bluuebunny/crossref_metadata_2025_split huggingface dataset:
The neural nitric oxide synthase is associated with cognitive decline and Alzheimer’s disease pathology
Authors: Justo Alberto Fernando Oliveira , Toscano Eliana Cristina de Brito , Leite Renata Elaine Paraizo , Paes Vitor Ribeiro , Grinberg Lea T. , Pasquallucci Carlos Augusto , Jacob‐Filho Wilson , Suemoto Claudia Kimie 
Published: December 2023

AbstractBackgroundAlthough Alzheimer’s disease (AD) etiology is partially explained by the deposition of aggregated forms of β‐amyloid proteins and neurofibrillary tangles in the brain, the process that triggers the deposition of these proteins is still unknown. The neural (nNOS), endothelial (eNOS), and inducible nitric oxide synthases (iNOS) may play an important role in AD pathophysiology according to previous studies in animals. However, studies on humans are scarce. Therefore, we aimed to quantify the expression of NOS enzymes in the hippocampus of individuals with AD compared to individuals without the disease and investigate the association of NOS expression with cognition and AD‐related pathology.MethodWe evaluated the nNOS, eNOS, and iNOS expressions using immunohistochemistry in the cornu ammonis (CA) and the dentate gyrus (DG) from hippocampi from subjects with pure AD and sex‐matched controls. Cognitive abilities were evaluated using the Clinical Dementia Rating (CDR) and AD pathology using Braak staging for neurofibrillary tangles and the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) for neuritic plaque. We used linear regression to examine the association of NOS with cognition and AD pathology.ResultsSociodemographic and clinical variables were similar between the control and AD groups (Table 1). In individuals with AD, nNOS was overexpressed in the CA (β = 23.62, IC95% = 5.13;42.10, p = 0.01) and DG (β = 16.76, IC95% = 0.35;3.18, p = 0.01), while eNOS and iNOS were increased only in the CA (β = 0.93, IC95% = 0.02;1.84, p = 0.04; β = 0.19, IC95% = 0.09;0.90, p = 0.01, respectively) (Figure 1). Larger nNOS levels in the CA and DG were associated with worse cognitive abilities, higher Braak and CERAD scores (Table 2).ConclusionThe nNOS, eNOS, and iNOS were increased in the hippocampi of subjects with AD. However, only nNOS overexpression was associated with cognitive impairment and AD‐related pathology.
Computerized serious game‐based cognitive training combined with tDCS for apathy treatment in Mild Neurocognitive Disorders
Authors: Daumas Lyne , Ettore Eric , Zory Raphael , Sacco Guillaume , Giordana Bruno , Guyomar Florent , Ferrandez Marion , Robert Philippe , Manera Valeria 
Published: December 2023

AbstractBackgroundApathy is a clinical syndrome, associated with numerous adverse outcomes, which is pervasive in neurocognitive disorders. Neural bases of apathy rest on dysfunctions of brain structures involved in initiation, execution and control of goal directed behavior, and notably implicate dysfunction of cognitive functions which are required to generate plan of actions. Cognitive training has shown efficacious effects in mild cognitive disorders, and non‐invasive brain stimulation seems to be a promising intervention for cognition and neuropsychiatric symptoms, as suggested by the benefit effects found on depression. The aim of this study is to assess the effectiveness of repeated sessions of computerized serious game‐based cognitive training combined with transcranial direct current stimulation (tDCS) on apathy among older adults with neurocognitive disorders.MethodThe study is an ongoing double‐blind randomized controlled trial. Patients are randomized into a 4‐week intervention of computerized “MeMo” serious game‐based cognitive training combined with either sham or active tDCS. The 4‐week intervention comprise 3 sessions per week, with 20 minutes of cognitive training plus simultaneously sham or active tDCS targeting the left dorsolateral prefrontal cortex with a 2mA of current intensity. The main outcome was the apathy, assessed using the Apathy Diagnostic Criteria, the Apathy Inventory and the self‐reported Apathy Motivation Index questionnaire. Secondary outcomes are cognitive performances, fatigue and daily motor activity.ResultPreliminary results of the ongoing study will be presented during the AAIC.ConclusionThese results will provide new evidence about the interest to use combined cognitive training and tDCS as neurorehabilitation intervention, for cognition and apathy treatment in mild neurocognitive disorders.
The utility of the digital clock drawing test as an accurate early screening tool for those with preclinical Alzheimer’s disease
Authors: Ding Huitong , Sunderaraman Preeti , Devine Sherral A. , Karjadi Cody , Ang Ting Fang Alvin , Au Rhoda 
Published: December 2023

