Using Classifiers To Predict Catalyst Design for Polyketone Microstructure.

We applied a classifier method to predict palladium catalysts for the formation of nonalternating polyketones via the copolymerization of CO and ethylene; current examples are limited to using phosphine sulfonate and diphosphazane monoxide supporting ligands. With the reported workflow, we discovered two new classes of palladium complexes capable of achieving the synthesis of nonalternating polyketones with a lower CO content than those made by known palladium catalysts. Our results show that we doubled the number of classes of palladium compounds that can catalyze the formation of this type of polymer.

Cognitive and Alzheimer's disease biomarker effects of oral nicotinamide riboside (NR) supplementation in older adults with subjective cognitive decline and mild cognitive impairment.

Introduction: Age-associated depletion in nicotinamide adenine dinucleotide (NAD+) concentrations has been implicated in metabolic, cardiovascular, and neurodegenerative disorders. Supplementation with NAD+ precursors, such as nicotinamide riboside (NR), offers a potential therapeutic avenue against neurodegenerative pathologies in aging, Alzheimer's disease, and related dementias. A crossover, double-blind, randomized placebo (PBO) controlled trial was conducted to test the safety and efficacy of 8 weeks' active treatment with NR (1 g/day) on cognition and plasma AD biomarkers in older adults with subjective cognitive decline and mild cognitive impairment.

Computerized cognitive testing to capture cognitive decline in Alzheimer's disease: Longitudinal findings from the ARMADA study.

Introduction: Timely detection and tracking of Alzheimer's disease (AD) -related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function-Cognition Battery (NIHTB-CB) detects AD-related cognitive decline.

Methods:  = 171 participants (age 76.

Life-Space Mobility Is Related to Loneliness Among Living-Alone Older Adults: Longitudinal Analysis With Motion Sensor Data.

Background: Life-space mobility can be a behavioral indicator of loneliness. This study examined the association between life-space mobility measured with motion sensors and weekly vs. annually reported loneliness.

I-CONECT intervention effects on weekly time spent outside of home and social contacts among socially isolated older adults.

Background: Socially isolated individuals tend to have less access to cognitively stimulating activities, which could adversely impact their cognitive health. The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) intervention was designed to deliver online conversation sessions to socially isolated older old adults to prevent cognitive decline. The current study examined the intervention efficacy on participants' weekly time spent out-of-home and their social interaction with family and friends.

Heat Waves and Adverse Health Events Among Dually Eligible Individuals 65 Years and Older.

Importance: Extensive research has found the detrimental health effects of heat waves. However, a critical gap exists in understanding their association with adverse health events among older dually eligible individuals, who may be particularly susceptible to heat waves.

Objective: To assess the association between heat waves and adverse health events among dually eligible individuals 65 years and older.

Using Apple Watches to Monitor Health and Behaviors of Individuals with Cognitive Impairment: A Case Series Study.

Objectives: This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment.

Method: Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and ii) AmissaOrbis, a centralized cloud portal for the collected data.

A multimodal cross-transformer-based model to predict mild cognitive impairment using speech, language and vision.

Mild Cognitive Impairment (MCI) is an early stage of memory loss or other cognitive ability loss in individuals who maintain the ability to independently perform most activities of daily living. It is considered a transitional stage between normal cognitive stage and more severe cognitive declines like dementia or Alzheimer's. Based on the reports from the National Institute of Aging (NIA), people with MCI are at a greater risk of developing dementia, thus it is of great importance to detect MCI at the earliest possible to mitigate the transformation of MCI to Alzheimer's and dementia.

Pathways to personalized medicine-Embracing heterogeneity for progress in clinical therapeutics research in Alzheimer's disease.

Biological and clinical heterogeneity is a major challenge in research for developing new treatments for Alzheimer's disease (AD). AD may be defined by its amyloid beta and tau pathologies, but we recognize that mixed pathologies are common, and that diverse genetics, central nervous system (CNS) and systemic pathophysiological processes, and environmental/experiential factors contribute to AD's diverse clinical and neuropathological features. All these factors are rational targets for therapeutic development; indeed, there are hundreds of candidate pharmacological, dietary, neurostimulation, and lifestyle interventions that show benefits in homogeneous laboratory models.

MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos.

Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model to distinguish MCI from those with normal cognition by analyzing facial features. The data comes from the I-CONECT, a behavioral intervention trial aimed at improving cognitive function by providing frequent video chats.

Biological effects of sodium phenylbutyrate and taurursodiol in Alzheimer's disease.

Introduction: Sodium phenylbutyrate and taurursodiol (PB and TURSO) is hypothesized to mitigate endoplasmic reticulum stress and mitochondrial dysfunction, two of many mechanisms implicated in Alzheimer's disease (AD) pathophysiology.

Methods: The first-in-indication phase 2a PEGASUS trial was designed to gain insight into PB and TURSO effects on mechanistic targets of engagement and disease biology in AD. The primary clinical efficacy outcome was a global statistical test combining three endpoints relevant to disease trajectory (cognition [Mild/Moderate Alzheimer's Disease Composite Score], function [Functional Activities Questionnaire], and total hippocampal volume on magnetic resonance imaging).

ω-3 PUFA for Secondary Prevention of White Matter Lesions and Neuronal Integrity Breakdown in Older Adults: A Randomized Clinical Trial.

