bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel PU.1 inhibitor for the treatment of Alzheimer’s disease
Authors: Ralvenius William T , Tsai Li‐Huei , Woolf Hannah , Cam Hugh , Mungenast Alison 
Published: December 2021

AbstractBackgroundIn 2015, we identified the microglia‐specific transcription factor PU.1, encoded for by SPI1, as a master regulator of neuroinflammation in a mouse model of Alzheimer’s Disease and in human brain tissue from deceased Alzheimer’s Disease patients1. In 2017, a GWAS SNP that delays onset of Alzheimer’s Disease, was shown to reduce PU.1 expression levels2. Here, we describe a high‐throughput screen for small molecules that inhibit PU.1 activity. Our goal is to develop novel agents that can rein in the abnormal neuroimmune response associated with Alzheimer’s dementia.MethodWe used a stable immortalized mouse microglia (BV2) reporter line that expresses luciferase in a PU.1‐dependent manner. We screened a total of 58’100 molecules from 3 separate chemical libraries and obtained 264 hits. We further validated these hits using multiple assays in human stem cell derived microglia. Finally, we administered our top hit (A11) to transgenic mouse models of Alzheimer’s Disease.ResultA11 exhibits anti‐inflammatory effects at a concentration of 20 nM in activated microglia, as assessed by RTqPCR and ELISA for IL1β, morphological analyses and lipid accumulation quantification. In mice, A11 preferentially distributed to brain tissues after systemic (intraperitoneal) application and reached a peak concentration at 4 hours. When dosing mice that develop severe neurodegeneration (CK‐p25 transgenic mice), we found a reduction of microglial proliferation, astrogliosis and retention of p25‐positive neurons in the hippocampus. No abnormalities were seen in the treated control littermates.ConclusionOur novel PU.1 inhibitor (A11) shows nanomolar potency and efficacy in multiple assays and cell lines. Mouse experiments demonstrate suitability for systemic application. Administration of A11 in CK‐p25 transgenic mice show an amelioration of neurodegeneration‐associated hallmarks, such as microglial and astrocytic activation and loss of p25‐positive neurons. References: (1) Gjoneska,E, A R Pfenning, H Mathys, G Quon, A Kundaje, L H Tsai and M Kellis. 2015. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nat. 518(7539)365‐9. (2) Huang,K L, E Marcora, A A Pimenova, A F Di Narzo et al. 2017. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat Neurosci. 20(8) 1052‐1061.
Asian cohort for Alzheimer’s disease (ACAD) data collection: Rationale, design and validation
Authors: Chow Tiffany W , Reyes‐Dumeyer Dolly , Gu Yian , Li Clara , Tee Boon Lead , Lee Haeok , Park Van Ta , Lieu Kevin , Lu Anna T , Peavy Guerry M , Hsiung Ging‐Yuek Robin , Vogel Briana , Ho Pei‐chuan , Yu W. Haung , Choi Yun‐Beom , Jun Gyungah R , Henderson Victor W , Chow Tiffany H , Mayeux Richard , Wang Li‐San ,   The Asian Cohort for Alzheimer's Disease Study
Published: December 2021

