Evidence map›Paper›PMID 28559787›Full record

ArticleFrontiers in neuroscience2017

A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability.

Kaihua Zhu, Liming Li, Xuyong Wei, Xiaohong Sui

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. The interplay between pulse width and activation depth in TENS: a computational study.Frontiers in pain research (Lausanne, Switzerland) · 2025
    Article
  5. Article
  6. Review
  7. High-Resolution Multi-Scale Computational Model for Non-Invasive Cervical Vagus Nerve Stimulation.Neuromodulation : journal of the International Neuromodulation Society · 2018
    Review
  8. Article
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.

Kaihua ZhuSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China.
Liming LiSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China.
Xuyong WeiSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China.
Xiaohong SuiSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tactile sensory feedback plays an important role in our daily life. Transcutaneous electrical nerve stimulation (TENS) is widely accepted to produce artificial tactile sensation. To explore the underlying mechanism of tactile sensation under TENS, this paper presented a novel 3D TENS computational model including an active Aβ tactile nerve fiber (TNF) model and a forearm finite element model with the fine-layered skin structure. The conduction velocity vs. fiber diameter and strength-duration relationships in this combined TENS model matched well with experimental data. Based on this validated TENS model, threshold current variation were further investigated under different stimulating electrode sizes with varied fiber diameters. The computational results showed that the threshold current intensity increased with electrode size, and larger nerve fibers were recruited at lower current intensities. These results were comparable to our psychophysical experimental data from six healthy subjects. This novel 3D TENS model would further guide the floorplan of the surface electrodes, and the stimulating paradigms for tactile sensory feedback.

Indexed as

3D computational modelingAβ tactile nerve fiberdouble-cable modelpsychophysical experimentstactile sensory feedbacktranscutaneous electrical nerve stimulation

Identifiers

PMID28559787
PMCPMC5432565

What OpenQuestion holds

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LicenceCC BY
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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.