bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting Undue Harm Arising From Elder Abuse Screening: Innovative Methods from the EASI‐ltc© Pilot Study
Authors: Wilchesky Machelle , Ballard Stephanie A. 
Published: January 2025

AbstractBackgroundScreening for elder abuse can cause victims to experience feelings of unpleasantness and/or relive painful memories which can be an ethical concern. Ensuring the safety of all participants/users, in our case long‐term care (LTC) residents, is of the utmost importance.MethodDrawing from approaches used in the intimate partner violence and clinical trials literature, we developed a novel typology of harm and a series of procedures to evaluate any negative consequences that might be incurred as a result of participating in the Piloting the Elder Abuse Suspicion Index‐long term care©: A Mixed Methods Feasibility Study. Our typology includes subjective and objective reporting from a multitude of sources, including: 1) Interviews with LTC residents using a modified Consequences of Screening Tool, adapted from MacMillan et al, 2009; Opinions of the 2) Study social worker conducting validation assessments and 3) LTC residents and institutional stakeholders; 4) Feedback from study research assistants who administer the tool using a specialized Adverse Events Form; and 5) Chart reviews. Procedures include the creation of an independent interdisciplinary pilot study safety committee, chaired by a geriatric psychiatrist and convened to conduct interim evaluations after the first 5, 10, and 20 residents have participated, the liberation of social work teams, who are on standby and available to intervene as necessary, and the creation of participant resource cards displaying pertinent phone numbers for immediate assistance.ResultBoth quantitative and qualitative analyses of harms detected among the first 50 participating LTC residents of this ongoing study will be presented.ConclusionTo our knowledge, this is the first elder abuse study to conduct a thorough analysis of assessment‐induced harm.
Improved detection of cerebral microbleeds in Alzheimer’s Disease using 7T susceptibility weighted imaging
Authors: Ashouri Laya 
Published: January 2025

AbstractBackgroundCerebral microbleeds (CMBs) are small, round aggregations of hemosiderin‐laden macrophages that indicate leakage of blood products from cerebral vessels damaged by β‐amyloid‐40 (Aβ), which is the basis of cerebral amyloid angiopathy (CAA). Pathology studies have demonstrated a high correlation between CAA and AD, both amyloid‐related pathologies. CMBs can be visualized commonly using T2*‐weighted MRI, as small foci of decreased signal intensity. The increased magnetic field strength at 7 Tesla (7T) MRI results in substantially enhanced sensitivity to variations in magnetic susceptibility. The more robust SWI at 7T MRI proves especially advantageous in the detection of CMBs, which are small hemorrhagic lesions distinguished by subtle alterations in magnetic susceptibility. In this study, we used the CLEAR SWI technique at 7T MRI to improve the visualization of CMBs that might be missed or undetectable with lower field strengths to better understand the role of CAA in mild cognitive impairment (MCI) and AD patients.MethodImaging with four MCI, two AD, and six healthy subjects (73 ± 6 years old) was performed using a 7T MRI scanner. CMBs were defined as small hypointense lesions within the brain parenchyma on the SWI images. The microbleeds were categorized into two groups according to their locations: non‐lobar region and lobar. Calcifications of the basal ganglia and deep cerebellar nuclei were considered as normal findings. The analysis was performed to evaluate the associations between the number of CMBs with the patient’s Aβ PET SUVR levels.ResultOur results showed that out of 51 observed CMBs in six MCI/AD patients 48 (94%) CMBs were lobar and 3 (6%) non‐lobar. A total of four CMBs were found in three AD/MCI patients. A total of three CMBs (one lobar and 2 non‐lobar) were observed in six healthy participants. A significant correlation was found between the number of lobar CMBs and the Aβ PET SUVR levels in AD/MCI patients.ConclusionCMBs have attracted attention in Amyloid immunization therapy trials in AD, therefore improved visualization of CMBs with CLEAR SWI sequences at 7T MRI may contribute to more accurate identification of CMBs, and better understanding of their role in MCI and AD.
