bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microglial Activation Captured by Diffusion‐Weighted MRI and sTREM2 in Alzheimer’s Disease
Authors: Zavradyan Gohar , Bennett Ilana J , Padmanaban Sneha , Kang Seerat , Kuncharapu Sindhuja 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is characterized by the accumulation of beta‐amyloid plaques and tau protein tangles and neurodegeneration, with growing interest in the role of neuroinflammation. The neuroinflammatory response to an insult is modulated by microglia, which transition from a resting state marked by ramified, branching processes to an activated stated in which they proliferate, migrate, and swell (processes shorten, somas enlarge). Animal studies have shown that diffusion‐weighted magnetic resonance imaging (MRI) is sensitive to these morphological differences in microglia, with higher diffusion in brain regions experiencing inflammation.MethodThe current study examined the sensitivity of diffusion‐weighted MRI to neuroinflammation in older adults diagnosed with AD, and its relation to an established CSF‐based biomarker of microglial activation (soluble Triggering Receptor Expressed on Myeloid cells 2, sTREM2), using data from the Alzheimer’s Disease Neuroimaging Initiative.ResultResults reveal significantly higher diffusion (mean diffusivity, MD) in the hippocampus and basal ganglia and higher sTREM2 levels in the AD group relative to the older adults diagnosed with mild cognitive impairment or with no cognitive deficits. In addition, higher MD in the basal ganglia (caudate), but not the hippocampus, was significantly related to sTREM2 levels in the AD group.ConclusionThese findings are consistent with microglia modulating a neuroinflammatory response in AD and indicate that diffusion‐weighted MRI is sensitive to the swollen morphology of activated microglia.
Age‐related variations in diffuse axonal injury following mild traumatic brain injury
Authors: Criado‐Marrero Marangelie , Ravi Sakthivel , Bhaskar Ekta , Febo Marcelo , Abisambra Jose F. F 
Published: January 2025

AbstractBackgroundRepetitive mild traumatic brain injury (rmTBI) represents a substantial health challenge, urging a more thorough investigation into its early effects and possible interventions. The collective consequences of rmTBI encompass various neurobiological and neuropsychological impairments, increasing susceptibility to diseases like Alzheimer's and related dementias. Employing the Closed‐Head Impact Model of Engineered Rotational Acceleration (CHIMERA) approach for TBI induction, our prior study revealed connectivity alterations within 53% of regions in young and aged wild‐type mice five days post‐injury.MethodGiven rmTBI's potential for causing diffuse axonal injury, the current study utilized advanced neuroimaging techniques—Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI)—to detect white matter damage in 36 regions of the brain.ResultMajor age‐dependent disruptions were observed in the corticospinal tract, hippocampal commissure, anterior cingulate, and optic tract. Surprisingly, mean diffusivity (MD), a reflection of overall water diffusion, showed unique age‐related effects. Three main regions were affected by injury in young mice: the entorhinal cortex, anterior cingulate, and optic tract. Regardless of injuries, aged animals exhibited an inverse correlation between fractional anisotropy (FA) and radial diffusivity (RD) in the secondary motor area, corticospinal tract, and hippocampal commissure.ConclusionOur discoveries are significant for revealing age‐specific changes and understanding the intricate connection between age and injury, shaping brain tissue integrity, particularly in the context of aging research.
Depressive Symptoms and Cognitive Function in Older Adults with Subjective Cognitive Decline: Longitudinal Findings From 2010 to 2020
Authors: Huang Jing , Perrin Nancy , Li Junxin 
Published: January 2025

