bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting cognitive stage transition using plasma p‐tau181, the Centiloid scale, and other measures
Authors: Kwon Hyuk Sung , Park Hyun‐Hee , Lee Eun‐Hye , Hwang Min A , Baek Jeongmin , Lee Eunji , Ryu Yejin , Lee Sunju , Lee Yongkyung , Yoo Hyun‐Jung , Koh Seong‐Ho 
Published: December 2023

AbstractBackgroundA combination of plasma phospho‐tau (p‐tau), amyloid beta (Aβ)‐PET, brain magnetic resonance imaging, cognitive function tests, and other biomarkers might predict future cognitive decline. This study aimed to investigate the efficacy of combining these biomarkers in predicting future cognitive stage transitions within 3 years.MethodAll participants were from the validation cohort of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE‐V) study. Among the participants in the KBASE‐V study, 49 mild cognitive impairment (MCI) and 113 cognitively unimpaired (CU) participants with Aβ‐PET and brain imaging data were analyzed. Main outcome was cognitive stage transitions within 3 years. The best combination model that predicts cognitive stage transition was considered by significant change of area under the curve (AUC) and Akaike information criterion value.ResultWithin 3 years, 19.1% of the participants showed a cognitive stage transition. Older age, increased plasma p‐tau, Aβ‐PET positivity, and decreased semantic fluency were independently associated with cognitive stage transitions. Plasma p‐tau181 (AUC = 0.673) and the Centiloid scale (AUC = 0.769) alone predicted cognitive stage transition well and correlated with each other significantly. Combining age, p‐tau181, the Centiloid scale, semantic fluency, and hippocampal volume produced better accuracy (AUC = 0.879). In MCI participants, the model combining age, p‐tau181, and the Centiloid scale demonstrated the highest predictive value (AUC = 0.963).ConclusionPlasma p‐tau181 and Centiloid scale, in combination with other biomarkers, might have high predictive value in predicting future cognitive stage transition in non‐dementia patients.
A retrospective cohort study of robotic‐assisted laparoscopic radical prostatectomy (RARP) to PONV scale effects on Alzheimer’s Disease‐Related Dementia patients’ sleep and vitality risks
Authors: Wu Jia‐Ping 
Published: December 2023

AbstractBackgroundProstate cancer is the most commonly safe with minimal morbidity and mortality rates of tumor cancer using microsurgical techniques. However, these rates have risen rapidly in the past two decades. This study aimed to determine comparing microsurgical prostatectomy techniques in National Comprehensive Cancer Network (NCCN) high‐risk and low‐risk robot‐assisted laparoscopic radical prostatectomy (RARP) and conventional laparoscopic radical prostatectomy (LRP) approach.MethodsRetrospective case observation study of single team laparoscopic and robotic‐assisted laparoscopic radical prostatectomy for NCCN (n = 855,893) high‐risk and low‐risk prostate cancer from 2009 to 2020. 307 Alzheimer’s Disease and Alzheimer’s Disease‐related Dementias patients were divided into two groups, 120 patients low‐risk and 187 patients high‐risk, undergoing radical prostatectomy for prostate cancer using LRP and RARP. To determine univariate and adjusted odds and hazard ratios for BCR, post‐operative detective, and post‐salvage therapy disease between low‐risk and high‐risk groups in undetectable PSA patients.Results307 patients in the detectable NCCN group 120 patients for very high‐risk and 187 patients in high‐risk groups. Very high‐risk groups have significant differences compared with high‐risk groups by PSA density (p<0.001), BCR in undetectable PSA (p<0.001), PSA rate (p<0.004), and salvage therapy (p<0.001). Univariate hazard ratios for BCR and univariate odds ratios for post‐operative detective PSA, post‐salvage therapy disease, and time to BCR in very high‐risk groups have significant differences (p<0.001) compared with high risk.ConclusionsRARP has better effects than conventional LRP available regarding lower incidence and risk factors. Very high‐risk prostate cancer is not good for radical prostatectomy and selected high‐risk group.
Bias in Acoustic‐ and Linguistic‐based Classifications of Alzheimer’s Disease
Authors: Parsapoor Mahboobeh , Dalmas Benjamin 
Published: December 2023

