bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single‐nuclei chromatin accessibility and transcriptomics unravels altered human oligodendrocyte heterogeneity in Alzheimer’s disease
Authors: Morabito Samuel , Miyoshi Emily , Michael Neethu , Shahin Saba , Swarup Vivek 
Published: December 2020

AbstractBackgroundAlzheimer’s disease (AD) is a devastating neurodegenerative disease, characterized by changes in cell‐type proportions and marked alterations of the epigenome and transcriptome. Recent “bulk” tissue RNA‐seq on thousands of postmortem AD brain samples by AMP‐AD consortia has shed light on new AD biology. Unfortunately, cell‐type specific disease biology is completely missed or averaged out using conventional “bulk” tissue RNA‐seq approaches. Single‐cell transcriptomics using single‐nuclei RNA‐seq (snRNA‐seq) and epigenomics (single‐nuclei ATAC‐seq) promises to capture cell‐type heterogeneity and unravel relevant biology related to cell‐to‐cell variability. Such single‐cell approaches have been recently used in psychiatric diseases through PsychENCODE consortia and integrated with bulk tissue RNA‐seq to understand cellular heterogeneity in these diseases. Our work is the first to define single‐nuclear clusters based on open‐chromatin (snATAC‐seq) and gene‐expression (snRNA‐seq) that are dysregulated during the progression of the disease.MethodUsing snATAC‐seq, we have profiled more than 150,000 single‐nuclei open chromatin profiles from normal human (n = 10) and late‐stage AD frontal cortex (n = 11) samples. We have also used same samples to further profile gene‐expression changes at single‐cell resolution using snRNA‐seq from more than 70,000 nuclei. In addition, we have performed bulk‐tissue RNA‐seq from control and AD frontal cortex (n = 50/group) samples and developed gene co‐expression methods to integrate bulk‐tissue and snRNA‐seq to unravel cell‐type specific co‐expression changes altered in AD.ResultOur analysis has identified distinct cell‐clusters based on both open‐chromatin (snATAC‐seq) and transcriptomics (snRNA‐seq) which are altered in late‐stage AD. We have identified several oligodendrocyte cell‐clusters which are increased only during disease progression. Pseudo‐time analysis of data indicates that AD‐specific oligodendrocyte clusters have a distinct developmental trajectory that define the terminal fate of these clusters. In addition, co‐expression analysis with bulk‐tissue RNA‐seq and snRNA‐seq data has identified functional modules (groups of genes) that are dysregulated in these AD‐specific oligodendrocyte clusters. These data highlight the potential role of myelination deficits that occur during the progression of disease.ConclusionOur tour‐de‐force work has identified distinct oligodendrocyte cell‐clusters that are altered in late‐stage AD. We have also identified core regulatory networks in these oligodendroglia cells that promises to provide novel therapeutic targets for AD.
Clinical correlation of cerebrospinal fluid total tau levels and depression as measured by PHQ9 total in a memory clinic
Authors: Pillai Malavika , Nair Anil 
Published: December 2020

AbstractBackgroundTau protein levels in cerebrospinal fluid is indicative of Alzheimer's dementia. We correlated PHQ‐9 screen severity of depression to CSF tau levels in a memory clinic sample.MethodWe analyzed data from patients attending a memory clinic in the south shore of Boston from 2010 to 2019 who had a lumbar puncture to obtain CSF tau levels. We correlated depression screening data from PHQ9 scores. Univariate analyses used Spearman correlation. A multivariate regression model was created including covariates of age, sex, and race.ResultsDepression as measured by PHQ9 total was not correlated to total tau levels in CSF.ConclusionIn a memory clinic sample, CSF tau levels did not correlate to PHQ9 scores.
Construct and validity of Persian Mini Mental Status Examination in illiterate elderly
Authors: Khodamoradi Zohre , Beheshti Maryam , Khodamoradi Mohammadreza 
Published: December 2020

