Evidence map›Paper›PMID 31115864›Full record

ReviewBiophysical reviews2019

Physical parameters describing neuronal cargo transport by kinesin UNC-104.

Kumiko Hayashi, Shiori Matsumoto, Miki G Miyamoto, Shinsuke Niwa

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Enhanced Single-Particle Upconversion Imaging via Energy Migration Boosting.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
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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.

Kumiko HayashiDepartment of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai, Japan. kumiko@camp.apph.tohoku.ac.jp.ORCID http://orcid.org/0000-0002-2895-8945
Shiori MatsumotoDepartment of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Miki G MiyamotoDepartment of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Shinsuke NiwaFrontier Research Institute for Interdisciplinary Sciences (FRIS) and Graduate School of Life Sciences, Tohoku University, Sendai, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this review, we focus on the kinesin-3 family molecular motor protein UNC-104 and its regulatory protein ARL-8. UNC-104, originally identified in Caenorhabditis elegans (C. elegans), has a primary role transporting synaptic vesicle precursors (SVPs). Although in vitro single-molecule experiments have been performed to primarily investigate the kinesin motor domain, these have not addressed the in vivo reality of the existence of regulatory proteins, such as ARL-8, that control kinesin attachment to/detachment from cargo vesicles, which is essential to the overall transport efficiency of cargo vesicles. To quantitatively understand the role of the regulatory protein, we review the in vivo physical parameters of UNC-104-mediated SVP transport, including force, velocity, run length and run time, derived from wild-type and arl-8-deletion mutant C. elegans. Our future aim is to facilitate the construction of a consensus physical model to connect SVP transport with pathologies related to deficient synapse construction caused by the deficient UNC-104 regulation. We hope that the physical parameters of SVP transport summarized in this review become a useful guide for the development of such model.

Indexed as

Cellular cargo transportKinesinMotor proteinsNeuronal disease

Identifiers

PMID31115864
PMCPMC6558098

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.