bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of amyloid β42 aggregation modulators from natural product library identifies promising scaffolds that suppress nuclei‐driven neurotoxicity
Authors: Murakami Kazuma 
Published: December 2021

AbstractBackgroundThe mechanism of Aβ42 aggregation mainly consists of two phases, nucleation and elongation (including a plateau region). During the nucleation phase, the monomer gradually forms toxic oligomers. During the elongation phase, each nucleus acts as a template and associates with monomers to initiate less‐toxic fibrillization. We previously proposed a method of classifying compounds into 9 groups based on their ability to modulate the nucleation and/or elongation phases. An orcein derivative (O4), which is a phenoxazine dye isolated from the lichen Roccella tinctoria and containing a 2,5‐cyclohexadienone moiety, was reported to convert oligomers into relatively inert fibrils, resulting in the reduction of the neurotoxicity of Aβ42 (Bieschke, J., et al. Nat. Chem. Biol.
2011, 8, 93‐101). Focusing on O4 in the pursuit of anti‐AD drugs, we herein screened 480 natural products including NPDepo (RIKEN).MethodThe ability of Aβ42 to aggregate or oligomerize was evaluated by thioflavin‐T fluorescence assay, transmission electron microscopy, and dot blotting. The neurotoxicity of Aβ42 was tested by MTT assay on mouse neuroblastoma Neuro2a cells.ResultThe 15 compounds that met the criteria of both delaying the nucleation phase and promoting the elongation phase were selected, and the signal intensities for Aβ42 oligomer treated with each of these compounds were lowered in dot blotting using anti‐oligomer antibody. On the other hand, the fibril formation of Aβ42 in the presence of these compounds was observed. Moreover, among the 15 compounds, 12 compounds (80%) reduced Aβ42‐induced toxicity.ConclusionGiven that some of these anti‐cytotoxic compounds such as warfarin contain 2‐pyrone or 4‐pyrone moiety as does O4, the current data suggest that hit compounds can shift the equilibrium of Aβ42 from toxic oligomer into less‐toxic fibrils by forming Michael adduct with Aβ42.
The effect of prolonged lockdown due to COVID‐19 on demented patients of different stages and on their caregivers
Authors: Tsapanou Angeliki , Zoi Panagiota , Kalligerou Faidra , Blekou Patra , Sakka Paraskevi 
Published: December 2021

AbstractBackgroundThe impact of the new coronavirus disease (COVID‐19) is deteriorating as time passes and the virus keeps spreading. The aim of this study was to examine the effect of prolonged isolation because of the COVID‐19 pandemic on people with dementia and their caregivers.MethodCaregivers answered online questions regarding their own physical and psychological burden, and of the person they take care of. The study took place in Athens, Greece, from November 2020 to April 2021. Participants were mostly members of online seminars of the Athens Alzheimer’s Association. Questions referred to their own burden, the overall decline of the persons they take care of, and changes in specific domains as well (i.e., mood, mobility, compliance with the new measures). Further, we also asked about any changes between the two major lockdown periods. Possible answers were: “not at all”, “moderately”, “a lot”. Statistical analysis included descriptive (mean, standard deviation, frequency, and percent) and inferential statistics (chi‐square test). Analysis was performed including the total sample and then, by three different stages of dementia (early, middle, late).ResultA total of 339 caregivers took part in the study. People suffering from dementia that were referred at, were mostly women, with a mean age of 81 (S.D.: 7.5) years old, mostly being in the middle stage of dementia. Results indicated significant decline, both in an overall aspect of the people with dementia, and in specific domains (mostly communication and mood). Regarding the caregivers, they reported having significantly increased physical and psychological burden, and also, noticing an overall change between the two lockdowns in their own burden. Analysis by dementia‐stage group indicated that significant decline occurred both in the middle‐stage and the late‐stage group.ConclusionAn urgency for further support of both the people with neurodegenerations and their caregivers is needed. Collaboration among care workers, online programs, governmental support, and day‐care centers should be planned to ensure continuity of care for those in need during the pandemic.
The association between plasma biomarkers, p‐tau181, glial fibrillary acidic protein, and neurofilament light and clinical AD risk: Results from prospective cohort study followed over 17 years
Authors: Stocker Hannah , Beyer Léon , Perna Laura , Schöttker Ben , Gerwert Klaus Bernhard , Brenner Hermann 
Published: December 2021

