Serum Tau-A and Tau-C Levels and Their Association with Cognitive Impairment and Dementia Progression in a Memory Clinic Derived Cohort.

BACKGROUND: Serum-measured fragments of Tau cleaved by ADAM-10 (Tau-A) and Caspase-3 (Tau-C) have been found linked to change in cognitive function and risk of dementia. OBJECTIVES: 1) To determine the discriminatory abilities of Tau-A, and Tau-C in subjects with either mild cognitive impairment (MCI) due to Alzheimer's disease (AD) or AD dementia compared to a control group. 2) To determine if there is a relation between Tau-A, and Tau-C and established cerebrospinal fluid (CSF) markers of AD- ß-Amyloid1-42 (AB42), Phosphorylated-tau-181 (p-tau), and total-tau. 3) To determine if Tau-A and Tau-C are associated with progression rate from MCI due to AD to AD dementia. DESIGN: Cross-sectional and a substudy using a retrospective cohort design. SETTING: Memory clinic derived subjects contributing to the Danish Dementia Biobank. PARTICIPANTS: Cognitively unimpaired subjects (n=49), patients with mild cognitive impairment (MCI) due to AD (n=45), and Alzheimer's dementia (n=52). MEASUREMENTS: Competitive enzyme-linked immunosorbent assay (ELISA)-measured serum levels of Tau-A, and Tau-C. RESULTS: The ratio between Tau-A and Tau-C differed between the three groups (p=0.015). Age- and sex-adjusted Tau-A differed between groups with lower ratios being associated with more severe disease (p=0.023). Tau-C was trending towards significant correlation to CSF-levels of AB42 (Pearson correlation coefficient 0.164, p=0.051). Those with Tau-C-levels in the 2nd quartile had a hazard ratio (HR) of 2.91 (95% CI 1.01 - 8.44, p=0.04) of progression compared to those in the 1st quartile. Those in the 3rd quartile was found to have a borderline significant (p=0.055) HR of 2.63 (95% CI 0.98 - 7.05) when compared to those in the lowest quartile. CONCLUSIONS: Tau-A and the ratio between Tau-A and Tau-C showed significant differences between groups and were correlated to CSF-AB42. Tau-C values in the middle range were associated with faster progression from MCI to dementia. This pilot study adds to the mounting data suggesting serum-measured Tau-A and Tau-C as biomarkers useful in relation to diagnosis and progression rate in AD but need further validation.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction.

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (ß-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease.

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Assessment of the Correlation and Diagnostic Accuracy between Cerebrospinal Fluid and Plasma Alzheimer's Disease Biomarkers: A Comparison of the Lumipulse and Simoa Platforms.

We compared the clinical and analytical performance of Alzheimer's disease (AD) plasma biomarkers measured using the single-molecule array (Simoa) and Lumipulse platforms. We quantified the plasma levels of amyloid beta 42 (Aß42), Aß40, phosphorylated tau (Ptau181), and total tau biomarkers in 81 patients with mild cognitive impairment (MCI), 30 with AD, and 16 with non-AD dementia. We found a strong correlation between the Simoa and Lumipulse methods. Concerning the clinical diagnosis, Simoa Ptau181/Aß42 (AUC 0.739, 95% CI 0.592-0.887) and Lumipulse Aß42 and Ptau181/Aß42 (AUC 0.735, 95% CI 0.589-0.882 and AUC 0.733, 95% CI 0.567-0.900) had the highest discriminating power. However, their power was significantly lower than that of CSF Aß42/Aß40, as measured by Lumipulse (AUC 0.879, 95% CI 0.766-0.992). Simoa Ptau181 and Lumipulse Ptau181/Aß42 were the markers most consistent with the CSF Aß42/Aß40 status (AUC 0.801, 95% CI 0.712-0.890 vs. AUC 0.870, 95% CI 0.806-0.934, respectively) at the ≥2.127 and ≥0.084 cut-offs, respectively. The performance of the Simoa and Lumipulse plasma AD assays is weaker than that of CSF AD biomarkers. At present, the analysed AD plasma biomarkers may be useful for screening to reduce the number of lumbar punctures in the clinical setting.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Nucleic acid aptamer-based electrochemical sensor for the detection of serum P-tau231 and the instant screening test of Alzheimer's disease.

