Evidence map›Paper›PMID 36213931›Full record

ArticleFrontiers in psychiatry2022

Fast identification and quantification of c-Fos protein using you-only-look-once-v5.

Na Pang, Zihao Liu, Zhengrong Lin, Xiaoyan Chen, Xiufang Liu, Min Pan, Keke Shi, Yang Xiao, Lisheng Xu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. 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
0.3field-weighted citation impact, top 48% 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, 3 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

9 authors at 4 institutions in 1 country.

Na PangThe College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Zihao LiuShenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China.
Zhengrong LinInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Xiaoyan ChenInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Xiufang LiuInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Min PanShenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China.
Keke ShiInstitute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Yang XiaoNational Innovation Center for Advanced Medical Devices, Shenzhen, China.
Lisheng XuThe College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Chinese Academy of Sciences · CNGuangzhou University of Chinese Medicine · CNNortheastern University · CNShenzhen Institutes of Advanced Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s,

Indexed as

c-Fosdeep learningneuron activityneurosciencequantitative statistics

Identifiers

PMID36213931
PMCPMC9537349
OpenAlexW4296830330

What OpenQuestion holds

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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.