Evidence map›Paper›PMID 37379617›Full record

ArticleComputers in biology and medicine2023

Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients.

Rana Raza Mehdi, Mohit Kumar, Emilio A Mendiola, Sakthivel Sadayappan, Reza Avazmohammadi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. In-silico heart model phantom to validate cardiac strain imaging.bioRxiv : the preprint server for biology · 2024
    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

5 authors.

Rana Raza MehdiDepartment of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.
Mohit KumarHeart, Lung, and Vascular Institute, Division of Cardiovascular Health and Disease, Department of Internal Medicine, Cincinnati, OH, USA; Department of Pharmacology and Systems Physiology, University of Cincinnati, Cincinnati, OH, USA.
Emilio A MendiolaDepartment of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.
Sakthivel SadayappanHeart, Lung, and Vascular Institute, Division of Cardiovascular Health and Disease, Department of Internal Medicine, Cincinnati, OH, USA; Department of Pharmacology and Systems Physiology, University of Cincinnati, Cincinnati, OH, USA.
Reza AvazmohammadiDepartment of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA; J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA. Electronic address: rezaavaz@tamu.edu.

Funding

The effect of systemic hypertension on prognosis of myocardial infarction: Understanding, prediction and therapy evaluationR00HL138288 · NHLBI · TEXAS ENGINEERING EXPERIMENT STATION · PI AVAZMOHAMMADI, REZA · 2020 to 2022
$727k
NHLBI NIH HHS R00 HL138288
6 · The paper itself

Abstract

Impaired relaxation of cardiomyocytes leads to diastolic dysfunction in the left ventricle. Relaxation velocity is regulated in part by intracellular calcium (Ca

Indexed as

CalciumSarcomeresAnimalsMachine LearningMiceMyocardial ContractionMyocytes, CardiacCalciumCalcium kineticsLayer wise relevance propagationMachine learningNeural networkRelaxation impairmentSarcomere length transientSoft voting classifier

Identifiers

PMID37379617
PMCPMC10525035

What OpenQuestion holds

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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.