Bioactive plasma coatings on orthodontic brackets: In Vitro metal ion release and cytotoxicity

The metal ion release characteristics and biocompatibility of meta-based materials are key factors that influence their use in orthodontics. Although stainless steel-based alloys have gained much interest and use due to their mechanical properties and cost, they are prone to localised attack after prolonged exposure to the hostile oral environment. Metal ions may induce cellular toxicity at high dosages. To circumvent these issues, orthodontic brackets were coated with a functional nanothin layer of plasma polymer and further immobilised with enantiomers of tryptophan. Analysis of the physicochemical properties confirmed the presence of functional coatings on the surface of the brackets. The quantification of metal ion release using mass spectrometry proved that plasma functionalisation could minimise metal ion release from orthodontic brackets. Furthermore, the biocompatibility of the brackets has been improved after functionalisation. These findings demonstrate that plasma polymer facilitated surface functionalisation of orthodontic brackets is a promising approach to reducing metal toxicity without impacting their bulk properties.Lasni Samalka Kumarasinghe, Neethu Ninan, Panthihage Ruvini Lakshika Dabare, Alex Cavallaro, Esma J. Dogramacı, Giampiero Rossi-Fedele ... et al

Renal hemofiltration prevents metabolic acidosis and reduces inflammation during normothermic machine perfusion of the vascularized composite allograft—A preclinical study

From Wiley via Jisc Publications RouterHistory: received 2021-05-17, rev-recd 2021-09-16, accepted 2021-09-24, pub-electronic 2021-11-26Article version: VoRPublication status: PublishedAbstract: Introduction: Recent experimental evidence suggests normothermic machine perfusion of the vascularized composite allograft results in improved preservation compared to static cold storage, with less reperfusion injury in the immediate post‐operative period. However, metabolic acidosis is a common feature of vascularized composite allograft perfusion, primarily due to the inability to process metabolic by‐products. We evaluated the impact of combined limb‐kidney perfusion on markers of metabolic acidosis and inflammation in a porcine model. Methods: Ten paired pig forelimbs were used for this study, grouped as either limb‐only (LO, n = 5) perfusion, or limb‐kidney (LK, n = 5) perfusion. Infrared thermal imaging was used to determine homogeneity of perfusion. Lactate, bicarbonate, base, pH, and electrolytes, along with an inflammatory profile generated via the quantification of cytokines and cell‐free DNA in the perfusate were recorded. Results: The addition of a kidney to a limb perfusion circuit resulted in the rapid stabilization of lactate, bicarbonate, base, and pH. Conversely, the LO circuit became progressively acidotic, correlating in a significant increase in pro‐inflammatory cytokines. Global perfusion across the limb was more homogenous with LK compared to LO. Conclusion: The addition of a kidney during limb perfusion results in significant improvements in perfusate biochemistry, with no evidence of metabolic acidosis

Optimizing Staining Protocols for Laser Microdissection of Specific Cell Types from the Testis Including Carcinoma In Situ

Microarray and RT-PCR based methods are important tools for analysis of gene expression; however, in tissues containing many different cells types, such as the testis, characterization of gene expression in specific cell types can be severely hampered by noise from other cells. The laser microdissection technology allows for enrichment of specific cell types. However, when the cells are not morphologically distinguishable, it is necessary to use a specific staining method for the target cells. In this study we have tested different fixatives, storage conditions for frozen sections and staining protocols, and present two staining protocols for frozen sections, one for fast and specific staining of fetal germ cells, testicular carcinoma in situ cells, and other cells with embryonic stem cell-like properties that express the alkaline phosphatase, and one for specific staining of lipid droplet-containing cells, which is useful for isolation of the androgen-producing Leydig cells. Both protocols retain a morphology that is compatible with laser microdissection and yield RNA of a quality suitable for PCR and microarray analysis

Greater male variability in daily energy expenditure develops through puberty

The authors also gratefully acknowledge funding from the Chinese Academy of Sciences (grant no. CAS153E11KYSB20190045) to J.R.S. and the US National Science Foundation (grant no. BCS-1824466) awarded to H.P. Acknowledgements Yvonne Schönbeck provided important information about morphometric measurements for Dutch children. A chat over dinner with Karsten Koehler, Eimear Dolan and Danny Longman brought up a number of thoughts that influenced this manuscript. The DLW database, which can be found at https://doublylabelled-waterdatabase.iaea.org/home, is hosted by the IAEA and generously supported by Taiyo Nippon Sanso and SERCON. We are grateful to the IAEA and these companies for their support and especially to Takashi Oono for his tremendous efforts at fundraising on our behalf.Peer reviewedPublisher PD

