Clinical Research Directory

Mild Cognitive Impairment Community Screening and Early Intervention Via Stem Cell Therapy and Wearable Brain Computer Interface Device.

This study aims to evaluate the efficacy of community-based early detection and targeted interventions, including stem cell therapy and wearable non-invasive brain-computer interface (BCI) devices, for Mild Cognitive Impairment (MCI) in adults aged 55 years and older residing in U.S. urban and suburban communities. Primary objectives include assessing improvements in MCI detection rates, cognitive outcomes, and progression delay compared to standard care.

Clinical Evaluation of Blood-Based Assays for Rapid Detection of Aβ Pathology in Alzheimer's Disease

Background: Blood-based biomarkers show promise in predicting Alzheimer's disease (AD) pathology and progression; however, inconsistencies in detection standards hinder clinical application. A head-to-head comparison of commercially available biomarkers is crucial for optimizing the clinical pathway for AD screening and diagnosis. Method: The CLEAR-AD study is an ongoing population-based cross-sectional study, currently recruiting 400 participants in ten centers in China. The study includes cognitively normal controls, individuals with mild cognitive impairment (MCI) - categorized as amyloid-positive and amyloid-negative - as well as patients with dementia, also divided into amyloid-positive and amyloid-negative groups. All participants undergo amyloid PET scans using tracers such as AV1, AV45, and PIB. Blood samples are collected within three months prior to the PET scan or from existing samples collected after January 1, 2024, that meet quality standards. After collection, these samples are analyzed at a central laboratory under blinded conditions using multiple detection methods to measure plasma levels of Aβ40, Aβ42, t-tau, and p-tau181/217. The detection technologies included single-molecule immunoassay, digital immunoassay chips, magnetic particle chemiluminescence, and flow cytometry fluorescence. The objective is to assess the sensitivity and specificity of different plasma biomarker levels in predicting amyloid pathology confirmed by Aβ-PET. Result: The study uses Aβ-PET as the reference standard to evaluate the sensitivity and specificity of various AD plasma biomarkers across different detection methods in diagnosing amyloid pathology. The analysis included generating receiver operating characteristic (ROC) curves, determining optimal cut-off values, and developing a predictive model that integrates multiple biomarker parameters and clinical data. Results is considered statistically significant with a p-value of less than 0.05.

Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis

The most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. If successful, this framework will become the most state-of-the-art approach to simulate the stereotypical pattern of intracerebral tau accumulation and distribution in vivo. Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI. The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.