AbstractBackgroundAs the first large Alzheimer’s Disease (AD) genetics cohort for Asians in the United States and Canada, the Asian Cohort for Alzheimer’s Disease (ACAD) has adapted existing instruments to collect data on sex, genetics, medical history, and lifestyle risk factors. We hypothesize that any and all of these factors impact AD risk differently for Asians. Initially ACAD focuses on US and Canadian participants of Chinese, Korean, and Vietnamese ancestry.MethodACAD’s Clinical Workgroup reviewed commonly used dementia research questionnaires, including the NACC Uniform Data Set, Religious Order Study, and NIA‐Late‐Onset AD to select, and in some cases adapt, questions that are most appropriate for ACAD. A combination of AD clinical researchers and community outreach specialists reviewed the data collection packet, administration procedures, and consensus diagnosis process for cultural sensitivity without sacrificing scientific rigor to gather non‐genetic and lifestyle risk factor data.ResultWe designed the ACAD Data Collection Packet (DCP) to create a friendly dialogue with participants through a socio‐culturally and linguistically sensitive 3‐part process. The DCP consists of some instruments already validated in Chinese, Vietnamese and Korean languages to screen for cognitive impairment (Cognitive Abilities Screening Instrument (CASI) and Modified Mini‐Mental State (3MS)). Part A collects origins, Mediterranean diet compliance, and Clinical Dementia Rating scores. The self‐administered Part B uses an adaptation of the Rush Cognitive Activity Questionnaire on early‐life enrichment. Part C structures the collection of medical history and cognitive testing with CASI vs 3MS, the Common Objects Memory Test, Category (not Letter) Fluency, and the 15‐item Clock Drawing Task. This assessment is designed to be administered either remotely or face to face.ConclusionOver the next year, we will pilot the ACAD Data Collection Packet, to set a community standard for dementia screening among Asian Americans and Canadians that can support data harmonization across epidemiological studies. While ACAD translation accommodates Chinese, Korean, and Vietnamese at this stage, we look forward to implementing additional major Asian languages.
Causal mediation analysis with longitudinal data: Relationships among sleep quality, diabetes, and subjective memory in older adults
Authors: Park Sang‐Mi , Lee Chang Dae 
Published: December 2021

AbstractBackgroundLow quality sleep or lack of sleep is related to both lower levels of quality of life and negative health problems such as cognitive decline or diabetes in older adults. Although the associations among sleep quality, cognitive function, and diabetes were reported in previous studies, the mechanisms of association and their causal link is unclear. The aim of this study was to explore the mechanism between quality of sleep and cognitive decline by examining the mediation model of quality of sleep, diabetes, and subjective memory.MethodThe longitudinal panel data from the National Health and Aging Trends Study (NHATS) which was released to the public for research was used for this study. Research hypothesis was that diabetes in the third wave mediates the association between sleep quality in the first wave and subjective memory in the fifth wave among consecutive five years of wave. Presence of diabetes was used as a mediation variable. Sleep quality was measured with three questions about frequency of difficulties in falling asleep or maintaining a good sleep at night within the last month. Subjective memory was measured by one question with a five‐point‐Likert scale. Causal mediation analysis was conducted using CAUSALMED procedure in SAS 9.4.ResultExcept for the cases which contained missing data, data from 3,238 older adults above the age of 65 was used for final analysis. After controlling for diabetes, education, subjective memory, economic well‐being, depression, medication, age, smoking history in the first wave, the total effect of higher sleep quality on better subjective memory was .011 (p=.036). However, indirect effect of sleep quality mediated by diabetes was not statistically significant (estimate = ‐.0003, p=.641). The percentage due to interaction effect of sleep quality and diabetes was not statistically significant as well (estimate = ‐31.269, p=.295).ConclusionAlthough both sleep quality and diabetes were reported to be important predictors of dementia in previous studies, the result did not support the mediation effect of diabetes between sleep quality and subjective cognitive decline. Further study to discover the mediation effect of predictors of dementia is needed for better understanding of the cognitive decline mechanism in older adults.
Differences between neurodegenerative disorders in conversational turn‐taking
Authors: Pressman Peter S. , Matthewson Gordon , Lemieux Eric , Levenson Robert W 
Published: December 2021

