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Alzheimer's disease (AD) is a progressive, neurodegenerative disease that creates complex challenges and a significant burden for patients and caregivers. Although underlying pathological changes due to AD may be detected in research studies decades prior to symptom onset, many patients in the early stages of AD remain undiagnosed in clinical practice. Increasing evidence points to the importance of an early and accurate AD diagnosis to optimize outcomes for patients and their families, yet many barriers remain along the diagnostic journey. Through a series of international working group meetings, a diverse group of experts contributed their perspectives to create a blueprint for a patient-centered diagnostic journey for individuals in the early stages of AD and an evolving, transdisciplinary care team. Here, we discuss key learnings, implications, and recommendations.
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http://dx.doi.org/10.3389/fneur.2020.592302 | DOI Listing |
Adv Sci (Weinh)
September 2025
Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Science (Ministry of Education), Fudan University, 2005 Songhu Road, Yangpu District, Shanghai, 200433, China.
Emerging evidence indicates that liquid-liquid phase separation of α-synuclein occurs during the nucleation step of its aggregation, a pivotal step in the onset of Parkinson's disease. Elucidating the molecular determinants governing this process is essential for understanding the pathological mechanisms of diseases and developing therapeutic strategies that target early-stage aggregation. While previous studies have identified residues critical for α-synuclein amyloid formation, the key residues and molecular drivers of its phase separation remain largely unexplored.
View Article and Find Full Text PDFNanoscale
September 2025
School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh 221005, India.
Early-stage cancer diagnosis is considered a grand challenge, and even though advanced analytical assays have been established through molecular biology techniques, there are still clinical limitations. For example, low concentration of target biomarkers at early stages of cancer, background values from the healthy cells, individual variation, and factors like DNA mutations, remain the limiting factor in early cancer detection. Volatile organic compound (VOC) biomarkers in exhaled breath are produced during cancer cell metabolism, and therefore may present a promising way to diagnose cancer at the early stage since they can be detected both rapidly and non-invasively.
View Article and Find Full Text PDFJ Neurochem
September 2025
Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Elucidating the earliest biological mechanisms underlying Alzheimer's disease (AD) is critical for advancing early detection strategies. While amyloid-β (Aβ) and tau pathologies have been central to preclinical AD research, the roles of peripheral biological processes in disease initiation remain underexplored. We investigated patterns of F-MK6240 tau positron emission tomography (PET) and peripheral inflammation across stages defined by Aβ burden and neuronal injury in n = 132 (64.
View Article and Find Full Text PDFInsect Sci
September 2025
State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, China.
The ectoparasitic honeybee (Apis mellifera) mite Tropilaelaps mercedesae represents a serious threat to Asian apiculture and a growing concern for global beekeeping due to its high reproductive capacity and host adaptability. However, the regulatory mechanisms underlying its host adaptation across life stages remain poorly characterized. Here, we performed integrated transcriptomic, proteomic, and metabolomic analyses of female mites at 4 key postembryonic developmental stages: protonymphs, deutonymphs, mature adults, and reproductive adults.
View Article and Find Full Text PDFFuture Med Chem
September 2025
Computational Science & Artificial Intelligence, Xenon Pharmaceuticals Inc, Burnaby, BC, Canada.
Aims: To develop a machine learning (ML) model for early-stage prediction of human half-life of oral central nervous system (CNS) drugs and to establish a curated dataset, including key and data, to support future modeling efforts.
Materials & Methods: Human and rat half-life, plasma protein binding (PPB), and liver microsomal clearance (LM) data for 76 diverse CNS drugs and candidates were obtained from public sources or evaluated at WuXi AppTec. Gradient tree boosting (GTB) models were constructed using ChemAxon's Trainer Engine.