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24-Month clinical evaluation of cervical restorations bonded using radio-opaque universal adhesive compared to conventional universal adhesive in carious cervical lesions: A randomized clinical trial
Abstract The aim of the current study was to evaluate the clinical performance of the novel radio-opaque universal adhesive “Scotchbond™ Universal Adhesive Plus” compared to conventional universal adhesive “Single Bond Universal” over 24 months in cervical carious lesions. Fifty participants with cervical carious lesions were randomly allocated into two groups (n = 25); either Scotchbond™ Universal Plus Adhesive (intervention) or Single Bond™ Universal Adhesive (control). Restorations were assessed at baseline, 12 and 24 months using the modified USPHS criteria. Data analysis was conducted using MedCalc software, version 22 for Windows. Intergroup comparisons at each follow-up were performed using the Chi-Square test (p ≤ 0.05). Intragroup comparisons within each intervention were conducted using Cochran’s Q test (p ≤ 0.016). After 24 months, all restorations in Scotchbond™ Universal Plus scored alpha, while in Single Bond™ Universal group, three restorations scored bravo after 24 months in marginal adaptation and discoloration. There was no statistically significant difference between both adhesives (p > 0.05) at all follow-up periods. Intragroup comparison within both adhesives has shown no statistically significant change across follow-up periods regarding all tested outcomes (p > 0.016) except for marginal adaptation within Single Bond Universal, where there was statistically significant difference (p = 0.005). Both adhesives exhibited satisfactory clinical performance in cervical restorations after 24-months. The present study emphasizes the clinical significance of using a new radio-opaque universal adhesive for restoring carious cervical lesions, providing radio-opacity, low viscosity, excellent handling, eliminating misinterpretation of MDP-based adhesive layer and generating reliable bonding performance to support long-term success in restorative dentistry.
Integrating angio-IMR and CMR-assessed microvascular obstruction for improved risk stratification of STEMI patients
A robust method for autofluorescence-free immunofluorescence using high-speed fluorescence lifetime imaging microscopy
Cadmium resistance microbes and TiO2 nanoparticles alleviate cadmium toxicity in wheat
Abstract Cadmium toxicity in the soil is an alarming issue, and among innumerable approaches, microbe-facilitated nanoparticle application for alleviation of Cd stress is a well-accepted technique. The present study explored the efficiency of combined TiO2-NPs and Staphylococcus aureus M1 strains for Cd mitigation in wheat plants. Results depicted that Cd stress attenuates the growth attributes while the collective application of NPs and microbes significantly upsurges the growth attributes as contrasted to Cd treatment. Combined TiO2-NPs and microbes application increased the total chlorophyll (12), a (10), b (11), and carotenoids (13%) under Cd (50 mg kg− 1) compared to microbial treatment. MDA (4), H2O2 (3), and EL (5%) were significantly down-regulated with combined TiO2-NPs and microbes application under Cd (50 mg kg− 1) compared to microbial treatment. CAT (17), SOD (7), POD (8), and APX (29%) were increased with combined TiO2-NPs and microbes application under Cd (50 mg kg− 1) comparison to microbial treatment. Cd accumulation in roots (34), shoots (23), and grains (27%) were significantly reduced under Cd (50 mg kg− 1) with combined TiO2-NPs and microbes application, contrary to microbial treatment. Subsequently, combined TiO2-NPs and microbial strains Staphylococcus aureus M1 application is a sustainable solution to boost crop production under Cd stress.
Hydration behaviors, workability, and strength variations in direct aqueous carbonation (DAC) of Portland cement paste
Robust multi-label surgical tool classification in noisy endoscopic videos
Abstract Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitising clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool classification using noisy endoscopic videos. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts through collective intelligence; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ strategies such as weighted data loaders and label smoothing to enable the models to learn difficult samples and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88% for the ensemble model-based self-training with class weights, and 80.88% without class weights for noisy tool labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness.
Fatigue failure of prefabricated crack HTPB(hydroxyl-terminated polybutadiene) propellant under strain control
Load sharing behaviour of bio-inspired root pile foundation in cohesionless soil under individual and combined loading conditions
The neuronal and glial cell diversity in the celiac ganglion revealed by single-nucleus RNA sequencing
The association between sex hormones and bone mineral density in US females
Investigating the role of gut microbiota in diabetic nephropathy through plasma proteome mediated analysis
Abstract Diabetic nephropathy (DN) is the leading cause of end-stage renal disease and poses significant threats to individuals with diabetes. The concept of gut–kidney axis has gained increasing attention in recent years and the in the occurrence and development of DN, alterations in the gut microbiota also plays a crucial and indispensable role. However, the specific causal relationships between various gut microbial communities and DN, as well as the underlying molecular mechanisms, remains unclear. This study utilized data from genome-wide association studies. After screening for qualified instrumental variables, mendelian randomization causal analyses were performed by inverse variance weighting, MR-Egger, weighted median, weighted mode and MR-RAPS methods. Additionally, sensitivity analyses such as heterogeneity, multiplicity, and the direction of the causal effect were carried out to ensure that the results were robust. After identifying significant gut microbiota, protein-proteomics mediation analysis was conducted on potential 3282 plasma proteins to determine those with mediating effects. Finally, Reactome enrichment analysis was performed to ascertain metabolic or signaling pathways with mediating effects. Mendelian randomization analysis indicated associations between 21 gut microbiota and DN. After adjusting significance levels, Catenibacterium and Parasutterella were found to have causal effects on the onset of DN. Subsequently, we identified 22 plasma proteins with mediating effects, along with 27 metabolic or signaling pathways including activated propionic acid metabolism. Increased in the abundance of Catenibacterium and Parasutterella intestinal bacteria are causative factors for DN. More importantly, the underlying mechanism by which the increased abundance of Catenibacterium and Parasutterella intestinal bacteria lead to DN were revealed, providing a blueprint for the involvement of gut–kidney axis in the pathogenesis of DN and paving the way for future studies.