Greater male variability in daily energy expenditure develops through puberty

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decrease in the degree of GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages

Greater male variability in daily energy expenditure develops through puberty

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decrease in the degree of GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages

Fuzzy Deep Neural Network for classification of overlapped data

Deep Learning is a popular and promising technique for classification problems. This paper proposes the use of fuzzy deep learning to improve the classification capability when dealing with overlapped data. Most of the research focuses on classification and uses traditional truth and false criteria. However, in reality, a data item may belong to different classes at different degrees. Therefore, the degree of belonging of each data item to a class needs to be considered for classification purposes in some cases. When a data item belongs to different classes with different degrees, then there exists an overlap between the classes. For this reason, this paper proposes a Fuzzy Deep Neural Network based on Fuzzy C-means clustering, fuzzy membership grades and Deep Neural Networks to address the over-lapping issue focused on binary classes and multi-classes. The proposed method converts the original attribute values to relevant cluster centres using the proposed Fuzzy Deep Neural Network. It then trains them with the original output class values. Thereafter, the test data is checked with the Fuzzy Deep Neural Network model for its performance. Using three popular datasets in overlapped and fuzzy data literature, the method presented in this paper outperforms the other methods compared in this study, which are Deep Neural Networks and Fuzzy classification

Fuzzy data augmentation for handling overlapped and imbalanced data

Class imbalance is a serious issue in classification as a traditional classifier is generally biased towards the majority class. The accuracy of the classifier could be further impacted in cases where additionally to the class imbalance, there are overlapped data instances. Further, data sparsity has shown to be a possible issue that may lead to non- invariance and poor generalisation. Data augmentation is a technique that can handle the generalisation issue and improve the regularisation of the Deep Neural Network (DNN). A method to handle both class overlap and class imbalance while also incorporating regularisation is proposed in this paper. In our work, the imbalanced dataset is balanced using SMOTETomek, and then the non-categorical attributes are fuzzified. The purpose of fuzzifying the attributes is to handle the overlapping in the data and provide some form of data augmentation that can be used as a regularisation technique. Therefore, in this paper, the invariance is achieved as the augmented data are generated based on the fuzzy concept. The balanced augmented dataset is then trained using a DNN classifier. The datasets used in the experiments were selected from UCI and KEEL data repositories. The experiments show that the proposed Fuzzy data augmentation for handling overlapped and imbalanced data can address the overlapped and imbalanced data issues, and provide regularisation using data augmentation for numerical data to improve the performance of a DNN classifier

A fuzzy data augmentation technique to improve regularisation

Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real-world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C-Means clustering and fuzzy membership grades. Fuzzy-related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias-variance tradeoff. Our proposed technique is evaluated using four popular data sets and is shown to provide better regularisation and higher classification accuracy compared with popular regularisation approaches

Surface chemistry mediated albumin adsorption, conformational changes and influence on innate immune responses

Available online 10 May 2022The surface chemistry of biomaterials plays a pivotal role in regulating the type, amount, and conformational changes of adsorbed proteins, which then modulates the subsequent innate immune responses. Albumin is the most abundant protein in serum and a major constituent of the protein corona that forms on biomaterial surfaces. Therefore, it is important to understand the role of surface chemistry in albumin adsorption, surface induced conformational changes, and how this affects the subsequent immune responses. To interrogate these events, we generated model substrata with four tailored surface chemistries rich in amines, oxazolines, carboxylic acid groups and pure hydrocarbons via plasma polymerization. The positively charged amine and oxazoline rich surface chemistries caused greatest albumin adsorption and protein conformational changes. Our data demonstrated that macrophages (differentiated THP-1 cells) were able to interact with unfolded albumin via their scavenger receptors. The innate inflammatory responses were studied by measuring the expression of pro- and anti- inflammatory markers. As an overall trend, the pre-adsorption of albumin resulted in a reduction in the level of expression of pro-inflammatory cytokines while the secretion of anti-inflammatory markers was stimulated. This study provides valuable information, which could aid in the design of future biomaterials that elicit predictable immune response.Panthihage Ruvini L Dabare, Akash Bachhuka, Dennis Palms, Emma Parkinson- Lawrence, John D Hayball, Agnieszka Mierczynska, Krasimir Vasile