ArticleBioelectricity2020
Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.
Article in Bioelectricity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 12 citations in OpenAlex.
- Personalized and genetically engineered animal models for next-generation surgical implant validation.Annals of medicine and surgery (2012) · 2025Review
- Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology.Micromachines · 2025Review
- Bioelectronic Therapeutics: A Revolutionary Medical Practice in Health Care.Bioelectricity · 2025Review
- Insights into Advances and Applications of Biomaterials for Nerve Tissue Injuries and Neurodegenerative Disorders.Macromolecular bioscience · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to address conditions with unmet needs, including heart failure, hypertension, inflammation, arthritis, asthma, Alzheimer's disease, and diabetes. Neural therapies, which target the brain, spinal cord, or peripheral nerves, are already being applied to conditions such as epilepsy, Parkinson's, and chronic pain. While today's therapies have made exciting advancements, their open-loop design-where stimulation is administered without collecting feedback-means that results can be variable and devices do not work for everyone. Stimulation effects are sensitive to changes in neural tissue, nerve excitability, patient position, and more. Closing the loop by providing neural or non-neural biomarkers to the system can guide therapy by providing additional insights into stimulation effects and overall patient condition. Devices currently on the market use recorded biomarkers to close the loop and improve therapy. The future of bioelectric medicine is more holistically personalized. Collected data will be used for increasingly precise application of neural stimulations to achieve therapeutic effects. To achieve this future, advances are needed in device design, implanted and computational technologies, and scientific/medical interpretation of neural activity. Research and commercial devices are enabling the development of multiple levels of responsiveness to neural, physiological, and environmental changes. This includes developing suitable implanted technologies for high bandwidth brain/machine interfaces and addressing the challenge of neural or state biomarker decoding. Consistent progress is being made in these challenges toward the long-term vision of automatically and holistically personalized care for chronic health conditions.
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.