bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of a novel Carbon Dot Inhibitor of Tau and Amyloid Beta Aggregation in Alzheimer’s Disease
Authors: Burr Hannah J , Smith Nathan Scott , Zhang Wei , Wang Chunyu 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disorder whose pathological hallmarks include tau and amyloid beta aggregation, a phenomenon that has been linked to inflammation and degradation of brain tissue. Prior data published in the Wang lab suggests that carbon dots (CDs) synthesized from citric acid and urea can inhibit aggregation. We sought to characterize the inhibitory effects of a new class of CDs synthesized from varied ratios of Congo red and citric acid. Since Congo red carbon dots (CRCDs) can cross the blood‐brain barrier (BBB), they have the potential to be used as a therapeutic.MethodA new type of CD was synthesized via the muffle furnace method using citric acid, urea, thiourea, selenourea, formamide, and congo red. The CDs have a diameter less than 20 nanometers, enabling them to cross both the BBB and the cell membrane, which was confirmed experimentally with zebrafish models. The newly synthesized CDs were used in Thioflavin T (ThT) aggregation assays to assess protein aggregation inhibition. This was verified visually by Atomic Force Microscopy (AFM).ResultAll three CRCDs inhibited tau aggregation, with CRCD1 being the most effective. CRCD1 had the lowest IC50 of 0.21ug/mL, while CRCD2 and CRCD3 had an IC50 of 0.50ug/mL and 2.55ug/mL, respectively. All three CRCDs also inhibited amyloid beta aggregation, with CRCD being the most effective. CRCD1 had the lowest IC50 of 1.76 ug/mL, while CRCD2 and CRCD3 had an IC50 of 3.34 ug/mL and 5.61 ug/mL, respectively.ConclusionThe ability to reduce tau aggregation in the brain has the potential to slow the growth of the disease and potentially alleviate symptoms. There are currently limited treatments available for AD, but CRCDs show great promise.
Disclosure of Alzheimer’s Disease Diagnosis: Patient Preferences and Physician Approach
Authors: Söderlund Maria Elvira , Guajardo Maria Elena , Cavagna Marina , Labos Edith , Schapira Marcelo , Seinhart Daniel Bernardo 
Published: January 2025

AbstractIntroductionThe announcement of an Alzheimer’s disease (AD) diagnosis is a complex process that requires careful consideration by healthcare professionals. This study aims to explore the desires of patients regarding the disclosure of their Alzheimer’s diagnosis and to understand the practices adopted by physicians in this regard.MethodsWithin the context of memory disorders consultations, patients were queried about their preferences regarding the disclosure of an AD diagnosis if it were determined to be the cause of their memory issues. Additionally, a survey was conducted among physicians specializing in the care of older adults, specifically at the Italian Hospital of Buenos Aires, to assess their practices and concerns related to the disclosure of AD diagnoses.ResultsOut of 250 patients, 223 expressed a desire to know their diagnosis, while 19 were uncertain, and 7 declined. Among the 76 participating physicians, the majority (82.9%) were aged between 35 and 65 years, with 68% being female. A significant portion (58.7%) were geriatricians while 27.8% were primary care physicians. Notably, 32% of physicians reported hardly ever using AD when delivering a diagnosis and 48% preferring the term “cognitive impairment”. Only 2 physicians always use AD in this context. The main reasons for avoiding this term were the potential risk of psychological impact (43.8%) and stigmatization (20.5%) associated with the diagnosis.ConclusionsThe incorporation of biomarkers enhances diagnostic certainty and therapeutic possibilities for AD. While most patients express a desire to know their diagnosis, a significant proportion of physicians opt for more general terms such as “cognitive impairment” instead of explicitly using the term AD. Understanding patient preferences and providing information in a sensitive manner are crucial aspects in the disclosure process, aiming to minimize potential harm. The right of patients to know or not to know their diagnosis should be respected, highlighting the importance of ethical and patient‐centered approaches in AD diagnosis disclosure.
