Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 4 de 4
Filter
1.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1364739

ABSTRACT

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.


Subject(s)
COVID-19 Vaccines/genetics , COVID-19/drug therapy , Drug Discovery , SARS-CoV-2/genetics , Artificial Intelligence , COVID-19/genetics , COVID-19/virology , COVID-19 Vaccines/chemistry , Drug Design , Humans , Machine Learning , Pandemics , SARS-CoV-2/chemistry , SARS-CoV-2/pathogenicity
2.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1286553

ABSTRACT

The rapid spread of SARS-CoV-2 infection around the globe has caused a massive health and socioeconomic crisis. Identification of phosphorylation sites is an important step for understanding the molecular mechanisms of SARS-CoV-2 infection and the changes within the host cells pathways. In this study, we present DeepIPs, a first specific deep-learning architecture to identify phosphorylation sites in host cells infected with SARS-CoV-2. DeepIPs consists of the most popular word embedding method and convolutional neural network-long short-term memory network architecture to make the final prediction. The independent test demonstrates that DeepIPs improves the prediction performance compared with other existing tools for general phosphorylation sites prediction. Based on the proposed model, a web-server called DeepIPs was established and is freely accessible at http://lin-group.cn/server/DeepIPs. The source code of DeepIPs is freely available at the repository https://github.com/linDing-group/DeepIPs.


Subject(s)
COVID-19/drug therapy , Phosphorylation/genetics , SARS-CoV-2/chemistry , Software , COVID-19/genetics , COVID-19/virology , Computational Biology , Deep Learning , Humans , Neural Networks, Computer , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity
3.
Int J Infect Dis ; 100: 507-512, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-982692

ABSTRACT

OBJECTIVE: To investigate olfactory and gustatory dysfunction in patients with coronavirus disease 2019 (COVID-19) in Wuhan using a telephone interview. METHODS: This retrospective telephone survey investigated 196 consecutive patients with COVID-19 at 3 months after discharge from two hospitals in Wuhan, China. The characteristics of the patient's disease course and time to recovery from olfactory and/or gustatory dysfunction (OD and/or GD) were collected by telephone interview. Demographic data were collected from the patient medical records. RESULTS: A total of 196 patients with COVID-19 completed the study. The most prevalent general symptoms were fever, cough, and fatigue. Overall, 19.9% of the patients reported OD and/or GD. In 87.2% of these cases, OD or GD appeared after the general symptoms. The time to recovery from OD and/or GD was more than 4 weeks in 51.4% of the patients. Patients with COVID-19 and OD and/or GD had significantly higher rates of cardiovascular disease than patients without OD and/or GD (p = 0.002). CONCLUSIONS: Recovery from chemosensory dysfunction (OD and/or GD) was slow, with over half of the patients taking more than 4 weeks to recover. Cardiovascular disease might be related to the development of olfactory or taste disorders in patients with COVID-19.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Olfaction Disorders/epidemiology , Pneumonia, Viral/complications , Taste Disorders/epidemiology , Adult , Aged , Aged, 80 and over , COVID-19 , Female , Hospitalization , Humans , Male , Middle Aged , Olfaction Disorders/physiopathology , Pandemics , Prevalence , Recovery of Function , Retrospective Studies , SARS-CoV-2 , Taste Disorders/physiopathology
4.
Ann Palliat Med ; 9(6): 4300-4307, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-961973

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) has increased the risk of infection among medical staff. Anesthetists may have direct or indirect contact with COVID-19 patients' saliva droplets, blood, and other secretions in their daily work. If infection-prevention measures are not appropriate, it will not only cause individual medical staff infection, but also cross-infection among patients and other medical staff. Therefore, it is important to establish infection-control practices for COVID-19 patients during anesthesia. The aim of the present study was to review the infection-prevention measures against COVID-19 during anesthesia. Previously published studies on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), as well as current studies on COVID-19, specifically in Wuhan, China, were analyzed. In the present study, we discuss the etiology, epidemiology, pathology, clinical manifestations, diagnosis, and treatment of SARS, MERS, and COVID-19 at first. And then we discuss preoperative preparation which include the preparation of operating room, pre-operative assessment, hand hygiene and staffing and psychological counseling. We also discuss the implementation of anesthesia, including anesthesia types, induction of general anesthesia and endotracheal intubation, postoperative recovery and patient transport. Finally, we consider the proper disposal procedure for anesthetic equipment and medical devices. COVID-19 infection can be effectively reduced by infection-prevention measures during the perioperative period.


Subject(s)
Anesthesia , COVID-19/prevention & control , Infection Control/methods , COVID-19/epidemiology , COVID-19/virology , China/epidemiology , Humans , Pandemics , Perioperative Period , SARS-CoV-2/isolation & purification
SELECTION OF CITATIONS
SEARCH DETAIL
...