AbstractBackgroundSubjective cognitive decline (SCD) is increasingly recognized as a symptom of preclinical Alzheimer’s disease (AD), characterized by self‐perceived cognitive worsening without objective cognitive deficits. Depressive symptoms have been linked to elevated risks of cognitive decline, yet research on depression’s role in SCD progression is still limited and inconsistent. Therefore, this study aims to examine the longitudinal association between depressive symptoms and cognitive function in older adults with SCD over a 10‐year period.MethodUsing six waves of biennial data from the Health and Retirement Study (2010‐2020), we selected older adults aged 50+ with SCD in 2010. SCD was defined by self‐reported memory decline compared with 2 years ago, absence of objective cognitive deficits (Modified Telephone Interview for Cognitive Status [TICS‐M]>11), and no self‐reported AD or dementia. Linear mixed models estimated the association between baseline depressive symptoms and cognitive function change from 2010 to 2020. Time, depression, and the time by depression interaction predicted cognitive function, measured by TICS‐M. Depressive symptoms were assessed by the eight‐item Center for Epidemiologic Studies Depression Scale (CESD‐8). CESD‐8 scores (0 – 8) were dichotomized into with and without depression using a cutoff of 3. Covariates included age, sex, race, education, marital status, poverty level, smoking, drinking, sleep disturbance, and number of chronic conditions.Result3058 older adults with SCD were identified (65.8 ± 10.5 years, 65.3% females). The baseline CESD score averaged 2.03 (SD = 2.32), and 31% were categorized as having probable depression. The mean TICS‐M score was 16.07 (SD = 3.00) in 2010, declining to 14.71 (SD = 4.28) in 2020. Compared with individuals without depression, those with depression at baseline exhibited a significantly greater decline in cognitive function over time when controlling for covariates (p = 0.001). Those with depression showed an average 0.14 greater decline in cognitive function at each biennial wave compared to those without depression (depression × time coefficient = ‐0.14, 95% CI = ‐0.22, ‐0.06).ConclusionThis study provides longitudinal evidence that depressive symptoms contribute to cognitive decline in SCD above other known factors. Addressing depressive symptoms may be pivotal in slowing further cognitive deterioration in older adults with SCD.
Pursuing dementia detection for better prevalence estimation with artificial intelligence and traditional modelling techniques in electronic health data
Authors: Collyer Taya , Liu Ming , Beare Richard , Andrew Nadine , Ilomaki Jenni , Bell Simon , Rocca Walter A. , St. Sauver Jennifer , Thrift Amanda G , Ung David , Lu Alicia , Siostrom Kristy , Moran Chris , Roberts Helene , Le Elizabeth , Kennedy John Clark , Srikanth Velandai K 
Published: January 2025

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.
Investigations of reported plasma amyloid b peptides and total tau protein assayed with immunomagnetic reduction in cognitively normal controls and patients with amnestic mild cognitive impairment and Alzheimer’s disease dementia
Authors: Yang Charles S.Y. 
Published: January 2025

AbstractBackgroundSince 2012, many groups have been using immunomagnetic reduction (IMR) for assaying plasma amyloid β 1‐40 (Aβ1‐40) peptide, Aβ1‐42 peptide and total tau protein (T‐Tau) in cognitively normal controls (NC) and patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD). Tremendous results have been independently reported. In this work, we summarize the reported levels of plasma Aβ1‐40, Aβ1‐42 and T‐Tau to investigate the consistence among studies.MethodResults of thirty studies were analyzed, including twenty‐five studies published in papers, two studies presented at conferences and three unpublished studies.ResultThe coefficient of variation (CV) in reported mean values of biomarker concentrations among studies are 8.1% for Aβ1‐40, 4.1% for Aβ1‐42 and 19.4% for T‐Tau in NC, 10.7% for Aβ1‐40, 8.4% for Aβ1‐42 and 15.0% for T‐Tau in aMCI, 10.6% for Aβ1‐40, 19.1% for Aβ1‐42 and 28.7% for T‐Tau in ADD. In the thirty studies, there are 1189 subjects of NC, 543 aMCI patients and 853 ADD patients. The averaged level of plasma Aβ1‐40 of subjects was found to significantly decrease from NC (59.72 pg/ml) to aMCI (45.92 pg/ml, p < 0.0001), which showed non‐significant difference from ADD (48.34 pg/ml, p > 0.05). The averaged level of plasma Aβ1‐42 significantly increased from NC (15.72 pg/ml) to aMCI (17.66 pg/ml, p < 0.0001), which was not significantly different from ADD (19.39 pg/ml, p > 0.05). However, the averaged level of plasma T‐Tau significantly increased from NC (17.92 pg/ml) to aMCI (28.26 pg/ml, p < 0.0001), further significantly increased to ADD (35.51 pg/ml, p < 0.001). The summarized results reveal important impacts to clinical utilities of plasma biomarkers in AD.ConclusionThe measured concentrations of plasma Aβ1‐40, Aβ1‐42 and T‐Tau with IMR are consistent and reliable among studies. Furthermore, the plasma Aβ1‐40, Aβ1‐42 and T‐Tau levels are able to discriminate NC from patients with aMCI and ADD. Plasma T‐Tau levels are disease‐severity dependent.
500nm blue‐green light therapy for cognitive function in Alzheimer’s continuum and factors affecting efficacy
Authors: Wang Ziqi , Yang Junlan , Feng Qiansen , Li Linlin , Chen Lei , Wang Jiayu , Wu Jiajing 
Published: January 2025