Longitudinal changes in cerebral metabolic rate of oxygen in older adults without and with cognitive impairment
Authors: Wu Jiani , Oishi Kumiko , Soldan Anja , Pettigrew Corinne , Lin Zixuan , Zhu Yuxin , Jiang Dengrong , Li Xin , Moghekar Abhay , Liu Peiying , Oishi Kenichi , Albert Marilyn S. , Lu Hanzhang 
Published: January 2025

AbstractBackgroundCerebral metabolic rate of oxygen (CMRO2) denotes the amount of O2 that the brain consumes. Changes in CMRO2 during aging and neurodegeneration have not been fully characterized. Using a non‐invasive, non‐contrast MRI CMRO2 technique, the present study reports CMRO2 changes in older adults from a total of 526 measurements, the largest CMRO2 dataset to date.MethodExperimental procedure: Analyses included 227 participants from the Biomarkers‐for‐Older‐Controls‐at‐Risk‐for‐Dementia (BIOCARD) cohort. Figure 1 shows the demographic information. Participants were categorized into 3 diagnostic categories: cognitively normal individuals who remained cognitively normal (n=187), individuals who progressed from normal cognition to Mild Cognitive Impairment (MCI) or dementia during follow‐up (n=13), and individuals with MCI/dementia at baseline (n=27). Participants were scanned on a 3T MRI (Philips) in a longitudinal study design. On average, participants had 2 scans (range = 1‐4) collected over 3.1 years of follow‐up. CMRO2 was estimated from the arterio‐venous difference in oxygen content, using a non‐invasive MRI technique. Data analysis: Linear mixed effect models were used to examine longitudinal changes in CMRO2 over time, as well as the effect of impairment (MCI/dementia) on CMRO2.ResultIn individuals who remained cognitively normal during follow‐up (Figure 2), there was an increase in CMRO2 over time (p<0.001), suggesting that the brain is “working harder” to compensate for its lower efficiency. However, the rate of increase in CMRO2 was attenuated among individuals with higher baseline ages (p=0.039). There were also main effects of baseline age (p=0.001) and sex (p=0.014), indicating lower CMRO2 levels among older than younger adults and among men than women. Next, we included participants who were cognitively impaired and those who progressed from normal cognition to cognitive impairment (Figure 3A). CMRO2 was lower among progressors and impaired participants than those who remained normal (p=0.001). There was an interaction effect between time and diagnosis category (p=0.044), indicating that participants who were impaired at baseline or progressed to impairment showed a different pattern of CMRO2 change over time compared to those who remained normal (Figure 3B).ConclusionCMRO2 revealed a non‐monotonic change in aging and cognitive impairment, suggesting a multi‐factorial and/or multi‐phase alteration of brain metabolism.
White Matter Changes in Alzheimer’s and Treated Huntington’s Disease Mouse Models Detected by Diffusion MRI
Authors: Liu Andy , Simmons Danielle , Alvarez Connor , Wang Yi , Spincemaille Pascal , Longo Frank M. , Zeineh Michael , Georgiadis Marios 
Published: January 2025

AbstractBackgroundMyelin integrity is central to healthy brains and is increasingly shown to be compromised in neurodegenerative diseases. Diffusion‐ and susceptibility‐based MRI metrics can detect myelin changes. We show advanced diffusion and susceptibility metrics can detect degenerative myelin changes in ex vivo AD and HD mouse models.MethodAD mice included 2 groups: 8‐month‐old male hAPP (Lond/Swe mutations, n = 4) and wild‐type (WT, n = 4). HD mice included 3 groups: 12‐week‐old vehicle‐treated controls (WT, n = 4), R6/2 HD treated for 7 weeks with vehicle (HD, n = 4) or LM11A‐31 (HD‐treated, n = 4). After perfusion‐fixation and brain extraction, 4‐shell diffusion (b = 1,2,5,10ms/µm2, 100 directions) and multi‐echo gradient echo (MGE) MRI (10 echoes, GRE = 4‐40ms) were performed on a Bruker 7T scanner. We used DESIGNER and MEDI to generate diffusion‐ and susceptibility‐based myelin‐sensitive metrics, respectively (diffusion: mean/radial/axial diffusivities ‐MD/RD/AD, kurtoses ‐MK/RK/AK, axonal water fraction ‐AWF; susceptibility: R2*, quantitative susceptibility mapping‐QSM) (Fig. 1A). Datasets were registered to the Allen Atlas through linear/non‐linear transformations using FSL flirt/ANTs (Fig. 1B). Median values were derived for two Allen Atlas white matter regions‐of‐interest ‐ROIs (corpus callosum and fornix). For AD, scan timing effects were included in a 2‐way ANOVA analysis. For HD, two‐sample t‐tests were performed between the 3 groups. For histological validation, a second set of R6/2 HD brains (WT, HD, HD‐treated n = 10‐13 mice/group) were myelin basic protein (MBP)‐stained, and the percent area of striatum immunostaining determined by ImageJ thresholding.ResultsAD mice showed increased RD and reduced AWF and RK compared to controls in both ROIs, suggesting a loss of myelinated axons. Susceptibility‐based metrics show no clear pattern. HD mice showed an increasing trend in AWF and RK and decreases in RD in both ROIs vs WT, suggesting increased myelination. Interestingly, LM11A‐31‐treated HD mice showed a significant reversal in RD and AWF. Histologically, myelin immunostaining was increased in the striatum of HD mice, reversed by LM11A‐31.ConclusionDiffusion MRI myelin‐sensitive metrics detected possibly compromised myelin in AD mouse white matter (corpus callosum and fornix). In HD mice, both diffusion MRI and histology showed a trend towards increased myelin, which was reversed by LM11A‐31 treatment.
