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Small RNA predictors for Alzheimer's disease
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A technology for Alzheimer's, predictors, applied in the field of assessing AD activity in subjects or patients, biomarkers, can solve problems such as effort barriers
Pending Publication Date: 2021-03-30
斯纳利蒂克斯股份有限公司
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However, these efforts have been hampered by the absence of clinically meaningful biomarkers to aid drug discovery and development
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Embodiment 1
[0092] Example 1: Identification of Alzheimer's Disease in Experimental or Comparative Groups of Brain Tissue, Cerebrospinal Fluid, or Serum binary classifier.
[0093]To identify binary small RNA predictors for Alzheimer's disease, small RNA sequencing data were downloaded from the GEO and dbGaP databases and used as Discovery Sets (Table 1A-Table 1B: Brain Samples, Table 3A-Table 3B: CSF samples and Table 6A-Table 6B: SER samples). All samples, regardless of material, were derived from post hoc validated Alzheimer's disease or non-Alzheimer's disease samples (healthy controls or other non-Alzheimer's disease-related neurological disorders such as Parkinson's disease, Parkinson's disease dementia, Huntington's disease, etc.).
[0094] The overall process is described as follows:
[0095] Number of diagnostic sample material samples (N)
[0096]
[0097]
[0098] CSF=cerebrospinal fluid, SER=serum.
[0099] The file was converted from .sra to .fastq format using ...
Embodiment 2
[0112] Example 2: Construction of a multiclass disease classifier for inflammatory bowel disease (IBD).
[0113] To construct a disease classifier that classifies IBD samples based on the presence or absence of specific sRNA molecules, the sRNA sets were identified in the sequence data.
[0114] sample
[0115] All samples were collected in accordance with the approval of their respective Institutional Review Board (IRB) with patient consent for unrestricted use. Data were collected from electronic medical records and chart reviews. Clinical data includes information such as age, sex, race, ethnicity, weight, body mass index, smoking history, drinking history, family history of disease. Disease-related data included information such as diagnosis, age at diagnosis of inflammatory bowel disease (IBD), current and previous medications, comorbidities, proctocolectomy and ileal pouch anal anastomosis (IPAA). Age and pouch age, time since ileostomy closure or pouch surgery (...
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Abstract
The present disclosure provides methods and kits for evaluating Alzheimer's disease (AD) activity, including in patients undergoing treatment for AD or a candidate treatment for AD, as well as in animal and cell models. Specifically, the present disclosure provides biomarkers (sRNA predictors) that are binary predictors of disease activity, and are useful for detecting and / or evaluating AD diseasestage, grade and progression, prognosis, and response to therapy or candidate therapy. The biomarkers are further useful in the context of drug discovery and clinical trials, to identify candidate pharmaceutical interventions (or other therapies) that are useful for the treatment of disease.
Description
[0001] priority [0002] This application claims the benefit of and priority to U.S. Provisional Application No. 62 / 703,172, filed July 25, 2018, the contents of which are incorporated herein by reference in their entirety. Background technique [0003] Alzheimer's disease (AD) is the most common neurodegenerative disorder as it accounts for nearly 70% of all dementia cases and affects up to 20% of individuals over the age of 80. Various morphological and histological changes in the brain are hallmarks of modern AD neuropathology. Specifically, two neurological phenomena have been observed: amyloid plaques and neurofibrillary tangles. Disease progression can be divided into Braak stages, in which six stages of disease transmission have been distinguished based on the location of tangle neurons in the brain and the severity of the changes: Braak stages I / II: Transentorhinal (temporal lobe) stages , a clinically asymptomatic case; Braak stage III / IV: limbic zone stage, incipie...
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