AbstractBackgroundLight therapy has emerged as an effective method for Alzheimer’s disease (AD). However, the efficacy on cognitive function in Alzheimer’s continuum and factors affecting efficacy have not been established. The study aimed to evaluate the efficacy of 500 nm blue‐green light therapy on cognitive function for Alzheimer’s continuum, and the main factors affecting light therapy efficacy were analyzed.MethodForty‐two participants were enrolled in this studies, including 13 healthy control [HC, equal to 0 standard deviation, (SD)], 9 subtle cognitive impairment (SCI, ≥0.5 SD), 8 early mild cognitive impairment (eMCI, ≥1.0 SD), 6 late MCI (lMCI, ≥1.5 SD), 6 dementia of AD (dAD, ≥2.0 SD). All the participants treated with 500 nm blue‐green light for 50 minutes with 4 weeks in the morning. A reduction of 4 or more points in the Alzheimer’s Disease Assessment Scale‐cognitive subscale (ADAS‐cog) scores was considered valid for cognitive improvement.ResultThe total efficacy after light therapy was 16.7%. The efficacy in HC, SCI, eMCI, lMCI and dAD after light therapy were 15.4%, 11.1%, 12.5%, 16.7% and 33.3%, respectively. Light therapy efficacy was not significantly correlated with the participant’s sex, age, diagnostic type, education, apolipoprotein E epsilon 4, the Mini‐mental State Examination scores; the Montreal Cognitive Assessment‐Basic scores, Hamilton Anxiety Scale scores, Hamilton Depression Scale scores, or the Pittsburgh Sleep Quotient Index. In multivariate analysis, the ADAS‐cog scores before light therapy (OR 1.279, 95% CI 1.021∼1.602, p = 0.033) was an independent predictor, while the Everyday Cognition (ECog) scores and the Mild Behavioral Impairment Checklist (MBI‐C) scores before light therapy didn’t influence the efficacy.ConclusionFour weeks of 500 nm blue‐green light therapy had different efficacy on cognitive function for Alzheimer’s continuum. Baseline cognitive status may influence light therapy efficacy.
Developing Novel Small Molecule Proteostasis Enhancers for Alzheimer’s Disease (AD)
Authors: Ariyath Ajish , Fernando Warnakulasuriya Mary Ann Dipika Binosha , Martins Ralph N , Bharadwaj Prashant 
Published: January 2025

AbstractBackgroundThe autophagy lysosomal pathway (ALP) and the ubiquitin‐proteasome system (UPS) are key proteostasis mechanisms in cells, which are dysfunctional in AD and linked to protein aggregation and neuronal death. Autophagy is over activated in Alzheimer’s disease brain whereas UPS is severely impaired. Activating autophagy has received most attention, however recent evidence suggests that UPS can clear aggregate proteins and a potential therapeutic target for AD and protein misfolding diseases.MethodWe previously developed an assay using the MC65 AD cell model and demonstrated that Amyloid Precursor Protein (APP) derived carboxy terminal peptides (APP‐C99) and amyloid‐β protein (Aβ) is rapidly cleared in this model. Using this model we screened a library of small molecule proteostasis modulators and identified IU1, a USP14 inhibitor that improved cell survival and promoted Aβ clearance. Our study investigated whether new analogues of IU1 could provide better neuroprotection in AD. Screening of 71 novel small molecule proteostasis inhibitors using this cell model, discovered two novel lead compounds C10 and C51. These compounds effect on autophagy, proteasome activity, and APP‐C99/Aβ clearance were further analyzed using techniques involving immunofluorescence staining, toxicity analysis, western blot, DUB, proteasome and luminescence assays.ResultInitial study used an in‐silico artificial intelligence screening platform from Atomwise to design and develop novel small molecule USP14 binding ligands. Cell studies have shown promising results with compounds C10 and C51 from the screened library of proteostasis modulators in alleviating AD proteostasis dysfunction, protein aggregation and regulating autophagy clearance. Cell survival by IU1 was 40% which was improved to 55% using C10 and C51, where it reduced accumulation of APP‐C99 and Aβ. Also, reduced levels of autophagy markers LC3 and p62, restored proteasomal activity in the AD cell model.ConclusionIn conclusion, ours is the first novel report of using IU1 in an AD model as a USP14 inhibitor. The novel IU1 analogues C10 and C51 looks more promising and hold potential as candidates for pre‐clinical validation in AD. The next steps involve testing therapeutic efficacy, target engagement, and brain bioavailability in AD animal models, with completed cell model assessments and upcoming focus on confirming deubiquitinating enzyme (DUB) specificity.
Dementia friends unite! Exploring the effectiveness of a dementia education initiative co‐produced with and for multicultural communities
Authors: Caballero Gabriela E , Tsouros Emy , Tran Mai , Jasim Nibras , Chan Sonia , Dinh Angie , Dinh Vivian , Chan Eliza , Richardson Kylie , Sartawy Sanna , Kaapro Sharyn , Hulm Zac , Siette Joyce , Dadich Ann , DiGiacomo Michelle , Steiner‐Lim Genevieve Z , Karamacoska Diana 
Published: January 2025

