targets.

2.
Ieee Transactions on Emerging Topics in Computational Intelligence ; : 10, 2022.
Article in English | Web of Science | ID: covidwho-1978407

ABSTRACT

The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine.

3.
IEEE Robotics and Automation Letters ; : 1-8, 2022.
Article in English | Scopus | ID: covidwho-1961414

ABSTRACT

We design a central controller system (CCS) and a tele-controlled system (TCS) with an aim of developing the integrated tele-monitoring/operation system that can enable the medical staff to tele-monitor the state of therapeutic devices utilized in the isolation intensive care unit (ICU) and to tele-operate its user interfaces. To achieve this aim, we survey the medical staff for medical requirements first and define the design guideline for tele-monitoring/operation functionality and field applicability. In designing the CCS, we focus on realizing the device having intuitive and user-friendly interfaces so that the medical staff can use the device conveniently without pre-training. Further, we attempt to implement the TCS capable of manipulating various types of user interfaces of the therapeutic device (e.g., touch screen, buttons, and knobs) without failure. As two core components of the TCS, the precision XY-positioner having a maximum positioning error of about 0.695 mm and the end-effector having three-degrees-of-freedom motion (i.e., pressing, gripping, and rotating) are applied to the system. In the experiment conducted for assessing functionality, it is investigated that the time taken to complete the tele-operation after logging into the CCS is less than 1 minute. Furthermore, the result of field demonstration for focus group shows that the proposed system could be applied practically to the medical fields when the functional reliability is improved. IEEE

4.
60th IEEE Conference on Decision and Control (CDC) ; : 3544-3550, 2021.
Article in English | Web of Science | ID: covidwho-1868528

ABSTRACT

We address the model identification and the computation of optimal vaccination policies for the coronavirus disease 2019 (COVID-19). We consider a stochastic Susceptible- Infected-Removed (SIR) model that captures the effect of multiple vaccine treatments, each requiring a different number of doses and providing different levels of protection against the disease. We show that the inclusion of vaccination data enables the estimation of the state of the model and key model parameters that are otherwise not identifiable. This estimates can, in turn, be used to design strategic approaches to vaccination that aim at minimizing the number of deaths and the economic cost of the disease. We illustrate these results with numerical examples.

5.
Emerging Markets Finance and Trade ; : 13, 2022.
Article in English | Web of Science | ID: covidwho-1852673

ABSTRACT

Income inequality is rising due to the risks and uncertainties related to the COVID-19 pandemic and other risks. This paper examines the effects of country risks (measured by economic/financial and political risks) and geopolitical risks on the income inequality in the panel dataset of 19 emerging market economies from 1985 to 2020. It is observed that all risk measures are positively related to income inequality. This evidence is also valid when different empirical models and estimation procedures are considered. The results are also robust for including various controls, excluding the extreme observations in the dataset, and considering the countries at the different income levels and regions.

6.
Advanced Nanobiomed Research ; 2(2):17, 2022.
Article in English | Web of Science | ID: covidwho-1813459

ABSTRACT

Lipid nanoparticles have attracted significant interests in the last two decades, and have achieved tremendous clinical success since the first clinical approval of Doxil in 1995. At the same time, lipid nanoparticles have also demonstrated enormous potential in delivering nucleic acid drugs as evidenced by the approval of two RNA therapies and mRNA COVID-19 vaccines. In this review, an overview on different classes of lipid nanoparticles, including liposomes, solid lipid nanoparticles, and nanostructured lipid carriers, is first provided, followed by the introduction of their preparation methods. Then the characterizations of lipid nanoparticles are briefly reviewed and their applications in encapsulating and delivering hydrophobic drugs, hydrophilic drugs, and RNAs are highlighted. Finally, various applications of lipid nanoparticles for overcoming different delivery challenges, including crossing the blood-brain barrier, targeted delivery, and various routes of administration, are summarized. Lipid nanoparticles as drug delivery systems offer many attractive benefits such as great biocompatibility, ease of preparation, feasibility of scale-up, nontoxicity, and targeted delivery, while current challenges in drug delivery warrant future studies about structure-function correlations, large-scale production, and targeted delivery to realize the full potential of lipid nanoparticles for wider clinical and pharmaceutical applications in future.