The instant screening of patients with a tendency towards developing Alzheimer's disease (AD) is significant for providing preventive measures and treatment. However, the current imaging-based technology cannot meet the requirements in the early stage. Developing biosensor-based liquid biopsy technology could be overcoming this bottleneck problem. Herein, we developed a simple, low-cost, and sensitive electrochemical aptamer biosensor for detecting phosphorylated tau protein threonine 231 (P-tau231), the earliest and one of the most efficacious abnormally elevated biomarkers of AD. Gold nanoparticles (AuNPs) were electrochemically synthesized on a glassy carbon electrode as the transducer, exhibiting excellent conductivity, and were applied to amplify the electrochemical signal. A nucleic acid aptamer was designed as the receptor to capture the P-tau231 protein, specifically through the formation of an aptamer-antigen complex. The proposed biosensor showed excellent sensitivity in detecting P-tau 231, with a broad linear detection range from 10 to 107 pg/mL and a limit of detection (LOD) of 2.31 pg/mL. The recoveries of the biosensor in human serum ranged from 97.59 to 103.26%, demonstrating that the biosensor could be used in complex practical samples. In addition, the results showed that the developed biosensor has good repeatability, reproducibility, and stability, which provides a novel method for the early screening of AD.

Tau- and α-synuclein-targeted gold nanoparticles: applications, opportunities, and future outlooks in the diagnosis and therapy of neurodegenerative diseases.

The use of nanomaterials in medicine offers multiple opportunities to address neurodegenerative disorders such as Alzheimer's and Parkinson's disease. These diseases are a significant burden for society and the health system, affecting millions of people worldwide without sensitive and selective diagnostic methodologies or effective treatments to stop their progression. In this sense, the use of gold nanoparticles is a promising tool due to their unique properties at the nanometric level. They can be functionalized with specific molecules to selectively target pathological proteins such as Tau and α-synuclein for Alzheimer's and Parkinson's disease, respectively. Additionally, these proteins are used as diagnostic biomarkers, wherein gold nanoparticles play a key role in enhancing their signal, even at the low concentrations present in biological samples such as blood or cerebrospinal fluid, thus enabling an early and accurate diagnosis. On the other hand, gold nanoparticles act as drug delivery platforms, bringing therapeutic agents directly into the brain, improving treatment efficiency and precision, and reducing side effects in healthy tissues. However, despite the exciting potential of gold nanoparticles, it is crucial to address the challenges and issues associated with their use in the medical field before they can be widely applied in clinical settings. It is critical to ensure the safety and biocompatibility of these nanomaterials in the context of the central nervous system. Therefore, rigorous preclinical and clinical studies are needed to assess the efficacy and feasibility of these strategies in patients. Since there is scarce and sometimes contradictory literature about their use in this context, the main aim of this review is to discuss and analyze the current state-of-the-art of gold nanoparticles in relation to delivery, diagnosis, and therapy for Alzheimer's and Parkinson's disease, as well as recent research about their use in preclinical, clinical, and emerging research areas.

Validity of claims-based definition of number of remaining teeth in Japan: Results from the Longevity Improvement and Fair Evidence Study.