AbstractBackgroundIt is crucial to identify patients with Alzheimer’s disease (AD). To do so, various attempts have been made to develop AI and non‐AI assessments, among which some focus on developing acoustic‐ and linguistic‐based classifiers. They are based on the detection of impairments in their language and speech, which can manifest years before other cognitive impairments associated with AD appear. The fact is that most of the current vocal and language classifiers of AD have been trained using the Pitt corpus, which is an imbalanced class and gender dataset. These two characteristics could be enough to bias such classifiers making them untrustworthy and unsuitable for integration into AD care settings. This paper presents a novel method for collecting vocal data to reduce the impact of potential sources of bias on acoustic and linguistic classifiers for AD.MethodWe will use a data collection approach that collects voices from participants (i.e., patients with AD and healthy controls) with diversity in race, gender, educational, socioeconomic, and cultural backgrounds. Participants will be asked to perform diverse language tasks, including word association, verbal fluency, and grammaticality judgment tasks. Following such an approach can ensure we collect oral data that wouldn’t be affected by selection, recruitment, sociolinguistics, and gender biases.ResultsThe main result of this study is the creation of a benchmark vocal dataset from diverse gender and racial groups and ensuring that the data is representative of the population with AD. We expect such data to be used for evaluating and validating and help AI developers to successfully develop fair acoustic and linguistic classifiers of AD. It, in its turn, can motivate healthcare professionals to employ these systems as assistants to identify patients with AD from their voices quickly.ConclusionThe Pitt corpus is a biased data set. Thus, acoustic and linguistic classifiers trained upon it can not be considered trustworthy AI systems. The AI developers that aim to deploy vocal systems into Alzheimer’s disease care settings would need unbiased vocal data. This study proposed a method to collect such verbal data.
Plasma amyloid‐β, total tau, and neurofilament light chain across the Alzheimer clinical spectrum: a population‐based study
Authors: Dong Yi , Hou Tingting , Liu Rui , Li Yuanjing , Cong Lin , Tang Shi , Wang Yongxiang , Du Yifeng , Qiu Chengxuan 
Published: December 2023

AbstractBackgroundPlasma biomarkers have emerged as a promising approach for defining mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We aimed to characterize Alzheimer’s plasma biomarkers across the clinical continuum of AD, and to assess their ability to differentiate between MCI, AD, and normal cognition.MethodThis population‐based study engaged 1446 rural‐dwelling older adults (age≥60 years, 61.0% women) derived from Multimodal Interventions to Delay Dementia and Disability in Rural China study; of these, 902 were defined with normal cognition, 402 with MCI, and 142 with AD according to the international criteria. Plasma amyloid‐beta (Aβ), total‐tau (t‐tau), and neurofilament light chain (NfL) concentrations were analysed using Simoa platform. Data were analysed using the logistic and general linear models, and receiver operating characteristic (ROC) analysis.ResultPlasma concentrations of Aβ40, Aβ42, t‐tau, and NfL were significantly increased with age (P<0.01). Women had higher plasma Aβ2 (age‐adjusted β = 0.62; 95%CI = 0.30‐0.94), Aβ42/Aβ40 ratio (0.19; 0.01‐0.36) and lower NfL concentrations (‐0.11; ‐0.16‐ ‐0.06). Plasma Aβ42 (age‐ and sex‐adjusted β = ‐0.73; ‐1.15‐ ‐0.31) and Aβ42/Aβ40 ratio (‐0.51; ‐0.74‐ ‐0.29) were lower in APOE ε4 carriers than that in non‐carriers. Plasma Aβ40 and Aβ42/Aβ40 ratio showed an increased and decreased trend, respectively, across AD clinical spectrum. Plasma t‐tau and t‐tau/Aβ42 ratio were higher in people with AD than those with MCI or normal cognition. Plasma NfL was increased across the AD clinical continuum and outperformed other Alzheimer’s biomarkers in differentiating AD from normal cognition (area under the ROC curve [AUC] = 0.75). However, all of these plasma biomarkers performed poorly to distinguish MCI from normal cognition (AUC<0.60). When adding plasma NfL to the basic model that included age, sex, education, and APOE genotype, the AUC was 0.87 for differentiating between AD and normal cognition, 0.79 between AD and MCI, and 0.64 between MCI and normal cognition.ConclusionAge, sex, and APOE genotype should be considered in clinical interpretation of Alzheimer’s plasma biomarkers. Plasma Aβ, t‐tau, and NfL concentrations differ across the cognitive continuum, but plasma NfL, which starts to elevate in the early stages of AD development, appears to be superior than plasma Aβ and t‐tau for defining cognitive continuum in old age.
Assessing motor cortex excitability and its relationship with brain atrophy in Ahlzeimer’s disease
Authors: Lawson Renee P , Zomorrodi Reza , Joseph Michael , Alhabbal Hiba , Asare Gifty , Blumberger Daniel M. , Daskalakis Zafiris J. , Fischer Corinne E. , Mulsant Benoit H. , Pollock Bruce G. , Rajji Tarek K. , Voineskos Aristotle N. , Kumar Sanjeev 
Published: December 2023