AbstractBackgroundDementia is a health concern all over the world. The prevalence of dementia is growing due to increasing elderly population. Mini‐Mental State Examination (MMSE) is common tool for assess cognitive disorders. We aim to construct and validate the Persian version of MMSE in illiterate elderly (60 years and older) and also assign cut off point for this population in Iran.MethodWe did a cross‐sectional study on 310 elderly individuals (209 healthy participants and 101 elderlies with dementia) in teaching center in Shiraz from 2012 to 2014. Our participants categorized to different stages of dementia with Clinical Dementia Rating Scale (CDR). We used Receiver operating characteristic analysis, kappa analysis and Cronbach’s α coefficient for statistical analysis.ResultOur results showed that MMSE score has significant association with age, marital status, living condition, occupation and CDR score (P< 0.001). The total Cronbach's alpha value for all domains of MMSE was more than 0.7. The cut‐off point of 21/5 of revised Persian MMSE for illiterate Persian elderly with 95% sensitivity and specificity rates of 72% was calculated. We showed cut off point for three age groups of elderly. We found 24.5, 21.5 and 22.5 for participants with 60‐69, 70‐80 and more than 80 years old respectively.ConclusionThe Persian version of MMSE was found to be useful and valid instrument for the determination of dementia in the illiterate elderly population.
Amyloid precursor protein (APP) knock‐in mouse model recapitulates transciptomic signature in human late‐onset Alzheimer’s disease
Authors: De Bastiani Marco Antônio , Zimmer Eduardo R. , Forner Stefania , Martini Alessandra Cadete 
Published: December 2020

AbstractBackgroundOver the years, improved rodent models have been generated aiming at recapitulating Alzheimer’s disease (AD) phenotype. However, the majority of these models harbor pathological mutations that are present in early‐onset AD, which represents only 5% of all cases. On the other hand, new models aiming at recapitulating late‐onset AD (LOAD) are of great interest. In this context, the knock‐in mouse model (hAβ‐KI line), in which murine amyloid precursor protein (APP) gene was humanized at the Aβ locus, was recently introduced as a potential LOAD model. Here, compare hippocampal transcriptomic profiles of human LOAD individuals and hAβ‐KI mouse model. We hypothesized that hippocampal transcriptomic profile of hAβ‐KI mouse model will be similar to the human LOAD.MethodPublicly available human AD/cognitively unimpaired (CU) transcriptomic profiles of hippocampus were collected from GEO (https://www.ncbi.nlm.nih.gov/geo/), merged and submitted to differential expression analysis and master regulator analysis (MRA) using R. RNAseq expression profile of hAβ‐KI mouse model was obtained at the AMP‐AD Knowledge Portal (https://www.synapse.org/) and also submitted to differential expression analysis and MRA.ResultWe observed that the hippocampus of human LOAD individuals and hAβ‐KI mice model share over 100 differentially expressed genes (DEGs). Functional enrichment of Gene Ontology terms using these genes revealed several biological processes such as gliogenesis, glial cell differentiation, axon development and ensheathment of neurons. We also found shared transcription factors between human LOAD individuals and the hAβ‐KI mice model (Figure 1).ConclusionThe novel hAβ‐KI mice, which expresses an humanized APP, has been proposed as a better model to mimic sporadic LOAD. Our early findings indicate that at the transcriptomic level the hAβ‐KI mice presents molecular alterations compatible to human LOAD condition. Thus, studies using this model may reveal important insights to understanding sporadic LOAD and lead to better translational concordance.
Age‐friendly initiative needs assessment survey: Los Angeles City and County employees
Authors: Aguilar Iris , Lloyd Donald A , Trejo Laura , Aranda Maria P 
Published: December 2020