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) have emerged as the future of AD prescreening measures and/or clinical risk assessment. In order to confirm clinical utility of plasma biomarkers, further validation in longitudinal cohort studies is necessary. Therefore, the aim of this study was to investigate the relationship between plasma biomarkers, phosphorylated tau (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) and incident clinical Alzheimer’s disease diagnosis in a community‐based cohort study prospectively followed over 17 years. Secondarily, disease specificity of these biomarkers was assessed with comparison to vascular (VD) and mixed dementia (MD), and the impact of fatal CVD risk on plasma biomarker prediction was explored.MethodP‐tau181, GFAP, and NfL concentration in plasma taken at baseline was measured using the SimoaTM HD‐X Analyzer and the 10‐year risk of fatal CVD using the Systematic Coronary Risk Evaluation (SCORE) was determined in 145 participants diagnosed with AD, 66 participants with VD, and 50 participants with MD during 17 years of follow‐up and 507 participants without dementia diagnosis. Cox regression models adjusted for age, sex, and education were used to assess the association between the plasma biomarker levels and risk of incident dementia subtypes.ResultHigher plasma levels of p‐tau181 and NfL were significantly associated to higher risk of AD development within the first nine years of follow‐up (p‐tau181, p=.02; NfL, p=.01) and were most elevated in AD cases in the first five years of follow‐up, however lacked associations between 9‐17 years. Contrastingly, GFAP levels were consistently elevated in AD cases and significant associations to higher AD risk were evident throughout the 17‐year follow‐up. NfL and GFAP levels additionally exhibited associations to a higher risk of VD and MD; while p‐tau181 did not. Significant interactions between fatal CVD risk and biomarker levels were evident for AD (p‐tau181, p=.03; NfL, p=.01).ConclusionHigher plasma p‐tau181 and NfL levels were associated with higher AD risk within nine years, while higher GFAP levels with higher AD risk consistently throughout 17 years. Plasma levels of p‐tau181 and NfL may be more suited to short‐term AD risk stratification; while GFAP may add important long‐term disease risk information.
Machine learning of transcripts and audio recordings of spontaneous speech for diagnosis of Alzheimer’s disease
Authors: Liu Ziming , Proctor Lauren , Collier Parker , Casenhiser Devin , Paek Eun Jin , Yoon Si On , Zhao Xiaopeng 
Published: December 2021

AbstractBackgroundLanguage deficits may occur in early stages of Alzheimer’s disease (AD). Analysis of individuals’ language changes has been shown to predict the diagnosis and severity of AD. This work explored machine learning methods to detect AD using voice recordings of the Cookie Theft picture description tasks.MethodAn open‐source dataset from Dementia Bank, which included manual transcripts and audio recordings of the Cookie Theft picture description task from 309 individuals with AD and 243 cognitively healthy older adults, was employed in the classification analysis. We analyzed transcripts generated using three different approaches (Figure 1), including a) automatically generated transcripts based on denoised voice recording; b) automatically generated transcripts without the denoising process; and c) manuscript transcripts. Denoising was implemented using noisereduce, a Python library to minimize background noise (CITATION). Automatic transcripts were produced using two toolkits: CMUSpinx pocketsphinx interpreter (PS) and Mozilla DeepSpeech (DS). The Small‐Bidirectional Encoder Representations from Transformers (BERT) model was implemented to extract high dimensional feature vectors to provide contextual representation of transcripts for classification analysis. The experiment was designed to compare the performance of transcript type (manual vs. automatic), recording quality (noise vs. denoise), as well as speech recognition methods (DS vs. PS). A neural network model was constructed to classify between those with AD and healthy controls. The classification analysis was conducted using 10‐fold cross validation. The classification was repeated 20 times and the averaged results were reported (see Table 1).ResultThe performance of each approach is shown as Table 1. Overall, the model that used the features extracted from DS transcripts with denoised speech recordings achieved the best performance which had 92.72% accuracy for AD diagnosis validation results. The comparison between raw and denoised speech recordings showed conflicting performances. Using features from PS transcripts with noisy speech recordings obtained better performance than the denoised one, which was different with features from DS transcripts.ConclusionThe analysis approach which combined transcription driven recognition and transfer learning indicated the potential of better performance in AD diagnosis. Further investigation is needed to understand the reason for its superior performance.
Machine learning–based detection model of early Alzheimer's disease using wearable device and gait assessment
Authors: Jeon YoungHoon , Ho Thi Kieu Khanh , Kang Jaeyong , Kim Byeong C. , Lee Kun Ho , Song Jong‐In , Gwak Jeonghwan 
Published: December 2021

