Evidence map›Paper›PMID 38383589›Full record

ArticleeNeuro2024

Biophysical Modeling of Actin-Mediated Structural Plasticity Reveals Mechanical Adaptation in Dendritic Spines.

Mayte Bonilla-Quintana, Padmini Rangamani

Open access · goldAbstract read
In one paragraph

Article in eNeuro, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Mayte Bonilla-QuintanaDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California 92093.
Padmini RangamaniDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California 92093 prangamani@ucsd.edu.ORCID https://orcid.org/0000-0001-5953-4347
University of California San Diego · US

Funding

Modeling and analysis of the mechanochemical processes that govern clathrin-mediated endocytosisR01GM132106 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI RANGAMANI, PADMINI · 2020 to 2024
$1.5M
NIGMS NIH HHS R01 GM132106
6 · The paper itself

Abstract

Synaptic plasticity is important for learning and memory formation; it describes the strengthening or weakening of connections between synapses. The postsynaptic part of excitatory synapses resides in dendritic spines, which are small protrusions on the dendrites. One of the key features of synaptic plasticity is its correlation with the size of these spines. A long-lasting synaptic strength increase [long-term potentiation (LTP)] is only possible through the reconfiguration of the actin spine cytoskeleton. Here, we develop an experimentally informed three-dimensional computational model in a moving boundary framework to investigate this reconfiguration. Our model describes the reactions between actin and actin-binding proteins leading to the cytoskeleton remodeling and their effect on the spine membrane shape to examine the spine enlargement upon LTP. Moreover, we find that the incorporation of perisynaptic elements enhances spine enlargement upon LTP, exhibiting the importance of accounting for these elements when studying structural LTP. Our model shows adaptation to repeated stimuli resulting from the interactions between spine proteins and mechanical forces.

Indexed as

ActinsDendritic SpinesActin CytoskeletonLong-Term PotentiationNeuronal PlasticitySynapsesActins3D modelactindendritic spinessLTPviscoelastic

Identifiers

PMID38383589
PMCPMC10928477
OpenAlexW4391992433

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.