AbstractBackgroundThe City and County of Los Angeles Age‐Friendly Cities initiative, Purposeful Aging Los Angeles (PALA), 2017 needs assessment included an online survey of the workforce expected to implement age friendly policies and programs. The aim of this study was to describe the rate and characteristics of family caregivers within in the City and County workforce (employees). The overall response rate was 6.5% (City 4.5%, County 7.5%) for a final sample size 9,330. The category of family caregiver was derived from the following survey questions: 1) Are you or someone else in your home caring for a family member or friends who are experiencing problems/thinking that is affecting their ability to work or live a normal life? and 2) Are you a family caregiver? The analysis revealed that 34% of all respondents were family caregivers with an almost even number of male to female ratio. Among all caregivers, the mean age for caregiving for someone with cognitive impairment not diagnosed dementia was 47 and diagnosed AD/Dementia was 49. The majority of caregivers (all types) were caring for either a child/children under the age of 18 (42.1%) followed by a parent(s) (23.1%). Nearly 15% of caregivers of adults with cognitive impairment reported caring for a child/children under the age of 18. When caregivers of adults with cognitive impairment were asked “what type of help might be most useful?”, a higher preference for services was indicated by those aged 45‐64 who selected all the help types as most useful (information, symptom management, someone to talk to, ID lost person, diagnosis and help with care, legal, support group, caregiving financial support, chores/respite, day care, out‐of‐home placement). The current family caregiving research literature suggests that family caregivers experience higher rates of depression, anxiety, and social isolation due to the caregiving demands. Caregiving demands have the potential to create work‐family conflicts that are important for employers to consider when designing wellness programs, and other supportive employment programs. It is critical to identify ways to address caregiving implications on social services, healthcare, and the environment.
Investigating CSF biomarker levels to predict which MCI patients will progress to AD within 5 years
Authors: Cherbuin Nicolas , Tabatabaei‐Jafari Hossein 
Published: December 2020

AbstractBackgroundMild cognitive impairment (MCI) is a heterogeneous condition whose progression is difficult to predict. In the population up to 60% of individuals found to have MCI revert to normal cognition at follow‐up assessments. Amongst those who do not, it is currently difficult to predict those who will progress to Alzheimer’s Disease in the next few years from those who will remain MCI in the longer term. The aim of this study was to investigate whether CSF biomarkers can help predict which individuals with MCI will convert to AD within 5 years.MethodCSF amyloid beta 42, total and phosphorylated tau were assayed in 304 individuals with Mild Cognitive Impairment (MCI) and 103 cognitively normal participants (CN). Participants were classified into longitudinal diagnosis groups based on disease progression (reverter, stable, converter) over at least 5 years to produce five categories (CNs, CNc, MCIr, MCIs, MCIc). Baseline differences in biomarker levels across categories were tested by linear regression analyses while controlling for age and sex. The predictive value of biomarker levels was tested by logistic regression to predict AD conversion among MCI participants and future disease risk across all participants (Low risk: CNs, MCIr; high risk: CNc, MCIs, MCIc).ResultOver the follow‐up 42 CN and 57 MCI participants remained stable, 35 MCI reverted, 61 CN converted to MCI, and 212 MCI converted to AD. Across all biomarkers progressively higher baseline pathology was detected across the diagnostic category gradient such that CNs < MCIr < CNc < MCIs < MCIc. When contrasting MCIc vs CNs, Abeta levels were 33% lower, and tTau/pTau levels were 40/52% lower in MCIc. With every 10% increase Abeta or decrease tTau/pTau CSF levels, MCI individuals were at significantly increased risk of conversion to AD within 5 years (Abeta: RR 1.77, 95%CI 1.55‐2.04; tTau: RR 0.85, 95%CI 0.80‐0.89; pTau: RR 0.29, 95%CI 0.19‐0.42).ConclusionMCI individuals with substantially lower Abeta or higher Tau levels are at a much higher risk of converting to AD within 5 years. CSF biomarkers may be clinically useful in identifying those with MCI at highest risk of conversion to AD.
Study on the nursing of patients with early dementia based on a hierarchical genetic algorithm (GA‐RBF) neural network system
Authors: Han Peiying 
Published: December 2020

AbstractObjectiveIn order to improve the efficiency and prediction accuracy of BP neural network for disease diagnosis, a genetic algorithm was proposed to optimize BP neural network for the diagnosis of Alzheimer's disease to verify the more practical and dementia diagnosis. Methods: Taking the electronic medical record data mining in hospital as an example, a predictive model was established for the diagnosis of Alzheimer's disease. The method firstly uses the genetic algorithm's search optimization technology to carry out feature reduction, and then uses the reduced feature as the input variable of BP neural network to train and construct the BP neural network model. The simulation experiment was carried out on the MATLAB software platform. Results: Compared with single BP neural network, genetic algorithm optimization BP neural network can reduce the training time of the model and improve the prediction accuracy. It is a feasible auxiliary diagnosis method for Alzheimer's disease. Conclusion: The application of hierarchical genetic algorithm GA‐RBF neural network system and early prediction of senile dementia and cognitive status can help to accurately predict the cognitive status of the elderly, prevent it in advance, and improve the quality of care for patients with Alzheimer's disease clinical promotion.
Family availability and its influence on informal and formal care used by adults with dementia in the United States
Authors: Choi HwaJung , Heisler Michele , Cho Tsai‐Chin , Connell Cathleen M 
Published: December 2020

