ReviewAdvanced healthcare materials2024
In Vitro Models for Peripheral Nerve Regeneration.
Review in Advanced healthcare materials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Decellularized matrix grafts and peripheral nerve regeneration.Neural regeneration research · 2026Article
- Select NSAIDs enhance peripheral nerve growth and calcium signaling through PPARγ activation.Molecular and cellular neurosciences · 2026Article
- Nerve tissue model on a micropatterned surface: Axon guidance and neural regeneration.Journal of materials science. Materials in medicine · 2025Article
- Multidimensional advances in neural interface technology for peripheral nerve repair: From material innovation to clinical translation.Materials today. Bio · 2025Review
- Mesenchymal stem cell-based therapy for peripheral nerve injuries: A promise or reality?World journal of stem cells · 2025Review
- Differential Effects of Calcium Alginate and Carboxymethylcellulose Wound Dressing Extracts on Human Sensory Neuron Regeneration and Secretome.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyArticle
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
Peripheral nerve injury (PNI) resulting in lesions is highly prevalent clinically, but current therapeutic approaches fail to provide satisfactory outcomes in many patients. While peripheral nerves have intrinsic regenerative capacity, the regenerative capabilities of peripheral nerves are often insufficient to restore full functionality. This highlights an unmet need for developing more effective strategies to repair damaged peripheral nerves and improve regenerative success. Consequently, researchers are actively exploring a variety of therapeutic strategies, encompassing the local delivery of trophic factors or bioactive molecules, the design of advanced biomaterials that interact with regenerating axons, and augmentation with nerve guidance conduits or complex prostheses. However, clinical translation of these technologies remains limited, emphasizing the need for continued research on peripheral nerve regeneration modalities that can enhance functional restoration. Experimental models that accurately recapitulate key aspects of peripheral nerve injury and repair biology can accelerate therapeutic development by enabling systematic testing of new techniques. Advancing regenerative therapies for PNI requires bridging the gap between basic science discoveries and clinical application. This review discusses different in vitro models of peripheral nerve injury and repair, including their advantages, limitations, and potential applications.
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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.