BACKGROUND: Secondary healthcare data use has been increasing in the dental research field. The validity of the number of remaining teeth assessed from Japanese dental claims data has been reported in several studies, but has not been tested in the general population in Japan. OBJECTIVES: To evaluate the validity of the number of remaining teeth assessed from Japanese dental claims data and assess its predictability against subsequent health deterioration. METHODS: We used the claims data of residents of a municipality that implemented oral health screening programs. Using the number of teeth in the screening records as the reference standard, we assessed the validity of the claims-based number of teeth by calculating the mean differences. In addition, we assessed the association between the claims-based number of teeth and pneumococcal disease (PD) or Alzheimer's disease (AD) in adults aged ≥65 years using Cox proportional hazards analyses. RESULTS: Of the 10,154 participants, the mean number of teeth assessed from the claims data was 20.9, that in the screening records was 20.5, and their mean difference was 0.5. During the 3-year follow-up, PD or AD onset was observed in 10.4% (3,212/30,838) and 5.3% (1,589/30,207) of participants, respectively. Compared with participants with ≥20 teeth, those with 1-9 teeth had a 1.29 (95% confidence interval [CI]: 1.17-1.43) or 1.19 (95% CI: 1.04-1.36) times higher risk of developing PD or AD, respectively. CONCLUSION: High validity of the claims-based number of teeth was observed. In addition, the claims-based number of teeth was associated with the risk of PD and AD.

Comparative studies of hair shaft components between healthy and diseased donors.

Globally, the rapid aging of the population is predicted to become even more severe in the second half of the 21st century. Thus, it is expected to establish a growing expectation for innovative, non-invasive health indicators and diagnostic methods to support disease prevention, care, and health promotion efforts. In this study, we aimed to establish a new health index and disease diagnosis method by analyzing the minerals and free amino acid components contained in hair shaft. We first evaluated the range of these components in healthy humans and then conducted a comparative analysis of these components in subjects with diabetes, hypertension, androgenetic alopecia, major depressive disorder, Alzheimer's disease, and stroke. In the statistical analysis, we first used a student's t test to compare the hair components of healthy people and those of patients with various diseases. However, many minerals and free amino acids showed significant differences in all diseases, because the sample size of the healthy group was very large compared to the sample size of the disease group. Therefore, we attempted a comparative analysis based on effect size, which is not affected by differences in sample size. As a result, we were able to narrow down the minerals and free amino acids for all diseases compared to t test analysis. For diabetes, the t test narrowed down the minerals to 15, whereas the effect size measurement narrowed it down to 3 (Cr, Mn, and Hg). For free amino acids, the t test narrowed it down to 15 minerals. By measuring the effect size, we were able to narrow it down to 7 (Gly, His, Lys, Pro, Ser, Thr, and Val). It is also possible to narrow down the minerals and free amino acids in other diseases, and to identify potential health indicators and disease-related components by using effect size.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Quantifying Putative Retinal Gliosis in Preclinical Alzheimer's Disease.

Purpose: Neuroinflammation plays a significant role in the pathology of Alzheimer's disease (AD). Mouse models of AD and postmortem biopsy of patients with AD reveal retinal glial activation comparable to central nervous system immunoreactivity. We hypothesized that the surface area of putative retinal gliosis observed in vivo using en face optical coherence tomography (OCT) imaging will be larger in patients with preclinical AD versus controls. Methods: The Spectralis II instrument was used to acquire macular centered 20 × 20 and 30 × 25-degrees spectral domain OCT images of 76 participants (132 eyes). A cohort of 22 patients with preclinical AD (40 eyes, mean age = 69 years, range = 60-80 years) and 20 control participants (32 eyes, mean age = 66 years, range = 58-82 years, P = 0.11) were included for the assessment of difference in surface area of putative retinal gliosis and retinal nerve fiber layer (RNFL) thickness. The surface area of putative retinal gliosis and RNFL thickness for the nine sectors of the Early Treatment Diabetic Retinopathy Study (ETDRS) map were compared between groups using generalized linear mixed models. Results: The surface area of putative retinal gliosis was significantly greater in the preclinical AD group (0.97 ± 0.55 mm2) compared to controls (0.68 ± 0.40 mm2); F(1,70) = 4.41, P = 0.039; Cohen's d = 0.61. There was no significant difference between groups for RNFL thickness in the 9 ETDRS sectors, P > 0.05. Conclusions: Our analysis shows greater putative retinal gliosis in preclinical AD compared to controls. This demonstrates putative retinal gliosis as a potential biomarker for AD-related neuroinflammation.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

Multiomics Blood-Based Biomarkers Predict Alzheimer's Predementia with High Specificity in a Multicentric Cohort Study.