AbstractBackgroundWith new drug treatments emerging, the need for early Alzheimer’s disease (AD) detection methods that can be done in a primary care setting has become urgent. Significant advances have been made in validating blood‐based AD biomarkers, but the absence of a validated, non‐invasive clinical indicator remains a barrier for determining those who might be optimal candidates for treatment. The digital clock drawing test (dCDT) has been previously shown to be related to AD PET biomarkers. However, it is unknown whether and the extent to which dCDT would predict AD as compared to blood‐based AD biomarkers. The current study used an interpretable machine learning approach to determine whether the dCDT, independently and in combination with blood‐based AD biomarkers (e.g., amyloid‐β 42 (Aβ42); total tau (T‐tau)), was predictive of incident AD.MethodThis study included participants from the Framingham Heart Study Generation 2 cohort (Gen 2; n = 527) who had dCDT measures, plasma Aβ42, and T‐tau. The fast interpretable greedy‐tree sums method was used to identify clinical decision rules for AD prediction. Synthetic minority oversampling technique was employed to handle the class imbalance. The predictive ability of plasma Aβ42, T‐tau and dCDT measures was evaluated by both area under the curve (AUC) and standardized partial AUC. The permutation feature importance method was used to show the importance of each measure.ResultInterpretable clinical decision rules were generated as multivariate biomarker signatures for AD prediction (Figure 1). The dCDT (AUC 0.859) was better at incident AD prediction than AD blood‐based biomarkers (AUC 0.839). The combination of dCDT, Aβ42, and T‐tau best predicted incident AD (AUC 0.884) (Figure 2). The combination of dCDT and blood‐based biomarkers also has higher partial AUC than individual ones (Table 1). The total score and information processing scores were the most important contributing dCDT measures.ConclusionThese results highlight the efficiency of detecting early AD through the integration of digital measures as a clinical indicator combined with AD specific biomarkers. The interpretable decision rules have the potential to provide automated results that can be used in practical clinical settings, although external validation is warranted.
Potential cognitive enhancers: Discovery and development of multi‐targeting aminoalkyl chalcones
Authors: Singh Manjinder , Sharma Pratibha , Singh Varinder , Singh Thakur Gurjeet 
Published: December 2023

AbstractBackgroundInhibition of acetylcholinesterase (AChE) enzyme is considered as the most promising approach to improve the cognitive deficit complex disorders such as Alzheimer’s disease, dementia etc. The AChE also promote the aggregation of amyloid‐β complexes, mitochondrial dysfunction, a buildup of oxidative stress and neuronal cell death. Consequently, the development of compounds having polyfunctional potential could manage the complex neurodegenerative disorders like Alzheimer’s disease.MethodThe chalcone analogues were developed using bioisosteric replacement and SBDD strategies having improved therapeutic potential. The kinetics and interaction pattern with target protein of designed analogues were further evaluated using in silico methods. The leading compounds were synthesized and evaluated against AChE, AGEs formation with additional radical scavenging potential.ResultThe docking results revealed that synthesized chalcones properly fit into the active gorge of the target enzyme and displayed crucial interactions with the peripheral and catalytic sites of and having drug like properties. The in vitro assays of designed compounds against different targets showed IC50 values in nano to micro molar range.ConclusionThe dynamic simulations of the designed compounds will be carried out to evaluate the stability of the target‐ligand complex. Thus, the designed chalcones could be taken further to improve cognition deficit mainly in Alzheimer’s disease.
Influence of demographic and clinical characteristics on circulating GFAP levels in Mexican American and non‐Hispanic white older adults
Authors: Gonzales Mitzi M. , Vela Gabriel A. , Philip Vinu , Trevino Hector , LaRoche Ashley , Wang Chen‐Pin , Parent Danielle , Kautz Tiffany F , Satizabal Claudia L. , Tanner Jeremy A. , O'Bryant Sid E. , Maestre Gladys E. , Tracy Russell P. , Seshadri Sudha 
Published: December 2023

