ArticleCell journal2023
The Effect of Biomaterials on Human Dental Pulp Stem Cell Neural Differentiation: A Scoping Review.
Article in Cell journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Mitochondrial dynamics and function in neural differentiation: a systematic review.European journal of medical research · 2025Pooled it
- Molecular Mechanisms and Clinical Applications of Neural Regeneration Through Dental Pulp Stem Cells.Stem cells international · 2026Review
- Multi-Organ Microphysiological Systems Targeting Specific Organs for Recapitulating Disease Phenotypes via Organ Crosstalk.Small science · 2024Article
- Application of hydrogel-loaded dental stem cells in the field of tissue regeneration.Human cell · 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
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural cells are the most important components of the nervous system and have the duty of electrical signal transmission. Damage to these cells can lead to neurological disorders. Scientists have discovered different methods, such as stem cell therapy, to heal or regenerate damaged neural cells. Dental stem cells are among the different cells used in this method. This review attempts to evaluate the effect of biomaterials mentioned in the cited papers on differentiation of human dental pulp stem cells (hDPSCs) into neural cells for use in stem cell therapy of neurological disorders. We searched international databases for articles about the effect of biomaterials on neuronal differentiation of hDPSCs. The relevant articles were screened by title, abstract, and full text, followed by selection and data extraction. Totally, we identified 731 articles and chose 18 for inclusion in the study. A total of four studies employed polymeric scaffolds, four assessed chitosan scaffolds (CS), two utilised hydrogel scaffolds, one investigation utilised decellularised extracellular matrix (ECM), and six studies applied the floating sphere technique. hDPSCs could heal nerve damage in regenerative medicine. In the third iteration of nerve conduits, scaffolds, stem cells, regulated growth factor release, and ECM proteins restore major nerve damage. hDPSCs must differentiate into neural cells or neuron-like cells to regenerate nerves. Plastic-adherent cultures, floating dentosphere cultures, CS, polymeric scaffolds, hydrogels, and ECM mimics have been used to differentiate hDPSCs. According to our findings, the floating dentosphere technique and 3D-PLAS are currently the two best techniques since they result in neuroprogenitor cells, which are the starting point of differentiation and they can turn into any desired neural cell.
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.