7.
Clinical Neurosurgery ; 68(SUPPL 1):72, 2022.
Article in English | EMBASE | ID: covidwho-1813118

ABSTRACT

INTRODUCTION: The COVID-19 pandemic forced the implementation of social distancing guidelines to minimize spread of the coronavirus. However, it is not yet understood what effects these precautions had on the rates of penetrating neurotrauma. METHODS: We retrospectively analyzed neurotrauma data from our institutional trauma registry from distinct periods defined as pre-COVID-19 (March 2019-September 2019) and COVID-19 (March 2020-September 2020). Demographics, injury characteristics, mechanisms of trauma, and past medical history (including psychiatric diagnosis) were collected. Data were analyzed for between-group differences and presented as odds ratios. RESULTS: We observed a significant rise in the number of neurotrauma cases in 2020 (558 vs. 630, OR 1.129 [1.0071, 1.2657]). There was a decrease in the proportion of male victims (71.3% vs. 68.6%, p = 0.03). There were significant differences noted in the mechanism of injury between groups. Patients in 2020 were less likely to present with falls (42.3% vs. 34.3%, OR 0.7119 [0.5627, 0.9005]) and more likely to present with GSW (4.48% vs. 7.78%, OR 1.7981 [1.0951, 2.9523]). Of the patients with penetrating cranial injuries, the most common motive was assault (56.7% vs. 60.0%), followed by self-inflicted (13.3% vs. 20.0%) and accidental (20.0% vs. 18.3%) with a significant difference between years (p = 0.0043). The presence of comorbid psychiatric illness or substance abuse did not confer an increased odds of presenting with penetrating injuries. No significant differences were noted in mean arrival or discharge GCS or injury severity as measured by ISS. However we did observe significant increases in patients presenting with bilaterally reactive pupils (48.3% vs 59.3%, p = 0.0025), patients discharged home (27.6% vs 37.3%, p = 0.0002), and survival at 6 months (41.4% vs. 54.2%, p = 0.0188). CONCLUSION: We observed a higher rate of penetrating neurotrauma while social distancing measures were in place. It is unclear if the psychosocial effects of quarantine and social distancing had a causative relationship with the increased rates of assault and self-inflicted penetrating injuries.

8.
60th IEEE Conference on Decision and Control, CDC 2021 ; 2021-December:3544-3550, 2021.
Article in English | Scopus | ID: covidwho-1746107

ABSTRACT

We address the model identification and the computation of optimal vaccination policies for the coronavirus disease 2019 (COVID-19). We consider a stochastic Susceptible-Infected-Removed (SIR) model that captures the effect of multiple vaccine treatments, each requiring a different number of doses and providing different levels of protection against the disease. We show that the inclusion of vaccination data enables the estimation of the state of the model and key model parameters that are otherwise not identifiable. This estimates can, in turn, be used to design strategic approaches to vaccination that aim at minimizing the number of deaths and the economic cost of the disease. We illustrate these results with numerical examples. © 2021 IEEE.

9.
8th International Conference on Dependable Systems and Their Applications, DSA 2021 ; : 639-646, 2021.
Article in English | Scopus | ID: covidwho-1672601

ABSTRACT

The quality of the dataset affects the accuracy of the artificial intelligence model, but it is a lot of work to manually detect errors related to the quality evaluation of the dataset, and it may not be possible to perform quality evaluation through simple viewing. Therefore, we propose an image dataset quality measurement model, including nine evaluation metrics, and analyze the evaluation metrics from three aspects: definition, calculation formula and description. Based on the label file, the quality of the dataset file and the content of the dataset is evaluated, and the evaluation standard is given to judge whether the quality of the dataset is qualified. The measurement model and evaluation criteria proposed in this article were verified against the Cifar-10 dataset and the COVID-CT dataset, and the problems of label accuracy and label category imbalance were found, which proved the effectiveness of the method in this paper. © 2021 IEEE.

10.
2021 International Conference on Green Communication, Network, and Internet of Things, GCNIoT 2021 ; 12085, 2021.
Article in English | Scopus | ID: covidwho-1642790

ABSTRACT

With the outbreak of the Covid-19, people's reliance on electronic devices has become more and more serious. Especially in the special period in present, the mode of online learning through electronic devices has been widely adopted by young students at different stages. There are various supports for electronic devices, but most of them can only be placed in a fixed position. Some display supports can be manually adjusted to a suitable position, which cannot provide more intelligent functions. Based on the current circumstances, by using computer software to build a 3D bracket model, and make it by 3D printer, with raspberry pi as the main control chip and stm32 as the system auxiliary chip, a set of intelligent display screen bracket based on IOT is developed. The smart bracket can independently adjust the position of the user and the screen, and can set auxiliary functions in real time according to needs, such as reminder of learning hours, sitting posture adjustment, voice interaction, etc. and can control core functions through the mobile terminal of the mobile phone. This paper aims to alleviate the problems of eye fatigue and high myopia rate caused by the improper posture of young people when using electronic display devices. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