Blood biomarkers of amyloid and tau pathologies, brain degeneration, inflammation, and oxidative stress in early‐ and late‐onset Alzheimer's disease
Authors: Xia Xinyi , Qin Qi , Tang Yi 
Published: January 2025

AbstractBackgroundBlood biomarkers provide a non‐invasive and accessible means of detection for Alzheimer's disease (AD). Specifically, biomarkers associated with early‐onset AD (EOAD) illuminate the underlying mechanisms and provide valuable guidance for potential therapies. We aim to present blood biomarker evidence in individuals with both EOAD and late‐onset AD (LOAD), as well as in age‐matched healthy controls.MethodsA total of 95 participants were enrolled in the study. We assessed levels of 18 distinct blood biomarkers, and concurrently examined cerebrospinal fluid amyloid and tau markers, neuropsychological test scores, APOEe4 carrier status, and neuroimaging markers. Group disparities, correlations, and the diagnostic utility of these biomarkers were investigated.ResultsIn patients with EOAD, levels of blood IL‐4, IL‐6, and TNF‐a were notably lower when compared to LOAD. AD patients exhibited higher blood levels of p‐tau181, NfL, GFAP, tAOC, and MDA, as well as lower levels of Aß1‐42, SOD, and IL‐12p70. Intricate interactions were observed among these blood biomarkers, with inflammatory markers demonstrating correlations with neuroimaging markers within the AD cohort. Individuals carrying the APOEe4 allele displayed elevated levels of GFAP and IL‐4 in comparison to non‐carriers. Additionally, blood levels of Aß1‐42, p‐tau181, NfL, and GFAP were found to correlate with neuropsychological scores and proved effective in distinguishing AD, with GFAP exhibiting particular relevance in early‐onset cases.ConclusionThe study demonstrated that individuals with EOAD may experience less inflammatory responses than older patients. Peripheral inflammation was associated with amyloid and tau pathologies, astrogliosis, oxidative stress, and alterations in brain structure and function. Blood Aß1‐42, p‐tau181, NfL, and GFAP showed promise in detecting cognitive decline and AD, with GFAP exhibiting relevance in early‐onset cases.
Predicting cognitive function from multimodal mesoscale neuroimaging data
Authors: Duchesne Simon , Potvin Olivier , Dieumegarde Louis 
Published: January 2025

AbstractBackgroundOur objective was to assess individual and joint relationships between various mesoscale indicators of brain health (e.g., neuronal, metabolic, and vascular integrity) and cognitive function.MethodWe used 7224 timepoint measures originating from 1825 participants of the ADNI study who were cognitively healthy or with mild cognitive impairment at initial assessment. Cognitive function outcomes were test results on the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog), the Rey auditory Verbal Learning Test immediate recall (RAVLTi), the Rey auditory Verbal Learning Test delayed recall (RAVLTd), the Trail Making Test A (Trail a), and the Trail Making Test B (Trail b) tasks. Four families of markers were used. First were three families of mesoscale neuroimaging assessed by MRI for 88 brain regions: 1. Neuronal integrity was estimated by FreeSurfer 6.0 produced volumes extracted from T1‐weighted (T1w) images; 2. Neurite integrity was estimated through T1w/T2‐weighted MRI ratios; 3. Cerebral metabolism integrity was measured by synthetic fluorodeoxyglucose‐positron emission tomography, generated by a model based on a latent‐space regularized generative adversarial net from T1w MRIs. Secondly, 4. Systemic markers of vascular health‐related factors were included in the model (history of cardiovascular diseases, alcohol abuse, smoking history, systolic and diastolic blood pressure, history of hypertension, type 2 diabetes, Hachinski score). Cross‐sectional and future cognitive outcomes were all predicted in a single machine learning model using all combinations of predictor families at baseline, time interval between outcomes and baseline as long as sex and education. Three types of regression models were tested (Ridge, Partial Least Squares and Support Vector).ResultIndividually, neuronal and metabolism integrity were the best predictors of cognitive function (Fig1; R2: .15‐.35 and MAE in Z score: .08‐.23). The combination of two or more mesoscale brain health markers improved predictions, with the best combination being neuronal integrity with metabolism (Figs2‐3; mean R2: .28; mean MAE: .12). However, increasing the number of markers led to similar results.ConclusionWhile the prediction of cognition improved with a combination of brain health mesoscale markers, a plateau seems to be reached after combining two families.