AbstractBackgroundCirculating levels of glial fibrillary acidic protein (GFAP), an intermediate filament protein of the astrocytic cytoskeleton and putative marker of reactive astrocytosis, increase with cerebral amyloid beta burden and associate with risk of incident all‐cause and Alzheimer’s disease (AD) dementia. However, further validation in diverse cohorts and evaluation of potential health disparities are necessary for broader generalization. The goal of the present study was to examine the associations between demographics, cardiovascular risk factors, and APOE ε4 status with serum GFAP levels among Mexican American and non‐Hispanic white older adults across the continuum from cognitively unimpaired to AD dementia.MethodParticipants included 1,156 Mexican American and 587 non‐Hispanic white adults, aged 55 years and older, who completed a blood draw, clinical and cognitive evaluations, and dementia consensus reviews as part of the Texas Alzheimer’s Research and Care Consortium. Serum levels of GFAP were assayed using a Simoa HD‐1 Analyzer (Quanterix). Associations between demographic and clinical characteristics with serum GFAP levels were evaluated using linear regression. The diagnostic accuracy of serum GFAP was further examined using area under the receiver operating characteristic curves (AUROC) in univariate and adjusted models and optimal cut‐points were derived using the maximum Kolmogorov‐Smirnov metric. All models were also stratified by ethnicity and disease stage.ResultIn the whole sample (Table 1), older age (b = 0.588, p<0.001), APOE ε4 status (b = 0.219, p<0.001), and the presence of cognitive impairment (b = 0.226, p<0.001) positively associated with serum GFAP. In contrast, higher body mass index (b = ‐0.243, p<0.001), diabetes (b = ‐0.110, p<0.001), and tobacco use (b = ‐0.066, p<0.001) were inversely associated with serum GFAP. AUROC values were generally comparable across ethnicities and model fit improved with inclusion of additional covariates (Table 2). However, optimal serum GFAP cut‐off values were consistently lower in Mexican Americans relative to non‐Hispanic whites (Table 3).ConclusionThe study results highlight the importance of understanding the role of broader demographic and clinical factors on circulating GFAP levels within diverse cohorts in order to enhance precision across clinical, research, and community settings.
Associations among depressive symptoms, hippocampal volume and cognition in community‐dwelling older Latinos
Authors: Giudicessi Averi , Gatchel Jennifer , Vila‐Castelar Clara , Ramirez‐Gomez Liliana A. , Aduen Paula , Noriega Daisy , Félix Nikole A Bonillas , Kaplan Elizabeth , Martinez Jairo E. , Fabrega Sofia , Castillo Crystal , Badillo‐Cabrera Alex L , Garza‐Naveda Ana Paola , Garcia Kelly A. Lopez , Properzi Michael J , Kirn Dylan , Amariglio Rebecca E. , Rentz Dorene M. , Johnson Keith A. , Sperling Reisa A. , Quiroz Yakeel T. 
Published: December 2023

AbstractBackgroundLatinos are the fastest growing subpopulation of older adults in the U.S. and have a higher risk for dementia than non‐Latino Whites. Depressive symptoms are prevalent among US Latinos. However, these symptoms often go undiagnosed and are undertreated. Depressive symptoms may be early manifestations of Alzheimer’s Disease (AD) before the onset of mild cognitive impairment and dementia. It’s unclear whether depressive symptoms in the presence of incipient neurodegeneration are associated with cognitive function in older Latino adults. Here, we examined the associations among depressive symptoms, cognition and hippocampal volume in older Latinos.MethodA total of 65 Spanish‐speaking Latinos enrolled in the Boston Latino Aging Study (BLAST) and the Harvard Aging Brain Study (HABS) were included. Depressive symptoms were assessed with the 30‐item‐Geriatric Depression Scale (GDS) and cognition with the Preclinical Alzheimer’s Cognitive Composite (PACC5), which includes the Mini‐Mental State Examination (MMSE), Digit Symbol Coding, Free and Cued Selective Reminding Test, Category Fluency (animals) and Logical Memory Delayed Recall (NEUROPSI Stories for BLAST participants). A subset of 34 participants also underwent a structural brain MRI on a 3T scanner. Spearman correlations were used to determine cross‐sectional associations among depressive symptoms, hippocampal volume, and cognition. Models were adjusted for age and education.ResultsParticipants had a mean age of 66 +/‐ 7.2 years, a mean education of 12.9 +/‐ 5.1 years, and 74% were female. They had a mean MMSE score of 26.7 +/‐ 2.8, a mean GDS score of 6.1 +/‐ 5.5 and a mean adjusted hippocampal volume of 7836.5 +/ = 630.9 mm3. Correlation analyses revealed that higher depressive symptoms were associated with lower years of education (r = ‐0.39, p<0.001), worse performance on cognitive measures (r = ‐0.33, p = 0.009) and smaller hippocampal volume (r = ‐0.61, p = <0.001).ConclusionsPreliminary results of this ongoing biomarker study suggest that depressive symptoms in older Latinos are associated with markers of neurodegeneration (i.e., hippocampal volume) and worse cognition. Future work will focus on investigating predictors of long‐term emotional and cognitive functioning in Spanish‐speaking, older Latino populations.
Retinal imaging biomarkers of Alzheimer’s disease: clinical evidence and perspectives
Authors: van Wijngaarden Peter 
Published: December 2023

