Evidence map›Paper›PMID 39554050›Full record

ArticlebioRxiv : the preprint server for biology2024

Streamlining Asymmetry Quantification in Fetal Mouse Imaging: A Semi-Automated Pipeline Supported by Expert Guidance.

S M Rolfe, D Mao, A M Maga

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

S M RolfeCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.ORCID 0000-0001-9514-7774
D MaoDepartment of Pediatrics, University of Washington, Seattle, WA, USA.
A M MagaCenter for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.ORCID 0000-0002-7921-9018

Funding

Utilization of Advanced Technologies for the Understanding of Human Structural Birth DefectsP01HD104435 · NICHD · SEATTLE CHILDREN'S HOSPITAL · PI BEIER, DAVID R. · 2021 to 2025
$8.4M
Deep Phenotyping of 3D Data for Candidate Gene Selection from Kids First StudiesR03OD032627 · OD · SEATTLE CHILDREN'S HOSPITAL · PI MAGA, ALI MURAT · 2021 to 2021
$330k
NICHD NIH HHS P01 HD104435NIH HHS R03 OD032627
6 · The paper itself

Abstract

Asymmetry is a key feature of numerous developmental disorders and in phenotypic screens is often used as a readout for environmental or genetic perturbations to normal development. A better understanding of the genetic basis of asymmetry and its relationship to disease susceptibility will help unravel the complex genetic and environmental factors and their interactions that increase risk in a range of developmental disorders. Large-scale imaging datasets offer opportunities to work with sample sizes needed to detect and quantify differences in morphology beyond severe deformities while also posing challenges to manual phenotyping protocols. In this work, we introduce a semi-automated open-source workflow to quantify abnormal asymmetry of craniofacial structures that integrates expert anatomical knowledge. We apply this workflow to explore the role of genes contributing to abnormal asymmetry by deep phenotyping 3D fetal microCT images from knockout strains acquired as part of the Knockout Mouse Phenotyping Program (KOMP2). Four knockout strains:

Indexed as

AsymmetryAutomatedDevelopmental InstabilityMicro-CTMouse

Identifiers

PMID39554050
PMCPMC11565955

What OpenQuestion holds

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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.