Improving detection of concomitant Alzheimer’s disease pathology in autopsied Lewy body disorders using CSF biomarkers
Authors: Albuja Isabela , Irwin David J , Chen‐Plotkin Alice , Shaw Leslie M. , Lee Eddie B , Wolk David A , Tropea Thomas F. , Cousins Katheryn A Q 
Published: January 2025

AbstractBackgroundWhile the cerebrospinal fluid (CSF) biomarkers β‐amyloid 1‐42 (Aβ42), β‐amyloid 1‐40 (Aβ40), total tau (t‐tau), and phosphorylated tau 181 (p‐tau181) can detect Alzheimer’s disease (AD) pathology during life, there is a need to evaluate CSF cutpoints for specific application in Lewy body spectrum disorders (LBSD) in order to maximize detection to AD co‐pathology. Using the Fujirebio LUMIPULSE G, we developed LBSD‐derived cutpoints for CSF biomarkers and their ratios (Aβ42/Aβ40, p‐tau181/Aβ42, t‐tau/Aβ42) to determine which best discriminates αSyn with AD (αSyn+AD) from αSyn without (αSyn). We compared our LBSD‐derived cutpoints to the established AD‐derived cutpoints.MethodParticipants were neuropathology‐confirmed LBSD, with (αSyn+AD; n=24) or without AD co‐pathology (αSyn; n=26). Amnestic AD without αSyn were included as a reference group (n=11). Receiver operating characteristic (ROC) and area under the curve (AUC) tested how accurately biomarkers discriminated between αSyn+AD and αSyn. Optimal cutpoints were identified by Youden's Index using bootstrapping (2000 iterations). Sensitivity, specificity, and accuracy were calculated at best cutpoints. Confidence intervals (95%CI) tested for differences between empirically‐defined LBSD‐specific cutpoints and previously established AD‐derived cutpoints. Chi‐square tests compared classification accuracy between LBSD‐ and AD‐derived cutpoints. Survival analyses tested if biomarker status predicted time to death.ResultDiscriminating between αSyn and αSyn+AD, the CSF Aβ42/Aβ40 ratio had the highest accuracy (0.90), followed by p‐tau181/Aβ42 ratio (0.87). We found that LBSD‐derived cutpoints were higher than established AD‐derived cutpoints for CSF Aβ42/Aβ40 ratio (0.080 [95%CI=0.069 – 0.089] vs. 0.058 pg/mL), and lower for CSF p‐tau181 (32.7 [95%CI=26.84 – 38.76] vs. 50.2 pg/mL) and t‐tau (227 [95%CI=203.51‐ 278.66] vs. 409 pg/mL). Furthermore, LBSD‐derived cutpoints identified individuals with AD co‐pathology more accurately than the AD‐derived cutpoints for CSF Aβ42/Aβ40 (X2= 23.26, P<0.001), p‐tau181 (X2=22.82, P<0.001) and t‐tau (X2= 15.09, P<0.001). Survival analysis show that AD‐positive individuals identified by the LBSD‐derived CSF Aβ42/Aβ40 ratio cutpoint associate with earlier death (HR = 2.98, 95%CI = 0.41 – 1.77, P = 0.0017).ConclusionThis study helps to establish AD CSF biomarkers for specific application in LBSD using the Fuijirebio LUMIPULSE G platform to maximize detection of AD co‐pathology. Validation in an independent cohort is needed.
Dynamic contrast enhanced MRI reveals impaired blood brain barrier in basal forebrain region in patients with Alzheimers disease: A pilot study
Authors: Lerch Ondrej , Kala David , Nedelska Zuzana , Hadzic Haris , Otahal Jakub , Hort Jakub 
Published: January 2025

AbstractBackgroundBlood brain barrier (BBB) is a protective layer of cells that separates the circulatory system from the brain. Its dysfunction is one of the possible mechanisms leading to onset of Alzheimer's disease (AD), a progressive neurodegenerative disease and a leading cause of dementia worldwide. Dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) is a imaging technique allowing regional assessment of BBB breakdown by estimating local metrics of capillary permeability such as K‐trans (volume transfer constant). We used DCE‐MRI to examine BBB dysfunction in regions affected early in the course of AD ‐ hippocampus, entorhinal cortex (EC) and basal forebrain (BF) nuclei.MethodA pilot group of 29 participants ‐ 17 cognitively normal, 12 with mild cognitive impairment (MCI) due to AD from Czech Brain Aging Study underwent DCE‐MRI. K‐trans maps were estimated using ROCKETSHIP software implemented within MATLAB 2023b. Segmentations of hippocampal head, body and tail, anterolateral and posteromedial EC and BF nuclei were obtained using in house developed pipeline. Average K‐trans values were extracted for each region. Regional differences in BBB permeability between groups were assessed using ANOVA.ResultParticipants in the MCI group had increased mean K‐trans in anterior (K‐transCN= 0.752 x10‐3min‐1; K‐transMCI= 0.991 x10‐3min‐1, p = 0.023) and posterior (K‐transCN = 0.656 x10‐3min‐1; K‐transMCI = 0.984 x10‐3min‐1, p = 0.018) part of nucleus basalis Meynerti (NBM). There were no other significant differences between regional BBB permeability in measures structures (p>0.05)ConclusionOur pilot data show regional BBB dysfunction in NBM region in patients with MCI due to AD. This may imply regional BBB vulnerability of BF or another ongoing process such as hyperactivation or inflammation, suggesting a link between BBB dysfunction and early pathological changes in the BF area. Verification of these results on larger dataset is necessary before any further conclusions can be made.