AbstractA growing body of research is devoted to retinal imaging biomarkers of Alzheimer’s disease (AD). Imaging methods such as optical coherence tomography (OCT) provide qualitative and quantitative measures of retinal and optic nerve neurodegeneration, whereas OCT‐angiography yields depth‐resolved maps of the retinal microvasculature in exquisite detail. Similarly, conventional and widefield retinal photography enable measurements of the retinal vascular geometry. Other emerging technologies, including hyperspectral imaging, retinal curcumin‐fluorescence imaging and fluorescence lifetime imaging ophthalmoscopy, provide insights into retinal changes at the molecular level, such as the accumulation of amyloid beta.Navigating the evidence in this field is challenging. Of the many studies that have examined associations between retinal imaging findings and AD, most have been based on clinical case ascertainment in the absence of biomarker findings. In this presentation, we will provide an accessible overview of this rapidly evolving field, with an emphasis on studies of retinal imaging biomarkers of the disease that align with the NIA‐AA Research Framework. Underpinning this are the findings of a systematic review and meta‐analysis that we have recently conducted (in press). Drawing on the anatomical and pathological insights provided by fellow speakers in the symposium, we highlight opportunities for further clinicopathological correlation of key imaging biomarkers. Finally, as a prelude to the last talk in the symposium, we highlight gaps in the existing clinical literature and point to efforts to harmonise study methods to generate the evidence needed for adoption of retinal imaging biomarkers of AD in practice.
Analogues Rescues Tau Pathologies and Memory Deficits Inhibiting GSK‐3 Signaling using Scaffold morphing and in silico approaches
Authors: Singh Manjinder , Goyal Shuchi , Thirumal Divya , Sharma Pratibha , Kaur Maninder , Singh Varinder , Singh Thakur Gurjeet 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD is caused by plaque agglomeration and entanglement in several areas of the neural cells, which leads to apoptosis. The main etiology of AD is senile dementia, which is linked to amyloid‐beta (Aβ) deregulation and tau perivascular pathogeny. Hyperphosphorylated tau has propensity for microtubules, which elevates the instability and tau‐protein congregates, leading to accumulation of neurofibrillary tangles (NFTs). Tau hyperphosphorylation is susceptible to GSK‐3 and is an emerging hypothesis for AD. As a result, attempts are made to conduct investigations and further advancements on new analogues capable of inhibiting the GSK‐3 protein, which are currently in the clinical trials.MethodIn this analysis, we have evaluated certain GSK‐3 inhibitor variants utilizing scaffolding and framework devise technique with pharmacological characteristics, accompanied by computational screenings (pharmacokinetics and docking).ResultThe structure‐based designed analogues interacted effectively with the active amino acids of GSK‐3β target protein. The in silico pharmacokinetic studies revealed their drug‐like properties.ConclusionThe analogues with best interactions and binding scores will be considered in the future to completely demonstrate their potential relevance as viable GSK ‐ 3 inhibitors.
A machine‐learning approach to biomarker evaluation for AD precision medicine
Authors: Climer Sharlee 
Published: December 2023