AbstractBackgroundCortical excitability detected using resting electroencephalography (EEG) or transcranial magnetic stimulation (TMS) combined with EEG has been proposed as a new neurophysiological marker of Alzheimer’s dementia (AD). However, the link between cortical excitability and structural changes in AD is not well understood. The objective of this study was to assess the relationship between cortical excitability and brain structure (cortical thickness) in the motor cortex of patients with AD and healthy older individuals.Methods63 participants (38 females) with AD (Mage = 74.5, SD = 8.0) and 48 healthy individuals (27 females) (Mage = 71.1, SD = 7.8) were included. hot spot’ over the left motor cortex was defined as the location that generated maximal motor‐evoked potentials in the abductor pollicis brevis muscle. Each participant’s resting motor threshold (rMT) was determined using the ‘hotspot’ by finding the lowest stimulus strength to evoke a motor‐evoked potential with a peak‐to‐peak amplitude of ≥50 µV in at least five of ten consecutive single pulse TMS trials. T1w MRI scans were pre‐processed using the anatomical pipeline in fMRIPrep v20.2.6. This pipeline includes recon‐all from Freesurfer v6.0.1, which estimates cortical thickness based on ROIs defined in the Desikan‐Killiany atlas.ResultsParticipants with AD had lower motor cortex thickness than healthy individuals (t(93) = ‐4.398, p = <0.001). Participants with AD had lower rMT (indicative of higher excitability) than healthy individuals (t(109) = ‐2.265, p = 0.026). Within the total sample, rMT was correlated with motor cortex thickness (r = 0.216, df = 95, p = 0.036).ConclusionsConsistent with previous literature, our study supports that AD is associated with decreased cortical thickness and higher motor cortex excitability. We also found that changes in cortical thickness positively correlated with rMT, suggesting that cortical hyperexcitability is influenced by cortical neurodegeneration. Future studies may wish to examine the association between cortical excitability and functional imaging measures to understand specific mechanisms.
Transformer Neural Networks, Information Biology, and Alzheimer’s Disease
Authors: Swan Melanie 
Published: December 2023

AbstractBackgroundAlzheimer’s disease affects one in ten people older than 65 years, with the prevalence expected to triple by 2030, together with commensurate increases in cost. A comprehensive causal model of Alzheimer’s disease etiology is lacking despite more than three decades of study. Modern computational methods provide a new approach to multiscalar biosystems by modeling neuropathology in its native three‐dimensional character in topological biophysics and quantum computational models.MethodThis work describes how an emerging standard in machine learning, responsible for the AlphaFold (protein folding structure) and Gato generalist agent projects, transformer neural networks, may be applied to the study of Alzheimer’s disease per proven results in bot text chat, image recognition and creation, video generation, software programming, and autonomy in robotics and driving. The self‐attention and self‐learning mechanisms of transformer neural networks are discussed in application to multifaceted Alzheimer’s disease data sets involving neuroimaging scans, whole human genome data, and blood and CSF biomarker data.ResultA machine learning‐based topological biophysics model of Alzheimer’s disease neuropathology hypothesis‐testing (misfolded proteins, gene expression, diabetes type‐3, and peptide immune system stimulation) is described.ConclusionThere is an opportunity to apply state‐of‐the‐art transformer neural network machine learning techniques to the automated analysis of large accruing data stores of Alzheimer’s information (imaging, genomic, proteomic, and self‐reported data) towards the causal understanding of the neuropathology.
Efficacy of Sailuotong in vascular dementia: an exploratory analysis of possible disease modifying activity
Authors: Chen Christopher , Rahman Nabilah 
Published: December 2023

