ArticleBMC musculoskeletal disorders2025
The cartilage-generated bioelectric potentials induced by dynamic joint movement; an exploratory study.
Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- The Effect of Physical, Biochemical, and Electrical Culture Conditions on Articular Chondrocytes Used for Cartilage Tissue Engineering: A Narrative Review.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundExcessive loading can damage knee cartilage, making it essential to assess and measure joint load effectively. Despite its importance, real-time monitoring of cartilage load in clinical settings remains challenging due to significant technical constraints. Electroarthrography, a recently introduced non-invasive technique, offers a promising solution by detecting load-generated potentials in joint cartilage through surface electrodes. While previous studies have primarily focused on static load applications, such as standing weight shift task or simple isometric contraction, our study explores its potential in dynamic loading scenarios.
methodsWe analyzed data from 20 knees in 20 subjects, using eight surface electrodes placed around each knee to capture electrical signals during three activities: active knee extension in a seated position, passive range of motion exercise in a decubitus position, and restricted squats. The recorded signals were processed into potential-time graphs, decomposed according to movement states, and analyzed through a deep neural network.
resultsThe results showed that cartilage-generated potentials were significantly higher during active extension compared to passive extension (1.62 mV vs. 0.87 mV; p < 0.05), with the deep neural network achieving an average classification accuracy of 98.77%.
conclusionThese findings highlight the feasibility of measuring and classifying cartilage-generated potentials during dynamic physical activities, providing valuable insights into load-related differences. This approach establishes a solid foundation for applications in rehabilitation medicine by facilitating the determination of appropriate exercise intensities, assessing risks associated with daily activities, and classifying physical activities. Further studies focusing on diverse biomechanical conditions will enhance its clinical utility.
Indexed as
Identifiers
What OpenQuestion holds
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.