AbstractBackgroundA method for diagnosing early stages of Alzheimer's disease (AD) is developed by analyzing human gait and training a machine learning classifier using signals obtained from inertial measurement unit. This patient‐friendly and cost‐effective method enables periodic monitoring, contributing to savings in treatment costs and effort.Method145 subjects (77 healthy controls, 68 Mild Cognitive Impairment) were employed to perform seven types of gait paradigms, including normal and complex walking. The gait of the employed subjects was measured by 7 shimmer devices, which consist of a three‐dimensional accelerometer and gyroscope. The acquired signal data is converted into 35 features including gait cycle. The extracted features are used to train an ensemble classifier based on machine learning with an oversampling method. In order to find the optimal combination in the ensemble model, an elimination technique was used to compare performance by removing single classifiers one by one. For performance comparison, the dataset is divided into training set/test set by leave‐one‐patient‐out cross‐validation, and several metrics such as accuracy and F1 score are used.ResultThe classifier for MCI detection classified severity to an accuracy of about 0.7 for 145 subjects. The combination of oversampling techniques and different single classifiers contributes to improved performance. In the seven walking experimental paradigms, the accuracy of the classification of normal walking without subtasks is about 0.67. O the other hand, the classification of walking with associative word‐speaking or counting numbers shows significant results with accuracy of 0.7413 and 0.7338, respectively.ConclusionOur proposed method predicts severity through efficient walking with less space‐time constraints. Subtasks that stress the brain in walking contribute to the performance improvement of the ensemble model with oversampling. These results demonstrate that our proposed method with gait features can be used as an early diagnosis model to quickly initiate early intervention and prevention strategies that slow the progress of dementia.
Characterization of AD pathology based on plasma biomarkers measured by a fully automated immunoassay system (HISCLTM series)
Authors: Watanabe Shunsuke , Ishiki Kengo , Yamashita Kazuto , Lukaszewska Teresa , Miura Masahiro , Irino Yasuhiro , Iwanaga Shigeki , Yoshida Tomokazu 
Published: December 2021

