Influence of Pre-Etched Area and Functional Monomers on the Enamel Bond Strength of Orthodontic Adhesive Pastes

This study was performed to investigate the influence of pre-etching area and functional monomers in orthodontic adhesive pastes on enamel bond strength. Bovine enamel was partially pre-etched with phosphoric acid for 30 s over areas with a diameter of 1.0, 2.0 or 3.0 mm, and metal brackets were then bonded with or without functional monomers in the orthodontic adhesive paste. For the baseline groups, the whole adherent area was pre-etched. The shear bond strength (SBS) and adhesive remnant index (ARI) were determined. The adhesive paste/enamel interfaces were observed by scanning electron microscopy (SEM). Although the adhesive paste with functional monomers showed higher SBS than the functional monomer-free adhesive paste in all groups, there were no significant differences in SBS between them regardless of the pre-etched area. The SBS increased with increasing pre-etched area in both orthodontic adhesive pastes. In SEM images of adhesive paste/enamel interfaces, although adhesive with functional monomers showed excellent adaptation, the functional monomer-free adhesive paste showed gap formation at the interface. These findings suggested that the pre-etching area greatly influenced bond strength, regardless of the presence or absence of the functional monomer in the orthodontic adhesive paste

Progressive Ataxia and Palatal Tremor Showing Characteristic Tau Depositions in [ F]PM-PBB3 PET.

Progressive ataxia and palatal tremor (PAPT) are slowlyprogressive neurodegenerative diseases characterized by pro-gressive ataxia and palatal tremor, which has been describedas a novel tauopathy based on neuropathologicalfindings.1,2The novel tau positron emission tomography (PET) tracer[18F]PM-PBB3 could detect 3- and 4-repeat tau deposits withhigh contrast in non-Alzheimer’s disease tauopathies.3,4Here,we report thefirst case of PAPT in which ante-mortem evalua-tions of tau retention were made using [18F]PM-PBB3

Effectiveness of <italic>c</italic>-Axis Aligned Crystalline IGZO FET as Selector Element and Ferroelectric Capacitor Scaling of 1T1C FeRAM

Aiming to reduce the area of a ferroelectric random access memory (FeRAM), we fabricated an FeRAM having a 1T1C configuration by using a ${c}$ -axis aligned crystalline In-Ga-Zn-O field-effect transistor, which we call OSFET, with a high breakdown voltage. A combination of the OSFET with ${L}/{W}$ of 60 nm/60 nm and a single damascene ferroelectric capacitor (FE-Cap) attained FE-Cap area reduction to $0.06~\mu \text{m}~^{\mathrm{ 2}}$ per cell. The FeRAM achieved a write time of 10 ns, a rewriting endurance of 109 cycles, and a data retention time of 100 min at 85&#x00B0;C. The OSFET is an optimal selector element for emerging memories

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Abstract Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid

Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status

Abstract Background Plasma biomarkers have emerged as promising screening tools for Alzheimer’s disease (AD) because of their potential to detect amyloid β (Aβ) accumulation in the brain. One such candidate is the plasma Aβ42/40 ratio (Aβ42/40). Unlike previous research that used traditional immunoassay, recent studies that measured plasma Aβ42/40 using fully automated platforms reported promising results. However, its utility should be confirmed using a broader patient population, focusing on the potential for early detection. Methods We recruited 174 participants, including healthy controls (HC) and patients with clinical diagnoses of AD, frontotemporal lobar degeneration, dementia with Lewy bodies/Parkinson’s disease, mild cognitive impairment (MCI), and others, from a university memory clinic. We examined the performance of plasma Aβ42/40, measured using the fully automated high-sensitivity chemiluminescence enzyme (HISCL) immunoassay, in detecting amyloid-positron emission tomography (PET)-derived Aβ pathology. We also compared its performance with that of Simoa-based plasma phosphorylated tau at residue 181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL). Results Using the best cut-off derived from the Youden Index, plasma Aβ42/40 yielded an area under the receiver operating characteristic curve (AUC) of 0.949 in distinguishing visually assessed 18F-Florbetaben amyloid PET positivity. The plasma Aβ42/40 had a significantly superior AUC than p-tau181, GFAP, and NfL in the 167 participants with measurements for all four biomarkers. Next, we analyzed 99 participants, including only the HC and those with MCI, and discovered that plasma Aβ42/40 outperformed the other plasma biomarkers, suggesting its ability to detect early amyloid accumulation. Using the Centiloid scale (CL), Spearman’s rank correlation coefficient between plasma Aβ42/40 and CL was -0.767. Among the 15 participants falling within the CL values indicative of potential future amyloid accumulation (CL between 13.5 and 35.7), plasma Aβ42/40 categorized 61.5% (8/13) as Aβ-positive, whereas visual assessment of amyloid PET identified 20% (3/15) as positive. Conclusion Plasma Aβ42/40 measured using the fully automated HISCL platform showed excellent performance in identifying Aβ accumulation in the brain in a well-characterized cohort. This equipment may be useful for screening amyloid pathology because it has the potential to detect early amyloid pathology and is readily applied in clinical settings

Additional file 4: Table S2. of Modeling neurological diseases with induced pluripotent cells reprogrammed from immortalized lymphoblastoid cell lines

List of primers. (PDF 397 kb