Evidence map›Paper›PMID 33654846›Full record

ArticleBio-protocol2019

A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning.

Shinji Urata, Tadatsune Iida, Yuri Suzuki, Shiou-Yuh Lin, Yu Mizushima, Chisato Fujimoto, Yu Matsumoto, Tatsuya Yamasoba

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

Article in Bio-protocol, 2019. 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
0.1field-weighted citation impact, top 53% 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, 4 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

8 authors at 1 institution in 1 country.

Shinji UrataDepartment of Cellular Neurobiology, University of Tokyo, Tokyo, Japan.
Tadatsune IidaDepartment of Cellular Neurobiology, University of Tokyo, Tokyo, Japan.
Yuri SuzukiDepartment of Cellular Neurobiology, University of Tokyo, Tokyo, Japan.
Shiou-Yuh LinDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Yu MizushimaDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Chisato FujimotoDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Yu MatsumotoDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Tatsuya YamasobaDepartment of Otolaryngology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
The University of Tokyo · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here, we describe a sorbitol-based optical clearing method, called modified Sca/eS that can be used to image all hair cells (HCs) in the mouse cochlea. This modification of Sca/eS is defined by three steps: decalcification, de-lipidation, and refractive index matching, which can all be completed within 72 h. Furthermore, we established automated analysis programs that perform machine learning-based pattern recognition. These programs generate 1) a linearized image of HCs, 2) the coordinates of HCs, 3) a holocochleogram, and 4) clusters of HC loss. In summary, a novel approach that integrates modified Sca/eS and programs based on machine learning facilitates quantitative and comprehensive analysis of the physiological and pathological properties of all HCs.

Indexed as

Auto hair cell analysisCochleaMachine learningOptical tissue clearingSca/eS

Identifiers

PMID33654846
PMCPMC7854069
OpenAlexW2969790415

What OpenQuestion holds

Textmetadata
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.