bluuebunny/crossref_metadata_2025_split huggingface dataset:
Grassroots Approaches to Community‐Based Representative Recruitment in a Large Multidomain Lifestyle Intervention Trial
Authors: Hill Marcus , Williams Kelvin , Meyer Oanh L. , Robinson Chartay , Omotoye Elizabeth , Alexander Ashley , Tang Tara , Lovato Laura , Wilmoth Sharon , Woolard Nancy , Hill Carl V. , Whitmer Rachel A. , Snyder Heather M , Carrillo Maria C. , Baker Laura D ,   for the U.S. POINTER Study Group
Published: December 2023

AbstractBackgroundU.S. POINTER is a randomized, controlled, multi‐domain 2‐year clinical trial in 2000 older Americans to slow cognitive decline via culturally‐appropriate healthy lifestyle interventions. Participant diversity is essential, with targeted enrollment of 50% women and 23% enrollees from communities of color (reflecting the U.S. population in the 2016 Census). Other recruitment priorities include individuals with lower traditional educational attainment, older age within our eligibility range (75‐79 years old), and rural residence. This diversification aims to (1) better represent our communities particularly affected by ADRD, and (2) help ensure that this intervention can be successful across broad cultural differences for long‐term implementation.MethodU.S. POINTER developed and deployed a community‐based recruitment strategy via a centrally‐coordinated grassroots outreach team at each site. The strategy includes evidence‐based and innovative community engagement tactics and is a multi‐faceted, agile approach that accounts for variations in: demographics across sites’ respective communities; levels of home institution‐community connectedness/integration; and outreach experience across team members. Tactics include: employing a multi‐tiered outreach framework that leverages existing community networks in order to augment team outreach capacity and ensure continuous community engagement; innovating approval procedures with central IRB to reduce barriers to producing timely outreach materials as needed; and standardizing onboarding for new team members across experience levels.ResultThe POINTER grassroots recruitment strategy has accounted for 22% of the trial’s overall enrollment. As of Jan 18, 2023, POINTER has enrolled 1879 participants, 29% of whom are from communities of color, 30% are men, 30% do not hold college degrees, and 13% are over 75 years old. Lessons learned include navigating local institutional reputations, retaining consistent team personnel for long‐term relationship building, setting trajectories for mutually‐beneficial long‐term community investment, and employing a formidable equity lens at all levels of staffing, outreach, and intervention design to better identify and eliminate barriers to participation, amongst others.ConclusionKey recommendations emerging from recruitment success in enrolling a diverse cohort include: approaching recruitment with long‐term vision and commitment toward community investment; leveraging existing community assets and working in partnership with respected community voices; and employing consistent, diverse, multimodal community outreach touchpoints for traction.
Explainable Artificial Intelligence using Model Agnostic Methods in detecting Alzheimer’s disease
Authors: Varghese Abraham , George Ben , Sherimon Vinu , Ahmed Saad Salman 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) results in cognitive dysfunction among older people, making their lives nearly impossible. Early intervention is likely to be the most effective way to slow its progression. Machine learning models are critical to determine whether a patient is demented or not at an early stage. But the most highly accurate models are non‐linear and less transparent, so the logic behind the predictions are not revealed. In this study, a neural network model was created, and two model‐agnostic methods were applied on it to understand the important features the model used to make predictions.MethodA total of 25 features were selected from Oasis‐2 dataset of 150 participants. A feature selection is performed using KRUSKAL algorithm and 10 potential features were selected. A feed forward neural network model with sigmoid hidden neurons and softmax output neurons was created for classification of demented and non‐demented cases. Then, two model agnostic methods, LIME (Local Interpretable Model‐Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were applied on the model to identify the features contributing to the prediction. LIME was used to generate a human‐readable explanation for a single prediction by approximating the behavior of the model locally around the instance being predicted. SHAP is a method based on the concept of Shapley values from cooperative game theory. It assigns a contribution value to each feature of the model, indicating how much that feature contributes to the final predictionResultNeural network resulted in an accuracy of 91.4%. It is found that Clinical Dementia Rating (CDR) is the most significant feature, followed by the normalized brain volume (nWBV). CDR is also observed to positively influence the detection of dementia cases, whereas the nWBV has a negative impact, which is also true clinically. Additionally, the contribution of each feature to the prediction is shown, which may assist in the evaluation of the model’s trustworthiness.ConclusionThe goal of this research is to create a neural network model that is highly accurate and highly interpretable so that any non‐technical stakeholder can understand the reasoning behind the predictions. So, accuracy combined with interpretability increases the reliability of AD prediction systems.