AbstractBackgroundMulti‐component, multi‐target approaches may be needed for treatment of Vascular Dementia (VaD). SaiLuoTong (SLT) consists of active ingredients derived from Ginkgo Biloba, Panax Ginseng and Crocus Sativus. A phase II, randomized, controlled, double‐blind study (NCT01978730) demonstrated the symptomatic efficacy of SLT in VaD, however, the slowing of disease progression remains unexplored. This exploratory analysis is to compare the Vascular Dementia Assessment Scale‐cognitive subscale (VaDAS‐cog) between early‐ and delayed‐starters over a 52‐week period so as to assess the disease‐modification effect of SLT.MethodSubjects with probable mild to moderate VaD were randomized to SLT 360 or 240 mg (early‐starters), or placebo (delayed‐starters). Whilst maintaining blinding, all early‐starters received SLT for 52 weeks and delayed‐starters switched to SLT 360 or 240 mg from placebo at week‐26. Using a modified intention‐to‐treat population of 325 subjects, comparison of change in VaDAS‐cog from baseline between early‐starters and delayed‐starters was performed using linear mixed‐effects models for repeated measurements, with fixed effect of intervention (early‐starters vs. delayed‐starters), time, time and intervention interaction, baseline VaDAS‐cog and randomization stratification factors. An unstructured variance‐covariance structure was employed.ResultEarly‐starters compared to delayed‐starters showed statistically significant improvement in adjusted mean change from baseline in VaDAS‐cog score at all timepoints: (Adjusted mean difference (AMD) [95% CI]: ‐1.53 [‐2.35, ‐0.71]; P<0.001) at week‐13, (AMD [95% CI]: ‐2.69 [‐3.69, ‐1.69]; P<0.001) at week‐26, (AMD [95% CI]: ‐2.15 [‐3.31, ‐0.98]; P<0.001) at week‐39 and (AMD [95% CI]: ‐1.94 [‐3.38, ‐0.51]; P = 0.008) at week‐52. The largest difference (early‐starters vs delayed‐starters) was observed at week‐26.ConclusionIn this analysis, the effect of SLT was observed at week‐13 in early‐starters and sustained until week‐52. Delayed‐starters did not catch up with early‐starters on mean change in VaDAS‐cog from baseline at week‐52, suggesting not only a symptomatic effect of SLT but also a possible disease‐modifying effect.
References: Jia J, Wei C, Chen S, et al. Efficacy and safety of the compound Chinese medicine SaiLuoTong in vascular dementia: A randomized clinical trial. Alzheimers Dement (N Y). 2018;4:108‐117. Published 2018 Mar 22. https://doi.org/10.1016/j.trci.2018.02.004
Age‐related relative comorbidity burden of mild cognitive impairment: A US database study
Authors: Li Gang , Toschi Nicola , Batrla Richard , Boccato Tommaso , Cho Min , Ferrante Matteo , Frech Feride H , Galvin James E , Henley David , Mattke Soeren , DeSanti Susan , Hampel Harald 
Published: December 2023

