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AIM: To explore the characteristics of astigmatism in preschool children before, during and after the COVID-19 epidemic, so as to provide a reference for further prevention and control of children's vision. *METHODS: In the consecutive four years from January 2018 to December 2021, a retrospective analysis of vision data was conducted on 2 273 preschool children (4546 eyes) younger than 4 years old who participated in children' s vision screening test in Baiyun district, Guangzhou. They were divided into 1 - year old group (ages<1-year old, 420 cases), 2-year old group (1-year <= ages < 2- year, 543 cases), 3- year old group (2- year <=ages <3-year, 614 cases), and 4 - year old group (3 - year <= ages< 4- year, 696 cases) according to ages. The analysis included astigmatic degrees of children's eyes as well as their conditions of astigmatism. *RESULTS: In 2018, the astigmatic degrees of the both eyes of 1-year-old group were higher than those of other groups (P<0.05). The binocular astigmatic degrees of the preschool children in four groups were obviously higher in 2020 than 2019 (P < 0. 05), while they were significantly decreased in 2021 when compared with 2020 (P < 0. 05). From 2019 to 2020, the increase of astigmatic degrees of the right eye is more considerable than the left eye of preschool children in those four groups (P < 0. 001). Furthermore, the morbidity of astigmatism basically echoes with the changing tendency of astigmatic degrees from 2018 to 2021. *CONCLUSIONS: Preschool children in Baiyun district, Guangzhou, have the highest degree of astigmatism and the fastest progression rate within 1 year old. Before COVID - 19 epidemic, the changes in astigmatism and prevalence were relatively stable;during COVID - 19 epidemic, the astigmatism and prevalence increased significantly and the astigmatic degrees of right eye increased more than that of the left eye;after the normalization of epidemic prevention and control, the astigmatism and prevalence decreased significantly.Copyright © 2023 International Journal of Ophthalmology (c/o Editorial Office). All rights reserved.
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In December 2019, the endemic of COVID-19 broke out in Wuhan, China. The disease is highly contagious and quickly spreads at home and abroad, causing great concern. However, there are no definite effective antiviral drugs in clinical use. Given the urgency of the COVID-19 outbreak, based on the diagnosis and treatment recommendation and relavant researches, this article describes the optional antiviral drugs such as remdesivir, oseltamivir, arbidol, lopinavir/ritonavir, ribavirin, and interferon-alpha to provide a reference for treatment of COVID-19.Copyright © 2020 by the Chinese Medical Association.
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In December 2019, the endemic of COVID-19 broke out in Wuhan, China. The disease is highly contagious and quickly spreads at home and abroad, causing great concern. However, there are no definite effective antiviral drugs in clinical use. Given the urgency of the COVID-19 outbreak, based on the diagnosis and treatment recommendation and relavant researches, this article describes the optional antiviral drugs such as remdesivir, oseltamivir, arbidol, lopinavir/ritonavir, ribavirin, and interferon-alpha to provide a reference for treatment of COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Since the last century, animal viruses have posed great threats to the health of humans and the farming industry. Therefore, virus control is of great urgency, and regular, timely, and accurate detection is essential to it. Here, we designed a rapid on-site visual data-sharing detection method for the Newcastle disease virus with smartphone recognition-based immune microparticles. The detection method we developed includes three major modules: preparation of virus detection vectors, sample detection, and smartphone image analysis with data upload. First, the hydrogel microparticles containing active carboxyl were manufactured, which coated nucleocapsid protein of NDV. Then, HRP enzyme-labeled anti-NP nanobody was used to compete with the NDV antibody in the serum for color reaction. Then the rough detection results were visible to the human eyes according to the different shades of color of the hydrogel microparticles. Next, the smartphone application was used to analyze the image to determine the accurate detection results according to the gray value of the hydrogel microparticles. Meanwhile, the result was automatically uploaded to the homemade cloud system. The total detection time was less than 50 min, even without trained personnel, which is shorter than conventional detection methods. According to experimental results, this detection method has high sensitivity and accuracy. And especially, it uploads the detection information via a cloud platform to realize data sharing, which plays an early warning function. We anticipate that this rapid on-site visual data-sharing detection method can promote the development of virus selfchecking at home.
