217 research outputs found
Characteristics of Particles and Debris Released after Implantoplasty: A Comparative Study
The work was supported by Graduate Periodontic, College of Dentistry, OUHSC and
Department of Occupational and Environmental Health, Hudson College of Public Health, OUHSC.
The work was also supported by the Spanish Government and the Ministry of Science and Innovation
of Spain by research projects RTI2018-098075-B-C21 and RTI2018-098075-B-C22 (co-funded by the
European Regional Development Fund (ERDF), a way to build Europe).Titanium particles embedded on peri-implant tissues are associated with a variety of
detrimental effects. Given that the characteristics of these detached fragments (size, concentration, etc.)
dictate the potential cytotoxicity and biological repercussions exerted, it is of paramount importance
to investigate the properties of these debris. This study compares the characteristics of particles
released among different implant systems (Group A: Straumann, Group B: BioHorizons and Group
C: Zimmer) during implantoplasty. A novel experimental system was utilized for measuring and
collecting particles generated from implantoplasty. A scanning mobility particle sizer, aerodynamic
particle sizer, nano micro-orifice uniform deposit impactor, and scanning electron microscope were
used to collect and analyze the particles by size. The chemical composition of the particles was
analyzed by highly sensitive microanalysis, microstructures by scanning electron microscope and the
mechanical properties by nanoindentation equipment. Particles released by implantoplasty showed
bimodal size distributions, with the majority of particles in the ultrafine size range (<100 nm) for all
groups. Statistical analysis indicated a significant difference among all implant systems in terms of the
particle number size distribution (p < 0.0001), with the highest concentration in Group B and lowest
in Group C, in both fine and ultrafine modes. Significant differences among all groups (p < 0.0001)
were also observed for the other two metrics, with the highest concentration of particle mass and
surface area in Group B and lowest in Group C, in both fine and ultrafine modes. For coarse particles
(>1 m), no significant difference was detected among groups in terms of particle number or mass,
but a significantly smaller surface area was found in Group A as compared to Group B (p = 0.02) and
Group C (p = 0.005). The 1 first minute of procedures had a higher number concentration compared
to the second and third minutes. SEM-EDS analysis showed different morphologies for various
implant systems. These results can be explained by the differences in the chemical composition and
microstructures of the different dental implants. Group B is softer than Groups A and C due to the
laser treatment in the neck producing an increase of the grain size. The hardest implants were those
of Group C due to the cold-strained titanium alloy, and consequently they displayed lower release
than Groups A and B. Implantoplasty was associated with debris particle release, with the majority
of particles at nanometric dimensions. BioHorizons implants released more particles compared to
Straumann and Zimmer. Due to the widespread use of implantoplasty, it is of key importance to
understand the characteristics of the generated debris. This is the first study to detect, quantify and
analyze the debris/particles released from dental implants during implantoplasty including the full
range of particle sizes, including both micro- and nano-scales.Graduate Periodontic, College of Dentistry, OUHSC and
Department of Occupational and Environmental Health, Hudson College of Public Health, OUHSCSpanish Government RTI2018-098075-B-C21 and RTI2018-098075-B-C22 ( European Regional Development Fund (ERDF)
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In Vitro Antimicrobial Activities of Organic Acids and Their Derivatives on Several Species of Gram-Negative and Gram-Positive Bacteria.
The objective of this study was to determine the in vitro antimicrobial activity of several organic acids and their derivatives against Gram-positive (G+) and Gram-negative (G-) bacteria. Butyric acid, valeric acid, monopropionin, monobutyrin, monovalerin, monolaurin, sodium formate, and ProPhorce-a mixture of sodium formate and formic acid (40:60 w/v)-were tested at 8 to 16 concentrations from 10 to 50,000 mg/L. The tested bacteria included G- bacteria (Escherichia coli, Salmonella enterica Typhimurium, and Campylobacter jejuni) and G+ bacteria (Enterococcus faecalis, Clostridium perfringens, Streptococcus pneumoniae, and Streptococcus suis). Antimicrobial activity was expressed as minimum inhibitory concentration (MIC) of tested compounds that prevented growth of tested bacteria in treated culture broth. The MICs of butyric acid, valeric acid, and ProPhorce varied among bacterial strains with the lowest MIC of 500-1000 mg/L on two strains of Campylobacter. Sodium formate at highest tested concentrations (20,000 mg/L) did not inhibit the growth of Escherichia coli, Salmonella Typhimurium, and Enterococcus faecalis, but sodium formate inhibited the growth of other tested bacteria with MIC values from 2000 to 18,800 mg/L. The MIC values of monovalerin, monolaurin, and monobutyrin ranged from 2500 to 15,000 mg/L in the majority of bacterial strains. Monopropionin did not inhibit the growth of all tested bacteria, with the exception that the MIC of monopropionin was 11,300 mg/L on Clostridia perfringens. Monolaurin strongly inhibited G+ bacteria, with the MIC value of 10 mg/L against Streptococcus pneumoniae. The MIC tests indicated that organic acids and their derivatives exhibit promising antimicrobial effects in vitro against G- and G+ bacteria that are resistant to antimicrobial drugs. The acid forms had stronger in vitro antimicrobial activities than ester forms, except that the medium chain fatty acid ester monolaurin exhibited strong inhibitory effects on G+ bacteria
