Evidence map›Paper›PMID 37131405›Full record

ArticleTalanta open2023

Carbon-based biosensors: Next-generation diagnostic tool for target-specific detection of SARS-CoV-2 (COVID-19).

Shivam Mishra, Bari Aamna, Sagarika Parida, Aritra Kumar Dan

Open access · goldAbstract read
In one paragraph

Article in Talanta open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Shivam MishraSchool of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India.
Bari AamnaSchool of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India.
Sagarika ParidaDepartment of Botany, School of Applied Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, 752050, India.
Aritra Kumar DanSchool of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India.
KIIT University · INCenturion University of Technology and Management · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was declared a global pandemic in 2020. Having rapidly spread around the globe, with the emergence of new variants, there is a crucial need to develop diagnostic kits for its rapid detection. Since it validated accuracy and reliability, the reverse transcription polymerase chain reaction (RT-PCR) test has been declared the gold standard for disease detection. However, despite its reliability, the requirement of specialized facilities, reagents, and duration of a PCR run limits its usage for rapid detection. There is thus a continuous increase in the design and development of rapid, point-of-care (PoC), and cost-effective diagnostic kits. In this review, we discuss the potential of carbon-based biosensors for target-specific detection of coronavirus disease 19 (COVID-19) and present an overview of investigation within the timeframe of the last four years (2019-2022), which have developed novel platforms using carbon nanomaterial-based approaches for viral detection. The approaches discussed offer rapid, accurate, and cost-effective strategies for COVID-19 detection for healthcare personnel and research workers.

Indexed as

Carbon biosensorNano-biomaterialsPoint-of-care testing (POCT)SARS-CoV-2Spike proteins

Identifiers

PMID37131405
PMCPMC10125215
OpenAlexW4366813950

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.