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In 2020, the outbreak of COVID-19 pneumonia has had a great impact on China's economic and social life. The construction and transportation industries have been greatly impacted and suffered from its high mobility. This paper studies the big data of migration between Xi'an and Chengdu from January 1, 2020 to March 15, 2020 and divides the epidemic situation into four stages according to the introduction of the elastic coefficient according to the development of the epidemic situation. In each stage, the elastic coefficient of index change is introduced in combination with the decreasing impact of epidemic prevention and control measures on transportation. Finally, a modified moving average method is formed, which is compared with the ordinary moving average method. The results show that the modified moving average method combined with Hadoop big data platform can improve the accuracy and efficiency of the intercity transportation flow prediction under the epidemic situation. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.
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BACKGROUND: Total joint arthroplasty (TJA) practices have been dramatically impacted by the COVID-19 pandemic. To date, no study has assessed trends in patient perceptions regarding the safety of elective TJA. METHODS: A single-institution, prospective cohort study was conducted between May 11th and August 10th, 2020. All patients who underwent elective hip and knee arthroplasty were contacted via telephone or emailed surveys. Two-hundred and thirty-five consecutive patients were screened, and 158 agreed to participate. The average age was 65.9 ± 11.5 years, with 51.0% of patients being female. The percentage of participants who underwent total knee, total hip, and unicompartmental knee arthroplasty was 41.4%, 37.6%, and 21.0%, respectively. Survey components assessed demographic data, level of concern and specific concerns about the pandemic, and factors increasing patient comfort in proceeding with surgery. RESULTS: Older age (P = .029) and female sex (P = .004) independently predicted higher concern on multivariate analysis. Race (P = .343), surgical site (knee vs hip, P = .58), and procedure type (primary vs revision, P = .26) were not significantly related to degree of concern. Most participants (71.5%) disagreed that the pandemic would negatively affect the outcome of their surgery. Patient concern mirrored statewide COVID-19 cases and deaths, rather than local municipal trends. The most cited reassuring factors were preoperative COVID-19 testing, personal protective equipment usage by hospital staff, and surgeon support. CONCLUSIONS: Patient concern regarding the safety of elective TJA may follow broader policy-level events rather than local trends. Surgeons should note that universal preoperative COVID-19 testing, adequate personal protective equipment, and surgeon support were reassuring to patients. LEVEL OF EVIDENCE: Level IV Therapeutic.
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Social networking services (SNSs) have become the main avenue, where people speak their thoughts. Accordingly, we can explore people's thoughts by analyzing topics in SNS. When do topics change? Do they ever come back? What do people mainly talk about? In this study, we design and propose a novel visual analytics system to answer these interesting questions. We abstract the topic per unit time as a point in a two-dimensional space through document embedding and dimensionality reduction techniques and provide supplemented charts that represent words appearing at a certain time and the time-series change of word occurrence over the entire period. We employ a novel text visualization technique, called semantic preserving word bubbles, to visualize words at a certain time. In addition, we demonstrate the effectiveness of our system using Twitter data about early COVID-19 trends in Japan. We propose our system to help users to explore and understand transitions in posted contents on SNS. © 2021 IEEE.
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In December 2019, the endemic of COVID-19 broke out in Wuhan, China. The disease is highly contagious and quickly spreads at home and abroad, causing great concern. However, there are no definite effective antiviral drugs in clinical use. Given the urgency of the COVID-19 outbreak, based on the diagnosis and treatment recommendation and relavant researches, this article describes the optional antiviral drugs such as remdesivir, oseltamivir, arbidol, lopinavir/ritonavir, ribavirin, and interferon-α to provide a reference for treatment of COVID-19. Copyright © 2020 by the Chinese Medical Association.