Thermal injury in tonsils and its relation to postoperative pain—a histopathological and clinical study

Objectives: The aim of this study was to compare thermal injury and depth of necrosis of using different monopolar power settings in partial tonsillectomy and correlate the results with the postoperative pain score. Results: The study included a total of 15 patients with mean of age of 5.7 ± 2.57 years. The mean depth of injury was significantly higher for the 25 W side (0.973 ± 0.613) versus the 15 W side (0.553 ± 0.218) (p = 0.023). The postoperative pain score showed no significant differences between both sides. Conclusion: The histopathologic depth of thermal injury is significantly higher with the 25 W monopolar microdissection in comparison to the 15 W; however, it does not seem to correlate with the postoperative pain level. Apparently, power settings of 25 W can be safely used for pediatric intracapsular tonsillectomies, without added postoperative morbidity despite the deeper tissue injury observed in the tonsil.The authors are grateful to the Histology and Electron Microscopy Service (HEMS) team at the i3S (Institute for Research and Innovation in Health, University of Porto) for providing the necessary equipment and the technical support for the electron microscopic analysis

PriNergy: A Priority-based Energy Efficient Routing Method for IoT Systems

The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energy-efficient routing (PriNergy) method is proposed. The method is based on the routing protocol for low-power and lossy network (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely the quality of service RPL (QRPL)

Privacy enhancing technologies (PETs) for connected vehicles in smart cities

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.4173 The accepted version of the publication may differ from the final published version.Many Experts believe that the Internet of Things (IoT) is a new revolution in technology that has brought many benefits for our organizations, businesses, and industries. However, information security and privacy protection are important challenges particularly for smart vehicles in smart cities that have attracted the attention of experts in this domain. Privacy Enhancing Technologies (PETs) endeavor to mitigate the risk of privacy invasions, but the literature lacks a thorough review of the approaches and techniques that support individuals' privacy in the connection between smart vehicles and smart cities. This gap has stimulated us to conduct this research with the main goal of reviewing recent privacy-enhancing technologies, approaches, taxonomy, challenges, and solutions on the application of PETs for smart vehicles in smart cities. The significant aspect of this study originates from the inclusion of data-oriented and process-oriented privacy protection. This research also identifies limitations of existing PETs, complementary technologies, and potential research directions.Published onlin

Modelling, data mining and visualisation of genetic variation data

University of Technology, Sydney. Faculty of Engineering and Information Technology.Data mining and knowledge discovery have been applied to datasets in various industries including biomedical informatics. The major challenges in data mining in the area stem from the fact that biomedical data comes in many forms with a highly dimensional nature. This research thesis focuses on one specific biomedical dataset, termed as genetic variation data in the form of genome-wide single nucleotide polymorphisms (SNPs) datasets. Advances in single nucleotide polymorphism genotyping technologies have revolutionised our ability to explore the genetic architecture and models underlying complex diseases by conducting studies based on the whole genome. These studies are called genome-wide association studies. The basic strategy used in these studies is to examine the relationship between the disease of interest and genetic markers across the whole genome. Many association studies have led to the discovery of single genetic variants associated with common diseases. However, complex diseases are not caused by single genes acting alone but are the result of complex non-linear interactions among genetic factors, with each gene having a small effect on disease risk. For this reason there is a critical need to implement new approaches that can take into account non-linear gene-gene interactions in searching for markers that jointly cause complex diseases. Several computational methods have been developed to deal with the genetic complexity of complex diseases. However, testing each SNP for main effects and different orders of gene-gene interaction is computationally infeasible for such high-dimensional data. Also, these methods do not scale well. Therefore, there is growing interest in applying non-parametric predictive models including data mining and machine learning approaches to understand genetic variation data. This thesis constructs models which incorporate genetic variation data in a manner that will alleviate the error induced by the high dimensionality of such data. Data mining approaches, specifically non-parametric ones, are developed for the modelling, exploration and visualization of patient-to-patient relationships based on genome-wide SNP data. This thesis focuses on three main issues in genetic variation studies: (1) feature selection and distance calculations, (2) framework for the task of disease diagnosis and prognosis, and (3) models for the comparison and visualisation of patient-to-patient relationships based on genome-wide SNP pro files. This thesis proposes efficient feature selection approaches to find an optimal subset of markers with the highest predictive power for the disease of interest, while managing the large search space required. The proposed approaches select genetic markers for marginal effects as well as gene-gene interaction effects. Markers with marginal effects are selected with an iterative random forest (RF) based procedure, called RF-RFE. The importance measure generated by random forest was chosen for estimating the importance of each SNP (weighting) and facilitates the selection of an appropriate set of SNPs. To deal with the large search space involved in detecting gene-gene interactions, putative markers are prioritized in the search using a new measure, called Interaction Effect (IE), that quantifies the potential for a SNP to be involved in gene-gene interaction. This measure can also be used as a splitting criterion in random forest construction to de ne a cut-off value of a ranked list of SNPs. The prioritized SNP set is used to construct new combined features, which carry the information to account for gene-gene interactions. This thesis proposes three new methods for calculating distances between genotype pro les based on a kernel-based weighting function including: RFK, using the RF variable importance measure; MAFK, based on the minor allele frequency measure and EK, using the entropy measure. The distances can be subsequently incorporated for the purpose of disease classifications, cluster analyses and visualizations. The feasibility of using genetic variation data for disease diagnosis and prognosis is explored with a new computational framework. The framework demonstrates the use of different phases of data processing and modelling to build reliable disease diagnostic and prognostic models using genetic variation data. The proposed feature selection approaches are incorporated in the framework to select an optimal subset of SNPs with the highest predictive power. The proposed framework is empirically evaluated using two case studies of acute lymphoblastic leukaemia. The results demonstrate that the framework can produce highly accurate diagnosis and prognosis models. This thesis shows that a significant improvement of models' performance requires including interaction markers. The results are consistent with known biology while the accuracy of the produced models is also high. Finally, several data reduction methods are used to visualize genetic variation data. For unsupervised-based visualization, they are compared based on the trustworthiness metric. For the supervised-based visualization, the performance is compared based on class discrimination. This thesis finds that the Neighbour Retrieval Visualizer method shows the best results for unsupervised-based visualization. Furthermore, in the supervised-based approach, the results highlight the importance of using feature selection to remove insignificant features. The visualization has the potential to assist clinicians and biomedical researchers in understanding relationships between patients and has the potential to lead to delivery of advanced personalized medicine. The methodologies and approaches presented in this thesis emphasise the critical role that genetic variation data plays in understanding complex disease. The availability of a flexible framework for the task of disease diagnosis and prognosis, as proposed in this thesis, will play an important role in understanding the genetic basis to common complex diseases. A comprehensive validation of the methods and approaches embedded in the framework is a matter of applying this framework to other complex diseases

