Evidence map›Paper›PMID 37056470›Full record

ArticlePNAS nexus2023

Mosquito bite prevention through self-assembled cellulose nanocrystals.

Daniel Voignac, Evyatar Sar-Shalom, Yossi Paltiel, Oded Shoseyov, Jonathan Bohbot

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2023. 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
0.2field-weighted citation impact, top 43% 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, 1 citations in OpenAlex.

  1. 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

5 authors at 1 institution in 1 country.

Daniel VoignacThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.ORCID https://orcid.org/0000-0002-0017-0304
Evyatar Sar-ShalomThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.
Yossi PaltielDepartment of Applied Physics and Center for Nanoscience and Nanotechnology, The Hebrew University, Jerusalem 9190401, Israel.
Oded ShoseyovThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.
Jonathan BohbotThe Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel.ORCID https://orcid.org/0000-0001-5204-6340
Hebrew University of Jerusalem · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mosquitoes are the deadliest of all combined insects and animals affecting millions and killing hundreds or thousands of people each year. Existing protection methods however are limited and include volatile compounds that actively repel mosquitoes such as N,N-Diethyl-meta-toluamide (DEET) or different essential oils such as geraniol and citronella. Most are odorous compounds and require organic solvents for dispersion. This work investigates the barrier properties of cellulose nanocrystals (CNCs). CNCs are known to self-assemble in strong, transparent, chemical barrier films. They are fully bio-based, and their surface chemistry is ideal for aqueous dispersion of many compounds. This work saw a significant 80% decrease in feeding on human skin when a thin CNC coat was applied. The effect was further confirmed by artificial feeding on

Indexed as

bite preventionmosquitoesnanocelluloseself-assembly

Identifiers

PMID37056470
PMCPMC10089072
OpenAlexW4364380391

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.