AbstractBackgroundPotential family availability may have a profound impact on the level of informal care received by older adults with dementia and hence formal care utilization including institutional care.MethodFrom a nationally representative study of older adults, the analysis sample included 4,954 adults (9,363 year‐persons) aged 55+ who had dementia during the years of 2002‐2014. We provided national estimates of the family availability of people with dementia. And, the adjusted, predicted probability of receiving informal and formal care was estimated separately by their spouse and adult child availability using multivariable, logistic regressions. Demographic, socioeconomic, and health measures were included in the adjusted models.ResultOnly 23% of adults with dementia had a non‐disabled spouse, but 66% had an adult child living less than 10 miles. There were substantial variations in family availability across demographic and socioeconomic groups. For example, 29% of non‐Hispanic blacks vs. 40% of other racial/ethnic groups (OR=0.63; 95% CI 0.53‐0.76) had a spouse. Only 16% of the bottom quartile in the wealth distribution had a spouse but 61% of the top quartile did (OR=0.13; 95% CI 0.11‐0.15). However, the greater share of non‐Hispanic blacks vs. non‐Hispanic whites had a coresident adult child (OR=2.07; 95% CI 1.70‐2.50) and a non‐employed adult child (OR=1.45; 95% CI 1.18‐1.79). Hispanics had the greatest family availability – both spouse and child availability.Having a spouse substantially increased the adjusted probability of receiving informal care, from 47% to 64% (OR=1.96; 95% CI 1.61‐2.40), but reduced that of receiving formal care from 47% to 33% (OR=0.54; 95% CI 0.46‐0.64) including institutional care from 30% to 18% (OR=0.50; 95% CI 0.39‐0.64). Having a coresident child substantially increased the adjusted probability of receiving informal care, from 47% to 71% (OR=2.80; 95% CI 2.18‐3.59), and lowered that of receiving formal care, from 46% to 24% (OR=0.37; 95% CI 0.29‐0.48).ConclusionThe presence of spouse and a co‐resident adult child may significantly reduce the use of formal care ‐ including institutional care ‐ by adults with dementia in the US. Intervention programs that promote family care however should consider substantial heterogeneity in potential family availability across racial/ethnic and socioeconomic groups.
Effect of matrix metalloproteinases on the longitudinal change in Alzheimer’s disease (AD) pathology and cognitive function in patients with mild cognitive impairment due to AD
Authors: Abe Kie , Chiba Yuhei , Hattori Saki , Ide Keiko , Yoshimi Asuka , Suda Akira , Hishimoto Akitoyo 
Published: December 2020

AbstractBackgroundMatrix metalloproteinases (MMPs), endopeptidases expressed in brain and immune cells, have been implicated in the pathogenesis of Alzheimer's disease (AD). However, the longitudinal effect of MMPs on AD patients is still unclear.MethodWe used the data of the Alzheimer's Disease Neuroimaging Initiative database. We included 95 ApoE4‐positive patients with mild cognitive impairment (MCI) due to AD confirmed with low Aβ42 and/or high phosphorylated‐tau (p‐tau) in cerebrospinal fluid (CSF). We obtained baseline demographic data and plasma MMP levels including MMP‐1, MMP‐2, MMP‐7, MMP‐9, and MMP‐10, and longitudinal data of Aβ42, total tau, and p‐tau in CSF, MRI‐measured hippocampal volume, and cognitive function evaluated on Mini‐Mental State Examination (MMSE) and AD Assessment Scale‐11 (ADAS‐11) over 4 years. We investigated the effect of baseline MMP levels on longitudinal changes in CSF AD biomarkers, hippocampus volume and cognitive function.ResultThere was no significant association between the baseline MMP levels and the longitudinal change in CSF biomarkers. Baseline MMP‐9 level was associated with a longitudinal decrease of hippocampal volume and deterioration of MMSE and ADAS‐11 scores.ConclusionHigh plasma MMP‐9 level in the patients with MCI due to AD would be a risk of consequent neurodegeneration and cognitive decline.
Cognitive function has a stronger correlation with “perceived age” than “chronological age,” especially in females
Authors: Kameyama Yumi Umeda , Kameyama Masashi , Kojima Taro , Ishii Masaki , Kidana Kiwami , Yakabe Mitsutaka , Ishii Shinya , Urano Tomohiko , Ogawa Sumito , Akishita Masahiro 
Published: December 2020

