The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Successful Differentiation of Mouse Neural Stem Cells on Layer-by-Layer Assembled Single-Walled Carbon Nanotube Composite
Abstract
The same properties that made carbon nanotube (CNT) composites interesting for electronics, sensing, and ultrastrong structural materials also make them an asset for biomedical engineering. The combination of electron conductivity, corrosion resistance, and strength are essential for neuroprosthetic devices. All of the studies in this area demonstrating cellular adhesion and signal transduction activity on CNT matrixes were conducted, so far, with terminally differentiated primary cells and cancerous cell lines. Neural stem cells are very plastic neural precursors capable of adapting to environmental conditions and recreating signal transduction pathways. Their intrinsic biological functionality not only makes the transition to stem cell cultures a difficult-to-avoid step but also implies several fundamentally important challenges. Here we demonstrate that mouse embryonic neural stem cells (NSCs) from the cortex can be successfully differentiated to neurons, astrocytes, and oligodendrocytes with clear formation of neurites on layer-by-layer (LBL) assembled single-walled carbon nanotube (SWNT)-polyelectrolyte multilayer thin films. Biocompatibility, neurite outgrowth, and expression of neural markers were similar to those differentiated on poly-L-ornithine (PLO), one of the most widely used growth substratums for neural stem cells.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Advances in the science and technology of carbon nanotubes and their composites: a review
Thostenson, E. T., Ren, Z., & Chou, T.-W. (2001) · Composites Science and Technology · doi:10.1016/s0266-3538(01)00094-x · Cited by 5,005
Marrow Stromal Cells as Stem Cells for Nonhematopoietic Tissues
Prockop, D. J. (1997) · Science · doi:10.1126/science.276.5309.71 · Cited by 4,629
Reynolds, B. A., & Weiss, S. (1992) · Science · doi:10.1126/science.1553558 · Cited by 4,482
In Vivo Imaging of Quantum Dots Encapsulated in Phospholipid Micelles
Dubertret, B., Skourides, P., Norris, D. J., Noireaux, V., Brivanlou, A. H., & Libchaber, A. (2002) · Science · doi:10.1126/science.1077194 · Cited by 3,019
Selective Differentiation of Neural Progenitor Cells by High-Epitope Density Nanofibers
Silva, G. A., Czeisler, C., Niece, K. L., Beniash, E., Harrington, D. A., Kessler, J. A., & Stupp, S. I. (2004) · Science · doi:10.1126/science.1093783 · Cited by 2,169
Beyond molecules: Self-assembly of mesoscopic and macroscopic components
Whitesides, G. M., & Boncheva, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082065899 · Cited by 1,616
Guest-Dependent Spin Crossover in a Nanoporous Molecular Framework Material
Halder, G. J., Kepert, C. J., Moubaraki, B., Murray, K. S., & Cashion, J. D. (2002) · Science · doi:10.1126/science.1075948 · Cited by 1,569
Chemistry of Single-Walled Carbon Nanotubes
Niyogi, S., Hamon, M. A., Hu, H., Zhao, B., Bhowmik, P., Sen, R., Itkis, M. E., & Haddon, R. C. (2002) · Accounts of Chemical Research · doi:10.1021/ar010155r · Cited by 1,556
Big returns from small fibers: A review of polymer/carbon nanotube composites
Breuer, O., & Sundararaj, U. (2004) · Polymer Composites · doi:10.1002/pc.20058 · Cited by 1,246
Sommer, I., & Schachner, M. (1981) · Developmental Biology · doi:10.1016/0012-1606(81)90477-2 · Cited by 1,134
Patolsky, F., Timko, B. P., Yu, G., Fang, Y., Greytak, A. B., Zheng, G., & Lieber, C. M. (2006) · Science · doi:10.1126/science.1128640 · Cited by 832
The therapeutic potential of neural stem cells
Martino, G., & Pluchino, S. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1908 · Cited by 744
A water-soluble tetrazolium salt useful for colorimetric cell viability assay
Tominaga, H., Ishiyama, M., Ohseto, F., Sasamoto, K., Hamamoto, T., Suzuki, K., & Watanabe, M. (1999) · Analytical Communications · doi:10.1039/a809656b · Cited by 716
Carbon Nanotube Substrates Boost Neuronal Electrical Signaling
Lovat, V., Pantarotto, D., Lagostena, L., Cacciari, B., Grandolfo, M., Righi, M., Spalluto, G., Prato, M., & Ballerini, L. (2005) · Nano Letters · doi:10.1021/nl050637m · Cited by 648
Molecular Functionalization of Carbon Nanotubes and Use as Substrates for Neuronal Growth
Mattson, M. P., Haddon, R. C., & Rao, A. M. (2000) · Journal of Molecular Neuroscience · doi:10.1385/jmn:14:3:175 · Cited by 636
Neuroscience and education: from research to practice?