AbstractBackgroundA smooth turn‐taking transition from one person to the next is an expected part of many conversations. Turn‐taking combines high temporal coordination between participants with the remarkable complexity and open‐endedness of the language and paralanguage that fills the turns. Turn‐taking may therefore serve as a naturally occurring marker of disorders that disrupt executive functioning, ability to interpret nonverbal cues, as well as social drive and motivation.MethodsCouples consisting of a healthy control and a person with a neurodegenerative condition were instructed to discuss a mutually selected topic of continuing disagreement in their relationship. Each conversation lasted between 10 to 15 minutes. Manual labeling of speakers, non‐speech vocalizations and background noise was then performed using Praat. Statistical analysis was then performed on numerous conversational metrics, including both count and duration of gaps, interruptions, and pauses throughout the conversations.ResultsOverall differences were found in number of times someone took the floor, interrupted the partner, and number of perfect exchanges. Compared to Alzheimer’s disease (AD), the semantic variant of primary progressive aphasia (svPPA) and progressive supranuclear palsy had more interruptions. Behavioral variant frontotemporal dementia had fewer perfect exchanges (< 0.1 second between speakers without interruption) than AD.ConclusionFloor exchanges, specifically numbers of interruptions and “perfect exchanges,” may be useful in distinguishing atypical AD from other dementia types. Next steps include comparing patients with neurodegenerative conditions against normal controls and correlating data with imaging and neuropsychiatric testing.
Gut microbiome alterations in Alzheimer’s patients from Kazakhstan
Authors: Askarova Sholpan , Kaiyrlykyzy Aiym , Kozhakhmetov Samat , Babenko Dmitriy , Olzhayev Farkhad , Kushugulova Almagul 
Published: December 2021

AbstractBackgroundRecent studies strongly suggest that gut microbiome is capable of influencing brain functions and contribute to the development of Alzheimer’s disease (AD). However, only few studies have characterized the human gut microbiome communities associated with AD. Moreover, reported changes in the gut microbiomes in AD patients from different countries are not similar. Therefore, more research is needed in order to reveal the relationships existing between human microbiomes and AD in different ethnic populations. Here, we report the first pilot case‐control study of diversity and composition of gut microbiomes isolated from the patients diagnosed with AD in comparison with healthy seniors from the Central Asian region.MethodMicrobial DNA was isolated from fecal samples of the patients with confirmed AD (n=11) and healthy age‐ and gender‐matched individuals (n=13). The composition of gut microbiomes were characterized by 16S ribosomal RNA MiSeq sequencing. Data analysis was performed using less operational taxonomic units scripts (LotuS) and R (v4.03).ResultOur results demonstrated that gut microbiota of AD individuals had overall higher α‐diversity compared to healthy controls, although this difference was not significant. The absence of statistical power might be explained by a low sample size. However, we have found significant differences of bacterial abundance at class, order and genus levels. At class level the amount of Bacilli was depleted in AD group; at order level, the relative abundance of Oscillospirales increased, while the abundance of Lactobacillales decreased in AD patients. Among bacterial genera, microbiomes of AD participants were characterized by a preponderance of unknown_Prevotellaceae and unknown_Ruminococcaceae. Linear discriminant analysis (LDA) has revealed that the relative abundances of Eubacterium coprostanoligenes group, Rikenellaceae RC9 gut group, Bacilli RF39, Oscillospirales UCG‐011 were significantly different in AD patients compared to healthy participants.ConclusionStudying the gut microbiome alterations in AD patients is of considerable interest. By focusing on the mechanism of interactions between human microbiota and the brain, we can uncover new pathophysiological pathways leading to the onset and progression of AD, evaluate the contributions of environment and lifestyle to the increase in prevalence of dementia and develop practical recommendations for the prevention and treatment of this severe pathology.
Mild behavioral impairment: Prevalence, clinical profile, and prognosis in a memory clinic cohort in Singapore
Authors: Kan Cheuk Ni , Cano Jemellee , Chan Victoria , Ismail Zahinoor , Zhao Xuhao , Chen Christopher , Xu Xin 
Published: December 2021