AbstractBackgroundPeople living with dementia and their carers, particularly those from culturally and linguistically diverse backgrounds, consistently demonstrate limited knowledge about and negative attitudes towards dementia. This can limit access to preventive and post‐diagnostic care. Culturally sensitive dementia education is an inclusive and practical intervention that can be used in multicultural contexts to increase awareness and destigmatise the condition. Our study aimed to explore the reach and effectiveness of a multilingual dementia education initiative, co‐produced by multicultural service providers and people with lived and caring experiences of dementia.MethodWe conducted a quasi‐experimental education intervention targeting English, Arabic, Vietnamese, Cantonese, Mandarin, and Greek‐speaking people in Sydney, Australia. The intervention involved three two‐hour information sessions delivered in‐language by trained bilingual co‐facilitators over three months. The RE‐AIM framework was used to analyse the intervention’s reach and effectiveness. Reach was assessed by the number of attendees at each session. Effectiveness was measured pre‐ and post‐intervention using an adapted survey incorporating 10‐items from the dementia knowledge assessment scale (DKAS) and 14‐items from the dementia attitudes scale (DAS). Mann‐Whitney U tests compared mean rank differences pre‐ and post‐intervention for each scale item.ResultsWe reached over 220 people across six languages. There were statistically significant improvements from pre‐ to post‐intervention on five DKAS items regarding causes, risk factors, and symptom management (all p ≤ 0.050), and eight DAS items regarding perceptions and stigma (all p ≤ 0.019).Discussion/ConclusionThe education initiative was effective in this multicultural context, but ongoing education is needed to address persistent dementia knowledge gaps and negative attitudes. Co‐production with multicultural stakeholders likely enhanced understanding of community needs, resulting in greater information access and acceptability. To measure longer‐term effectiveness, the adapted survey will be re‐administered to capture changes in dementia knowledge and attitudes, 12‐months post‐intervention. While culturally tailored education might overcome nuanced learning and support needs, a culturally sensitive approach is likely to be more effective to enact dementia‐friendly community principles in multicultural contexts.
Plasma NfL and GFAP for predicting VCID brain changes in the population
Authors: Raghavan Sheelakumari , Hofrenning Ekaterina I. , Fought Angela J. , Algeciras‐Schimnich Alicia , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Lowe Val J. , Graff‐Radford Jonathan , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundVascular contributions to cognitive impairment and dementia (VCID) are often comorbid with Alzheimer’s disease and increase the risk of dementia. Blood‐based biomarkers may be promising for identifying individuals at high risk for VCID due to small vessel disease (SVD).MethodWe included 1709 participants from the Mayo Clinic Study of Aging who had concurrent MRI, plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) measured on the HD‐X Simoa Quanterix platform, and clinical dementia rating scale (CDR). We categorized participants into SVD vs. not using WMH/TIV≥1.3% which corresponds to Fazekas>=2 cutoff and cognitively impaired (CI) vs. not using CDR≥0.5. We obtained four groups using SVD and cognitively normal and impaired classifications: cognitively unimpaired without SVD (CU), CU with SVD (Vas‐CU), CI with SVD (VCI), and CI without SVD (Non‐vas‐CI). Univariate and multivariable logistic regression models were used to obtain odds ratios, confidence intervals and ROC curves to evaluate the association between plasma markers and each disease group compared to CU. We also evaluated these analyses in amyloid positive and negative participants.ResultThere was a high proportion of CU (Vas‐CU/(CU+Vas‐CU)=18%) and CI (VCI/(VCI+ Non‐vas‐CI)=47%) participants with SVD in the population‐based sample. Higher plasma levels were associated with greater vascular burden and differed by amyloid status (Figure 1). ROC analyses showed that both NfL and GFAP provided discrimination between Vas‐CU and CU groups (AUC=0.83) as well as VCI and CU groups (AUC=0.88) (Figure 2, Table 1). Though GFAP and NfL are non‐specific and may also reflect non‐vascular damage, we found that these plasma markers did not clearly separate NonVas‐CI from the CU group in the overall cohort (AUC=0.69). When amyloid status was available, the discriminative power of NfL and GFAP in separating the 4 groups was very low in amyloid positive participants. On the other hand, NfL and GFAP separated Vas‐CU and VCI groups from CI reasonably well in the amyloid negative group.ConclusionPlasma biomarkers associated with SVD‐burden provided reliable discriminative power in predicting VCID, particularly in the absence of amyloid, suggesting their utility in the prognosis and stratification for clinical trials.