Goswami, U. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1907 · Cited by 623
Zimmerman, L., Lendahl, U., Cunningham, M., McKay, R., Parr, B., Gavin, B., Mann, J., Vassileva, G., & McMahon, A. (1994) · Neuron · doi:10.1016/0896-6273(94)90148-1 · Cited by 583
Schwartz, A. B. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144233 · Cited by 559
Fundamental Electronic Properties and Applications of Single-Walled Carbon Nanotubes
Ouyang, M., Huang, J.-L., & Lieber, C. M. (2002) · Accounts of Chemical Research · doi:10.1021/ar0101685 · Cited by 537
UNUSUAL PROPERTIES AND STRUCTURE OF CARBON NANOTUBES
Dresselhaus, M. S., Dresselhaus, G., & Jorio, A. (2004) · Annual Review of Materials Research · doi:10.1146/annurev.matsci.34.040203.114607 · Cited by 507
Nantotechniques and approaches in biotechnology
Curtis, A., & Wilkinson, C. (2001) · Trends in Biotechnology · doi:10.1016/s0167-7799(00)01536-5 · Cited by 499
Bansal, R., Warrington, A. E., Gard, A. L., Ranscht, B., & Pfeiffer, S. E. (1989) · Journal of Neuroscience Research · doi:10.1002/jnr.490240413 · Cited by 419
Emulating biology: Building nanostructures from the bottom up
Seeman, N. C., & Belcher, A. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.221458298 · Cited by 419
Neuroscience nanotechnology: progress, opportunities and challenges
Silva, G. A. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1827 · Cited by 382
Johnson, G. V. W., & Jope, R. S. (1992) · Journal of Neuroscience Research · doi:10.1002/jnr.490330402 · Cited by 361
Zhou, O., Shimoda, H., Gao, B., Oh, S., Fleming, L., & Yue, G. (2002) · Accounts of Chemical Research · doi:10.1021/ar010162f · Cited by 301
Haddon, R. C. (2002) · Accounts of Chemical Research · doi:10.1021/ar020259h · Cited by 280
A Generic Organometallic Approach toward Ultra-Strong Carbon Nanotube Polymer Composites
Blake, R., Gun’ko, Y. K., Coleman, J. N., Cadek, M., Fonseca, A., Nagy, J. B., & Blau, W. J. (2004) · Journal of the American Chemical Society · doi:10.1021/ja0474805 · Cited by 233
From molecular biology to nanotechnology and nanomedicine
Bogunia-Kubik, K., & Sugisaka, M. (2002) · Biosystems · doi:10.1016/s0303-2647(02)00010-2 · Cited by 226
Recognition Molecule Directed Interfacing Between Semiconductor Quantum Dots and Nerve Cells
Winter, J. O., Liu, T. Y., Korgel, B. A., & Schmidt, C. E. (2001) · Advanced Materials · Cited by 203
Single molecule nanobioscience
Ishijima, A., & Yanagida, T. (2001) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(01)01860-6 · Cited by 195
Engineered self-organization of neural networks using carbon nanotube clusters
Gabay, T., Jakobs, E., Ben-Jacob, E., & Hanein, Y. (2005) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.11.007 · Cited by 192
Chemical Doping of Single-Wall Carbon Nanotubes
Fischer, J. E. (2002) · Accounts of Chemical Research · doi:10.1021/ar0101638 · Cited by 164
Pappas, T. C., Wickramanyake, W. M. S., Jan, E., Motamedi, M., Brodwick, M., & Kotov, N. A. (2007) · Nano Letters · doi:10.1021/nl062513v · Cited by 162