AbstractBackgroundMild behavioral impairment (MBI) is characterized as later‐life emergent and persistent neuropsychiatric symptoms (NPS) that are suggestive of early manifestations of Alzheimer’s Disease (AD) and dementia. This study aims to investigate the prevalence, clinical correlates, cognitive trajectories, and dementia risk of MBI in a clinical cohort.MethodIn a Singaporean longitudinal cohort, 309 dementia‐free participants (mean age=71.6 [SD=7.8], 52% females) underwent annual neuropsychological, neuropsychiatric, and clinical assessments for up to four years. The diagnosis of MBI was ascertained using neuropsychiatric inventory assessments at two time‐points. Global and domain‐specific cognitive Z‐scores were calculated from a comprehensive neuropsychological battery, and Clinical Dementia Rating sum‐of‐boxes (CDR‐SoB) scores were used to assess disease progression.ResultA total of 72 (23.3%) participants had MBI. Cross‐sectionally, MBI patients performed poorer in global cognition (β=‐0.27 [‐0.51–0.03], p=0.026), particularly in memory (β=‐0.56 [‐0.79–0.33], p<0.001), executive function (β=‐0.33 [‐0.62–0.03], p=0.030), and visuomotor speed (β=‐0.20 [‐0.39–0.02], p=0.031) domains. On follow‐up, MBI patients showed faster decline in global cognition (β=‐0.19 [‐0.27–0.11], p<0.001) (Fig. 1) and faster increment in CDR‐SoB (β=0.96 [0.74‐1.19], p<0.001) (Fig. 2) as compared to non‐MBI elderly, regardless of cognitive status. On follow‐up, 40.3% (n=29) of MBI patients progressed to dementia, compared with 8% (n=19) of non‐MBI elderly. MBI diagnosis was associated with a significantly higher risk of dementia (Hazard Ratio=6.28 [3.51‐11.23], p<0.001) (Fig. 3).ConclusionMBI may be an early neurobehavioral marker of cognitive decline leading to dementia. Incorporating MBI evaluations into clinical assessments may identify high risk patients for dementia who may benefit from intervention.
Differences in sex distribution between genetic and sporadic FTD
Authors: de Boer Sterre C.M. , Van Der Lee Sven J , Diehl‐Schmid Janine , Riedl Lina , Piguet Olivier , Halliday Glenda M , Galimberti Daniela , Sorrentino Federica , Ducharme Simon , Fieldhouse Jay L.P. , Reus Lianne M. , Pijnenburg Yolande A.L. 
Published: December 2021

AbstractBackgroundThe reported sex distribution differs between frontotemporal dementia (FTD) cohorts. Possible explanations are the evolving clinical criteria of FTD and its subtypes and the discovery of FTD causal genetic mutations that have resulted in variable findings. Our aim was to determine the sex distribution in a large international retrospective cohort of sporadic and genetic FTD.MethodWe included patients with probable and definite behavioural variant frontotemporal dementia (bvFTD), non‐fluent variant primary progressive aphasia (nfvPPA), semantic variant primary progressive aphasia (svPPA) and right temporal variant frontotemporal dementia (rtvFTD) from the Amsterdam Dementia Cohort, the Montreal Neurological Institute Cohort, the University of Ulm and Technical University of Munich Cohort (part of the German Consortium of Frontotemporal Lobal Degeneration), the Policlinico Milan Cohort and the Sydney FRONTIER Cohort. We compared sex distribution between genetic and sporadic FTD using χ2 tests.ResultA total of 910 subjects were included (56.3% male), of whom 654 had bvFTD, 99 nfvPPA, 117 svPPA and 40 rtvFTD. Of these, 215 had genetic FTD and the sex distribution was equal (51.2% male), which did not differ significantly from sporadic FTD (57.8% male, χ2 p=0.081). In the sporadic bvFTD subgroup, we found a male predominance (61.6% males compared to 52.9% males in the bvFTD genetic group, χ 2 p=0.04). No sex distribution differences between sporadic and genetic cases were found in the other clinical FTD subgroups (all p>0.05).ConclusionDifferences in sex distribution between genetic and sporadic behavioural variant of FTD may provide important clues for its differential pathogenesis and warrants further research.
Racial differences in AD CSF biomarkers in persons with MCI: Implications and insights
Authors: Garrett Stephanie L. , McDaniel Darius , Obideen Malik , Trammell Antoine R. , Shaw Leslie M. , Goldstein Felicia C. , Hajjar Ihab 
Published: December 2021

