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Impact of polishing, glazing and firing, restoration thickness, point of loading and aging on the edge chipping resistance of lithium silicate ceramics
No full textObjectives
To investigate the edge chipping resistance (ECR) of four lithium silicate ceramics at different thicknesses and points of loading after various surface treatment, firing and aging protocols.
Methods
288 rectangular specimens were cut from CAD/CAM ceramics (lithium-di-silicate: Amber Mill, Amber Mill Direct, IPS e.max CAD; lithium-alumino-silicate: CEREC Tessera) in three thicknesses (1.5 mm, 2 mm, 3 mm) and underwent different surface treatments (polishing, glazing, no surface treatment) and/or firing protocols (high translucency, medium opacity). Specimens were bonded to 4 mm thick dentine analogues and loaded 0.25 mm or 0.30 mm from the edge using a Vickers diamond indenter. ECR was determined initially, after thermocycling (5/55 °C, 10,000 cycles) and after hydrothermal aging (134 °C, 0.2 MPa, 120min). Force when chipping occurred was recorded and ECR calculated. Data were analyzed with Kolmogorov-Smirnov, Kruskal-Wallis, Mann-Whitney U, Friedmann and Wilcoxon tests (p < 0.05).
Results
For 7/18 groups, glazed and medium opacity fired Amber Mill showed higher ECR than all other groups. In comparison with polishing or exclusive firing, a surface treatment with glazing led to the highest ECR. The influence of specimen thickness and point of loading was negligible. While aging reduced the ECR in 50 % of the glazed groups, the ECR of those groups remained among the highest.
Significance
With the majority of groups showing no impact of the specimen thickness, a reduced restoration thickness of 1.5 mm seems to present limited disadvantages and should thus be considered for minimal invasive treatments. With regards to ECR, glazing can be recommended as the preferred surface treatment method for CAD/CAM lithium silicate ceramics
Colchicine as a food contaminant: rare occurrence but persistent in stored honey and during yogurt fermentation
Full text linkFAPI PET for monitoring of rheumatological treatment in multifocal peritoneal nodular fibrosis: a case study
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Extended Technical and Clinical Validation of Deep Learning‐Based Brainstem Segmentation for Application in Neurodegenerative Diseases
Full text linkDisorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of pathologies and T1-weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (n = 257) was employed in the training of deep learning models based on multi-dimensional gated recurrent units (MD-GRU) or the nnU-Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan-rescan repeatability (n = 46) and inter-scanner reproducibility (n = 20, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1-year follow-up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD-GRU and nnU-Net models demonstrated very good segmentation performance (median Dice coefficients ≥ 0.95 each) and outperformed a previously published model trained on a narrower dataset. Scan–rescan repeatability and inter-scanner reproducibility yielded similar Bland–Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability/reproducibility 0.68/1.85), MD-GRU (0.72/1.46), and nnU-Net (0.48/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration
SCD‐plus features and AD biomarkers in cognitively unimpaired samples: A meta‐analytic approach for nine cohort studies
Full text linkIntroduction:
Specific features of subjective cognitive decline (SCD-plus) have been proposed to indicate an increased risk of preclinical Alzheimer's disease (AD). However, few studies have examined how these features relate to AD biomarkers in cognitively unimpaired (CU) older adults.
Methods:
Meta-analyses were performed using cross-sectional data from nine cohorts (n = 7219, mean age (SD): 71.17 (5.9), 56.5% female) to determine associations of SCD-plus features with positron emission tomography (PET)– or cerebrospinal fluid (CSF)–derived amyloid beta (Aβ) and tau biomarkers.
Results:
Participants with preclinical AD (community-based only) were more likely to fulfill SCD-plus features. The presence of self-reported memory decline, associated concern/worry, and a higher number of fulfilled features were all associated with high Aβ levels. Only the latter was associated with abnormal tau.
Discussion:
Simultaneous endorsement of multiple SCD-plus features is a robust indicator of abnormal AD biomarkers in CU older adults, whereas isolated SCD features seem only sensitive to elevated Aβ, supporting their value as early behavioral markers of preclinical AD
Antibiotic drug use in the five years preceding the diagnosis of multiple sclerosis
Full text linkBackground: Microbiota may play a role in autoimmune disease pathogenesis, including multiple sclerosis (MS). Antibiotic use disrupts the microbiome and may increase the risk of autoimmune diseases. We evaluated the relationship between MS diagnosis and antibiotic, antimycotic and antiviral drug use in the 5 years preceding diagnosis.
