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)

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

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. a–c 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 546 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

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

Grazing exclosures solely are not the best methods for sustaining alpine grasslands

Background Grazing is widely regarded as a critical factor affecting the vegetation structure, productivity and nutritional value of natural grasslands. To protect and restore degraded grasslands, non-grazed exclosures are considered as a valuable tool. However, it is not clear whether long term non-grazed exclosures of grazers can improve the condition and nutritional value of vegetation and soil properties. Methods We have compared the impact of long-term non-grazed and continuous grazed management strategy on vegetation structure, nutritional values and soil properties of alpine meadow of the Qinghai-Tibet Plateau by field investigation (11–13 years) and indoor analysis during 2015–2017. Results Our results showed that long-term non-grazed exclosures clearly increased the aboveground biomass and coverage of plant functional types. Long-term non-grazed exclosures improved the development of all vegetation types, except NG (GG, grass species type; SG, sedge species type; LG, leguminous species type; FG, forbs species type and NG, noxious species type). Long-term non-grazed exclosures significantly improved all six measured soil properties (TN, total nitrogen; TP, total phosphorus; TK, total potassium; AN, available nitrogen; AP, available phosphorus and AK, available potassium) in 0–10 cm soil layer, considerable effect on the improvement of all measured soil properties, except TK in 10–20 cm soil layer and all measured soil properties, except TN and TK in 20–30 cm soil layer were observed. However, long-term non-grazed exclosures significantly decreased biodiversity indicators i.e., species richness, Shannon diversity index and Evenness index of vegetation. A substantial decrease in the density, biodiversity and nutritional values (CP (crude protein), IVTD (in vitro ture digestibility) and NDF (neutral detergent fiber)) of all vegetation types, except NG were recorded. While a downward trend in aboveground biomass and all measured soil properties except TP and TK were observed during 2015–2017 in alpine meadows due to long-term grazed treatment. The density, diversity and nutritional value (CP and IVTD) of long-term non-grazed alpine meadows showed a downward trend over time (2015–2017). By considering the biodiversity conservation and grassland livestock production, long-term non-grazed exclosures are not beneficial for the improvement of density, biodiversity and nutritional values of plant functional types. Thus, our study suggests that rotational non-grazed and grazed treatment would be a good management strategy to restore and improve the biodiversity and nutritional values of plant functional types in natural grassland ecosystems