The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Gene expression correlates of neurofibrillary tangles in Alzheimer's disease
Open accessAbstract
Neurofibrillary tangles (NFT) constitute one of the cardinal histopathological features of Alzheimer's disease (AD). To explore in vivo molecular processes involved in the development of NFTs, we compared gene expression profiles of NFT-bearing entorhinal cortex neurons from 19 AD patients, adjacent non-NFT-bearing entorhinal cortex neurons from the same patients, and non-NFT-bearing entorhinal cortex neurons from 14 non-demented, histopathologically normal controls (ND). Of the differentially expressed genes, 225 showed progressively increased expression (AD NFT neurons > AD non-NFT neurons > ND non-NFT neurons) or progressively decreased expression (AD NFT neurons < AD non-NFT neurons < ND non-NFT neurons), raising the possibility that they may be related to the early stages of NFT formation. Immunohistochemical studies confirmed that many of the implicated proteins are dysregulated and preferentially localized to NFTs, including apolipoprotein J, interleukin-1 receptor-associated kinase 1, tissue inhibitor of metalloproteinase 3, and casein kinase 2, beta. Functional validation studies are underway to determine which candidate genes may be causally related to NFT neuropathology, thus providing therapeutic targets for the treatment of AD.
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“This dataset was later re‐analyzed to show that neurons with high levels of tau were enriched for signatures of DNA damage and senescence (Dunckley et al, 2006; Musi et al, 2018).”
From Mechanisms of DNA damage-mediated neurotoxicity in neurodegenerative disease (2022) · cites this paper as Dunckley et al, 2006
Citations
Cites
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
Randomization and Monte Carlo Methods in Biology.
Foutz, R. V., & Manly, B. F. J. (1992) · Journal of the American Statistical Association · doi:10.2307/2290299 · Cited by 1,627
Ebneth, A., Godemann, R., Stamer, K., Illenberger, S., Trinczek, B., Mandelkow, E.-M., & Mandelkow, E. (1998) · The Journal of Cell Biology · doi:10.1083/jcb.143.3.777 · Cited by 820
Oddo, S., Billings, L., Kesslak, J. P., Cribbs, D. H., & LaFerla, F. M. (2004) · Neuron · doi:10.1016/j.neuron.2004.07.003 · Cited by 780
Inhibition of neurite polarity by tau antisense oligonucleotides in primary cerebellar neurons
Cáceres, A., & Kosik, K. S. (1990) · Nature · doi:10.1038/343461a0 · Cited by 656
Li, Y., Liu, L., Barger, S. W., & Griffin, W. S. T. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-05-01605.2003 · Cited by 563
The calpain family and human disease
Huang, Y., & Wang, K. (2001) · Trends in Molecular Medicine · doi:10.1016/s1471-4914(01)02049-4 · Cited by 480
DeMattos, R. B., O'dell, M. A., Parsadanian, M., Taylor, J. W., Harmony, J. A. K., Bales, K. R., Paul, S. M., Aronow, B. J., & Holtzman, D. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162228299 · Cited by 359
Backstrom, J. R., Lim, G. P., Cullen, M. J., & Tökés, Z. A. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-24-07910.1996 · Cited by 331 · cited 1×
“Further, matrix metalloproteinase 9, which is a target of TIMP inhibition, has been reported to be upregulated in plasma of AD patients [23] and has been implicated in the degradation of toxic β-amyloid species [2].”
