MedNexus
2023年 · 第03卷第01期
MedNexus
The coronavirus disease 2019 (COVID-19) pandemic is ravaging the world. Many therapies have been explored to treat COVID-19. This report aimed to assess the global research trends for the development of COVID-19 therapies.
We searched the relevant articles on COVID-19 therapies published from January 1, 2020, to May 25, 2022, in the Web of Science Core Collection Database (WOSCC). VOSviewer 1.6.18 software was used to assess data on the countries, institutions, authors, collaborations, keywords, and journals that were most implicated in COVID-19 pharmacological research. The latest research and changing trends in COVID-19-relevant pharmacological research were analyzed.
After manually eliminating articles that do not meet the requirements, a total of 5,289 studies authored by 32,932 researchers were eventually included in the analyses, which comprised 95 randomized controlled trials. 3,044 (57.6%) studies were published in 2021. The USA conducted the greatest number of studies, followed by China and India. The primary USA collaborators were China and England. The topics covered in the publications included: the general characteristics, the impact on pharmacists’ work, the pharmacological research, broad-spectrum antiviral drug therapy and research, and promising targets or preventive measures, such as vaccine. The temporal diagram revealed that the current research hotspots focused on the vaccine, molecular docking, Mpro, and drug delivery keywords.
Comprehensive bibliometric analysis could aid the rapid identification of the principal research topics, potential collaborators, and the direction of future research. Pharmacological research is critical for the development of therapeutic and preventive COVID-19-associated measures. This study may therefore provide valuable information for eradicating COVID-19.
The Omicron variant of SARS-COV-2 is replacing previously circulating variants around the world in 2022. Sporadic outbreaks of the Omicron variant into China have posed a concern how to properly response to battle against evolving coronavirus disease 2019 (COVID-19).
Based on the epidemic data from website announced by Beijing Center for Disease Control and Prevention for the recent outbreak in Beijing from April 22nd to June 8th in 2022, we developed a modified SEPIR model to mathematically simulate the customized dynamic COVID-zero strategy and project transmissions of the Omicron epidemic. To demonstrate the effectiveness of dynamic-changing policies deployment during this outbreak control, we modified the transmission rate into four parts according to policy-changing dates as April 22nd to May 2nd, May 3rd to 11st, May 12th to 21st, May 22nd to June 8th, and we adopted Markov chain Monte Carlo (MCMC) to estimate different transmission rate. Then we altered the timing and scaling of these measures used to understand the effectiveness of these policies on the Omicron variant.
The estimated effective reproduction number of four parts were 1.75 (95% CI 1.66-1.85), 0.89 (95% CI 0.79-0.99), 1.15 (95% CI 1.05-1.26) and 0.53 (95% CI 0.48-0.60), respectively. In the experiment, we found that till June 8th the cumulative cases would rise to 132,609 (95% CI 59,667-250,639), 73.39 times of observed cumulative cases number 1,807 if no policy were implemented on May 3rd, and would be 3,235 (95% CI 1,909-4,954), increased by 79.03% if no policy were implemented on May 22nd. A 3-day delay of the implementation of policies would led to increase of cumulative cases by 58.28% and a 7-day delay would led to increase of cumulative cases by 187.00%. On the other hand, taking control measures 3 or 7 days in advance would result in merely 38.63% or 68.62% reduction of real cumulative cases. And if lockdown implemented 3 days before May 3rd, the cumulative cases would be 289 (95% CI 211-378), reduced by 84%, and the cumulative cases would be 853 (95% CI 578-1,183), reduced by 52.79% if lockdown implemented 3 days after May 3rd.
The dynamic COVID-zero strategy might be able to effectively minimize the scale of the transmission, shorten the epidemic period and reduce the total number of infections.
To describe the information technology and artificial intelligence support in management experiences of the pediatric designated hospital in the wave of COVID-19 in Shanghai.