AbstractBackgroundThere have been a lot of studies on classification of Alzheimer’s disease (AD) based on its pathological process. These pathological changes have been defined by the ATN classification system (A : amyloid β (Aβ) deposition, T : pathogenic tau, N : neurodegeneration) based on cerebrospinal fluid (CSF) biomarker levels or neuroimaging. In order to promote research and practical application, it is necessary to develop a cost effective and minimally invasive technology. Therefore, we have developed plasma Aβ1‐40, Aβ1‐42, threonine‐181 phosphorylated tau (p‐tau181) and total‐tau immunoassay systems and reported that these plasma biomarkers levels were significantly different between AD and cognitively normal (CN). In this study we have measured biomarkers in other sample sets including mild cognitive impairment (MCI), and evaluated the performance in characterizing AD pathology, in the context of the ATN classification system.MethodWe measured commercially available plasma and CSF samples on the HISCLTM series, which can achieve high degrees of sensitivity and fast measurement times of 17 minutes, requiring only 10 to 30 µl of samples. The concentration of plasma and CSF biomarkers levels (Aβ1‐40, Aβ1‐42, p‐tau181, total‐tau) were measured and the significance of the difference between AD, MCI and CN groups was assessed.ResultThe AD group had lower Aβ1‐42/Aβ1‐40 ratio and higher levels of p‐tau181 and t‐tau compared to MCI or CN groups. The levels of plasma biomarkers in MCI group showed wide distribution compared to AD or CN group, suggesting these plasma biomarkers reflect the various pathological states of dementia in MCI patients. This might indicate that our plasma biomarker measurement systems represent characterization of AD pathological processes.ConclusionOur assay system revealed that the distribution of plasma biomarkers showed characteristic distribution in AD, MCI and CN groups, respectively. This result may indicate the possibility of practical diagnosis of AD pathology based on plasma biomarker ATN classification system. We aim to continue to measure other clinical sample sets for evaluation of the usefulness and universality of plasma biomarkers.
Stability in BMI over time is associated with a better cognitive trajectory in older adults
Authors: Beeri Michal Schnaider , Tirosh Amir , Lin HungMo , Golan Sapir , Boccara Ethel , Sano Mary , Zhu Carolyn W 
Published: December 2021

AbstractBackgroundDespite the relevance of body mass index (BMI) to the risk of incident cognitive impairments and the subtle but consistent decrease in BMI which accompanies old age, there is limited evidence on the simultaneous changes in BMI and cognitive decline, which better reflect the natural course of both health phenomena. Whether BMI stability contributes to better cognitive trajectories is essentially unknown. To address this gap in knowledge we examined the longitudinal associations of changes in BMI over time with changes in cognition.MethodWe exploited longitudinal clinical data from 15,977 initially non‐demented older adults participating in the Alzheimer’s Disease Centers across the United States followed for an average of 5 years. In this longitudinal study, changes in BMI were defined as 1) last minus first BMI, 2) mean of all follow‐up BMIs minus first BMI, and 3) standard deviation (SD‐representing variability) of BMI change from baseline and all follow‐up visits. Linear mixed effect models assessed the association of each of the BMI change variables with cognitive decline, adjusting for a broad range of sociodemographic, cardiovascular, and functional measures.ResultParticipants averaged 73.5 (7.6) years of age and 15.6 (3.6) years of education; 59.2% were women and 80.9% Caucasians. Participants who had significant changes in BMI defined as an increase or decrease of >5% in BMI, or who had greater variability in BMI, had a faster rate of cognitive decline in global cognition and the specific cognitive domains. Furthermore, this pattern was consistent irrespective of whether a person had normal (BMI<25; N= 5747), overweight (BMI=25 to 29.99; N=6302), or obese (BMI>30; N=3928) BMI at baseline.ConclusionOur study provides new evidence that greater changes (or variability) in BMI over time are associated with a faster rate of cognitive decline suggesting that in initially non‐demented older adults, stability in BMI predicts a better cognitive trajectory. These results suggest a potential clinical value in tracking BMI change, which is simple to measure, as it may point to individuals whose cognition is declining.
Early identification of persons living with dementia at point‐of‐care hospitalization using the electronic medical record
Authors: Tan Zaldy S , Rhinehart Deana , Spivack Erica , Keklikian Maria , Cheng Susan 
Published: December 2021