BACKGROUND: The primary criteria for diagnosing mild cognitive impairment (MCI) due to Alzheimer's Disease (AD) or probable mild AD dementia rely partly on cognitive assessments and the presence of amyloid plaques. Although these criteria exhibit high sensitivity in predicting AD among cognitively impaired patients, their specificity remains limited. Notably, up to 25% of non-demented patients with amyloid plaques may be misdiagnosed with MCI due to AD, when in fact they suffer from a different brain disorder. The introduction of anti-amyloid antibodies complicates this scenario. Physicians must prioritize which amyloid-positive MCI patients receive these treatments, as not all are suitable candidates. Specifically, those with non-AD amyloid pathologies are not primary targets for amyloid-modifying therapies. Consequently, there is an escalating medical necessity for highly specific blood biomarkers that can accurately detect pre-dementia AD, thus optimizing amyloid antibody prescription. OBJECTIVES: The objective of this study was to evaluate a predictive model based on peripheral biomarkers to identify MCI and mild dementia patients who will develop AD dementia symptoms in cognitively impaired population with high specificity. DESIGN: Peripheral biomarkers were identified in a gene transfer-based animal model of AD and then validated during a retrospective multi-center clinical study. SETTING: Participants from 7 retrospective cohorts (US, EU and Australia). PARTICIPANTS: This study followed 345 cognitively impaired individuals over up to 13 years, including 193 with MCI and 152 with mild dementia, starting from their initial visits. The final diagnoses, established during their last assessments, classified 249 participants as AD patients and 96 as having non-AD brain disorders, based on the specific diagnostic criteria for each disorder subtype. Amyloid status, assessed at baseline, was available for 82.9% of the participants, with 61.9% testing positive for amyloid. Both amyloid-positive and negative individuals were represented in each clinical group. Some of the AD patients had co-morbidities such as metabolic disorders, chronic diseases, or cardiovascular pathologies. MEASUREMENTS: We developed targeted mass spectrometry assays for 81 blood-based biomarkers, encompassing 45 proteins and 36 metabolites previously identified in AAV-AD rats. METHODS: We analyzed blood samples from study participants for the 81 biomarkers. The B-HEALED test, a machine learning-based diagnostic tool, was developed to differentiate AD patients, including 123 with Prodromal AD and 126 with mild AD dementia, from 96 individuals with non-AD brain disorders. The model was trained using 70% of the data, selecting relevant biomarkers, calibrating the algorithm, and establishing cutoff values. The remaining 30% served as an external test dataset for blind validation of the predictive accuracy. RESULTS: Integrating a combination of 19 blood biomarkers and participant age, the B-HEALED model successfully distinguished participants that will develop AD dementia symptoms (82 with Prodromal AD and 83 with AD dementia) from non-AD subjects (71 individuals) with a specificity of 93.0% and sensitivity of 65.4% (AUROC=81.9%, p<0.001) during internal validation. When the amyloid status (derived from CSF or PET scans) and the B-HEALED model were applied in association, with individuals being categorized as AD if they tested positive in both tests, we achieved 100% specificity and 52.8% sensitivity. This performance was consistent in blind external validation, underscoring the model's reliability on independent datasets. CONCLUSIONS: The B-HEALED test, utilizing multiomics blood-based biomarkers, demonstrates high predictive specificity in identifying AD patients within the cognitively impaired population, minimizing false positives. When used alongside amyloid screening, it effectively identifies a nearly pure prodromal AD cohort. These results bear significant implications for refining clinical trial inclusion criteria, facilitating drug development and validation, and accurately identifying patients who will benefit the most from disease-modifying AD treatments.

Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The identification and staging of Alzheimer's Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC). Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD. METHODS: One hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses. RESULTS: Two episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI. CONCLUSIONS: Our findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

[Features of the clinical and neuroimaging picture in patients with early-onset Alzheimer's disease]. / Osobennosti klinicheskoi i neirovizualizatsionnoi kartiny u patsientov s bolezn'yu Al'tsgeimera s rannim nachalom.

