ReviewRSC advances2022
Insight into prognostics, diagnostics, and management strategies for SARS CoV-2.
Review in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed, 9 citations in OpenAlex.
- Antiviral Properties of Green-Synthesized Silver and Zinc Oxide Nanoparticles Against Coronaviruses: A Review.International journal of microbiology · 2026Review
- Digital microfluidics methods for nucleic acid detection: A mini review.Biomicrofluidics · 2024Review
- Evaluation of the analytical performance of the 15-minute point-of-care DASH™ SARS-CoV-2 RT-qPCR test.Diagnostic microbiology and infectious disease · 2024Article
- The critical role of engineering in the rapid development of COVID-19 diagnostics: Lessons from the RADx Tech Test Verification Core.Science advances · 2023Review
- Nanotechnology meets immunology towards a rapid diagnosis solution: the COVID-19 outbreak challenge.RSC advances · 2022Article
- The role of electrochemical biosensors in SARS-CoV-2 detection: a bibliometrics-based analysis and review.RSC advances · 2022Review
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The foremost challenge in countering infectious diseases is the shortage of effective therapeutics. The emergence of coronavirus disease (COVID-19) outbreak has posed a great menace to the public health system globally, prompting unprecedented endeavors to contain the virus. Many countries have organized research programs for therapeutics and management development. However, the longstanding process has forced authorities to implement widespread infrastructures for detailed prognostic and diagnostics study of severe acute respiratory syndrome (SARS CoV-2). This review discussed nearly all the globally developed diagnostic methodologies reported for SARS CoV-2 detection. We have highlighted in detail the approaches for evaluating COVID-19 biomarkers along with the most employed nucleic acid- and protein-based detection methodologies and the causes of their severe downfall and rejection. As the variable variants of SARS CoV-2 came into the picture, we captured the breadth of newly integrated digital sensing prototypes comprised of plasmonic and field-effect transistor-based sensors along with commercially available food and drug administration (FDA) approved detection kits. However, more efforts are required to exploit the available resources to manufacture cheap and robust diagnostic methodologies. Likewise, the visualization and characterization tools along with the current challenges associated with waste-water surveillance, food security, contact tracing, and their role during this intense period of the pandemic have also been discussed. We expect that the integrated data will be supportive and aid in the evaluation of sensing technologies not only in current but also future pandemics.
Identifiers
What OpenQuestion holds
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.