Assessments for Prodromal Alzheimer’s Disease Having Positive Amyloid Pathology Using Plasma Biomarkers And APOE Genotype
Authors: Wang Pei‐Ning , Chiu Ming‐Jang , Sabbagh Marwan N. , Yang Charles S.Y. 
Published: January 2025

AbstractBackgroundWith the success in developing drugs able to slow cognitive decline in Alzheimer’s disease (AD), the disease managements of AD become aggressive and positive. The target patients of drug are the prodromal AD, i.e. clinical dementia rating being 0.5 and mini‐mental status examination being between 20 and 28, having positive amyloid pathology. In practical, it seems to have big burdens in finding prodromal AD patients have positive amyloid pathology at neurological departments in hospitals. The impact of drug in managing AD is intensively suppressed. A pre‐screening tool for finding target patients is needed. By integrating the reported assessments of prodromal AD and positive amyloid positron emission tomography (PET) using plasma biomarkers, a pre‐screening flow chart is proposed.MethodA multi‐centered study was done to validate assessments of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD) using plasm concentration ratio of amyloid β 1‐42 (Aβ1‐42) to Aβ1‐40 (Aβ1‐42/Aβ1‐40) and the concentration product of Aβ1‐42 and total tau protein (T‐Tau) (Aβ1‐42xT‐Tau), which were assayed with immunomagnetic reduction (IMR). Explorations of pre‐screening model of amyloid pathology in prodromal using plasma Aβ1‐42 and APOE genotype in prodromal AD was investigated in the multi‐centered study.ResultThe levels of Aβ1‐42/Aβ1‐40 or Aβ1‐42xT‐Tau in plasma show high accuracy (> 85%) in assessing prodromal AD. The combination of plasma Aβ1‐42 and APOE genotypes show the high prediction (> 84%) in positive amyloid PET in prodromal AD. These results demonstrate the feasibilities of assessing prodromal AD using plasma Aβ1‐42/Aβ1‐40 or Aβ1‐42xT‐Tau, followed by combing plasma Aβ1‐42 with APOE genotypes to predict the positive amyloid PET.ConclusionSuch pre‐screening flow chart could breakdown the bottleneck of finding prodromal AD having positive amyloid PET in clinic and promote the impact of drug to the managements of AD.