We retrospectively concluded the management experiences at the largest pediatric designated hospital from March 1st to May 11th in 2022 in Shanghai. We summarized the application of Internet hospital, face recognition technology in outpatient department, critical illness warning system and remote consultation system in the ward and the structed electronic medical record in the inpatient system. We illustrated the role of the information system through the number and prognosis of patients treated.
The COVID-19 designated hospitals were built particularly for critical patients requiring high-level medical care, responded quickly and scientifically to prevent and control the epidemic situation. From March 1st to May 11th, 2022, we received and treated 768 children confirmed by positive RT-PCR and treated at our center. In our management, we use Internet Information on the Internet Hospital, face recognition technology in outpatient department, critical illness warning system and remote consultation system in the ward, structed electronic medical record in the inpatient system. No deaths or nosocomial infections occurred. The number of offline outpatient visits dropped, from March to May 2022, 146,106, 48,379, 57,686 respectively. But the outpatient volume on the internet hospital increased significantly (3,347 in March 2022 vs. 372 in March 2021; 4,465 in April 2022 vs. 409 in April 2021; 4,677 in May 2022 vs. 538 in May 2021).
Information technology and artificial intelligence has provided significant supports in the management. The system might optimize the admission screening process, increases the communication inside and outside the ward, achieves early detection and diagnosis, timely isolates patients, and timely treatment of various types of children.
The objective of this study was to develop a robust method for rapid detection and identification of the virus based on Raman spectroscopy combined with machine learning approach.
We have used saliva spiked with different bacterial viruses such as P1 Phage, M13 Phage, and Lambda Phage, for demonstrating the utility of this method for virus detection. The Raman spectra collected from a large number of independent samples, each of different phages with and without saliva were used to train a supervised convolutional neural network (CNN) with its hyperparameters optimized by Bayesian optimization. The CNN method was not only able to detect the presence of a phage but was also able to identify the phage type using unprocessed Raman spectra having high noise. In addition, a semi-supervised auto-encoder was utilized for differentiating healthy saliva from saliva spiked with phages thereby making it possible to detect the presence of phages in saliva samples.
The CNN could identify the virus with an accuracy of 98.86% based on ten-fold cross-validation, precision of 98.8%, recall of 98.7%, and F1 score of 98.7%. The area under the curve of receiver operating characteristic curve was 0.99. Autoencoder was capable of differentiating healthy saliva from the virus spiked saliva with an accuracy of 99.7% in a semi-supervised manner. Thus, Raman spectroscopy coupled with machine learning approach was able to directly detect and identify the virus without consuming time for lengthy sample processing.
A robust method based on Raman spectroscopy coupled with machine learning may be capable of detection and identification of the virus even from the signal with low intensity and high noise. This label-free method is fast, sensitive, specific, and cost effective.
Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.
Deep learning approaches, especially convolutional neural networks (CNNs), have become the method of choice in the field of medical image analysis over the last few years. This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner, not only for 2D/3D images in the Euclidean space, but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field. The brain cerebral cortex is a highly convoluted and thin sheet of gray matter (GM) that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere. Accordingly, novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data. This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field. Specifically, we surveyed the use of deep learning techniques for cortical surface reconstruction, registration, parcellation, prediction, and other applications. We concluded by discussing the open challenges, limitations, and potentials of these techniques, and suggested directions for future research.
Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. In the field of medical image analysis, transformers have also been successfully used in to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. This paper aimed to promote awareness of the applications of transformers in medical image analysis. Specifically, we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic components. Second, we reviewed various transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigated key challenges including the use of transformers in different learning paradigms, improving model efficiency, and coupling with other techniques. We hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.
International movement plays an important role in spatial spread of infectious diseases. Here, we share two successful COVID-19 interventions based on real-time digital information collected from international passengers, which have been performed in Greece and China respectively. Both of the interventions demonstrated good performance and showed the potential of real-time digital data in containing the spread. However, several key points should not be ignored when we promote similar strategies.
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