AbstractBackgroundHospitalization for Alzheimer's disease and related dementias (ADRD) is the single largest expense in the U.S., accounting for two thirds of Medicare costs for ADRD estimated at $155 billion in 2020. Persons living with dementia (PLWD) are twice as likely to be hospitalized, have longer average lengths of stay, higher readmission and mortality rates and are at increased risk for institutionalization. A major challenge is the low rates of identification of PLWD upon admission to the hospital, preventing early institution of person‐centered collaborative care interventions that can improve patient outcomes.MethodTwo trained dementia nurse practitioners performed a prospective daily review of all patients age >70 years admitted to a large academic medical center for a period of 8‐weeks (1/26/2021 – 3/23/2021). The NPs performed a systematic keyword search of the EMR for ADRD diagnoses, which were confirmed through review of past medical history, outpatient primary care and consult notes, problem and medication lists. Hospitalized PLWD were analyzed for quality of care indicators.ResultOf the 249 hospitalized patients (mean age 86.5 years;61% female) with ADRD diagnosis, 47 (18%) had dementia included in the admission diagnosis while 151 (61%) had dementia included in the admission history and/or assessment/plan. 71 patients (29%) had restraints ordered, 120 (48%) were prescribed psychoactive medications and 95 (38%) were put on restraints. Advance directives were completed for 122 (49%) of PLWD and 157 (63%) had a full code status. 149 (60%) had a previous inpatient admission and 57 (23%) had an ED visit in the last 12 months. Ninety‐five (38%) of hospitalized PLWD were discharged to institutional facilities.ConclusionThere are low rates of inclusion of dementia in admission diagnoses and notes of hospitalized PLWD. In contrast, there are high rates of restraint use, psychoactive medication order and discharge to institutional facilities. Most PLWD admitted to the hospital has had a previous hospitalization in the past year. To our knowledge, this is the first study that utilized EMR‐based identification of ADRD diagnosis and tracking of hospital outcomes of PLWD. These findings represent opportunities for improvement in quality and outcomes of hospital care for PLWD.
Targeting microRNA‐485‐3p blocks Alzheimer's disease progression
Authors: Ryu JinHyeob , Koh HanSeok , Lee SangJoon , Lee HyoJin , Min JaeWoong , Iwatsubo Takeshi , Teunissen Charlotte E. , Cho HyunJeong 
Published: December 2021

AbstractBackgroundAlzheimer’s disease (AD) is a form of dementia characterized by progressive memory decline and cognitive dysfunction, which affects more than 44 million people worldwide. Currently, there is no effective therapy for AD despite its increasing global incidence; thus, effective treatment strategies for AD are urgently needed. While several drugs that decrease amyloid beta (Aβ) production or increase Aβ clearance in the brain have been identified, treatment with these drugs is poorly correlated with improvements in AD severity and cognitive dysfunction.MethodExpression of miR‐485‐3p was analysed by real‐time PCR in the human frontal cortex (8 healthy controls (HC), 7 AD patients), precentral gyrus (6 HC, 8 AD), cerebrospinal fluid (CSF) (6 HC, 7 AD), plasma exosomes (10 HC, 17 mild cognitive impairment (MCI), 12 AD). Aβ 1–42 plaque immunofluorescence and tau pathology were imaged in primary cultured mouse neurons after lentivirus‐derived miR485‐3p transduction. MiR‐485‐3p antisense oligonucleotide (ASO, 1.5 μg) or control oligonucleotide formulated with the in vivo jetPEI reagent was injected into 8‐month‐old 5XFAD mice by ICV injection once weekly for two weeks. Behavioral tests were performed at 8 months and their brain pathology was examined after 8‐week‐washout at 10 months.ResultWe found that the miR‐485‐3p is overexpressed in brain tissues and CSF of AD patients, and its ASO reduces Aβ plaques, tau pathology, neuroinflammation, and cognitive decline in a transgenic mouse model of AD. Mechanistically, miR‐485‐3p ASO enhanced Aβ clearance via CD36‐mediated phagocytosis of Aβ in vitro and in vivo. We found that miR‐485‐3p ASO reduces apoptosis, which effectively decreases truncated tau levels. Further, miR‐485‐3p ASO reduced secretion of pro‐inflammatory cytokines, including IL‐1β and TNF‐α, and eventually relieved cognitive impairment.ConclusionCollectively, our findings suggest that miR‐485‐3p is a useful biomarker as well as a causative factor of the inflammatory pathophysiology of AD; furthermore, miR‐485‐3p ASO represents a candidate therapy for AD pathology and cognitive decline, establishing a new paradigm in the AD field.