Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 8 de 8
Filter
1.
World Journal of Emergency Medicine ; 13(6):459-466, 2022.
Article in English | Web of Science | ID: covidwho-2124061

ABSTRACT

BACKGROUND: Beijing 2022 Olympic Winter Games was the second Games held amid the COVID-19 pandemic. To a certain extent, it has altered the way sporting activities operate. There is a lack of knowledge on injury risk and illness occurrence in elite winter sport athletes amid the COVID-19 pandemic. This study aimed to describe the incidence of injuries and illnesses sustained during the XXIV Olympic Winter Games in Beijing from February 4 to 20, 2022. METHODS: We recorded the daily number of injuries and illnesses among athletes reported by Beijing 2022 medical staff in the polyclinic, medical venues, and ambulance. We calculated injury and illness incidence as the number of injuries or illnesses occurring during competition or training, respectively, with incidence presented as injuries/illnesses per 100 athlete-days. RESULTS: In total, 2,897 athletes from 91 nations experienced injury or illness. Beijing 2022 medical staff reported 326 injuries and 80 illnesses, equaling 11.3 injuries and 2.8 illnesses per 100 athletes over the 17-day period. Altogether, 11% of the athletes incurred at least one injury and nearly 3% incurred at least one illness. The number of injured athletes was highest in the skating sports (n=104), followed by alpine skiing (n=53), ice track (n=37), freestyle skiing (n=36), and ice hockey (n=35), and was the lowest in the Nordic skiing disciplines (n=20). Of the 326 injuries, 14 (4.3%) led to an estimated absence from training or competition of more than 1 week. A total of 52 injured athletes were transferred to hospitals for further care. The number of athletes with illness (n=80) was the highest for skating (n=33) and Nordic skiing (n=22). A total of 50 illnesses (62.5%) were admitted to the department of dentistry/ophthalmology/ otolaryngology, and the most common cause of illness was other causes, including preexisting illness and medicine (n=52, 65%). CONCLUSION: Overall, 11% of athletes incurred at least one injury during the Games, which is similar to the findings during the Olympic Winter Games in 2014 and 2018. Regarding illness, 2% of athletes were affected, which is approximately one-third of the number affected in the 2018 Olympic Winter Games.

2.
Zhonghua Yu Fang Yi Xue Za Zhi ; 56(11): 1663-1667, 2022 Nov 06.
Article in Chinese | MEDLINE | ID: covidwho-2119387

ABSTRACT

Due to the wide variety of pathogens causing respiratory tract infection and the close symptoms, coronavirus disease 2019 (COVID-19) needs to be differentiated from other common infections. Early comprehensive detection and accurate identification of respiratory infection pathogens is of great value for early diagnosis, curative effect, as well as monitor of the diseases. Combined detection of multiple pathogens can quickly and accurately detect and distinguish the pathogens, then provide rapid and reliable laboratory diagnostic basis for further treatment. This article elaborates the application and development of multiplex detection assay in the diagnosis of COVID-19 according to the recent research.


Subject(s)
COVID-19 , Respiratory Tract Infections , Humans , COVID-19/diagnosis , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/therapy , Sensitivity and Specificity
3.
Asian J Androl ; 2022.
Article in English | PubMed | ID: covidwho-1975058

ABSTRACT

Studies have investigated the effects of androgen deprivation therapy (ADT) use on the incidence and clinical outcomes of coronavirus disease 2019 (COVID-19);however, the results have been inconsistent. We searched the PubMed, Medline, Cochrane, Scopus, and Web of Science databases from inception to March 2022;13 studies covering 84 003 prostate cancer (PCa) patients with or without ADT met the eligibility criteria and were included in the meta-analysis. We calculated the pooled risk ratios (RRs) with 95% confidence intervals (CIs) to explore the association between ADT use and the infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and severity of COVID-19. After synthesizing the evidence, the pooled RR in the SARS-CoV-2 positive group was equal to 1.17, and the SARS-CoV-2 positive risk in PCa patients using ADT was not significantly different from that in those not using ADT (P = 0.544). Moreover, no significant results concerning the beneficial effect of ADT on the rate of intensive care unit admission (RR = 1.04, P = 0.872) or death risk (RR = 1.23, P = 0.53) were found. However, PCa patients with a history of ADT use had a markedly higher COVID-19 hospitalization rate (RR = 1.31, P = 0.015) than those with no history of ADT use. These findings indicate that ADT use by PCa patients is associated with a high risk of hospitalization during infection with SARS-CoV-2. A large number of high quality studies are needed to confirm these results.

4.
26th Pacific Symposium on Biocomputing (PSB) ; : 91-94, 2021.
Article in English | Web of Science | ID: covidwho-1743974

ABSTRACT

AI for infectious disease modelling and therapeutics is an emerging area that leverages new computational approaches and data in this area. Genomics, proteomics, biomedical literature, social media, and other resources are proving to be critical tools to help understand and solve complicated issues ranging from understanding the process of infection, diagnosis and discovery of the precise molecular details, to developing possible interventions and safety profiling of possible treatments.