The most common cause of severe cognitive impairment in adults is Alzheimer's disease (AD). Depending on the age of onset, AD is divided into early (<65 years) and late (≥65 years) forms. Early-onset AD (EOAD) is significantly less common than later-onset AD (LOAD) and accounts for only about 5-10% of cases. However, its medical and social significance, as a disease leading to loss of ability to work and legal capacity, as well as premature death in patients aged 40-64 years, is extremely high. Patients with EOAD compared with LOAD have a greater number of atypical clinical variants - 25% and 6-12.5%, respectively, which complicates the differential diagnosis of EOAD with other neurodegenerative diseases. However, the typical classical amnestic variant predominates in both EOAD and LOAD. Also, patients with EOAD have peculiarities according to neuroimaging data: when performing MRI of the brain, patients with EOAD often have more pronounced parietal atrophy and less pronounced hippocampal atrophy compared to patients with LOAD. The article pays attention to the features of the clinical and neuroimaging data in patients with EOAD; a case of a patient with EOAD is presented.

[EEG changes in patients with Alzheimer's disease]. / Izmeneniya elektroentsefalogrammy u patsientov s bolezn'yu Al'tsgeimera.

The prevalence of cognitive impairment is steadily increasing compared to previous years. According to the World Health Organization, the number of people living with dementia will increase reaching 82 million in 2030 and 152 million in 2050. The most common cause is Alzheimer's disease (AD). The pathophysiological process in AD begins several years before the onset of clinical symptoms; so identifying it at an early stage would likely improve the clinical prognosis. The article presents EEG changes in patients with AD, and discusses the possibility of using EEG as a screening method for examining patients with cognitive impairment.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (µPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on µPADs can further facilitate the realization of smartphone µPADs platforms for efficient disease detection. RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on µPADs. Our platform successfully applied sandwich c-ELISA for detecting the ß-amyloid peptide 1-42 (Aß 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aß 1-42, particularly in areas with low resources and limited communication infrastructure.

Plasma biomarkers increase diagnostic confidence in patients with Alzheimer's disease or frontotemporal lobar degeneration.

BACKGROUND: The recent development of techniques to assess plasma biomarkers has changed the way the research community envisions the future of diagnosis and management of Alzheimer's disease (AD) and other neurodegenerative disorders. This work aims to provide real world evidence on the clinical impact of plasma biomarkers in an academic tertiary care center. METHODS: Anonymized clinical reports of patients diagnosed with AD or Frontotemporal Lobar Degeneration with available plasma biomarkers (Aß42, Aß42/Aß40, p-tau181, p-tau231, NfL, GFAP) were independently assessed by two neurologists who expressed diagnosis and diagnostic confidence three times: (T0) at baseline based on the information collected during the first visit, (T1) after plasma biomarkers, and (T2) after traditional biomarkers (when available). Finally, we assessed whether clinicians' interpretation of plasma biomarkers and the consequent clinical impact are consistent with the final diagnosis, determined after the conclusion of the diagnostic clinical and instrumental work-up by the actual managing physicians who had complete access to all available information. RESULTS: Clinicians assessed 122 reports, and their concordance ranged from 81 to 91% at the three time points. At T1, the presentation of plasma biomarkers resulted in a change of diagnosis in 2% (2/122, p = 1.00) of cases, and in increased diagnostic confidence in 76% (91/120, p < 0.001) of cases with confirmed diagnosis. The change in diagnosis and the increase in diagnostic confidence after plasma biomarkers were consistent with the final diagnosis in 100% (2/2) and 81% (74/91) of cases, respectively. At T2, the presentation of traditional biomarkers resulted in a further change of diagnosis in 13% (12/94, p = 0.149) of cases, and in increased diagnostic confidence in 88% (72/82, p < 0.001) of cases with confirmed diagnosis. CONCLUSIONS: In an academic tertiary care center, plasma biomarkers supported clinicians by increasing their diagnostic confidence in most cases, despite a negligible impact on diagnosis. Future prospective studies are needed to assess the full potential of plasma biomarkers on clinical grounds.

