The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Single App knock-in mouse models of Alzheimer’s disease
Abstract
Experimental studies of Alzheimer's disease have largely depended on transgenic mice overexpressing amyloid precursor protein (APP). These mice, however, suffer from artificial phenotypes because, in addition to amyloid β peptide (Aβ), they overproduce other APP fragments. We generated knock-in mice that harbor Swedish and Beyreuther/Iberian mutations with and without the Arctic mutation in the APP gene. The mice showed typical Aβ pathology, neuroinflammation and memory impairment in an age-dependent manner.
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In the citing paper
“We elected to use the AppNL-G-F knock-in mouse model for this study as it expresses App under its endogenous promoter in a physiological manner with regards to both cell type and amount, and avoids the issue of APP overexpression and abnormal expression patterns.”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [33]
In the citing paper
“AppNL-G-F mice [C57BL/6-App<tm3(NL-G-F)Tcs> (No.RBRC06344)] were provided by the RIKEN BRC through the National BioResource Project of the MEXT, Japan [33].”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [33]
In the citing paper
“we employed APPNLGF knock-in mice that express a humanized and triple-mutated APP in the endogenous App locus to circumvent transgenic mouse artifacts.”
From Oligodendrocytes produce amyloid-β and contribute to plaque formation alongside neurons in Alzheimer’s disease model mice (2024) · cites this paper as [34]
In the citing paper
“While novel APP knock-in models, such as APPNL, APPNL-F, or APPNL-G-F mice overproduce Aβ1–42 in the absence of potentially confounding APP overexpression [139], these mice still contain one or more independent FAD mutations, which might result in an abnormal Aβ conformation [140].”
From Neuron Loss in Alzheimer’s Disease: Translation in Transgenic Mouse Models (2020) · cites this paper as [139]
In the citing paper
“By 6 months of age, App NL‐G‐F mice exhibit a substantial amyloid plaque burden in the brain”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“These were bred to the App NL‐G‐F AD‐like model to determine responses in the context of amyloid‐driven pathology (Saito et al. 2014).”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“The App NL‐G‐F mice are characterized by progressive amyloid deposition from 2 months to cognitive decline by 6 months (Saito et al. 2014).”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“The App NL‐G‐F knock‐in model, however, has physiological App expression levels, avoiding the overexpression artifacts found in other transgenic models”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“but is strongly biased toward very high Aβ42:40 ratios (~10–100:1) because of the familial mutations”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“In AppNL-G-F mice, which harbor all three mutations, Aβ amyloidosis is aggressive, and neuroinflammation is observed in subcortical structures as well as cortical regions [10–12].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“By contrast, AppNL mice that carry only the Swedish mutation produce significantly higher levels of Aβ40 and Aβ42 without overt AD-related brain pathology such as extracellular Aβ plaques or neuroinflammation [10, 11, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“alternative mouse models have been generated via knock-in (KI) of a humanized Aβ sequences harboring familial AD mutations (Swedish (NL), Beyreuther/Iberian (F), and Arctic (G)) in order to model Aβ amyloidosis without non-physiological overexpression of APP [10].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“By contrast, AppNL mice that carry only the Swedish mutation produce significantly higher levels of Aβ40 and Aβ42 without overt AD-related brain pathology such as extracellular Aβ plaques or neuroinflammation [10, 11, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“To overcome these issues, KI mouse models with familial AD mutations have been generated [10, 24, 31, 32].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“despite overproducing human Aβ40 and Aβ42 in their brains [10, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“These results are consistent with recent studies reporting negligible cognitive deficits and the lack of Aβ plaque formation in AppNL/NL mice (Fig. 6) [10, 11, 13, 14, 24].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“Both models express human APP at physiological levels.