Evidence map›Paper›PMID 34150335›Full record

ArticleMRS advances2021

Surface adhesion of viruses and bacteria: Defend only and/or

Manoj Kolel-Veetil, Ayusman Sen, Markus J Buehler

Open access · bronzeAbstract read
In one paragraph

Article in MRS advances, 2021. 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
0.7field-weighted citation impact, top 29% 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, 4 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

3 authors at 3 institutions in 1 country.

Manoj Kolel-VeetilChemistry Division, US Naval Research Laboratory, Washington, DC 20375 USA.
Ayusman SenDepartments of Chemistry and Chemical Engineering, Pennsylvania State University, University Park, PA 16802 USA.
Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA USA.
Massachusetts Institute of Technology · USPennsylvania State University · USUnited States Naval Research Laboratory · US

Funding

Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020
$5.2M
NIBIB NIH HHS U01 EB014976
6 · The paper itself

Abstract

Coronaviruses COVID-19, SARS-CoV and NL63 use spikes in their corona to bind to angiotensin converting enzyme 2 (ACE2) sites on cytoskeletal membranes of host cells to deliver their viral payload. While groups such as disulfides in ACE2's zinc metallopeptidase, and also in COVID-19's spikes, facilitate such binding, it is worth exploring how similar complementary sites on materials such as polymers, metals, ceramics, fabrics, and biomaterials promote binding of viruses and bacteria and how they could be further engineered to prevent bioactivity, or to act as agents to collect viral payloads in filters or similar devices. In that vein, this article offers a perspective on novel tools and approaches for chemically and topologically modifying most utilitarian surfaces via defensive topological vibrational engineering to either prevent such adhesion or to enhance adhesion and elicit vibrational characteristics/'musical signatures' from the surfaces so that the structure of the binding sites of viruses and bacteria is permanently altered and/or their cellular machinery is permanently disabled by targeted chemical transformations. Graphic abstract: Supplementary Information: The online version contains supplementary material available at 10.1557/s43580-021-00079-0.

Identifiers

PMID34150335
PMCPMC8204927
OpenAlexW3169380854

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.