Diagnostic Utility of Plasma Biomarkers to Differentiate Controls, SCD, and MCI Subjects
Authors: Boutajangout Allal , Masurkar Arjun V. , Osorio Ricardo S. , Ahmed Wajiha , Debure Ludovic , Vedvyas Alok , Links Jon , Marsh Karyn , Wisniewski Thomas 
Published: January 2025

AbstractBackgroundPlasma biomarkers of Alzheimer’s disease (AD) are less invasive and have lower costs than cerebrospinal fluid (CSF) biomarkers. Blood biomarkers are potential instruments for diagnosis, prognosis, and monitoring of disease progression. We assessed the diagnostic potential of several plasma biomarkers for the detection of early stages of AD.Method152 participants (age>60) were recruited at the NYU Alzheimer's Disease Research Center. This cohort includes n=55 with normal cognition (NL), n=70 with subject cognitive decline (SCD), and n=27 with mild cognitive impairment (MCI). Comprehensive neuropsychological and magnetic resonance imaging evaluations were conducted for all patients. Plasma biomarkers assay Aβ40, Aβ42, total tau [t‐tau], neurofilament light [NfL], glial fibrillary acid protein [GFAP], ubiquitin carboxyl‐terminal hydrolase L1 [UCH‐L1], and pTau181 were measured using the SIMOA SR‐X a novel technology that employs highly sensitive immunoassays with a limit of detection (LOD) under 100 fg/ml.ResultThe levels of Aβ40, Aβ42, and t‐tau were measured using the Neurology 3‐plex A. Aβ42 levels showed a significant difference between NL and MCI (one‐tailed t‐test p=0.0262). The level of Aβ42/Aβ40 ratio was also significantly different between SCD and MCI (one‐tailed t‐test p=0.0206). pTau181 level showed a statistically significant difference between NL and SCD (one‐tailed t‐test p=0.0341). The pTau181/Aβ42 ratio was significantly different between NL and SCD; and NL and MCI (one‐tailed t‐test p=0.0190 and p=0.0037, respectively). In addition, the t‐tau/Aβ42 ratio was significantly different between NL and SCD, and NL and MCI (p=0.0359 and p=0.0091, respectively). Correlation of these biomarkers with brain imaging and cognitive measures is underway.ConclusionBiomarker levels of Aβ42, pTau181, pTau181/ Aβ42 ratio, and t‐tau/Aβ42 in plasma samples showed significant differences between the three groups of subjects. Plasma biomarkers can be useful for the diagnosis of AD‐related pathology at early stages.
Association between medial temporal and neocortical Tau SUVR across tau imaging agents: HEAD Study
Authors: Rahmouni Nesrine , Tissot Cécile , Hosseini Seyyed Ali , Macedo Arthur C. , Servaes Stijn , Therriault Joseph , Lussier Firoza Z , Arias Jaime Fernandez , Wang Yi‐Ting , Stevenson Jenna , Trudel Lydia , Gauthier Serge , Baker Suzanne L. , Pascoal Tharick Ali , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundThe association between medial temporal and neocortical SUVR depends on availability of cortical tau. However, tracer differences in affinity and off‐target binding might interfere in these associations. Here, we examined the association between medial temporal and neocortical SUVR using voxel‐based approach.MethodWe included 92 individuals (46 Cognitively Unimpaired and 46 Cognitively Impaired) from the HEAD Study recruited at McGill University. All individuals were assessed for amyloid‐β (Aβ) deposition using [18F]AZD4694‐PET and at least two tau‐PET ligands ([18F]MK6240 and [18F]Flortaucipir). A subset of 25 individuals had 2 additional tau‐PETs scans: [18F]RO948 and [18F]PI‐2620. Voxel‐based regression models evaluated the relationship between PET tracers’ uptake in the entorhinal cortex (EC) and its association with the tracer’s uptake in later Braak regions. All models include Aβ‐PET, age, and sex as covariates and RFT was used to account for multiple comparisons. Additionally, we extracted the number of significant voxels in the associations within each Braak regions.ResultPositive associations were observed between tau‐PET SUVR in the EC (Figure 1). [18F]MK6240 EC was correlated with tracer uptake in the whole cortex. [18F] Flortaucipir was associated with tracer uptake in similar regions, albeit showing a lower t‐value. [18F]PI2620 EC uptake correlated with binding in regions Braak II‐V. Finally, [18F]RO948 was associated with uptake in regions Braak II‐IV. We further validated our results by extracting the number of significant voxels in each Braak regions. [18F]MK6240 showed the highest percentage of spatial extent of tau spreading in all Braak regions, followed by [18F]Flortaucipir and [18F]RO948. [18F]PI2620 demonstrated the lowest spatial extent.Conclusion[18F]MK6240 SUVR better predicted the spatial extent of tau spreading in the whole cortex. Results were concordant at the voxel and region‐on‐interested levels. [18F]Flortaucipir showed similar results, in lower magnitude. Finally, [18F]PI2620 and [18F]RO948 were further lower. Nevertheless, the later analyses were conducted in a smaller sample. Our results support the concept that MK6240 affinity possibly explain these results.