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Items 101–177 of 177
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Barbash, S., & Sakmar, T. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-00250-4 · Cited by 19
Rai, H., Singh, R., Bharti, P. S., Kumar, P., Rai, S., Varma, T., Chauhan, B. S., Nilakhe, A., Debnath, J., Dhingra, R., et al. (2024) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2024.135364 · Cited by 18
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Prasad, R., Jung, H., Tan, A., Song, Y., Moon, S., Shaker, M. R., Sun, W., Lee, J.-H., Ryu, H., Lim, H. K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99562-9 · Cited by 16
Robust Gene Dysregulation in Alzheimer's Disease Brains
Feng, X., Bai, Z., Wang, J., Xie, B., Sun, J., Han, G., Song, F., Crack, P. J., Duan, Y., & Lei, H. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140147 · Cited by 16
Shukla, M., & Vincent, B. (2023) · Current Neuropharmacology · doi:10.2174/1570159x21666230314142505 · Cited by 17
Transcriptional signatures mediated by acetylation overlap with early-stage Alzheimer’s disease
Vadnal, J., Houston, S. A., Bhatta, S., Freeman, E. J., & McDonough, J. A. (2012) · Experimental Brain Research · doi:10.1007/s00221-012-3172-y · Cited by 18
Ruffini, N., Klingenberg, S., Heese, R., Schweiger, S., & Gerber, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.866886 · Cited by 16
Wang, C., Wang, J., Liu, D., & Wang, Z. (2010) · Analytical Methods · doi:10.1039/c0ay00438c · Cited by 14
Liu, W., Chen, S., Rao, X., Yang, Y., Chen, X., & Yu, L. (2023) · Biomedicines · doi:10.3390/biomedicines11010194 · Cited by 14
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Beck, J. S., Madaj, Z., Cheema, C. T., Kara, B., Bennett, D. A., Schneider, J. A., Gordon, M. N., Ginsberg, S. D., Mufson, E. J., & Counts, S. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac001 · Cited by 12
Fiscon, G., Sibilio, P., Funari, A., Conte, F., & Paci, P. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12101731 · Cited by 12
Prognostic transcriptional association networks: a new supervised approach based on regression trees
Nepomuceno-Chamorro, I. A., Azuaje, F. J., Devaux, Y., Nazarov, P. V., Muller, A., Aguilar–Ruiz, J. S., & Wagner, D. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq645 · Cited by 12
Mean Expression of the X-Chromosome is Associated with Neuronal Density
Swingland, J. T., Durrenberger, P. F., Reynolds, R., Dexter, D. T., Pombo, A., Deprez, M., Roncaroli, F., & Turkheimer, F. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00161 · Cited by 12
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Mintoo, M. J., Rajagopalan, V., & O’Bryan, J. P. (2024) · Biochemical Society Transactions · doi:10.1042/bst20211241 · Cited by 11
da Silva, E. M. G., Fischer, J. D. S. D. G., Signorini-Souza, I. D. L., Andrade, A. C. C., Souza, L. C., Andrade, M. K., Carvalho, P. C., de Souza, R. L. R., Vital, M. A. B. F., & Passetti, F. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052772 · Cited by 12
Rare Variants and Transcriptomics in Alzheimer disease
Humphries, C., & Kohli, M. A. (2014) · Current Genetic Medicine Reports · doi:10.1007/s40142-014-0035-9 · Cited by 11
Narad, P., Kumar, A., Chakraborty, A., Patni, P., Sengupta, A., Wadhwa, G., & Upadhyaya, K. C. (2016) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-016-0168-5 · Cited by 11
Investigating the impact human protein–protein interaction networks have on disease-gene analysis
Browne, F., Wang, H., & Zheng, H. (2016) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-016-0503-5 · Cited by 11
Wang, Y. H., Luo, P. P., Geng, A., Li, X., Liu, T., He, Y. J., Huang, L., & Tang, Y. Q. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1183119 · Cited by 13
Liu, G., & Sun, Y. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251374149 · Cited by 11
Genomic and Proteomic Biomarker Discovery in Neurological Disease
Robeson, R. H., Siegel, A. M., & Dunckley, T. L. (2008) · Biomarker Insights · doi:10.4137/bmi.s596 · Cited by 11
