Evidence map›Paper›PMID 38673819›Full record

ReviewInternational journal of molecular sciences2024

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

Mikhail Paveliev, Anton A Egorchev, Foat Musin, Nikita Lipachev, Anastasiia Melnikova, Rustem M Gimadutdinov, Aidar R Kashipov, Dmitry Molotkov, Dmitry E Chickrin, Albert V Aganov

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

10 authors at 2 institutions in 2 countries.

Mikhail PavelievNeuroscience Center, University of Helsinki, Haartmaninkatu 8, 00290 Helsinki, Finland.ORCID 0000-0002-2905-2272
Anton A EgorchevInstitute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia.
Foat MusinInstitute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia.
Nikita LipachevInstitute of Physics, Kazan Federal University, Kremlyovskaya 16a, Kazan 420008, Tatarstan, Russia.ORCID 0000-0002-5145-7532
Anastasiia MelnikovaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Karl Marx 74, Kazan 420015, Tatarstan, Russia.ORCID 0000-0001-7686-1075
Rustem M GimadutdinovInstitute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia.
Aidar R KashipovInstitute of Artificial Intelligence, Robotics and Systems Engineering, Kazan Federal University, Kremlyovskaya 18, Kazan 420008, Tatarstan, Russia.
Dmitry MolotkovBiomedicum Imaging Unit, University of Helsinki, Haartmaninkatu 8, 00014 Helsinki, Finland.
Dmitry E ChickrinInstitute of Artificial Intelligence, Robotics and Systems Engineering, Kazan Federal University, Kremlyovskaya 18, Kazan 420008, Tatarstan, Russia.
Albert V AganovInstitute of Physics, Kazan Federal University, Kremlyovskaya 16a, Kazan 420008, Tatarstan, Russia.
Kazan Federal University · RUUniversity of Helsinki · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer's disease, stroke, post-traumatic conditions, and some other brain disorders. The functional role of the PNN microstructure in brain pathologies has remained largely unstudied until recently. Here, we review recent research implicating PNN microstructural changes in schizophrenia and other disorders. We further concentrate on high-resolution studies of the PNN mesh units surrounding synaptic boutons to elucidate fine structural details behind the mutual functional regulation between the ECM and the synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain's complexity through a wide range of organization levels-from synaptic molecular events to large scale tissue rearrangements and the whole-brain connectome function. This scope matches exactly the complex role of PNN in brain physiology and pathology processes, and the first AI-assisted PNN microscopy studies have been reported. To that end, we report here on a machine learning-assisted tool for PNN mesh contour tracing.

Indexed as

Artificial IntelligenceBrainAnimalsBrain DiseasesExtracellular MatrixHumansMicroscopyNerve NetNeuronsSynapsesantidepressantartificial intelligencebrain plasticityepilepsyextracellular matrixmachine learningperineuronal netschizophreniasynapse

Identifiers

PMID38673819
PMCPMC11049984
OpenAlexW4394717363

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.