AbstractBackgroundThe perceived age of older adults, as measured by their facial appearances, has been demonstrated to be a robust biomarker of aging predictive of survival, telomere length, and DNA methylation and reportedly correlates with carotid atherosclerosis and bone status. This study aims to determine whether metrics of dementia, including general cognition, vitality, depressive state, and self‐supportability, have stronger correlations with perceived age than with chronological age.MethodStudy subjects were admitted to the Department of Geriatric Medicine, The University of Tokyo Hospital, on account of being suspected of cognitive decline between April 2012 and September 2019. Participants were photographed from the front and in profile from both sides. The mini‐mental state examination (MMSE), Vitality Index, geriatric depression scale 15 (GDS15), instrumental activity of daily living (IADL), and Barthel index were performed. Five experienced geriatricians and five experienced clinical psychologists determined the perceived age of participants based on photographs.ResultThis study included 126 participants (47 male: average age 80.4±6.1, average MMSE 21.3±5.4, 79 female: average age 81.1±6.8, average MMSE 21.8±5.2 ) . The average values of the 10 raters showed excellent reliability (ICC(3,10)=0.941). Steiger's test revealed that perceived age showed a significantly better correlation with MMSE (female), vitality index (total, female), and IADL (total) than did chronological age, but not with GDS15 or the Barthel Index.ConclusionPerceived age was demonstrated to be a reliable biomarker for cognitive assessment especially in female.
Prediction of longitudinal clinical status changes in mild cognitive impairment by network structural covariance
Authors: Dang Mingxi , Yang Caishui , Xu Chang , Zhang Zhanjun 
Published: December 2020

AbstractBackgroundMild cognitive impairment (MCI) is the transitional zone between normal aging and Alzheimer’s dementia (AD), representing a group of subjects with higher risk of conversion to AD (Petersen, 2010). However, as accumulating evidence shows that there are a non‐negligible number of MCI subjects who revert to be cognitive normal (CN), additional measures are needed for better characterizations on the MCI groups (Petersen, 2014). Thus, the purpose of this study is to explore the structural covariance (SC) feature of subjects with different longitudinal clinical status changes.MethodParticipants were from the Beijing Aging Brain Rejuvenation Initiative (BABRI), with two clinical cognitive assessments and the baseline high‐resolution structural MRI data. And four groups were defined, the stable CN (sCN, n = 99), CN progressing to MCI (pCN, n = 23), stable MCI (sMCI, n = 29), and MCI reverting to CN (rMCI, n = 33), and the mean age and follow‐ups interval for CN and MCI subjects were 64.74 and 66.63, 2.46 and 1.45 years, respectively. And the seed‐based multivariate method (Alexander‐Bloch, 2013) was applied to identify the SC of default‐mode network (scDN), frontoparietal network (scFN) and hippocampal network (scHN), of which the covariance scores were calculated. To obtain the SC templates of three networks, 69 sCN were randomly selected, and the rest 30 sCN, with the other three groups, were included in establishing prediction model of clinical status changes based on network SC scores, by using the support vector machine method and leave‐one‐out cross‐validation. Moreover, baseline cognition was also added in the model.ResultFor classification between sCN and sMCI, the scHN score presented the highest accuracy (area under curve (AUC) = 0.89), and it was the scDN score showing the best performance in classifying sMCI and rMCI (AUC = 0.82), with the scFN score outperforming others in distinguishing pCN from sCN (AUC = 0.68). And when baseline cognition was also included, prediction accuracies of clinical changes were improved (AUC = 0.84 for sMCI vs. rMCI, AUC = 0.83 for sCN vs. pCN).ConclusionTaking together, our findings indicated the potential of network structural covariance in classifying MCI subjects with different risk of conversion.