Self-citationFinding a disease-related gene from microarray data using random forest
Nishiwaki, K., Kanamori, K., & Ohwada, H. (2016) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2016.7862090 · Cited by 10
Provenance of Microarray Experiments for a Better Understanding of Experiment Results
Deus, H., Zhao, J., Zoology, D. O., Sahoo, S., & Samwald, M. (2011) · Cited by 10
Harutyunyan, A., Jones, N. C., Kwan, P., & Anderson, A. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.821343 · Cited by 9
Age-related disturbances in DNA (hydroxy)methylation in APP/PS1 mice
Chouliaras, L., Lardenoije, R., Kenis, G., Mastroeni, D., Hof, P. R., van Os, J., Steinbusch, H. W. M., van Leeuwen, F. W., Rutten, B. P. F., & van den Hove, D. (2018) · Translational Neuroscience · doi:10.1515/tnsci-2018-0028 · Cited by 10
Self-citationOverview of Proteomic Analysis of Amyloid Plaques and Neurofibrillary Tangles in Alzheimer’s Disease
Grewal, A., Raikundalia, S., Zaia, J., & Sethi, M. K. (2025) · Biomolecules · doi:10.3390/biom15091310 · Cited by 9
Qi, S., Wang, S., Tan, Y., Pan, C., & Bi, X. (2025) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-025-01621-6 · Cited by 10
Adaptive type2-possibilistic C-means clustering and its application to microarray datasets
Husseini, Z. M., Zarandi, M. H. F., & Ahmadi, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10380-5 · Cited by 8
Climer, S., Templeton, A. R., Garvin, M. R., Jacobson, D., Lane, M. J., Hulver, S., Scheid, B. H., Chen, Z., Cruchaga, C., & Zhang, W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.28.923730 · Cited by 8
The lemur tail kinase family in neuronal function and disfunction in neurodegenerative diseases
Larose, A., Miller, C. C. J., & Mórotz, G. M. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05480-0 · Cited by 7
Kazemi, M., Sanati, M., Khaniani, M. S., & Ghafouri‐Fard, S. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149595 · Cited by 8
Patel, A. O., Caldwell, A. B., Ramachandran, S., & Subramaniam, S. (2023) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-220098 · Cited by 7
Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest
Nishiwaki, K., Kanamori, K., & Ohwada, H. (2017) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2017040102 · Cited by 7
Jepsen, W. M., Both, M. D. D., Siniard, A. L., Ramsey, K. M., Piras, I. S., Naymik, M., Henderson-Smith, A., & Huentelman, M. J. (2020) · Biology Open · doi:10.1242/bio.054627 · Cited by 7
Self-citationColeman, P. D., Delvaux, E., Kordower, J. H., Boehringer, A. V., & Huseby, C. J. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14555 · Cited by 7
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Shen, J., & Mariano, V. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3437656 · Cited by 6
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Li, J., Gao, X., Tang, M., Wang, C., Liu, W., & Tian, S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2009545 · Cited by 6
Analyzing Alzheimer's disease gene expression dataset using clustering and association rule mining
Queau, B. L., Shafiq, O., & Alhajj, R. (2014) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2014.7051901 · Cited by 6
Krishnamurthy, V., Issac, N. S., & Natarajan, J. (2009) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.1000113 · Cited by 6
Integrating Gene Expression and Phenotypic Information to Analyze Alzheimer's Disease
Ray, M., & Zhang, W. (2009) · Journal of Alzheimer s Disease · doi:10.3233/jad-2009-0917 · Cited by 6
Sun, X., Wang, Y., Zhang, Y., Han, R., Wang, M., Zhang, J., Sun, T., Liu, Y., Hu, G., Cao, L., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.012 · Cited by 5
Jaye, S., Sandau, U. S., McFarland, T. J., Woltjer, R. L., & Saugstad, J. A. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1426180 · Cited by 5
Phelix, C. F., LeBaron, R. G., Roberson, D. J., Villanueva, R. E., Villareal, G., Rahimi, O. B., Siedlak, S. L., Zhu, X., & Perry, G. L. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.98 · Cited by 5
A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease.