11.
European Journal of Integrative Medicine ; 48, 2021.
Article in English | EMBASE | ID: covidwho-1587797

ABSTRACT

Introduction: This living rapid review aims to systematically update evidence from randomised controlled trials (RCTs) on the efficacy and safety of any zinc formulation or dose compared to any control, for preventing or treating SARS-CoV-2 and other acute viral respiratory tract infections (RTIs) in adults. Methods: Protocol registration was 27-April-2020 (PROSPERO: CRD42020182044). Eight databases (one Chinese), four clinical trial registries (one Chinese) and two pre-print servers were then searched with no language or date restrictions. Post-protocol/pre-data extraction, the inclusion criteria was restricted to adults. Meta-analysis used weighted, random-effects models. Cochrane RoB 2.0 tool and GRADE were used to appraise evidence certainty. Searches for COVID-19 evidence are updated 6-monthly. Results: As of Oct-2020, 1,907 articles and protocols were screened, and 28 RCTs involving 5,403 participants (none with SARS-CoV-2 infections) were included. Compared to placebo, oral or intranasal zinc prevented 5 RTIs/100 person-months (95%CI: 1-9, NTT=20) in adults without zinc deficiency (moderate-certainty), but not pre/post exposure prevention following human rhinovirus inoculation (RR 0.96, 95%CI: 0.77-1.21, moderate-certainty). There was low-certainty evidence of clinically important RTI treatment outcomes. Compared to placebo, sublingual or intranasal zinc improved day-3 symptom severity (MD 1.2 points lower, 95%CI: 0.7-1.7) and reduced symptom duration (MD 2 days shorter, 95%CI: 0.2-3.5;HR 0.55 over 7-days, 95% CI: 0.32-0.91, NNT=5). There was an increased risk of non-serious adverse events (e.g. nausea, or mouth or nasal irritation) (ARR 14/100 adults, 95%CI: 4-16, NNH=7). In the 25 RCTs that reported adverse events, none were serious, including copper deficiency or anosmia. The April-2021 update search identified, four COVID-19 RCTs with 572 participants and 7 registered RCTs. These results will be included in the next update. Conclusion: Preliminary evidence suggests there may be a role for zinc in the COVID-19 pandemic. Further research and regular updating of the evidence is warranted. Keywords: Zinc, Complementary medicine, Common cold, Respiratory infections, Viral infections, COVID-19

12.
6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021 ; : 297-302, 2021.
Article in English | Scopus | ID: covidwho-1379521

ABSTRACT

The rapid development of computer vision makes human-computer interaction possible and has a wide application prospect. Since the first case of COVID-19 was found, up to now, the global number of infected people has reached 119220681 [1]. In China, although the epidemic is under control, affected by the external environment, the epidemic will also spread through cold chain transportation, and imported fruits may also carry the virus. In order to solve this problem, based on deep learning, we collected 300 fruit images and used YOLOV5 to recognize some common fruit images. The experimental results are shown in the proposed method, and the average accuracy can reach about 84%. © 2021 IEEE.

13.
International Journal of Clinical and Experimental Medicine ; 14(7):2123-2131, 2021.
Article in English | EMBASE | ID: covidwho-1346973

ABSTRACT

Coronavirus disease (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) still has no specific laboratory markers to assess severity. As a novel acute infectious disease, early recognition of severe cases (nearly 20%) is essential for early triage and corresponding treatment. This study aimed to summarize the potential practical predictors for clinicians to identify severe cases during hospitalization. We collected the clinical laboratory data as well as the demographic, epidemiological and clinical information from 58 COVID-19 patients (26 severe cases, 32 mild cases) in Xiangyang Central Hospital (Xiangyang, China) during their hospitalization. The correlation between laboratory parameters and disease severity, laboratory parameters dynamics and the outcome of severe COVID-19 patients were fully analyzed. Finally, we compared the characteristics between severe and mild cases and summarized several laboratory parameters. The median age, concomitant diseases, PT, FIB, DD, ISTH/CDSS score, UN, CK, ESR and CRP were significantly higher in the severe cases, while the LYM count, viral nucleic acid Ct value, and Alb were significantly lower. Logistic regression analysis and AUC of ROC showed that Ct, Alb, CK, ESR and CRP may be good predictors for the severity of COVID-19 cases and patient prognosis. Laboratory parameter dynamics indicated the repletion of LYM, Alb, D-D, UN, CK, ESR and CRP may be important for the recovery of severe cases. Low Ct value and other parameters may have the potential to discriminate mild and severe COVID-19 cases and could be used as prognostic markers to guide treatment.