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Online continual learning (CL) aims to learn new knowledge and consolidate
previously learned knowledge from non-stationary data streams. Due to the
time-varying training setting, the model learned from a changing distribution
easily forgets the previously learned knowledge and biases toward the newly
received task. To address this problem, we propose a Continual Bias Adaptor
(CBA) module to augment the classifier network to adapt to catastrophic
distribution change during training, such that the classifier network is able
to learn a stable consolidation of previously learned tasks. In the testing
stage, CBA can be removed which introduces no additional computation cost and
memory overhead. We theoretically reveal the reason why the proposed method can
effectively alleviate catastrophic distribution shifts, and empirically
demonstrate its effectiveness through extensive experiments based on four
rehearsal-based baselines and three public continual learning benchmarks.Comment: Accepted by ICCV 202
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Continuous-time dynamic graph modeling is a crucial task for many real-world
applications, such as financial risk management and fraud detection. Though
existing dynamic graph modeling methods have achieved satisfactory results,
they still suffer from three key limitations, hindering their scalability and
further applicability. i) Indiscriminate updating. For incoming edges, existing
methods would indiscriminately deal with them, which may lead to more time
consumption and unexpected noisy information. ii) Ineffective node-wise
long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a
backbone, which has been demonstrated to be incapable of fully capturing
node-wise long-term dependencies in event sequences. iii) Neglect of
re-occurrence patterns. Dynamic graphs involve the repeated occurrence of
neighbors that indicates their importance, which is disappointedly neglected by
existing methods. In this paper, we present iLoRE, a novel dynamic graph
modeling method with instant node-wise Long-term modeling and Re-occurrence
preservation. To overcome the indiscriminate updating issue, we introduce the
Adaptive Short-term Updater module that will automatically discard the useless
or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further
propose the Long-term Updater to realize more effective node-wise long-term
modeling, where we innovatively propose the Identity Attention mechanism to
empower a Transformer-based updater, bypassing the limited effectiveness of
typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are
also encoded into a graph module for informative representation learning, which
will further improve the expressiveness of our method. Our experimental results
on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic
graph modeling
Graph Prompt Learning: A Comprehensive Survey and Beyond
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet
its integration with graph data, a cornerstone in our interconnected world,
remains nascent. This paper presents a pioneering survey on the emerging domain
of graph prompts in AGI, addressing key challenges and opportunities in
harnessing graph data for AGI applications. Despite substantial advancements in
AGI across natural language processing and computer vision, the application to
graph data is relatively underexplored. This survey critically evaluates the
current landscape of AGI in handling graph data, highlighting the distinct
challenges in cross-modality, cross-domain, and cross-task applications
specific to graphs. Our work is the first to propose a unified framework for
understanding graph prompt learning, offering clarity on prompt tokens, token
structures, and insertion patterns in the graph domain. We delve into the
intrinsic properties of graph prompts, exploring their flexibility,
expressiveness, and interplay with existing graph models. A comprehensive
taxonomy categorizes over 100 works in this field, aligning them with
pre-training tasks across node-level, edge-level, and graph-level objectives.
Additionally, we present, ProG, a Python library, and an accompanying website,
to support and advance research in graph prompting. The survey culminates in a
discussion of current challenges and future directions, offering a roadmap for
research in graph prompting within AGI. Through this comprehensive analysis, we
aim to catalyze further exploration and practical applications of AGI in graph
data, underlining its potential to reshape AGI fields and beyond. ProG and the
website can be accessed by
\url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and
\url{https://github.com/sheldonresearch/ProG}, respectively
Melt electrowritten scaffolds containing fluorescent nanodiamonds for improved mechanical properties and degradation monitoring
Biocompatible fluorescent nanodiamonds (FNDs) were introduced into polycaprolactone (PCL) – the golden standard material in melt electrowriting (MEW). MEW is an advanced additive manufacturing technique capable of depositing high-resolution micrometric fibres. Due to the high printing precision, MEW finds growing interest in tissue engineering applications. Here, we introduced fluorescent nanodiamonds (FNDs) into polycaprolactone prior to printing to fabricate scaffolds for biomedical applications with improved mechanical properties. Further FNDs offer the possibility of their real-time degradation tracking. Compared to pure PCL scaffolds, the functionalized ones containing 0.001 wt% of 70 nm-diameter nanodiamonds (PCL-FNDs) showed increased tensile moduli (1.25 fold) and improved cell proliferation during 7-day cell cultures (2.00 fold increase). Furthermore, the addition of FNDs slowed down the hydrolytic degradation process of the scaffolds, accelerated for the purpose of the study by addition of the enzyme lipase to deionized water. Pure PCL scaffolds showed obvious signs of degradation after 3 h, not observed for PCL-FNDs scaffolds during this time. Additionally, due to the nitrogen-vacancy (NV) centers present on the FNDs, we were able to track their amount and location in real-time in printed fibres using confocal microscopy. This research shows the possibility for high-resolution life-tracking of MEW PCL scaffolds’ degradation.</p