Zanzoni, A. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-2627-5_26 · Cited by 4
Siniard, A. L., Corneveaux, J. J., Both, M. D. D., Chawla, M. K., Barnes, C. A., & Huentelman, M. J. (2014) · Neuromethods · doi:10.1007/978-1-4939-2247-5_4 · Cited by 4
Pérez-Rando, M., Dujardin, S., Bennett, R. E., Commins, C., Nibhanupudy, T. J., & Hyman, B. T. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-01049-7 · Cited by 4
Liang, J., LaFleur, B., Hussainy, S., & Perry, G. L. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-221241 · Cited by 4
Self-citationPremkumar, T., Katta, B., Sajitha Lulu, S., & Sundararajan, V. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2256878 · Cited by 5
Rahman, M. R., Islam, T., Shahjaman, M., Rana, M. H. K., Holsinger, R. M. D., Quinn, J. M. W., Göv, E., & Moni, M. A. (2020) · Biointerface Research in Applied Chemistry · doi:10.33263/briac112.86868701 · Cited by 4
Ghosh, D. K., & Ranjan, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/780379 · Cited by 4
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Sharma, D., Manjunatha, P. M., Babu, T. H., Dhungel, A., Swaraj, S., Janadari, S., Sharma, U., Surendra, V., Taj, N., & Gayathri, S. V. (2024) · Current Pharmacology Reports · doi:10.1007/s40495-024-00376-w · Cited by 3
An Exploratory Analysis of Conservation of Co-Expressed Genes across Alzheimer's disease Progression
Chowriappa, P. (2013) · Journal of Computer Science & Systems Biology · doi:10.4172/jcsb.1000119 · Cited by 3
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Kwong, R., Lupton, M. K., & Janitz, M. (2012) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-012-9797-3 · Cited by 2
Khanna, M. R., & Fortini, M. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-141491 · Cited by 2
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Browne, F., Wang, H., & Zheng, H. (2014) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2014.6999189 · Cited by 2
Rahman, M. R., Islam, T., Göv, E., Quinn, J. M. W., & Moni, M. A. (2021) · Experimental Results · doi:10.1017/exp.2020.65 · Cited by 1
Skorkina, M., Kovaleva, T. F., Predeina, I., Smirnov, N., & Mukhina, I. V. (2026) · Cells · doi:10.3390/cells15040324 · Cited by 1
Building Transcriptional Association Networks in Cytoscape with RegNetC
Nepomuceno-Chamorro, I. A., Marquez-Chamorro, A. E., & Aguilar–Ruiz, J. S. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2385702 · Cited by 1
Epigenetics and Late-Onset Alzheimer’s Disease
Schumacher, A., Bihaqi, S. W., & Zawia, N. H. (2011) · Springer eBooks · doi:10.1007/978-1-84882-644-1_12 · Cited by 1
Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest
Nishiwaki, K., Kanamori, K., & Ohwada, H. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-2460-2.ch070 · Cited by 1
Alzheimer’s Disease Genomics and Clinical Applications
Lee, T.-S. W., & Sian, M. (2011) · InTech eBooks · doi:10.5772/18773
Desarrollo de algoritmos bioinformáticos para estudios de genómica funcional: aplicaciones en cáncer
Fontanillo, C. (2013) · Gredos (University of Salamanca) · doi:10.14201/gredos.122975
Epigenetic dysregulation and the pathophysiology of Alzheimer’s disease
Mastroeni, D. (2013) · doi:10.26481/dis.20130503dm
Self-citationAthié, M. C. P. (2010) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.41.2010.tde-13082010-142129
Synergies of genes in Alzheimer's disease.