14.
Chinese Journal of Applied Chemistry ; 38(5):592-604, 2021.
Article in Chinese | Scopus | ID: covidwho-1335485

ABSTRACT

In December 2019, the global outbreak of Corona Virus Disease 2019 (COVID-19) made biosafety an attractive and crucial development direction globally. Rapid, accurate and low-cost detection of pathogenic microorganisms is one of the important issue to ensure biosafety, and is the key to epidemic prevention, control and diagnosis. This review elaborately introduces the applications of biosensors based on nucleic acid sequencing, isothermal amplification and gene editing (CRISPR/Cas) in integrated portable devices. The development of these technologies offers the possibility of developing “low-cost, high-efficiency and result-visualization” integrated detection methods. It is expected to provide technology guarantees for the effective prevention and control of pathogenic microorganisms. © 2021, Science in China Press. All rights reserved.

15.
34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 ; 2021-June:521-526, 2021.
Article in English | Scopus | ID: covidwho-1334354

ABSTRACT

Artificial Intelligence (AI) has made leapfrogs in development across all the industrial sectors especially when deep learning has been introduced. Deep learning helps to learn the behaviour of an entity through methods of recognising and interpreting patterns. Despite its limitless potential, the mystery is how deep learning algorithms make a decision in the first place. Explainable AI (XAI) is the key to unlocking AI and the black-box for deep learning. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand. The end users can be domain experts, regulatory agencies, managers and executive board members, data scientists, users that use AI, with or without awareness, or someone who is affected by the decisions of an AI model. Chest CT has emerged as a valuable tool for the clinical diagnostic and treatment management of the lung diseases associated with COVID-19. AI can support rapid evaluation of CT scans to differentiate COVID-19 findings from other lung diseases. However, how these AI tools or deep learning algorithms reach such a decision and which are the most influential features derived from these neural networks with typically deep layers are not clear. The aim of this study is to propose and develop XAI strategies for COVID-19 classification models with an investigation of comparison. The results demonstrate promising quantification and qualitative visualisations that can further enhance the clinician's understanding and decision making with more granular information from the results given by the learned XAI models. © 2021 IEEE.

16.
Journal of Xi'an Jiaotong University (Medical Sciences) ; 42(2):186-189, 2021.
Article in Chinese | Scopus | ID: covidwho-1190618

ABSTRACT

Objective: To assess the epidemiological characteristics and transmission risk of asymptomatic COVID-19 infection in Shaanxi Province. Methods: A dense population database of COVID-19 cases in Shaanxi Province was constructed as of March 26, 2020, and 28 asymptomatic infected patients were selected through case epidemiological investigation report for descriptive analysis. Results: In Shaanxi Province, the majority of asymptomatic COVID-19 infections were in the 20-59 years old group, and women took up a higher proportion than men. 82.14% of asymptomatic infections were found in the centralized isolation state. Nearly 80% of asymptomatic infections and confirmed cases were mainly exposed by living together in the family. The median number of days from last contact with the source of infection for all asymptomatic infected persons was 13, with 3 (10.71%) showing clinical symptoms. Epidemiological investigations showed that asymptomatic infections could spread as a result of shared family life. Conclusion: At present, imported cases in Shaanxi Province continue to exist. Considering the concealed transmission of asymptomatic infections, prevention and control work is still facing challenges. © 2021, Editorial Board of Journal of Xi'an Jiaotong University (Medical Sciences). All right reserved.

18.
Clinical Pharmacology & Therapeutics ; 109:S27-S28, 2021.
Article in English | Web of Science | ID: covidwho-1136823
19.
Clinical Pharmacology & Therapeutics ; 109:S60-S60, 2021.
Article in English | Web of Science | ID: covidwho-1136809
20.
Chinese Journal of Clinical Pharmacology and Therapeutics ; 25(3):329-333, 2020.
Article in Chinese | EMBASE | ID: covidwho-683936

ABSTRACT

Public health emergency is a huge challenge for all countries or regions. It is impossible for any country or region to make adequate preparations in advance. How to effectively prevent and control the spread of the epidemic is an unprecedented challenge for China and even the whole international community. This paper holds that the current prevention and control measures and efforts of the Chinese government are effective and important for preventing and controlling the disease rapidly, but the ethical concerns cannot be ignored. In fact, good ethical practices can help to achieve long-term prevention and control goals. Based on the current situation of the epidemic, we propose six ethical points to further stimulate dialogue on how to implement disease prevention and control measures to better protect public safety, protect personal privacy, prevent stigma and discrimination, and guide society to follow the right ethical value orientation.

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