The liver proteome of individuals with a natural UGT2B17 complete deficiency
Analysis of post-transcriptional regulatory signatures and immune cell subsets in premature ovarian insufficiency based on full-length transcriptome
Population shift in antibody immunity following the emergence of a SARS-CoV-2 variant of concern
Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) exhibit escape from pre-existing immunity and elicit variant-specific immune responses. In South Africa, the second wave of SARS-CoV-2 infections was driven by the Beta VOC, which coincided with the country-wide National COVID-19 Antibody Survey (NCAS). The NCAS was conducted between November 2020 and February 2021 to understand the burden of SARS-CoV-2 infection through seroprevalence. We evaluated 649 NCAS sera for spike binding and pseudovirus neutralizing antibodies. We classified individuals as ancestral or D614G neutralizers (114/649), Beta neutralizers (96/649), double neutralizers (375/649) or non-neutralizers (62/649). We observed a consistent decrease in preferential neutralization against the D614G variant from 68 to 18% of individuals over the four sampling months. Concurrently, samples with equivalent neutralization of both variants, or with enhanced neutralization of the Beta variant, increased from 32 to 82% of samples. Neutralization data showed that geometric mean titers (GMTs) against D614G dropped 2.4-fold, while GMTs against Beta increased 2-fold during this same period. A shift in population humoral immunity in favor of Beta-directed or cross-neutralizing antibody responses, paralleled the increase in genomic frequency of the Beta variant in South Africa. Understanding similar population immunity shifts could elucidate immunity gaps that drive SARS-CoV-2 evolution.
Actively forming microbial mats provide insight into the development of microdigitate stromatolites
Abstract Stromatolites can be traced back to ∼3.5 billion years. They were widespread in the shorelines of ancient oceans and seas. However, they are uncommon nowadays, and basic information is lacking about how these unique carbonate structures developed. Here we study the unusually thick (3–5 cm) biofilms of the 79.2 °C outflow from Köröm thermal well (Hungary) and demonstrate that its microbial mat – carbonate architecture is similar to fossilized microdigitate stromatolites. Our observations reveal vertically oriented fibrous mineral fabrics, typical of stromatolites, in the red biofilm and clotted mesostructures, typical of thrombolites, in the green biofilm. These layers contain carbonate peloids and show network structures, formed by filamentous microbes. The 16S rRNA gene-based amplicon sequencing implies that numerous undescribed taxa may contribute to the carbonate mineralisation. The biofilms abundantly contain the phyla Bacteroidota, Pseudomonadota and Cyanobacteria. Geitlerinema PCC-8501 and Raineya are characteristic for the green biofilm, whereas uncultured Oxyphotobacteria, unc. Saprospiraceae and unc. Cytophagales are abundant in the red biofilm. A hydrogen-oxidizing Hydrogenobacter within the phylum Aquificota and unclassified Bacteria together with the phylum Deinococcota dominate the water and carbonate samples. The morphological structure and taxonomic composition of Köröm biofilm is a unique representation of the development processes of microbialite formations.
Research on the evolutionary game of reversed online public opinion based on the dual-helix structure mechanism
Citation manipulation through citation mills and pre-print servers
Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners
Abstract While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer’s disease, their clinical applicability is hindered by the assumption that both modalities are always available during model inference. In practice, clinicians adjust diagnostic tests based on available information and specific clinical contexts. We propose a novel MRI- and FDG PET-based multi-modal deep learning approach that mimics clinical decision-making by incorporating uncertainty estimates of an MRI-based model (generated using Monte Carlo dropout and evidential deep learning) to determine the necessity of an FDG PET scan, and only inputting the FDG PET to a multi-modal model when required. This approach significantly reduces the reliance on FDG PET scans, which are costly and expose patients to radiation. Our approach reduces the need for FDG PET by up to 92% without compromising model performance, thus optimizing resource use and patient safety. Furthermore, using a global model explanation technique, we provide insights into how anatomical changes in brain regions—such as the entorhinal cortex, amygdala, and ventricles—can positively or negatively influence the need for FDG PET scans in alignment with clinical understanding of Alzheimer’s disease.