Nepomuceno-Chamorro, I. A., & Aguilar–Ruiz, J. S. (2013) · Deposito de Investigacion Universidad de Sevilla (University of Seville)
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Bradford, M. K. (2015)
Current Incorporation and Impact of Computational Biology in Alzheimer's Disease
Naaz, S., Rani, S. G., & Pati, P. (2022) · International Journal of Health Sciences and Research · doi:10.52403/ijhsr.20220345
Sándor, S., Tátrai, K., Czeibert, K., & Kubinyi, E. (2019) · Research Square · doi:10.21203/rs.2.14657/v1
Prasad, R., Jung, H., Tan, A., Song, Y., Moon, S., Shaker, M. R., Sun, W., Lee, J.-H., Ryu, H., Lim, H. K., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-474622/v1
de Ávila, C., Suazo, C., Nolz, J., Cochran, J. N., Wang, Q., Velázquez, R., Dammer, E. B., Readhead, B., & Mastroeni, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.14.553279
Self-citationAnalysis of genes associated with Alzheimer's disease and endoplasmic reticulum stressPreprint
Liu, Z., & Li, F. (2024) · Research Square · doi:10.21203/rs.3.rs-4334229/v1
Sun, X., Wu, L., Orr, T. C., Heck, A., Young, K., Dunaway, N., Navarro, J. C., Beechem, J., & Orr, M. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.19.689127
Oxidative Stress Associated Signal Transduction Cascades in Alzheimer Disease
Petersen, R. B., Nunomura, A., Lee, H., Casadesús, G., Perry, G. L., Smith, M. A., & Zhu, X. (2009) · Contemporary clinical neuroscience · doi:10.1007/978-1-60327-342-8_8
Transcriptome analysis of Alzheimer's disease identifies links to cardiovascular disease
Ray, M. (2008) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7jw8c4j
Additional file 1 of Sex-specific associations of matrix metalloproteinases in Alzheimer’s disease
Aksnes, M., Edwin, T. H., Saltvedt, I. T., Eldholm, R. S., Chaudhry, F. A., Halaas, N. B., Myrstad, M., Watne, L. O., & Knapskog, A.-B. (2023) · Figshare · doi:10.6084/m9.figshare.23118925
Cho, Y.-J., Kim, S.-H., Ryu, S.-H., Chung, C., Lee, N., Kim, N. H., Jeong, M., Kim, Y.-J., Gwak, J., Choi, S.-Y., et al. (2026) · Molecular Neurodegeneration · doi:10.1186/s13024-026-00936-2
Molecular mechanisms of action of MT-MMPs involved in the pathogenesis of Alzheimer’s disease
García‐González, L. (2020) · theses.fr (ABES) · doi:10.70675/a04672c0z1f4az472ezbcd5zbea446eac50c
Metadata
- Authors
- Dunckley, T., Beach, T. G., Ramsey, K. E., Grover, A., Mastroeni, D., Walker, D. G., LaFleur, B. J., Coon, K. D., Brown, K. M., Caselli, R., Kukull, W., Higdon, R., McKeel, D., Morris, J. C., Hulette, C., Schmechel, D., Reiman, E. M., Rogers, J., & Stephan, D. A.
- Affiliations
- Translational Genomics Research Institute (US)
- Vanderbilt University (US)
- Mayo Clinic (US)
- National Alzheimer's Coordinating Center (US)
- Duke University (US)
- Corresponding author
- Stephan, Dietrich A.
- ORCID
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 27
- Issue
- 10
- Pages
- 1359-1371
- ISSN
- 0197-4580 (ISSN-L)
- PMC
- PMC2259291
- OpenAlex
- W2005889734
- Cited by
- 177
- References
- 35
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 8 · 2023: 14 · 2022: 11 · 2021: 10 · 2020: 15 · 2019: 10 · 2018: 10 · 2017: 5 · 2016: 9 · 2015: 12 · 2014: 10 · 2013: 9 · 2012: 9
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1k01ag024079-01a2)
- National Institute on Aging (P30-AG-19610, AG05 128, R01AG023193, 1-RO1-AG023193, U01AG016976, K01-AG-024079, P50AG005128, P 50 AG05681, P50AG005681, P30AG019610, P01 AG0 3991, P01AG003991)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- neurofibrillary tangles, Alzheimer's disease, gene expression profiling, differentially expressed genes, clusterin, TIMP-3, casein kinase 2, therapeutic target
- MeSH terms
- Aged, 80 and over, Female, Humans, Male, Nerve Tissue Proteins, Gene Expression, Neurofibrillary Tangles, Entorhinal Cortex, Gene Expression Profiling
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