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“The AppNL-G-F and AppNL-F show neuropathology and hippocampus-dependent cognitive impairments at 6 or 18 months, respectively.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“Our result in female mice is consistent with the cognitive phenotype in the Y maze reported in this genotype25,42.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“Human APP knockin (KI) models develop robust Aβ pathology, yet do not exhibit artifacts associated with APP over-expression, such as elevated levels of APP cleavage products (Saito et al., 2014).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These studies all suffer because they exhibit artifacts arising from the over-expression APP (WT or mutant) and in some cases mutant presenilins (Balducci and Forloni, 2011; Saito et al., 2014; Sasaguri et al., 2017).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“Human MAPT knock-in models have also been developed, and these models produce tau pathology, but only very late in the murine life span (~15 months) (Saito et al., 2014; Sakakibara et al., 2018; Sasaguri et al., 2022).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“However, over-expressing APP and presenilin 1 cause effect resulting from the over-expression that are unrelated to the disease process (Saito et al., 2014; Sakakibara et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These mice develop robust plaque pathology beginning as early as 3 months of age, however they develop little tau pathology and little neurodegeneration (Saito et al., 2014; Sakakibara et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These patterns of labeling or Aβ and MAPT are consistent with prior studies of APPNL-G-F and P301S MAPT mice (Yoshiyama et al., 2007; Saito et al., 2014; Sasaguri et al., 2017; Apicco et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“As expected,15 22-month-old AppNL-G-F mice had robust AD-related pathology, including amyloid deposition and neuroinflammation (Figures 1C and 1D).”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“As suggested,15,20 App KI mice may model pre-clinical stages of AD, characterized by progressive amyloidosis and gliosis without prominent cognitive deterioration, tau pathology, or neurodegeneration.”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“Humanized App knockin (KI) and APP transgenic mouse models mimic certain aspects of AD pathogenesis, including progressive amyloidosis, gliosis, and neuroinflammation, as well as network hyperexcitability and cognitive impairment, albeit to different degrees of severity across models and paradigms.15-27”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“Whereas plaque deposition only increases by less than twofold between 9 and 12 months of age, it increases tenfold between 12 and 18 months (Saito et al., 2014).”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“By contrast, NHE6 ablation led to glial activation and decreased plaque load in ApoeAPOE4 (Sullivan et al., 1997) and AppNL-F (Saito et al., 2014) double knockin mice.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“The AppNL-F mouse model used in our study develops plaques at 12 months (Saito et al., 2014) in the presence of murine ApoE.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“In AppNL-F mice, plaques can be identified as early as 9 months of age.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“These mice, however, do not show cognitive impairments in spatial learning tests like Morris water maze (Saito et al., 2014) (own unpublished observations).”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“We show in a well-studied App knock-in mouse model (Saito et al. , 2014) the gradual establishment of a multicellular co-expressed gene response encompassing 57 Plaque-Induced Genes or PIGs in the microenvironments of amyloid plaques (Figure 5).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Saito et al., 2014
In the citing paper
“App NL-G-F knock-in (Saito et al., 2014) mice express Swedish (KM670/671NL), Beyreuther/Iberian (I716F), and Arctic (E693G) mutations in the App gene under the endogenous promoter on the C57Bl/6J background.”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Saito et al., 2014
In the citing paper
“AD mice, in which APP with a humanised Aβ region containing 3 AD-related mutations (APPNL-G-F) is knocked in (49), to avoid artefacts associated with over-expressing APP, were crossed with NG2-DsRed mice in which pericytes express DsRed (19).”
