Evidence map›Paper›PMID 28256230›Full record

ArticleBiophysical journal2017

Design Rules for Membrane-Embedded Voltage-Sensing Nanoparticles.

Kyoungwon Park, Shimon Weiss

Abstract read
In one paragraph

Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Coherent heteroepitaxial growth of I-III-VINature communications · 2023
    Article
  3. Optoelectronic Neural Interfaces Based on Quantum Dots.ACS applied materials & interfaces · 2022
    Review
  4. Time for NanoNeuro.Nature methods · 2021
    Review
  5. Review
  6. Electrical Control ofBioelectricity · 2020
    Article
  7. Article
  8. Article
  9. Review
  10. 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

2 authors.

Kyoungwon ParkDepartment of Electrical Engineering, University of California Los Angeles, Los Angeles, California.
Shimon WeissDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California; Department of Physiology, University of California Los Angeles, Los Angeles, California; California NanoSystems Institute, University of California Los Angeles, Los Angeles, California. Electronic address: sweiss@chem.ucla.edu.

Funding

Multimodal Qdot Probes for Bioimaging of Cells and Tumors in Small AnimalsR01EB000312 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WEISS, SHIMON · 2002 to 2010
$7.5M
NIBIB NIH HHS R01 EB000312
6 · The paper itself

Abstract

Voltage-sensing dyes and voltage-sensing fluorescence proteins have been continually improved and as a result have provided a wealth of insights into neuronal circuits. Further improvements in voltage-sensing dyes and voltage-sensing fluorescence proteins are needed, however, for routine detection of single action potentials across a large number of individual neurons in a large field-of-view of a live mammalian brain. On the other hand, recent experiments and calculations suggest that semiconducting nanoparticles could act as efficient voltage sensors, suitable for the above-mentioned task. This study presents quantum mechanical calculations, including Auger recombination rates, of the quantum-confined Stark effect in membrane-embedded semiconducting nanoparticles, examines their possible utility as membrane voltage sensors, and provide design rules for their structure and composition.

Indexed as

Electrophysiological PhenomenaNanoparticlesCell MembraneFeasibility StudiesLuminescent ProteinsQuantum TheoryLuminescent Proteins

Identifiers

PMID28256230
PMCPMC5340176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.