Method: Our population-based case-control study used German ambulatory claims data from 2012 to 2022. We defined cohorts of 13,053 MS patients, 22,898 Crohn's disease patients, and 15,037 matched controls without autoimmune diseases, aged 21–70. Logistic and Poisson regression models explored the relationship between MS diagnosis and antimicrobial usage. Two sub-analyses were performed: a separate analysis of patients with clinically isolated syndrome (CIS) and a sensitivity analysis of newly diagnosed MS patients without preceding neurological symptoms.
Results: Patients with MS had higher exposure to antibiotic (Odd Ratio (OR) = 1.27, 95 % CI 1.21–1.33), antimycotic (OR = 1.27, 95 % CI 1.12–1.45), and antiviral drugs (OR = 1.28, 95 % CI 1.15–1.43) in the five years before diagnosis compared to patients with no autoimmune diseases. Similar findings were obtained for the CIS cohort and in the sensitivity analysis. Antibiotic use peaked 5 years before MS diagnosis, declining closer to diagnosis, while antiviral and antimycotic drug use showed the opposite. This effect was not observed in the sensitivity analysis and CIS cohorts. Antibiotic use was higher in Crohn's disease than in MS (OR = 0.86, 95 % CI 0.82–0.90), with no consistent differences in antimycotic and antiviral use.
Conclusions: The association and kinetic of antibiotic use before MS and CIS diagnosis supports the role of microbiota in MS pathogenesis and suggests antibiotic use to be related to the development of autoimmune diseases, including MS. Additional studies are warranted to clarify whether increased antibiotic use is part of the MS prodrome or a true risk factor for MS
Can an app designed to reduce repetitive negative thinking decrease depression and anxiety in young people? Results from a randomized controlled prevention trial
Full text linkBackground and objectives: Rates of mental health disorders are rising among adolescents and young adults. Therefore, scalable methods for preventing psychopathology in these age groups are needed. As repetitive negative thinking (RNT) is a risk factor for depression and anxiety disorders, targeting RNT via smartphone app promises to be an effective, scalable strategy. The current three-arm, parallel group, randomized controlled trial tested whether a self-help app designed to reduce RNT decreased psychopathological symptoms and RNT in adolescents and young adults at risk for mental disorders.
Method: A sample of 16–22-year-olds with elevated levels of RNT (N = 365) were randomly allocated to either use a one of two self-help apps designed to reduce RNT for 6 weeks or to a waitlist. The full RNT-focused intervention app encompassed a variety of RNT-reducing strategies, whereas the concreteness training app focused on one of these strategies, namely, concrete thinking.
Results: The apps did not decrease depressive symptoms, anxiety symptoms and RNT relative to the waitlist. However, exploratory analyses using a minimum dose criterion showed that participants who used the full-RNT-focused intervention app more often, reported greater baseline to follow-up decreases in depressive symptoms compared to waitlist.
Limitations: Include decreased power due to slightly more dropout than expected and limited generalizability due to the mostly female and highly educated sample.
Conclusions: RNT-focused prevention via a self-help app did not decrease depression and anxiety, presumably due to too little engagement with the app content provided
An STM study on the diffusion of O atoms on a CO-covered Ru(0001) surface—The role of domain boundaries
Full text linkWe investigate tracer diffusion at the domain boundaries in an adsorption layer, an effect that corresponds to grain boundary diffusion in 3D polycrystalline solids. Experiments were performed on adsorbed O atoms on a Ru(0001) surface in a layer of CO molecules. The CO molecules form a structure which displays translational domains. High-speed scanning tunneling microscopy (STM) was used to image the motion of the O atoms. The data show that single O atoms preferentially move along the domain walls which in the STM movies appear as disordered, fluctuating stripes between the ordered domains. The diffusion coefficient of the O atoms is one order of magnitude higher than the diffusion coefficient in the ordered domains. By comparison with previous experiments on completely disordered CO layers, it is concluded that the diffusion is similarly promoted by the enhanced fluctuations in the disordered domain walls