5.
2020 35th International Conference on Image and Vision Computing New Zealand ; 2020.
Article in English | Web of Science | ID: covidwho-1349145

ABSTRACT

The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.

6.
Proceedings of the Vldb Endowment ; 13(12):2841-2844, 2020.
Article in English | Web of Science | ID: covidwho-1031191

ABSTRACT

Spatio-temporal data analysis is very important in many time-critical applications. We take Coronavirus disease (COVID-19) as an example, and the key questions that everyone will ask every day are: how does Coronavirus spread? where are the high-risk areas? where have confirmed cases around me? Interactive data analytics, which allows general users to easily monitor and explore such events, plays a key role. However, some emerging cases, such as COVID-19, bring many new challenges: (C1) New information may come with different formats: basic structured data such as confirmed/suspected/serious/death/recovered cases, unstructured data from newspapers for travel history of confirmed cases, and so on. (C2) Discovering new insights: data visualization is widely used for storytelling;however, the challenge here is how to automatically find "interesting stories", which might be different from day to day. We propose DEEPTRACK, a system that monitors spatio-temporal data, using the case of COVID-19. For (C1), we describe (a) how we integrate and clean data from different sources by existing modules. For (C2), we discuss (b) how to build new modules for ad-hoc data sources and requirements, (c) what are the basic (or static) charts used;and (d) how to generate recommended (or dynamic) charts that are based on new incoming data. The attendees can use DeepTrack to interactively explore various COVID-19 cases.

7.
Chinese Traditional and Herbal Drugs ; 51(5):1147-1152, 2020.
Article | WHO COVID | ID: covidwho-52361

ABSTRACT

To dig out and analyze the drug rule of COVID-19 prevention prescriptions from provinces and cities by using Traditional Chinese Medicine Inheritance Support System, summarize and explore its potential new prescription. The Chinese medicine prevention programs for COVID-19 were collected and searched from the official website of the Health Commission and State Administration Medicine of Traditional Chinese Medicine of the country and provinces, autonomous regions, and municipalities. TCM prevention programs in 17 provinces including Heilongjiang, Beijing, Tianjin, Hebei, Henan, Jiangxi, Sichuan, Hubei, and Hunan, etc were received. A total of 82 herbs were included in 64 prescriptions, the most frequently used Chinese herbs were Astragalus membranaceus, Lonicera japonica, etc. Tonifying deficiency drugs with sweet and warm natures were used the most, the Chinese herbs distributed in the lung channel was the most in channel tropism drugs. Analysis by association rule, eight combinations of commonly used drugs were obtained. Based on entropy clustering method rule analysis, seven potential new prescriptions were obtained. Tonifying deficiency drugs are often used in various places to prevent COVID-19, focusing on the lung, spleen and stomach. Although the specific details are different, they all reflect the preventive thinking of traditional Chinese medicine.

8.
Eur Rev Med Pharmacol Sci ; 24(6): 3411-3421, 2020 03.
Article in English | MEDLINE | ID: covidwho-49973

ABSTRACT

OBJECTIVE: On December 8, 2019, many cases of pneumonia with unknown etiology were first reported in Wuhan, China, subsequently identified as a novel coronavirus infection aroused worldwide concern. As the outbreak is ongoing, more and more researchers focused interest on the COVID-19. Therefore, we retrospectively analyzed the publications about COVID-19 to summarize the research hotspots and make a review, to provide reference for researchers in the world. MATERIALS AND METHODS: We conducted a search in PubMed using the keywords "COVID-19" from inception to March 1, 2020. Identified and analyzed the data included title, corresponding author, language, publication time, publication type, research focus. RESULTS: 183 publications published from 2020 January 14 to 2020 February 29 were included in the study. The first corresponding authors of the publications were from 20 different countries. Among them, 78 (42.6%) from the hospital, 64 (35%) from the university and 39 (21.3%) from the research institution. All the publications were published in 80 different journals. Journal of Medical Virology published most of them (n=25). 60 (32.8%) were original research, 29 (15.8%) were review, 20 (10.9%) were short communications. 68 (37.2%) epidemiology, 49 (26.8%) virology and 26 (14.2%) clinical features. CONCLUSIONS: According to our review, China has provided a large number of research data for various research fields, during the outbreak of COVID-19. Most of the findings play an important role in preventing and controlling the epidemic around the world. With research on the COVID-19 still booming, new vaccine and effective medicine for COVID-19 will be expected to come out in the near future with the joint efforts of researchers worldwide.


Subject(s)
Bibliometrics , Coronavirus Infections , Pandemics , Pneumonia, Viral , Betacoronavirus , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Disease Outbreaks , Humans , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , SARS-CoV-2
SELECTION OF CITATIONS
SEARCH DETAIL