Evidence map›Paper›PMID 38180277›Full record

ReviewAging cell2024

Microphysiological systems for human aging research.

Seungman Park, Thomas C Laskow, Jingchun Chen, Prasun Guha, Buddhadeb Dawn, Deok-Ho Kim

Abstract readReview
In one paragraph

Review in Aging cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  12. Microphysiological systems to advance human pathophysiology and translational medicine.Journal of applied physiology (Bethesda, Md. : 1985) · 2024
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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

6 authors.

Seungman ParkDepartment of Mechanical Engineering, University of Nevada, Las Vegas, Las Vegas, Nevada, USA.ORCID 0000-0001-7275-8345
Thomas C LaskowDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0003-0173-1588
Jingchun ChenNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, Nevada, USA.ORCID 0000-0001-9408-0117
Prasun GuhaNevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, Nevada, USA.ORCID 0000-0003-1224-7361
Buddhadeb DawnDepartment of Internal Medicine, Kirk Kerkorian School of Medicine, University of Nevada, Las Vegas, Las Vegas, Nevada, USA.
Deok-Ho KimDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-6989-6074

Funding

Microphysiological Model of Human Cardiac Sympathetic InnervationR01HL164936 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI KIM, DEOK-HO, KWON, CHULAN · 2022 to 2025
$3.2M
Engineering Clinical Trials on a Chip for Dystrophin-Deficient Muscular DystrophyUH3TR003271 · NCATS · JOHNS HOPKINS UNIVERSITY · PI KASS, DAVID ALAN, KIM, DEOK-HO · 2022 to 2024
$2.4M
Transcriptomic Entropy to Quantify Maturation of PSC-Derived CardiomyocytesR01HL156947 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI KIM, DEOK-HO, KWON, CHULAN · 2021 to 2024
$2.3M
DISEASE MODELING AND PHENOTYPIC DRUG SCREENING FOR DYSTROPHIC CARDIOMYOPATHYR01HL146436 · NHLBI · UNIVERSITY OF WASHINGTON · PI KIM, DEOK-HO · 2019 to 2022
$2.2M
A Human iPSC-based 3D Microphysiological System for Modeling Cardiac Dysfunction in MicrogravityUH3TR003519 · NCATS · JOHNS HOPKINS UNIVERSITY · PI KIM, DEOK-HO · 2020 to 2022
$2.0M
Convolutional Neural Network for Disease Prediction, Biomarker Discovery, and Validation in Alzheimer's DiseaseR15AG083618 · NIA · UNIVERSITY OF NEVADA LAS VEGAS · PI Jingchun Chen · 2024 to 2026
$751k
Characterization of relation between tissue viscoelasticity and tumor progression in aging tissuesK25AG070286 · NIA · UNIVERSITY OF NEVADA LAS VEGAS · PI PARK, SEUNGMAN · 2021 to 2025
$734k
NCATS NIH HHS UH3 TR003271NCATS NIH HHS UH3 TR003519NHLBI NIH HHS R01 HL146436NHLBI NIH HHS R01 HL156947NHLBI NIH HHS R01 HL164936NIA NIH HHS K25 AG070286NIA NIH HHS R15 AG083618NIH HHS K25AG070286NIH HHS R01HL146436NIH HHS R01HL156947NIH HHS R01HL164936NIH HHS UH3TR003271NIH HHS UH3TR003519
6 · The paper itself

Abstract

Recent advances in microphysiological systems (MPS), also known as organs-on-a-chip (OoC), enable the recapitulation of more complex organ and tissue functions on a smaller scale in vitro. MPS therefore provide the potential to better understand human diseases and physiology. To date, numerous MPS platforms have been developed for various tissues and organs, including the heart, liver, kidney, blood vessels, muscle, and adipose tissue. However, only a few studies have explored using MPS platforms to unravel the effects of aging on human physiology and the pathogenesis of age-related diseases. Age is one of the risk factors for many diseases, and enormous interest has been devoted to aging research. As such, a human MPS aging model could provide a more predictive tool to understand the molecular and cellular mechanisms underlying human aging and age-related diseases. These models can also be used to evaluate preclinical drugs for age-related diseases and translate them into clinical settings. Here, we provide a review on the application of MPS in aging research. First, we offer an overview of the molecular, cellular, and physiological changes with age in several tissues or organs. Next, we discuss previous aging models and the current state of MPS for studying human aging and age-related conditions. Lastly, we address the limitations of current MPS and present future directions on the potential of MPS platforms for human aging research.

Indexed as

Lab-On-A-Chip DevicesMicrophysiological SystemsGeroscienceHumansLiverage-related changesage-related diseasesagingaging phenotypesmicrophysiological systems

Identifiers

PMID38180277
PMCPMC10928588

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.