Evidence map›Paper›PMID 40718504›Full record

ReviewJournal of tissue engineering

Yuannan Kang, Deepak M Kalaskar, Darren J Player

Abstract readReview
In one paragraph

Review in Journal of tissue engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Yuannan KangCentre for 3D Models of Health and Disease, Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, UK.
Deepak M KalaskarUCL Division of Surgery and Interventional Science, Royal Free Hospital Campus, University College London, UK.ORCID https://orcid.org/0000-0002-0576-1816
Darren J PlayerCentre for 3D Models of Health and Disease, Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, UK.ORCID https://orcid.org/0000-0003-2867-5416

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Muscle spindles are key proprioceptive mechanoreceptors composed of intrafusal fibres that regulate kinaesthetic sensations and reflex actions. Traumatic injuries and neuromuscular diseases can severely impair the proprioceptive feedback, yet the regenerative potential and cell-matrix interactions of muscle spindles remain poorly understood. There is a pressing need for robust tissue-engineered models to study spindle development, function and regeneration. Traditional approaches, while insightful, often lack physiological relevance and scalability. Three-dimensional (3D) bioprinting offers a promising approach to fabricate biomimetic, scalable, and animal-free muscle spindle constructs with controlled cellular architecture. Various bioprinting techniques - including inkjet, extrusion, digital light projection and laser-assisted bioprinting - have been explored for skeletal muscle fabrication, but replicating intrafusal fibre complexity remains a challenge. A major challenge lies in bioink development, where biocompatibility, printability and mechanical strength must be balanced to support intrafusal fibre differentiation and proprioceptive function. Recent molecular insights into spindle anatomy, innervation and extracellular matrix composition are shaping biofabrication strategies. This review discusses the current state of muscle spindle modelling, the application of 3D bioprinting in intrafusal fibre engineering, key challenges and future directions.

Indexed as

bioinkbioprintinghydrogelintrafusal fibremuscle spindleproprioceptionskeletal musclethree-dimensional (3D) bioprintingtissue engineering

Identifiers

PMID40718504
PMCPMC12290365

What OpenQuestion holds

Textmetadata
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.