Ruminococcus‐based metagenomic‐brain signature linked to cognitive performance and Alzheimer’s disease markers
Authors: Labus Jennifer S , Heston Margo B. , Mayer Emeran A , Mohanty Ipsita , Tran Thai , Huynh Kevin , Gupta Arpana , Rey Federico E. , González Antonio , Dilmore Amanda H. , Knight Rob , Kaddurah‐Daouk Rima , Bendlin Barbara B. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

AbstractBackgroundThe aim of this study was to identify a gut microbial signature associated with patterns of gray matter volume in AD, and to validate the microbial signature by testing it against measures of AD pathology and cognitive performance. Prior literature suggests that microbial species involved in bile acid production and inflammation may be implicated in the microbial signature.MethodThe sample comprised 204 Microbiome in Alzheimer’s Risk Study participants (22 AD, 10 MCI, and 172 CN; 129 Females, 78 APOE+) from the Wisconsin Alzheimer’s Disease Research Center and Wisconsin Registry for Alzheimer’s Prevention. With shotgun metagenomic sequencing data and regional gray matter volumes calculated using T1‐weighted neuroimaging (Destrieux, Harvard Oxford subcortical atlases), sparse partial least squares regression was used to determine correlated sets of microbial features and gray matter regions that maximally explained the variance in the two datasets. General linear models were then used to link the derived microbial signature with an aggregated score of cognitive performance (PACC3), an estimated cognitive score (ADAS‐Cog‐13) derived using participants’ serum lipidomics, and amyloid and tau positivity status.ResultGreater abundance of 14 microbial features dominated by Ruminococcus species was associated (r=0.39, p=7.7e‐09) with greater volume in 13 regions dominated by the hippocampus, amygdala, and temporal regions, which are implicated in AD (Figure 1). This relationship was especially strong among APOE ε4 carriers. Higher scores on the microbial signature (associated with higher gray matter volume) were also associated with pTau negative status, and with better performance on the PACC3 and estimated ADAS‐Cog‐13.ConclusionWith roles in producing secondary bile acids and short chain fatty acids, Ruminococcus species may benefit the gut mucosal barrier and abrogate inflammatory mechanisms. This study suggests these microbes could protect against neurotoxic processes including tau hyperphosphorylation, resulting in preserved cortical volume and better cognitive performance.Funding: Alzheimer Gut Microbiome Project, NIA U19 AG063744
Alzheimer’s disease diagnosis using gray matter of T1‐weighted sMRI data and vision transformer
Authors: Aghdam Maryam Akhavan , Bozdag Serdar , Saeed Fahad 
Published: January 2025

AbstractBackgroundAlzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. Traditional diagnostic methods, mainly based on cognitive, memory, and behavioral tests, have limitations, particularly in the early detection of AD. Structural magnetic resonance imaging (sMRI) has emerged as a key tool in understanding the brain changes associated with AD, focusing particularly on alterations in gray matter (GM). However, the complexity of brain changes in AD requires sophisticated analysis methods. In recent years, machine learning (ML) models have shown great potential in interpreting complex neuroimaging data. These models can detect intricate patterns in neuroimaging data, making them invaluable in enhancing the diagnostic accuracy and early AD diagnosis. Therefore, combining the neuroimaging data with ML models presents a promising direction in improving the early‐diagnosis and understanding of AD.MethodWe propose a novel approach to diagnose AD using Vision Transformer (ViT) (Figure 1) [1], a cutting‐edge class of ML model, and GM of T1‐weighted sMRI data. The proposed approach leverages the power of deep‐learning (DL) to detect the GM changes that are indicative of AD. We used pretrained ViT model to extract features from the GM sagittal and coronal slices of sMRI data and classify AD from cognitively normal (CN). We employed ADNI dataset, focusing on subjects with T1‐weighed MPRAGE sMRI scans, including 70 AD patients and 85 CN individuals.ResultThe study achieved an average classification accuracy of 97.6% in sagittal slices and 97.7% in coronal slices (Figures 2 and 3). These results indicate a significantly higher accuracy in diagnosing AD using the proposed method compared to other state‐of‐the‐art models based on sMRI data. The high accuracy underscores the model's capability in effectively distinguishing between AD patients and CN individuals, demonstrating its potential utility in clinical settings.ConclusionThe proposed approach demonstrates a significant advancement in the accurate diagnosis of AD, which might be useful for early‐diagnosis. Our proposed ML model represents a considerable improvement over existing ML methods, offering a new avenue for research and application in the field of neurodegenerative diseases.