ReviewProcess biochemistry (Barking, London, England)2020
A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19.
Review in Process biochemistry (Barking, London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prevalence and clinical outcomes of pleural effusion in COVID-19 patients: A systematic review and meta-analysis.Journal of medical virology · 2022Pooled it
- COVID 19 repercussions in ophthalmology: a narrative review.Sao Paulo medical journal = Revista paulista de medicinaPooled it
- Contribution ofCurrent issues in molecular biology · 2025Review
- Research on the Stability and Response of Food Packaging Polystyrene Resin Materials to γ-ray Irradiation.ACS omega · 2024Article
- Performance assessment of a new serological diagnostic test for COVID-19 with candidate peptides from spike and nucleocapsid viral proteins.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Diclofenac-loaded PLGA nanoparticles downregulate LasI/R quorum sensing genes in pathogenic P. aeruginosa isolates.Archives of microbiology · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Investigating the mode of transmission of COVID-19 through genital secretions, semen, the birth canal, and lactation: A systematic review.Journal of education and health promotion · 2024Article
- Article
- An overview of SARS-CoV-2 transmission and engineering strategies to mitigate risk.Journal of building engineering · 2023Review
- COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection.Diagnostics (Basel, Switzerland) · 2023Article
- A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023Article
- Pleural Effusion in COVID-19 Pneumonia: Clinical and Prognostic Implications-An Observational, Retrospective Study.Journal of clinical medicine · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- Coronavirus disease (COVID-19) cases analysis using machine-learning applications.Applied nanoscience · 2023Article
- A review on role of nitrous oxide nanoparticles, potential vaccine targets, drug, health care and artificial intelligence to combat COVID-19.Applied nanoscience · 2023Review
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- Diagnosis Methods for COVID-19: A Systematic Review.Micromachines · 2022Review
- Preparation of surface-functionalized electrospun PVA nanowebs for potential remedy for SARS-CoV-2.Journal of hazardous materials advances · 2022Article
- MicroRNA of N-region from SARS-CoV-2: Potential sensing components for biosensor development.Biotechnology and applied biochemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the new cases of COVID-19 are growing every daysince January 2020, the major way to control the spread wasthrough early diagnosis. Prevention and early diagnosis are the key strategies followed by most countries. This study presents the perspective of different modes of transmission of coronavirus,especially during clinical practices and among the pediatrics. Further, the diagnostic methods and the advancement of the computerized tomography have been discussed. Droplets, aerosol, and close contact are thesignificantfactors to transfer the infection to the suspect. This study predicts the possible transmission of the virus through medical practices such as ophthalmology, dental, and endoscopy procedures. With regard to pediatric transmission, as of now, only afew child fatalities had been reported. Childrenusually respond to the respiratory virus; however, COVID-19 response ison the contrary. The possibility of getting infected is minimal for the newborn. There has been no asymptomatic spread in children until now. Moreover, breastfeedingwould not transmit COVID-19, which is encouraging hygiene news for the pediatric. In addition, the current diagnostic methods for COVID-19 including Immunoglobulin M (IgM) and Immunoglobulin G (IgG)and chest computed topography(CT) scan, reverse transcription-polymerase chain reaction (RT-PCR) andimmunochromatographic fluorescence assay, are also discussed in detail. The introduction of artificial intelligence and deep learning algorithmhas the ability to diagnose COVID-19 in precise. However, the developments of a potential technology for the identification of the infection, such as a drone with thermal screening without human intervention, need to be encouraged.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.