Self-citationGlial Fibrillary Acidic Protein: Dynamic Property and Regulation by Phosphorylation
Inagaki, M., IMakamura, Y., Takeda, M., Nishimura, T., & Inagaki, N. (1994) · Brain Pathology · doi:10.1111/j.1750-3639.1994.tb00839.x · Cited by 157
Gheith, M. K., Pappas, T. C., Liopo, A. V., Sinani, V. A., Shim, B. S., Motamedi, M., Wicksted, J. P., & Kotov, N. A. (2006) · Advanced Materials · doi:10.1002/adma.200600878 · Cited by 154
Self-citationGuided neurite growth on patterned carbon nanotubes
ZHANG, X., PRASAD, S., Niyogi, S., MORGAN, A., Ozkan, M., & Ozkan, C. S. (2005) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2004.10.039 · Cited by 140
Using multi-neuron population recordings for neural prosthetics
Chapin, J. K. (2004) · Nature Neuroscience · doi:10.1038/nn1234 · Cited by 139
Evidence for a Computational Distinction Between Proximal and Distal Neuronal Inhibition
Vu, E. T., & Krasne, F. B. (1992) · Science · doi:10.1126/science.1553559 · Cited by 112
Immunohistochemistry of the Glial Fibrillary Acidic Protein: Basic and Applied Considerations
McLendon, R. E., & Bigner, D. D. (1994) · Brain Pathology · doi:10.1111/j.1750-3639.1994.tb00837.x · Cited by 72
Challenges in quantum dot-neuron active interfacing
Gomez, N. V., Winter, J. O., Shieh, F., Saunders, A. E., Korgel, B. A., & Schmidt, C. E. (2005) · Talanta · doi:10.1016/j.talanta.2005.06.041 · Cited by 62
Remodeling of lesioned kitten visual cortex after xenotransplantation of fetal mouse neopallium
Ourednik, J., Ourednik, W., & Mitchell, D. E. (1998) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19980525)395:1<91::aid-cne7>3.0.co;2-c · Cited by 20
Cited by
Yang, J. C., Mun, J., Kwon, S. Y., Park, S., Bao, Z., & Park, S. (2019) · Advanced Materials · doi:10.1002/adma.201904765 · Cited by 1,641
Three-Dimensional Cell Culture Matrices: State of the Art
Lee, J., Cuddihy, M. J., & Kotov, N. A. (2008) · Tissue Engineering Part B: Reviews · doi:10.1089/teb.2007.0150 · Cited by 1,047
Self-citationChallenges and breakthroughs in recent research on self-assembly
Ariga, K., Hill, J. P., Lee, M. V., Vinu, A., Charvet, R., & Acharya, S. (2008) · Science and Technology of Advanced Materials · doi:10.1088/1468-6996/9/1/014109 · Cited by 759
A Review of Organic and Inorganic Biomaterials for Neural Interfaces
Fattahi, P., Yang, G., Kim, G., & Abidian, M. R. (2014) · Advanced Materials · doi:10.1002/adma.201304496 · Cited by 580
Nanomaterials for Neural Interfaces
Kotov, N. A., Winter, J. O., Clements, I. P., Jan, E., Timko, B. P., Campidelli, S., Pathak, S., Mazzatenta, A., Lieber, C. M., Prato, M., et al. (2009) · Advanced Materials · doi:10.1002/adma.200801984 · Cited by 504
Self-citation
Metadata
- Authors
- Jan, E., & Kotov, N. A.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-04-24
- Volume
- 7
- Issue
- 5
- Pages
- 1123-1128
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2081924426
- Cited by
- 334
- References
- 42
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.