Importance: Older adults with lower intake and tissue levels of long-chain ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA; 20:5) and docosahexaenoic acid (DHA; 22:6) have more brain white matter lesions (WMLs), an association suggesting that small-vessel ischemic disease, a major contributor to the development of dementia, including Alzheimer disease, may be preventable through ω-3 treatment.

Objective: To determine whether ω-3 treatment reduces WML accumulation in older adults without dementia harboring WMLs and with suboptimal ω-3 status.

Design, Setting, And Participants: This quadruple-blinded, placebo-controlled, randomized clinical trial with treatment stratification by apolipoprotein E ε4 allele (APOE*E4) carrier status used linear mixed-effects models to estimate mean annual change between groups.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.

Background: Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility.

Objective: To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study.

Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.

Early detection of Mild Cognitive Impairment (MCI) leads to early interventions to slow the progression from MCI into dementia. Deep Learning (DL) algorithms could help achieve early non-invasive and low-cost detection of MCI. This paper presents the detection of MCI in older adults using DL models based only on facial features extracted from video-recorded conversations at home.

Decentralized clinical trials for medications to reduce the risk of dementia: Consensus report and guidance.

Introduction: Recent growth in the functionality and use of technology has prompted an increased interest in the potential for remote or decentralized clinical trials in dementia. There are many potential benefits associated with decentralized medication trials, but we currently lack specific recommendations for their delivery in the dementia field.

Methods: A modified Delphi method engaged an expert panel to develop recommendations for the conduct of decentralized medication trials in dementia prevention.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA).

Who Benefited Most from the Internet-Based Conversational Engagement RCT (I-CONECT)? Application of the Personalized Medicine Approach to a Behavioral Intervention Study.

Background: Many Alzheimer's Disease (AD) clinical trials have failed to demonstrate treatment efficacy on cognition. It is conceivable that a complex disease like AD may not have the same treatment effect due to many heterogeneities of disease processes and individual traits.

Objectives: We employed an individual-level treatment response (ITR) approach to determine the characteristics of treatment responders and estimated time saved in cognitive decline using the Internet-based Conversational Engagement Clinical Trial (I-CONECT) behavioral intervention study as a model.

White matter hyperintensities and the surrounding normal appearing white matter are associated with water channel disruption in the oldest old.

Introduction: Age-related magnetic resonance imaging (MRI) T2 white matter hyperintensities (WMHs) are common and associated with neurological decline. We investigated the histopathological underpinnings of MRI WMH and surrounding normal appearing white matter (NAWM), with a focus on astroglial phenotypes.

Methods: Brain samples from 51 oldest old Oregon Alzheimer's Disease Research Center participants who came to autopsy underwent post mortem (PM) 7 tesla MRI with targeted histopathological sampling of WMHs and NAWM.

Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Background: Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition.

Objective: We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency.

A 10-week intergenerational program bringing together community-living older adults and preschool children (INTERACTION): a pilot feasibility non-randomised clinical trial.

Background: Social isolation and low levels of physical activity are strong drivers for frailty, which is linked to poor health outcomes and transition to long-term care. Frailty is multifactorial, and thus an integrated approach is needed to maintain older adults' health and well-being. Intergenerational programs represent a novel multifactorial approach to target frailty, social isolation and physical decline but these have not yet been rigorously tested in Australia.

Cerebrospinal fluid biomarker profiling of diverse pathophysiological domains in Alzheimer's disease.

Introduction: While Alzheimer's disease (AD) is defined by amyloid-β plaques and tau tangles in the brain, it is evident that many other pathophysiological processes such as inflammation, neurovascular dysfunction, oxidative stress, and metabolic derangements also contribute to the disease process and that varying contributions of these pathways may reflect the heterogeneity of AD. Here, we used a previously validated panel of cerebrospinal fluid (CSF) biomarkers to explore the degree to which different pathophysiological domains are dysregulated in AD and how they relate to each other.

Methods: Twenty-five CSF biomarkers were analyzed in individuals with a clinical diagnosis of AD verified by positive CSF AD biomarkers (AD, = 54) and cognitively unimpaired controls negative for CSF AD biomarkers (CU-N, = 26) using commercial single- and multi-plex immunoassays.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown.

Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.

Mild cognitive impairment (MCI) represents the early stage of dementia including Alzheimer's disease (AD) and is a crucial stage for therapeutic interventions and treatment. Early detection of MCI offers opportunities for early intervention and significantly benefits cohort enrichment for clinical trials. Imaging and in vivo markers in plasma and cerebrospinal fluid biomarkers have high detection performance, yet their prohibitive costs and intrusiveness demand more affordable and accessible alternatives.

Including Socially Isolated Black, Older Old Adults (Aged 80 and Above) with and without Mild Cognitive Impairment in a Clinical Trial: Recruitment Strategies and Perspectives.

Objective: This study describes strategies for the recruitment of socially isolated older old Black individuals to participate in the "Internet-based conversational engagement clinical trial (I-CONECT)" (Clinical Trial.gov: NCT02871921) and lessons learned in this critical population segment.

Methods: Best practice strategies to recruit the target population included mass mailings, advertisements, and direct community outreach, including the collaboration with a community group created to reach Black individuals interested in research participation.