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Submission of Evidence on Online Violence Against Women to the UN Special Rapporteur on Violence Against Women, its Causes and Consequences, Dr Dubravka Šimonović
Figure S1. B3GALNT2 levels determined by W.B. and ROC curve. aâc Relative mRNA expression of B3GALNT2 in HCC tumor tissues and normal liver tissues obtained from GSE76427, GSE36376, and TCGA-LIHC datasets. d Western blot analysis of B3GALNT2 levels in 24 pairs of HCC tissues. T HCC tumor tissue, N adjacent non-tumor tissue. e ROC curve analysis of the sensitivity and specificity for the predictive value of TNM model, B3GALNT2 expression, and the combination model. (TIFF 546Â kb
11β-HSD1 inhibition ameliorates diabetes-induced cardiomyocyte hypertrophy and cardiac fibrosis through modulation of EGFR activity
11β-HSD1 has been recognized as a potential therapeutic target for type 2 diabetes. Recent studies have shown that hyperglycemia leads to activation of 11β-HSD1, increasing the intracellular glucocorticoid levels. Excess glucocorticoids may lead to the clinical manifestations of cardiac injury. Therefore, the aim of this study is to investigate whether 11β-HSD1 activation contributes to the development of diabetic cardiomyopathy. To investigate the role of 11β-HSD1, we administered a selective 11β-HSD1 inhibitor in type 1 and type 2 murine models of diabetes and in cultured cardiomyocytes. Our results show that diabetes increases cortisone levels in heart tissues. 11β-HSD1 inhibitor decreased cortisone levels and ameliorated all structural and functional features of diabetic cardiomyopathy including fibrosis and hypertrophy. We also show that high levels of glucose caused cardiomyocyte hypertrophy and increased matrix protein deposition in culture. Importantly, inhibition of 11β-HSD1 attenuated these changes. Moreover, we show that 11β-HSD1 activation mediates these changes through modulating EGFR phosphorylation and activity. Our findings demonstrate that 11β-HSD1 contributes to the development of diabetic cardiomyopathy through activation of glucocorticoid and EGFR signaling pathway. These results suggest that inhibition of 11β-HSD1 might be a therapeutic strategy for diabetic cardiomyopathy, which is independent of its effects on glucose homeostasis
Prompt Learning on Temporal Interaction Graphs
Temporal Interaction Graphs (TIGs) are widely utilized to represent
real-world systems. To facilitate representation learning on TIGs, researchers
have proposed a series of TIG models. However, these models are still facing
two tough gaps between the pre-training and downstream predictions in their
``pre-train, predict'' training paradigm. First, the temporal discrepancy
between the pre-training and inference data severely undermines the models'
applicability in distant future predictions on the dynamically evolving data.
Second, the semantic divergence between pretext and downstream tasks hinders
their practical applications, as they struggle to align with their learning and
prediction capabilities across application scenarios.
Recently, the ``pre-train, prompt'' paradigm has emerged as a lightweight
mechanism for model generalization. Applying this paradigm is a potential
solution to solve the aforementioned challenges. However, the adaptation of
this paradigm to TIGs is not straightforward. The application of prompting in
static graph contexts falls short in temporal settings due to a lack of
consideration for time-sensitive dynamics and a deficiency in expressive power.
To address this issue, we introduce Temporal Interaction Graph Prompting
(TIGPrompt), a versatile framework that seamlessly integrates with TIG models,
bridging both the temporal and semantic gaps. In detail, we propose a temporal
prompt generator to offer temporally-aware prompts for different tasks. These
prompts stand out for their minimalistic design, relying solely on the tuning
of the prompt generator with very little supervision data. To cater to varying
computational resource demands, we propose an extended ``pre-train,
prompt-based fine-tune'' paradigm, offering greater flexibility. Through
extensive experiments, the TIGPrompt demonstrates the SOTA performance and
remarkable efficiency advantages.Comment: 11 pages, 8 figure
Fluorescent Nanodiamonds for Tracking Single Polymer Particles in Cells and Tissues
Polymer nanoparticles are widely used in drug delivery and are also a potential concern due to the increased burden of nano- or microplastics in the environment. In order to use polymer nanoparticles safely and understand their mechanism of action, it is useful to know where within cells and tissues they end up. To this end, we labeled polymer nanoparticles with nanodiamond particles. More specifically, we have embedded nanodiamond particles in the polymer particles and characterized the composites. Compared to conventional fluorescent dyes, these labels have the advantage that nanodiamonds do not bleach or blink, thus allowing long-term imaging and tracking of polymer particles. We have demonstrated this principle both in cells and entire liver tissues.</p
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