ReviewInternational journal of molecular sciences2026
In Vitro Modeling of Sarcopenia-Related Pathology and Exercise-Associated Adaptations: Integration with In Silico Approaches.
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.
What it found
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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.
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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.
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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.