Development and validation of a natural language processing system that extracts cognitive test results from clinical notes
Authors: Chen Jinying , Li Xuyang , Aguilar Byron J. , Shishova Ekaterina , Morin Peter J , Berlowitz Dan , Miller Donald R , O'Connor Maureen K. , Nguyen Andrew H. , Zhang Raymond , Monfared Amir Abbas Tahami , Zhang Quanwu , Xia Weiming 
Published: December 2023

AbstractBackgroundCognitive test results from electronic health records (EHRs) are key information for assessing the severity and progression of patients with mild cognitive impairment (MCI) and Alzheimer’s’ disease (AD). However, such information is often recorded in unstructured clinical notes rather than in an administrative database. We developed and validated a natural language processing (NLP) system to extract cognitive test results from clinical notes in the Veterans Affair (VA) Healthcare System.MethodAn NLP system was developed using regular expression‐based rules and Python to extract results for six tests that have been used most frequently in VA: Mini‐Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), Saint Louis University Mental Status Examination (SLUMS), Mini‐cog, Boston Naming Test (BNT), and Benton Visual Retention Test (BVRT). The system extracted test results from each note in two steps: (1) searched a test name, or variations and abbreviation of the test name and, if successful, (2) searched the quantitative results (e.g., 12/30 for MMSE, 74th percentile for BNT) and/or descriptive results (e.g., “borderline impairment” for BVRT) within 5 words or one sentence before or after the test name. To balance the system performance and speed, we developed 3‐8 extraction rules per test based on a manual review of 30‐50 notes for each test. We further validated NLP performance on 6 held‐out datasets (50‐200 notes/test). We automatically sampled the development and held‐out notes by searching the test name and its variations/abbreviation to increase positive cases, i.e., notes that contained the above test results.ResultThe NLP system achieved 0.72‐0.92 predictive positive values (PPV), 0.96‐1.00 recall, and 0.83‐0.95 F1 scores on the validation test sets (Table 1). In addition, it demonstrated a scalable performance (processed 200,000 notes in 7 min), allowing an extraction of millions of notes from ∼1 million patients within hours.ConclusionRule‐based NLP can extract cognitive test results with adequate performance and scalable capability for clinical notes from patients with MCI or AD within the VA Healthcare System.
AI‐based Digital Biomarkers for Mild Dementia Decision Support using Mobile Playful Exercises with High Adherence
Authors: Paletta Lucas , Pszeida Martin , Russegger Silvia , Orgel Thomas , Draxler Sandra , Berger Martin , Koini Marisa , Fellner Maria , Spat Stephan , Schüssler Sandra , Zuschnegg Julia , Strobl Bernhard , Ploder Karin , Hofmarcher‐Holzhacker Maria 
Published: December 2023

AbstractBackgroundEarly detection of cognitive decline and monitoring of cognitive functioning in mild dementia are fundamental for timely adaptation of lifestyle and intervention strategies. The development of digital dementia biomarkers through playful exercises with high adherence rate was a key objective of the national project multimodAAL (no. FFG 875345). The results of a study on computer‐based cognitive and physical training (CCPT) in persons diagnosed with mild Alzheimer’s disease (PwAD) are presented.MethodTablet‐PC‐based intervention was applied within 6 months in Austria, engaging PwADs living at home by means of playful multimodal training and activation (n = 11; female N = 8, male N = 3; age M = 76.6 / SD = 9.2 years, MMSE score M = 21.50 / SD = 4.41). PwADs interacted with a prototypical version of the BRAINMEE app that included a suite of cognitive exercises (puzzle, pairs, text gap filling) based on audiovisual information. The playful training app was introduced and assisted by mobile care professionals with weekly visits, however, PwDs played alone between these visits.ResultPwADs applied training with high adherence, finalizing M = 72 (M = 32) digital exercises per day within the first (last) month of the study. Duration of using exercise type ‘outsider’(p = .028*) and ‘quiz’ (p = .001**), averaged about 2 week figures, each provided statistically significant correlations (Spearman) with MMSE test scores, as well as ‘spot‐the‐difference’ (p = .003**) with Trail Making Test A, ‘outsider’ (p = .005**) with Auditory Verbal Learning Test (AVLT), respectively. A neural network (Support Vector Machine, linear kernel, 11‐fold cross validation) using duration of use of ‘quiz’, ’outsider’ and ‘hearing’ (guessing animal sounds) as input data resulted in M = 2.16 absolute error in MMSE score estimation on test data.ConclusionThe work outlined within the Austrian study on digital biomarker development indicates successful steps towards daily use of cognitive assessment using highly adherent playful training. The playful training app is applied in the European project MARA (no. FFG 886427) to enable continuous estimates of MCI’s mental state over time. The app was very well accepted by both PwADs and persons with MCI. It offers with its pervasive mental assessment tool a large potential for future long‐term monitoring in dementia prevention, early detection as well as in numerous dementia care services.
