Evidence map›Paper›PMID 37771321›Full record

ArticleHardwareX2023

A custom-built planar biaxial system for soft tissue material testing.

Salvatore Pasta, Chiara Catalano, Fabrizio Crascì, Roberta Scuoppo

Abstract read
In one paragraph

Article in HardwareX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Salvatore PastaDepartment of Engineering, Viale delle Scienze, Università degli Studi di Palermo, Palermo, Italy.
Chiara CatalanoDepartment of Engineering, Viale delle Scienze, Università degli Studi di Palermo, Palermo, Italy.
Fabrizio CrascìDepartment of Engineering, Viale delle Scienze, Università degli Studi di Palermo, Palermo, Italy.
Roberta ScuoppoDepartment of Engineering, Viale delle Scienze, Università degli Studi di Palermo, Palermo, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate material characterization of soft tissues is crucial for understanding the physiopathology of cardiovascular diseases. However, commercial biaxial testing systems are expensive, prompting the need for affordable custom solutions. This study aimed to develop a low-cost custom biaxial system capable of accurately characterizing the mechanical behavior of soft tissues. The biaxial system was constructed using 3D printing technology and non-captive linear actuators for precise displacement control. A real-time marker tracking system was implemented to estimate dis-placements without the need for costly hardware. The system's performance was evaluated through tests on a calibration spring and frozen porcine aorta samples. The linear actuators demonstrated excellent response to user position input after motor tuning, showing no discrepancies between commands and actual positions. The experimental testing of the calibration spring showed good agreement with the analytical solution, validating the system's ability to accurately test materials. Testing on porcine aorta samples revealed stress-strain responses consistent with existing literature, accounting for potential variations due to tissue preservation and regional material property heterogeneity. Overall, this custom biaxial system demonstrates promising performance in accurately assessing the mechanical behavior of soft tissues, providing researchers with a valuable tool for cardiovascular disease research and tissue engineering applications.

Indexed as

3D printingBiaxial systemBiomechanicsMaterial testingSoft tissue

Identifiers

PMID37771321
PMCPMC10523007

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.