From Amyloid β oligomers constrict human capillaries in Alzheimer's disease via signaling to pericytes (2019) · cites this paper as [49]
In the citing paper
“In contrast, knock-in of Swedish and Iberian mutations results in plaque development beginning at 6 months, and gliosis, synaptic alterations and memory impairment from 18 months [129].”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
In the citing paper
“Additional knock-in of the Arctic mutation into these mice results in more rapid pathology development including plaque development beginning at 2 months that is more widespread throughout the brain and memory impairment from 6 months [129].”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
In the citing paper
“In contrast, initial studies show that more physiological knock-in mouse models develop cognitive impairment many months after plaque development [129], which is more similar to humans.”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
Citations
Cites
Correlative Memory Deficits, Aβ Elevation, and Amyloid Plaques in Transgenic Mice
Hsiao, K., Chapman, P., Nilsen, S., Eckman, C., Harigaya, Y., Younkin, S., Yang, F., & Cole, G. (1996) · Science · doi:10.1126/science.274.5284.99 · Cited by 4,210
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.263 · Cited by 3,459
Amyloid deposition as the central event in the aetiology of Alzheimer’s disease
Hardy, J., & Allsop, D. (1991) · Trends in Pharmacological Sciences · doi:10.1016/0165-6147(91)90609-v · Cited by 2,538
Reducing Endogenous Tau Ameliorates Amyloid ß-Induced Deficits in an Alzheimer's Disease Mouse Model
Roberson, E. D., Scearce-Levie, K., Palop, J. J., Yan, F., Cheng, I. H., Wu, T., Gerstein, H., Yu, G.-Q., & Mucke, L. (2007) · Science · doi:10.1126/science.1141736 · Cited by 1,892
Citron, M., Oltersdorf, T., Haass, C., McConlogue, L., Hung, A. Y., Seubert, P., Vigo-Pelfrey, C., Lieberburg, I., & Selkoe, D. J. (1992) · Nature · doi:10.1038/360672a0 · Cited by 1,757
A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline
Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., Stefansson, H., Sulem, P., Gudbjartsson, D., Maloney, J., et al. (2012) · Nature · doi:10.1038/nature11283 · Cited by 1,738
Two amyloid precursor protein transgenic mouse models with Alzheimer disease-like pathology
Sturchler-Pierrat, C., Abramowski, D., Duke, M., Wiederhold, K.-H., Mistl, C., Rothacher, S., Ledermann, B., Bürki, K., Frey, P., Paganetti, P. A., et al. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.24.13287 · Cited by 1,507
Saido, T. C., Iwatsubo, T., Mann, D. M. A., Shimada, H., Ihara, Y., & Kawashima, S. (1995) · Neuron · doi:10.1016/0896-6273(95)90301-1 · Cited by 587
Self-citationAβ Secretion and Plaque Formation Depend on Autophagy
Nilsson, P., Loganathan, K., Sekiguchi, M., Matsuba, Y., Hui, K., Tsubuki, S., Tanaka, M., Iwata, N., Saito, T., & Saido, T. C. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.08.042 · Cited by 459
Self-citationSarnyai, Z., Sibille, E. L., Pavlides, C., Fenster, R. J., McEwen, B. S., & Tóth, M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.26.14731 · Cited by 396
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Self-citationSaido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00023-1
Self-citationBiology of time: The temporal distance between cause and effect
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Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00015-2
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Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00020-6
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Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00012-7
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Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00013-9
Self-citationYaman, Ü., Banks, G. T., Gustavsson, E. K., Mumford, P., Magusali, N., Taso, O. S., Macpherson, H. L., Carmona, S., Murray, M., Bains, R. S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.10.607237
Sahay, A., Vicidomini, C., Goode, T. D., McAvoy, K. M., Yu, R., Beveridge, C. H., Iyer, S. N., Victor, M. B., Leary, N., Steinbaugh, M. J., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4920738/v1
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Vale, A. M., & Bernardino, L. I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22268-9.00017-x
