Evidence map›Paper›PMID 42883854›Full record

ReviewInternational journal of molecular sciences2026

In Vitro Modeling of Sarcopenia-Related Pathology and Exercise-Associated Adaptations: Integration with In Silico Approaches.

Kyung-Wan Baek, Yu Ri Jeong, Ji-Seok Kim, Ah Young Lee

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2026. 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

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

4 authors.

Kyung-Wan BaekResearch Institute of Pharmaceutical Sciences, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0002-8445-3773
Yu Ri JeongDepartment of Food Science and Nutrition, Gyeongsang National University, Jinju 52828, Republic of Korea.
Ji-Seok KimResearch Institute of Pharmaceutical Sciences, Gyeongsang National University, Jinju 52828, Republic of Korea.
Ah Young LeeDepartment of Food Science and Nutrition, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0002-3489-7798

Funding

Gyeongsang National University Glocal University 30 Project in 2025
6 · The paper itself

Abstract

Sarcopenia is a progressive and generalized skeletal muscle disorder characterized primarily by reduced muscle strength, with low muscle quantity or quality confirming the diagnosis and poor physical performance indicating greater severity. Although human studies and animal models remain essential, their use is constrained by ethical considerations, inter-individual variability, species-specific differences, cost, and the difficulty of isolating individual mechanisms within whole-body systems. This review critically evaluates in vitro models that reproduce selected sarcopenia-related pathological phenotypes, experimental approaches for studying exercise-associated adaptations, and in silico methods for translational benchmarking. We compare myogenic cell cultures, three-dimensional engineered muscle tissues, senescence-inducing systems, and microphysiological platforms according to the phenotypes modeled, principal endpoints, strengths, and limitations. We then assess electrical pulse stimulation, mechanical stretch, and pathway-targeted pharmacological probes as reductionist tools for examining contraction-associated, load-sensitive, humoral, and metabolic responses rather than reproducing whole-body exercise. Finally, we discuss how multi-omics profiling, machine learning, deep learning, public transcriptomic datasets, and cell-type-aware analyses can benchmark in vitro signatures against in vivo data while accounting for batch effects, dataset heterogeneity, cell-composition confounding, overfitting, interpretability, and external validation. Integrating experimentally validated findings with clearly identified hypothesis-generating observations may improve model selection, reduce reliance on animal experiments where appropriate, and accelerate the development of interventions to preserve skeletal muscle function during aging.

Indexed as

Adaptation, PhysiologicalExerciseModels, BiologicalSarcopeniaAnimalsComputer SimulationHumansMuscle, Skeletalengineered muscle tissueexercise-associated adaptationsin vitro modelsmachine learningmulti-omicssarcopeniaskeletal muscle

Identifiers

PMID42883854
PMCPMC13665164

What OpenQuestion holds

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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.