Probing Protein Structural Changes in Alzheimer's Disease via Quantitative Cross-linking Mass Spectrometry.

Alzheimer's disease (AD) is a progressive neurological disorder featuring abnormal protein aggregation in the brain, including the pathological hallmarks of amyloid plaques and hyperphosphorylated tau. Despite extensive research efforts, understanding the molecular intricacies driving AD development remains a formidable challenge. This study focuses on identifying key protein conformational changes associated with the progression of AD. To achieve this, we employed quantitative cross-linking mass spectrometry (XL-MS) to elucidate conformational changes in the protein networks in cerebrospinal fluid (CSF). By using isotopically labeled cross-linkers BS3d0 and BS3d4, we reveal a dynamic shift in protein interaction networks during AD progression. Our comprehensive analysis highlights distinct alterations in protein-protein interactions within mild cognitive impairment (MCI) states. This study accentuates the potential of cross-linked peptides as indicators of AD-related conformational changes, including previously unreported site-specific binding between α-1-antitrypsin (A1AT) and complement component 3 (CO3). Furthermore, this work enables detailed structural characterization of apolipoprotein E (ApoE) and reveals modifications within its helical domains, suggesting their involvement in MCI pathogenesis. The quantitative approach provides insights into site-specific interactions and changes in the abundance of cross-linked peptides, offering an improved understanding of the intricate protein-protein interactions underlying AD progression. These findings lay a foundation for the development of potential diagnostic or therapeutic strategies aimed at mitigating the negative impact of AD.

Sleep duration, sleep efficiency, and amyloid ß among cognitively healthy later-life adults: a systematic review and meta-analysis.

BACKGROUND: Abnormal amyloid ß (Aß) deposits in the brain are a hallmark of Alzheimer's disease (AD). Insufficient sleep duration and poor sleep quality are risk factors for developing AD. Sleep may play a role in Aß regulation, but the magnitude of the relationship between sleep and Aß deposition remains unclear. This systematic review examines the relationship between sleep (i.e., duration and efficiency) with Aß deposition in later-life adults. METHODS: A search of PubMed, CINAHL, Embase, and PsycINFO generated 5,005 published articles. Fifteen studies met the inclusion criteria for qualitative syntheses; thirteen studies for quantitative syntheses related to sleep duration and Aß; and nine studies for quantitative syntheses related to sleep efficiency and Aß. RESULTS: Mean ages of the samples ranged from 63 to 76 years. Studies measured Aß using cerebrospinal fluid, serum, and positron emission tomography scans with two tracers: Carbone 11-labeled Pittsburgh compound B or fluorine 18-labeled. Sleep duration was measured subjectively using interviews or questionnaires, or objectively using polysomnography or actigraphy. Study analyses accounted for demographic and lifestyle factors. Based on 13 eligible articles, our synthesis demonstrated that the average association between sleep duration and Aß was not statistically significant (Fisher's Z = -0.055, 95% CI = -0.117 ~ 0.008). We found that longer self-report sleep duration is associated with lower Aß (Fisher's Z = -0.062, 95% CI = -0.119 ~ -0.005), whereas the objectively measured sleep duration was not associated with Aß (Fisher's Z = 0.002, 95% CI = -0.108 ~ 0.113). Based on 9 eligible articles for sleep efficiency, our synthesis also demonstrated that the average association between sleep efficiency and Aß was not statistically significant (Fisher's Z = 0.048, 95% CI = -0.066 ~ 0.161). CONCLUSION: The findings from this review suggest that shorter self-reported sleep duration is associated with higher Aß levels. Given the heterogeneous nature of the sleep measures and outcomes, it is still difficult to determine the exact relationship between sleep and Aß. Future studies with larger sample sizes should focus on comprehensive sleep characteristics and use longitudinal designs to better understand the relationship between sleep and AD.