AbstractBackgroundAs novel therapies emerge, timely detection and diagnosis of early Alzheimer’s disease (AD) become increasingly relevant. While primary care would be the most suitable setting for early detection, diagnosis rates of mild cognitive impairment (MCI) are low and systematic screening of asymptomatic individuals is currently not recommended. We investigated whether a predictive algorithm based on established AD risk factors can help to identify individuals at elevated risk of having undiagnosed MCI, in particular in younger age cohorts.MethodOur retrospective study used MarketScan insurance claims data to identify individuals aged ≥50 years with MCI (but without dementia) and assemble controls without MCI/dementia diagnoses matched on age, sex, and geographic region. Medical conditions previously identified as AD risk factors in the literature were considered MCI risk factors if their prevalence in MCI individuals was statistically significantly higher than in controls. Logistic regression and extreme gradient boosting (XGBoost; a machine learning method) were applied to discriminate MCI from control individuals based on area under the curve (AUC). Analyses were conducted for the overall cohort and by age group (50‐64, 65‐79, and ≥80 years).ResultTwenty‐five medical conditions had statistically higher frequency in the MCI cohort (n = 5,185) compared to controls (n = 15,555). The top MCI risk factors (with odds ratio for the 50‐64, 65‐79, and ≥80 years subgroups, respectively) were: depression (4.4, 3.1, 1.9); stroke/transient ischemic attack, TIA (6.4, 3.0, 2.1); obstructive sleep apnea, OSA (3.6, 2.5, 1.7); hyperlipidemia (1.7, 1.5, 1.4); and hypertension (1.6, 1.5, 1.5) (all p‐values <0.0001). The logistic regression models for identifying MCI individuals reached AUCs of 0.75, 0.69, and 0.66, for the 3 age groups, respectively. The XGBoost approach reached AUC = 0.94 (training set) and AUC = 0.78 (test set). Each approach was most accurate when discriminating MCI for the youngest age group.ConclusionOur two modeling approaches were able to predict MCI with acceptable accuracy. The predictions were more accurate in the 50‐64 age group than in the older groups, suggesting that the use of prediction models for triage in primary care will play a greater role in younger age groups.
Baseline cerebrovascular reactivity predicts longitudinal enlarged perivascular space burden in the basal ganglia among cognitively normal older adults
Authors: Libecap Timothy Justin , Bauer Christopher E. , Zachariou Valentinos , Pappas Colleen , Raslau Flavius D , Liu Peiying , Lu Hanzhang , Gold Brian T. 
Published: December 2023

AbstractBackgroundIncreasing evidence suggests that enlarged perivascular spaces (ePVS) on magnetic resonance images (MRIs) are associated with cognitive dysfunction in aging. However, the etiology of ePVS remains unknown. Here we tested the possibility that cerebrovascular dysfunction, as assessed by an MRI measure of cerebrovascular reactivity (CVR), may contribute to ePVS development.Method79 cognitively normal, older adults (46 women, age range 60‐84) were recruited to undergo baseline MRI scanning and follow‐up scanning approximately 2.5 years later. Of the 79 participants scanned at baseline, 50 returned for follow‐up. Participants were scanned on a 3T Siemens Prisma scanner with a 64‐channel head coil. All scanning methods were identical across time points and included collection of T1‐weighted, T2‐weighted FLAIR, and quantitative susceptibility mapping (QSM) images. Cerebrovascular dysfunction was assessed using CVR, a measure of the ability of cerebrovasculature to dilate in response to vasoactive stimuli. Participants underwent blocked administration of breathing either room air or hypercapnic gas, while blood‐oxygen‐level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) was performed. CVR values were computed for baseline scans in specific regions by dividing change in BOLD signal by change in end‐tidal CO2. Baseline and follow‐up ePVS counts were performed by a single rater who was blinded to participant demographics. ePVS were individually and manually counted using STRIVE criteria in a representative, axial slice of the basal ganglia (BGePVS) and centrum semiovale (CSePVS).ResultBGePVS and CSePVS significantly increased during the follow‐up period. Mixed effects models indicated that baseline basal ganglia CVR values significantly predicted increased BGePVS counts after controlling for age, sex, education, and time point (p = <0.001). Furthermore, this effect was present bilaterally: Baseline CVR in the left hemisphere predicted increased BGePVS in the left hemisphere (p = <0.001) and baseline CVR in the right hemisphere predicted increased BGePVS in the right hemisphere (p = <0.001). Conversely, there was no longitudinal relationship between baseline CVR and increased CSePVS (p = 0.449).ConclusionThese results demonstrate that low baseline CVR is a risk factor for later development of ePVS. Localization of these findings to the basal ganglia suggests that it is particularly vulnerable to cerebrovascular dysfunction.