AbstractBackgroundThe implications for potential racial differences in tau CSF biomarkers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are unclear for standard cutoffs used for determining enrollment criteria into novel clinical trials, and insight into interaction with markers of non‐biological determinants.MethodSetting & Participants‐ Data presented were obtained from baseline assessment of a case control study conducted from March 1, 2016 through January 31, 2019. Participants were 50 years or older adult volunteers recruited from Atlanta, Georgia into the Brain Stress Hypertension and Aging Research Program (B‐SHARP, PI: Hajjar R01AG051633) at the Emory Goizueta Alzheimer’s Disease Research Center. Exposures‐ Self‐reported race and cognitive status per modified Petersen criteria. Outcome measures‐ CSF levels of Aβ1‐42, tau, P‐tau181, ratio of tau or P‐tau181 to Aβ1‐42, and hippocampal volume.Result362 study participants were analyzed (mean [SD] age, 65.6 [7.9] years of age), where 152 (42.0%) were African American, 230 (63.5%) were women, and 189 (52.2%) had MCI. After adjusting for multiple determinants, including age, sex, cognitive performance, body mass index (BMI), education, and family history of AD, results suggest Black Americans compared to white individuals with MCI had lower mean (SE) levels of CSF tau (52.4 [5.9] vs. 78.98 [5.02] pg/mL; P=.001) and P‐tau181 (15.42 [2.06] vs. 25.24 [1.75] pg/mL; P=.001). After adjusting for intracranial volume and other covariates in the total sample hippocampal volumes did not differ by race. However, using ROC analyses, AUC comparisons suggest that Aβ1‐42 (0.632 vs 0.779; P=.02) and P‐tau181 (0.638 vs 0.768;P=.03) had lower specificity and sensitivity in Black Americans to distinguish between MCI and normal cognition.ConclusionWe recommend caution in using current biomarker cutoffs to determine enrollment criteria for potential Black American study cohorts. While we hypothesized that these differences could be explained by increased vascular contributors, our supposition was not supported; after adjusting for hypertension and diabetes, racial differences yet remained. Further investigation should clarify the impact of biological factors like vascular disease, interaction with more explicit and well‐defined environmental determinants like education quality and quantity, and their life‐course occurrence.
Early detection of mild cognitive impairment with convolutional neural network based on brain structural connectivity
Authors: Chen Qianyun , Yan Taiyu , Abrigo Jill , Chu Winnie C.W. 
Published: December 2021

AbstractBackgroundStructural connectivity derived from diffusion tensor imaging (DTI) reflects white matter network changes during Alzheimer’s Disease (AD) progression, which can potentially serve as a biomarker for prediction. In this study we explored the ability of structural connectivity to predict mild cognitive impairment (MCI) by implementing a convolutional neural network (CNN).Method613 DTI scans (194 controls, 291 MCI and 128 AD) from 206 patients (113 male, 93 female; age range: 55‐91 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Phase GO and 2 were included in this study. The clinical diagnosis at last ADNI follow‐up visit served as reference for prediction (average: 44.4 ± 26.7 months). DTI scans were acquired with 5 b0 and 41 diffusion weighted volumes with b=1000 s/mm2 at 2.7×2.7×2.7mm3 resolution. Preprocessing included eddy current correction and co‐registration to normalized T1‐weighted scans.Whole brain white matter fiber was tracked by deterministic tractography using DSI Studio under Q‐space diffeomorphic reconstruction scheme, which calculates the quantitative anisotropy mapping prior to normalization to standard space. An Automated Anatomical Labeling atlas 2 with 120 region‐of‐interests (ROI) was used to parcellate the brain, where each ROI represented a node of the network. The weight of the edge was calculated by number of tracts connecting two ROIs and normalized by medial length. Therefore a 120×120 structural connectivity matrix was generated for each scan. 80% of data was used for training and 10% of data for validation. The parameters that achieved highest accuracy on validation set was applied to the remaining 10% (hold‐out test set) for final evaluation. A seven‐layer CNN model was constructed for differentiating three groups. The performance was evaluated by accuracy and sensitivity.ResultOn an average of 3.7 years before the diagnosis, the prediction accuracy was 86.9% in validation set and 85.7% in hold‐out test set. The sensitivity to predict controls and MCI were 95.0% and 93.3%, respectively.ConclusionUsing CNN, structural connectivity allowed good ability to predict MCI from cohorts at an early stage before diagnosis.