Transcriptomic and proteomic characterization of the locus coeruleus in a rat model of Alzheimer’s disease
Authors: Kelberman Michael , McCann Katharine , Hurst Cheyenne D , Duong Duc M. , Marriott Alexia , Seyfried Nicholas T , Weinshenker David 
Published: December 2023

AbstractBackgroundThe noradrenergic locus coeruleus (LC) is the first region to accumulate hyperphosphorylated tau pathology (p‐tau) in Alzheimer’s disease (AD). Using TgF344‐AD transgenic rats, which develop endogenous p‐tau in the LC prior to pathology in forebrain regions, we have recently demonstrated age‐dependent effects of p‐tau on LC firing rates. Specifically, LC neurons exhibit early hyperactivity followed by late hypoactivity. These changes in LC firing rates are coincident with the emergence of behavioral phenotypes that characterize different stages of AD (neuropsychiatric symptoms followed by cognitive deficits). Here, we sought to identify molecular mechanisms that could contribute to these changes using RNAscope and proteomics in TgF344‐AD rats.Methods32 male and female TgF344‐AD rats and wild‐type (WT) littermates aged 6 and 15 months were used for this study. Brains were flash‐frozen in isopentane on dry ice and stored at ‐80°C until being sectioned. At the level of the LC, the left hemisphere LC was collected with a 1 mm tissue punch. Punches of the cerebellum were taken as a control to verify LC enrichment in the brainstem punches. The right LC hemisphere was sectioned at 16 µm for processing with fluorescent in situ hybridization using the RNAscope Multiplex Fluorescent V2 Assay according to the manufacturer’s protocol. Probes included noradrenergic markers (tyrosine hydroxylase, norepinephrine transporter) and those that influence LC activity (α2‐adrenergic receptors, GRIA2, GRIN, ADCYAP1R1). Separately, tissue punches were submitted to the Emory Integrated Proteomics Core for label‐free quantification of proteins.ResultsRNAscope revealed no differences in transcript levels of noradrenergic markers at either age. Similarly, there were no differences in transcript levels of α2‐adrenergic receptors, GRIA, or ADCYAP1R1 at either age, or GRIN in 6‐month animals. We observed an increase of GRIN mRNA in 15‐month TgF344‐AD animals, suggesting an upregulation of excitatory neurotransmission.ConclusionsNoradrenergic neuronal identity remains in‐tact up to 15 months of age, a timepoint when there is no LC cell death in these rats. We have also uncovered a potential compensatory mechanism to maintain LC activity in late stages of disease. Proteomics and RNAscope analysis of other markers that may affect LC activity are ongoing.