AbstractBackgroundLate‐onset Alzheimer disease (AD) is a heterogeneous disease, as demonstrated by its wide range of genetic and environmental risk factors, as well as diversity of clinical manifestations. Cognizant of the futility of a one‐size‐fits‐all approach for heterogeneous diseases, precision medicine aims to treat each subtype appropriately, but methods for successful diagnosis of AD subtypes has been limited. Real‐valued biomarkers, such as omics measurements in plasma or CSF, frequently are evaluated based on fold change (FC) and/or the area under the receiver operating characteristic curve (AUC) metric. We previously demonstrated the inability for FC and AUC to capture signals for subtypes that comprise less than 50% of the diseased individuals due to inherently low true positive rates and proposed an alternative approach based on the bimodality of the data [Smith and Climer, https://doi.org/10.1101/2022.02.14.22270972]. This approach is based on statistical characteristics of the data, including skewness, cardinality, and kurtosis. k‐medians clustering, a machine learning approach to identify natural clusters in data, removes these statistical assumptions, but optimally solving k‐medians is generally NP‐hard, yielding computational intractability for datasets of interest. Lloyd’s approximation algorithm is commonly used in practice, however, its accuracy is highly sensitive to the random start state, thus is not suitable when accuracy is paramount.MethodOwing to the nature of the problem, we present an evaluation metric based on the optimal k‐medians objective with k = 2. Additionally, due to the single dimensionality of the data, we introduce a dynamic programming algorithm to improve computational performance.ResultAlthough optimally solving k‐medians is NP‐hard, our 2‐medians objective over 1‐dimensional data using dynamic programming has an average time complexity of O(n log n), and worst‐case complexity of O(n
2) where n is the number of data values. This objective has no reliance upon false positive rate, skewness, or kurtosis, making it robust for real‐valued data drawn from heterogeneous AD subtypes.ConclusionBiomarker data drawn from heterogeneous AD subtypes will have inherently low true positive rates, resulting with low FC and AUC values. Our machine‐learning based approach for evaluating potential biomarkers efficiently produces optimal clustering of the data and scales well to large datasets.
Classification of patients with dementia in a late life depression cohort using structural MRI
Authors: Hadji Mohammad , Weinstein Andrea M. , Jeyachandra Jerrold , Brown Patrick , Butters Meryl A. , Lavretsky Helen , Lenze Eric J. , Miller J. Philip , Mulsant Benoit H. , Voineskos Aristotle N. , Shimony Joshua S. 
Published: December 2023

AbstractBackgroundLate life depression (LLD) is a risk factor for cognitive impairment, with up to 40% of depressed older adults showing persistent cognitive deficits even after symptom remission. Over 20% of Alzheimer’s disease patients exhibit depression‐like symptoms which can represent an early marker of disease (Tsuno et Homma., 2009). We hypothesized that automated classification algorithms trained on structural MRI could predict adjudicated diagnosis of cognitive impairment (CI) in this cohort.MethodOur study evaluated 295 patients with LLD. An adjudication committee consisting of two board‐certified psychiatrists and a clinical neuropsychologist determined ground‐truth labels for patient diagnostic results of CI (N = 145) or No CI (N = 150). Patients underwent T1‐weighted scans, which were segmented with Freesurfer 6.0.1 (freesurfer.net) packaged with fMRIPrep 1.5.8 (fmriprep.org) using the Desikan‐Killiany atlas. Cortical thickness and volumetric data were obtained along with demographic parameters. Freesurfer data was then regressed against estimated total intracranial volume. 11 classifications algorithms implemented in R were trained and tested on 209 and 88 patients, respectively, to classify if a patient has CI. All continuous‐valued data were normalized with respect to the training set mean and standard deviation.ResultTable 1 shows results from multiple classifications algorithms, including high sensitivity, specificity, and accuracy from adaptive boosting algorithms. The latter algorithms iteratively optimize a set of binary classification tree algorithms (Freund et Schapire., 1996).ConclusionVolumetric structural MRI biomarkers trained with machine learning classifiers can adequately categorize depressed older adults with CI which could assist the adjudication process. Future work incorporating structural MRI biomarkers with gold standard clinical data may improve earlier detection of cognitive impairment stemming from a neurodegenerative etiology.References:1. Tsuno, N., & Homma, A. (2009). What is the association between depression and Alzheimer’s disease?. Expert review of neurotherapeutics, 9(11), 1667‐1676.2. Freund, Y., & Schapire, R. E. (1996, July). Experiments with a new boosting algorithm. In icml (Vol. 96, pp. 148‐156).