Increased blood‐brain barrier permeability following COVID‐19 and its correlation with Alzheimer's biomarkers
Authors: Sun Zhe , Frontera Jennifer , Masurkar Arjun V. , Boutajangout Allal , Jiang Li , Thawani Sujata , Links Jon , Vedvyas Alok , Betensky Rebecca A , Debure Ludovic , Ahmed Wajiha , Wisniewski Thomas , Ge Yulin 
Published: January 2025

AbstractBackgroundPost‐COVID cognitive dysfunctions, impacting attention, memory, and learning, might be linked to inflammation‐induced blood‐brain barrier (BBB) impairment. This study explores post‐COVID BBB permeability changes using a non‐contrast water‐exchange based MRI and their associations with blood Alzheimer’s biomarkers.MethodSixty‐seven participants were classified based on COVID (COV) and cognitive (COG) statuses into three groups: COV+/COG‐ (n=34), COV+/COG+ (n=23), and COV‐ (n=10) for comparisons (COV+: Laboratory‐verified SARS‐CoV‐2 infection; COV‐: No history of SARS‐CoV‐2 infection and negative SARS‐CoV‐2 nucleocapsid antibody test.). All patients underwent neuropsychological testing and were coded as having cognitive impairment (COG+) based on neuropsychological testing (=1 abnormal test result). Clinician interview with the patient and informant. Patients were imaged with the following 3T‐MRI sequences: 1) T1‐MPRAGE; 2) Phase contrast (PC)‐MRI for quantification of cerebral blood flow (ƒ). 3) Water‐extraction‐with‐phase‐contrast‐arterial‐spin‐tagging (WEPCAST) [1], estimating permeability‐surface‐area‐product (PS) calculated with Renkin‐Crone Model: PS = ‐ln(1‐E) *ƒ (Figure 1). Plasma biomarkers of Alzheimer’s disease (AD), including Aß40, Aß42, NFL, and pTau181, were measured on the same day as the MRI. PS was compared between groups. Correlations were computed between PS and AD biomarkers.ResultWe included 67 non‐hospitalized participants (age: 64.7, F/M: 43/24) who successfully completed their clinical and MRI exams. Illustrative WEPCAST images and the results of group comparisons indicated that BBB permeability (PS) was higher in COV+ than that in COV‐ subjects (119 vs 96 mg/100 g/min, P=0.006), as well as in any patient with cognitive dysfunction (COG+ vs COG‐, 122 vs 111 mg/100 g/min, P=0.0319). Similarly, among COV+ subjects, PS was significantly higher in COV+COG+ compared to COV+COG‐ subjects (129 vs 113 mg/100 g/min, P=0.0041; Figure 2). After adjusting age, PS was negatively correlated with plasma Aß42 (r=‐0.28, P=0.02), and positively correlated with pTau181 (r=0.24, P=0.046) in all subjects. (Figure 3). There was no significant correlation between PS and NLF or Aß40.ConclusionOur findings demonstrate that SARS‐CoV‐2 virus has an impact on BBB integrity. This effect correlates with several AD biomarkers, including plasma Aß42 and pTau181, highlighting the potential contribution of increased BBB permeability in post‐COVID to AD pathology. Reference: 1. Lin Z, et al. Magn. Reson. Med. 2018;80:1507–1520.