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Sheoran, P., Tripathi, R. D., Mishra, S. K., Ghatak, T., & Singh, S. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15540-6.00011-2
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Ikezu, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-68237-7_22
Mo, D., Zhao, Y. C., Liu, Y., Brosius, J., & Xiao, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.18.633698
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Abdallah, I. B., Sourty, M., Rame, M., Degiorgis, L., Isik, A., Mathis, C., & Harsan, L. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.28.635128 · Cited by 1
Bansal, V. A., Tan, J. M., Soon, H. R., Zainolabidin, N., Saido, T. C., & Ch’ng, T. H. (2025) · eLife · doi:10.7554/elife.92069.3.sa3
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Self-citationAdachi, Y., Katori, K., Ishiyama, S., Morikawa, S., Saito, T., Saido, T. C., Ikegaya, Y., & Takeuchi, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651821
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Arancibia‐Cárcamo, I. L., Davis, E., Gatto, N., Meleckyte, R., Varsally, W., Mitchener, V. F. T., Thackray, M. J., Fiers, M. W. E. J., Strooper, B. D., & Turner, J. M. A. (2025) · Research Square · doi:10.21203/rs.3.rs-6558560/v1
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Nollet, M., Ba, W., Soto, B. A., Yin, C., Lignos, L., Jović, K., Wong, S., Vyssotski, A. L., Yustos, R., Franks, N. P., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.09.663930
Heo, E. Y., Salinas‐Birt, A., Ye, L., Sajadi, S., Sarker, M. K., Yang, Y., & Paukert, M. (2025) · iScience · doi:10.1016/j.isci.2025.113120
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Aljawder, A. I., Choudhury, H., Cummings, D. M., Wood, J. I., & Edwards, F. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.22.671714
Kempermann, G. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780197519233.003.0012
Nakano, A., Maekawa, S., Murakami, T., Fujimaki, A., Takizawa, S., Murata, J., Ohuchi, K., Kurita, H., Takata, K., & Inden, M. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251378462
Kim, T. A., Syty, M. D., Wang, F., & Ge, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.18.677216
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Guo, L., Gaunt, J. R., Cheah, C. C. H., Chen, A. I., Claudine, S., Dawe, G. S., Goh, E. L. K., Kho, S.-H., Kumar, P., Lim, G. G. Y., et al. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1629830
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Mukhamejanova, A. (2025) · American journal of student research. · doi:10.70251/hyjr2348.36393400
Shin, J., Brady, E. S., Chen, C., Lauderdale, K., Agrawal, A., Zhang, Y., Jiang, X., Nambiar, P., Herbert, J., Mallen, D. D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.11.687887
Ohi, Y., Hada, K., Murata, Y., Kodama, D., & Wakiya, Y. (2025) · Journal of Pharmacological Sciences · doi:10.1016/j.jphs.2025.11.003
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Self-citation
Metadata
- Authors
- Saito, T., Matsuba, Y., Mihira, N., Takano, J., Nilsson, P., Itohara, S., Iwata, N., & Saido, T. C.
- Affiliations
- RIKEN Center for Brain Science (JP)
- Corresponding author
- Saido, Takaomi C
- Venue
- Nature Neuroscience
- Published
- 2014-04-13
- Volume
- 17
- Issue
- 5
- Pages
- 661-663
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2090620788
- Cited by
- 1,366
- References
- 26
- Citations by year
- 2026: 159 · 2025: 164 · 2024: 209 · 2023: 167 · 2022: 157 · 2021: 155 · 2020: 92 · 2019: 73 · 2018: 64 · 2017: 49 · 2016: 37 · 2015: 24 · 2014: 13
- FWCI
- 15.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (23680039, 26290019)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, knock-in mice, amyloid precursor protein, APP mutations, Swedish mutation, Arctic mutation, amyloid-beta pathology, neuroinflammation, memory impairment, age-dependent phenotype
- MeSH terms
- Age Factors, Alzheimer Disease, Analysis of Variance, Animals, Disease Models, Animal, Enzyme-Linked Immunosorbent Assay, Humans, Mice, Inbred C57BL, Mice, Transgenic, Mutation, Amyloid beta-Peptides, Amyloid beta-Protein Precursor, Maze Learning, Mice, Gene Knock-In Techniques
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