Evidence map›Paper›PMID 38392027›Full record

ReviewBiosensors2024

Hierarchical Nanobiosensors at the End of the SARS-CoV-2 Pandemic.

Jael Abigail Medrano-Lopez, Isaela Villalpando, Ma Isabel Salazar, Carlos Torres-Torres

Abstract readReview
In one paragraph

Review in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

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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.

Jael Abigail Medrano-LopezSección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
Isaela VillalpandoCentro de Investigación para los Recursos Naturales, Salaices 33941, Mexico.
Ma Isabel SalazarDepartamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, Mexico.
Carlos Torres-TorresSección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico.ORCID 0000-0001-9255-2416

Funding

Consejo Nacional de Humanidades, Ciencias y Tecnologías CF-2023-I-2042Instituto Politécnico Nacional SIP-2023
6 · The paper itself

Abstract

Nanostructures have played a key role in the development of different techniques to attack severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Some applications include masks, vaccines, and biosensors. The latter are of great interest for detecting diseases since some of their features allowed us to find specific markers in secretion samples such as saliva, blood, and even tears. Herein, we highlight how hierarchical nanoparticles integrated into two or more low-dimensional materials present outstanding advantages that are attractive for photonic biosensing using their nanoscale functions. The potential of nanohybrids with their superlative mechanical characteristics together with their optical and optoelectronic properties is discussed. The progress in the scientific research focused on using nanoparticles for biosensing a variety of viruses has become a medical milestone in recent years, and has laid the groundwork for future disease treatments. This perspective analyzes the crucial information about the use of hierarchical nanostructures in biosensing for the prevention, treatment, and mitigation of SARS-CoV-2 effects.

Indexed as

Biosensing TechniquesCOVID-19NanostructuresVirusesHumansPandemicsSARS-CoV-2biosensorsdetection limithierarchical nanostructuresnanoparticlesSARS-CoV-2

Identifiers

PMID38392027
PMCPMC10887370

What OpenQuestion holds

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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.