Examining Methylation at the Intersection of Genetic Risk To Alzheimer's Disease And Alcohol Use Disorder
Authors: Pathak Gita , Pietrzak Robert H , Levey Daniel , Montalvo‐Ortiz Janitza , Kranzler Henry , Gelernter Joel , Polimanti Renato A 
Published: January 2025

AbstractBackgroundAlcohol Use Disorder (AUD) affects over 15 million individuals in the United States, contributing to oxidative stress, neuroinflammation, and elevating the risk of neurodegeneration. Despite this, the connection between AUD and aging conditions, particularly Alzheimer’s disease (AD), remains unclear. AD, with a heritability of 60‐80%, is genetically linked, necessitating an exploration of the molecular implications of AUD and genetic susceptibility to AD.MethodParticipants were assessed for AUD using DSM‐5 criteria, and genotype data were imputed with the TOPMED reference panel. The polygenic score (PGS) of AD was calculated in European ancestry participants (mean±SD, 40.12 ±13.3 yrs; N= 4,915), adjusting for age, sex, and genetic ancestry. Methylation was assayed using the Illumina EPIC array using blood to conduct an epigenome‐wide association study (EWAS) on the interaction between PGS‐AD and AUD at 657,226 CpG sites in a subset of participants (N=452). Intrinsic and extrinsic epigenetic age acceleration (IEAA, EEAA) were calculated using Hannum and Horvath’s epigenetic clocks.ResultWe observed a significant association between PGS of AD and AUD diagnosis (cases=34%; OR=1.44; p=0.005). The subsequent EWAS revealed top CpG sites in genes associated with AD pathogenesis, including RLF, CD2AP, GLUL, GJA3, TRIM26, JAG2, RERE, SRRM3, COL11A2. For these top CpGs, we investigated the beta (methylation intensity) in individuals with high‐PGS of AD (top 25% quantile) and stratified them by AUD diagnosis. Briefly, the top two CpG sites in AUD cases‐cg01101221(RLF; p=5.51E‐07) was hypomethylated (p=0.0205); and cg05804888(CD2AP; p=4.61E‐06) was hypermethylated (p=0.0142). We also observed interaction of AUD and PGS of AD is associated with accelerated aging‐ IEAA and EEAA from both Horvath and Hannum’s clocks (p<8.56E‐06<0.0092).ConclusionThe identified genes have been implicated in AD pathogenesis, such as CD2AP's association with Aß production and GLUL's role in brain lipid metabolism. The study also highlighted the association between AUD and PGS‐AD interaction and accelerated epigenetic aging. While the CpG associations did not reach Bonferroni significance, our ongoing research aims to extend these findings to other ancestries and cohorts to improve statistical power. Thus providing a comprehensive understanding of the complex interactions between AUD diagnosis and genetic susceptibility to AD.
PhysicOmics: Combining MR physics, proteomics, and transcriptomics to study cerebral aging and neurodegeneration
Authors: Bae Jonghyun , Gong Zhaoyuan , Guo Alex , Laporte John P , Faulkner Mary E , Bouhrara Mustapha 
Published: January 2025

AbstractBackgroundNeuroimaging‐based evidence suggests that changes in cerebral tissue determinants, including axonal density and myelin content, are associated with aging and neurodegenerative diseases. While neuroimaging markers show strong association with physiological changes, direct validation of their specificity remains challenging. Histology provides useful information for validation, however, faces limitations including denaturation of the sample during preparation. However, in the era of omics, validation of neuroimaging markers becomes accessible with dataset from various biological measurements. We provide two examples of validations of our MR physics‐based markers of myelin water fraction (MWF), a surrogate of myelin content, and neurite density index (NDI), a proxy of axonal density, using proteomics and transcriptomics (Fig.1).MethodAssociation between neurofilament light chain (NfL) and NDI: 58 participants (age = 21‐83yrs.) underwent our multishell diffusion imaging protocol for NDI determination using C‐NODDI. Blood was collected at the time of MRI to quantify the NfL level, a biomarker of axonal integrity, using Simoa assays. The association between NDI and NfL was evaluated using a multiple linear regression analysis. Association between myelin gene expression and MWF: 132 subjects (age = 22‐94yrs.) underwent our relaxometry protocol for MWF determination using BMC‐mcDESPOT. Further, microarray data were obtained from the Allen Human Brain Atlas, providing gene expression data from 6 healthy donors. Using Pearson correlations, we assessed the association between regional MWF measurements (averaged across participants) and regional gene expressions, with a focus on Myelin Basic Protein (MBP) and transcription factors involved in myelin synthesis.ResultFigure 2 shows strong negative regional correlations between NfL and NDI derived using C‐NODDI. Further, we found strong positive correlations between MWF and genes involved in myelin production (Fig.3).ConclusionGiven the strong correlations between NfL or MBP and NDI or MWF, this work provides evidence of the specificity of these MRI biomarkers of axonal density and myelin content, making C‐NODDI and BMC‐mcDESPOT powerful and relevant MR methods for clinical investigations and trials. While further investigations are still required in the context of neurodegeneration, our work demonstrates the potential of combining MR physics and omics to delve deeper into the neurobiological and molecular mechanisms underlying aging and age‐related diseases.