Evidence map›Paper›PMID 35888544›Full record

ReviewMaterials (Basel, Switzerland)2022

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.

Mohammed S Alqahtani, Mohamed Abbas, Mohammed Abdulmuqeet, Abdullah S Alqahtani, Mohammad Y Alshahrani, Abdullah Alsabaani, Murugan Ramalingam

Open access · goldAbstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Review
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

7 authors at 4 institutions in 5 countries.

Mohammed S AlqahtaniRadiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0001-7425-3578
Mohamed AbbasElectrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0002-3141-2900
Mohammed AbdulmuqeetElectrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia.
Abdullah S AlqahtaniPathology and Clinical Laboratory Medicine Administration (PCLMA), King Fahad Medical City, Riyadh 59046, Saudi Arabia.
Mohammad Y AlshahraniDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0002-7096-0221
Abdullah AlsabaaniDepartment of Family and Community Medicine, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia.
Murugan RamalingamInstitute of Tissue Regeneration Engineering, Department of Nanobiomedical Science, BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea.ORCID 0000-0001-6498-9792
King Khalid University · SAChengdu University · CNKing Fahd Medical City · SAUniversity of Leicester · GB

Funding

King Khalid University 4-N-20/21
6 · The paper itself

Abstract

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

Indexed as

biosensorelectro-chemical sensorelectronic nosegas sensornanoparticlesnon-invasive sensing approachesRT-PCR

Identifiers

PMID35888544
PMCPMC9317545
OpenAlexW4286433241

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.