Using non-standard finite difference scheme to study classical and fractional order SEIVR model

In this study, we considered a model for novel COVID-19 consisting on five classes, namely (Formula presented.), susceptible; (Formula presented.), exposed; (Formula presented.), infected; (Formula presented.), vaccinated; and (Formula presented.), recovered. We derived the expression for the basic reproductive rate (Formula presented.) and studied disease-free and endemic equilibrium as well as local and global stability. In addition, we extended the nonstandard finite difference scheme to simulate our model using some real data. Moreover, keeping in mind the importance of fractional order derivatives, we also attempted to extend our numerical results for the fractional order model. In this regard, we considered the proposed model under the concept of a fractional order derivative using the Caputo concept. We extended the nonstandard finite difference scheme for fractional order and simulated our results. Moreover, we also compared the numerical scheme with the traditional RK4 both in CPU time as well as graphically. Our results have close resemblance to those of the RK4 method. Also, in the case of the infected class, we compared our simulated results with the real data

Distal hereditary motor neuronopathy of the Jerash type is caused by a novel SIGMAR1 c.500A>T missense mutation

BACKGROUND: Distal hereditary motor neuronopathies (dHMN) are a group of genetic disorders characterised by motor neuron degeneration leading to muscle weakness that are caused by mutations in various genes. HMNJ is a distinct form of the disease that has been identified in patients from the Jerash region of Jordan. Our aim was to identify and characterise the genetic cause of HMNJ. METHODS: We used whole exome and Sanger sequencing to identify a novel genetic variant associated with the disease and then carried out immunoblot, immunofluorescence and apoptosis assays to extract functional data and clarify the effect of this novel SIGMAR1 mutation. Physical and neurological examinations were performed on selected patients and unaffected individuals in order to re-evaluate clinical status of patients 20 years after the initial description of HMNJ as well as to evaluate new and previously undescribed patients with HMNJ. RESULTS: A homozygous missense mutation (c.500A>T, N167I) in exon 4 of the SIGMAR1 gene was identified, cosegregating with HMNJ in the 27 patients from 7 previously described consanguineous families and 3 newly ascertained patients. The mutant SIGMAR1 exhibits reduced expression, altered subcellular distribution and elevates cell death when expressed. CONCLUSION: In conclusion, the homozygous SIGMAR1 c.500A>T mutation causes dHMN of the Jerash type, possibly due to a significant drop of protein levels. This finding is in agreement with other SIGMAR1 mutations that have been associated with autosomal recessive dHMN with pyramidal signs; thus, our findings further support that SIGMAR1 be added to the dHMN genes diagnostic panel