Spatial navigation performance differentiates Alzheimer´s disease biomarker positive and biomarker negative cognitively impaired older adults
Authors: Laczó Martina , Svacova Zuzana , Sedlakova Veronika , Svatkova Radka , Kalinova Jana , Vyhnalek Martin , Hort Jakub , Hornberger Michael , Laczó Jan 
Published: December 2023

AbstractBackgroundSpatial navigation deficits including egocentric (body‐centered) and allocentric (world‐centered) navigation impairment are present early in Alzheimer´s disease (AD). We evaluated a potential of a realistic‐looking spatial navigation task to differentiate older adults with amnestic mild cognitive impairment (aMCI) who have positive AD biomarkers (AD aMCI) from those with negative AD biomarkers (non‐AD aMCI).MethodParticipants with AD aMCI (n = 24), non‐AD aMCI (n = 25), mild AD dementia (n = 14) and cognitively normal (CN) older adults (n = 35) underwent spatial navigation assessment in the virtual Supermarket task. Participants passively travelled through the supermarket and followed different routes to reach specific locations. In the final location, the participants had to indicate their starting location (egocentric heading) and their current location and final heading orientation on an aerial spatial map of the supermarket (distance from the correct location and allocentric heading, respectively, both measures of allocentric navigation).ResultIn the egocentric heading, the AD aMCI and mild AD dementia groups indicated their original starting location less accurately than the CN group (p<0.001) and with similar accuracy as the non‐AD aMCI group (p> = 0.219). The non‐AD aMCI group did not significantly differ from the CN group (p = 0.055). In the first allocentric task (distance from the correct location), the AD aMCI group and mild AD dementia groups indicated their positions on a paper aerial map of the supermarket with greater distance errors compared to the non‐AD aMCI group (p< = 0.013) and CN (p<0.001) groups. The non‐AD aMCI group had comparable performance to the CN group (p = 0.172). In the second allocentric task (allocentric heading), both the AD aMCI and mild AD dementia groups made more errors indicating final heading orientation than the non‐AD aMCI (p< = 0.040) and the CN (p<0.001) groups. The non‐AD aMCI group had worse performance than the CN group (p = 0.004). The sensitivity and specificity for discriminating AD aMCI and non‐AD aMCI was 72% and 71%, respectively.ConclusionBoth, egocentric and allocentric spatial navigation tasks detected spatial navigation impairment in older adults with AD aMCI. However, only the allocentric tasks differentiated AD aMCI from non‐AD aMCI. The virtual Supermarket task has a potential to become a screening tool for early AD‐related spatial navigation deficits.
Tracking the intracellular itinerary of APP and de novo Aβ generation using click chemistry
Authors: Xu Isabella , Czerniewski Leah R , Xiao Qingli , Yan Ping , Benz Ann , Cashikar Anil G , Mawuenyega Kwasi G. , Crick Scott L , Mennerick Steven , Bateman Randall J. , Hanson Phyllis I , Lee Jin‐Moo 
Published: December 2023

AbstractBackgroundAmyloid‐beta (Aβ), is generated by sequential cleavage of the amyloid precursor protein (APP) by beta‐ and gamma‐secretase. The intracellular sites of cleavage are unknown due to the lack of tools that can detect de novo Aβ production.MethodUsing unnatural amino acid (UAA) mutagenesis and click chemistry, a single fluorophore (magenta) site‐specifically binds to Aβ within the APP construct. The C‐terminus of APP is fused with the green fluorescent protein mClover, allowing for a distinction between full‐length APP and Aβ, as the uncleaved APP will appear white (magenta+green) from the bifluorescence, contrasting with the magenta of Aβ alone when separated from the green mClover C‐terminus. Live cells expressing the APP‐mClover construct (green) were incubated with cell‐impermeable tetrazine‐AlexaFluor 594 (magenta) at 4°C, which click‐labeled the UAA on Aβ on cell surface APP. The cells were then incubated at 37°C, resuming endocytosis of bifluorescent APP.ResultAfter click‐labeling, the APP construct resides exclusively at the cell surface. Once warmed to 37°C, the intracellular bifluorescence increases with time, suggesting APP endocytosis (Fig 1). Using object‐based analyses, the magenta click‐label:green mClover ratio was determined from super‐resolution z‐stack images (Fig 2). The magenta:green ratio was lower in cells treated with gamma‐secretase inhibitor (GSI).ConclusionA qualitative and semi‐quantitative picture can be created of Aβ’s localization by following the separation of the bifluorescent signal into intraluminal magenta click‐label and membrane‐bound green mClover. This technology will be coupled with immunocytochemistry of subcellular markers, which will allow for further specificity on the location of Aβ travel within a cell, with regards to the endolysosomal pathway. Furthermore, this technology can be applied to examine Aβ within the unique cytoarchitecture of primary neurons which may help inform the design of new therapeutics in this cell type central to AD pathogenesis.
Genetic associations with neuropathological outcomes revealed a novel role in neuritic plaque formation
Authors: Lin Eugene , Lerch Melissa , Chan Kwun C , Biber Sarah , Kukull Walter A. 
