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treatment of Alzheimer disease","volume":"32","author":"Vitek","year":"2023","journal-title":"Expet Opin Invest Drugs"},{"key":"10.1016\/j.apsb.2025.08.015_bib89","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1056\/NEJMoa2212948","article-title":"Lecanemab in early Alzheimer's disease","volume":"388","author":"van Dyck","year":"2023","journal-title":"N Engl J Med"},{"key":"10.1016\/j.apsb.2025.08.015_bib90","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1016\/j.apsb.2024.03.034","article-title":"Monoclonal antibodies and aptamers: the future therapeutics for Alzheimer's disease","volume":"14","author":"Barrera-Ocampo","year":"2024","journal-title":"Acta Pharm Sin B"},{"key":"10.1016\/j.apsb.2025.08.015_bib91","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s41582-018-0116-6","article-title":"A critical appraisal of amyloid-\u03b2-targeting therapies for Alzheimer disease","volume":"15","author":"Panza","year":"2019","journal-title":"Nat Rev 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