Published: December 2023

AbstractBackgroundNumerous genome‐wide association studies (GWASs) have been performed for the determination of genes and variants contributing to Alzheimer’s disease (AD). We hypothesized that it is important to explore whether the previously‐reported AD‐associated genes in GWASs can have neuropathological effects in the brain and can be employed as potential biomarkers for neuropathological outcomes such as neuritic plaque development. Neuritic plaques (also known as amyloid plaques) are extracellular deposits of the amyloid beta protein primarily found in the grey matter of the brain and are the most discernible neuropathological alterations observed in individuals with dementia.MethodHere, we report the genetic association study of neuropathology data such as neuritic plaque measurements in an AD cohort at the National Alzheimer’s Coordinating Center (NACC) using a sample of 1,283 individuals. We acquired the genetic data from the Alzheimer’s Disease Sequencing Project (ADSP) which generated whole genome sequencing data through the ADSP Quality Control pipeline. We employed 18 loci where the associations of these loci with AD risk were validated by the recent GWASs. Analysis was obtained using a linear mixed model after adjustment for covariates including age at death, gender, the number of the APOE e4 allele (i.e., 0, 1, and 2), ancestry representative principle components (i.e., population structure), and a genetic relatedness matrix (i.e., a polygenic effect due to genetic relatedness among individuals).ResultFrom our analysis, we generalized to the NACC cohort significant associations after Bonferroni correction with neuritic plaque measurements and one previously identified locus such as BIN1 on chromosome 2 (p = 4.65×10−5). Furthermore, we detected, at the significance level after Bonferroni correction, a novel locus for neuritic plaque formation that has not previously been reported: PICALM (or nearby LINC02695 or nearby RNU6‐560P) on chromosome 11 (p = 1.02×10−3). PICALM is a strong candidate for playing a role in neuritic plaque formation because deficiency in PICALM results in diminished amyloid‐β clearance.ConclusionThe results from our genetic association study of neuritic plaque formation in the AD cohort underline the potential of utilizing neuropathology data to discover additional support for relevant loci being associated with AD across diverse populations.
Phonetic‐phonological disturbances of spontaneous speech in autosomal dominant Alzheimer disease due to A431E PSEN1 mutation
Authors: Preciado‐Baron Ana Karen , Figuera‐Villanueva Luis Eduardo , Gallegos Alfonso , Orozco Maribel , Berumen‐Ocegueda Isaac Enrique , González Victor J. Sánchez , Ringman John M , Matute Esmeralda 
Published: December 2023

AbstractBackgroundConsiderable cognitive research in Alzheimer Disease (AD) has focused on memory as it is often the primary symptom; nevertheless, in A431E PSEN1 mutation carriers in Jalisco, a rapidly language impairment and verbal fluency decrease during the disease is reported (Petersen‐Dumois, et al., 2020) though this deficit is not well‐characterized. The aim of this study was to identify and characterize phonetic‐phonological abnormalities in the spontaneous speech through different stages of A431E PSEN1 mutation‐related AD.MethodEleven symptomatic A431E PSEN1 mutation carriers, with a mean age of 42.54 years (DE = 4.17) participated in the study: five with Mild Cognitive Impairment (MCI), three with Mild Dementia (MiD) and five with Moderate Dementia (MoD). We used three language tasks: trip planning, picture description, and narrative discourse of a risky and happy moment of their lives which were recorded and transcribed. For each participant, we computed the frequency of phonetic‐phonological features: stuttering, unintelligible productions, phonological paraphasias, fillers and pauses (≥2 seconds) and then converted into percentages dividing the number of occurrences of each feature by the number of words used on the speech sample (for pauses, time in seconds). Then, we obtained the percentage mean of each feature per group.ResultWe analyzed a total of 134.65 minutes of speech sample. Regarding verbal fluency, words per minute (means) varied across the groups MCI: 85.55 (SD = 39.34); MiD: 128.56 (SD = 34.14) MoD: 67.80. (SD = 32.71). All phonological features were present, stuttering: MCI: 1.38%; MiD: 3.2%; MoD: 4.05%; unintelligible productions: MCI: .22%; MiD: .64%; MoD: .76%; phonological paraphasias; MCI: 0.002%; MiD: .01% MoD: .01%; fillers: MCI: 2.05%; MiD: .29%; MoD: 2.88%; number of pauses: MCI: .04%; MiD: .05%; MoD: .03%. and pauses average: MCI: 3.42 (SD = .62); MiD: 2.55 (SD = .03) MoD = 3.43. (SD: 1.61).ConclusionWe found that A431E PSEN1 mutation carriers present phonetic‐phonological traits even in the earliest stage (MCI) and for three of them (stuttering, unintelligible productions, and phonological paraphasias) their frequency is greater the more severe the impairment (MoD). Stuttering is their most prominent feature. These disturbances in the spontaneous speech could be a phenotype trait of this ADAD mutation.