Search

When there is a mismatch between the cardinality of a periodic task set and the priority levels supported by the underlying hardware systems, multiple tasks are grouped into one class so as to maintain a specific level of confidence in their accuracy. However, such a transformation is achieved at the expense of the loss of schedulability of the original task set. We further investigate the aforementioned problem and report the following contributions: (i) a novel technique for mapping unlimited priority tasks into a reduced number of classes that do not violate the schedulability of the original task set and (ii) an efficient feasibility test that eliminates insufficient points during the feasibility analysis. The theoretical correctness of both contributions is checked through formal verifications. Moreover, the experimental results reveal the superiority of our work over the existing feasibility tests by reducing the number of scheduling points that are needed otherwise.

Objective: Duchenne muscular dystrophy (DMD) is a rare X-linked neurodegenerative disorder caused by mutations in the DMD gene. This study examined the efficacy and safety of ataluren, the first oral treatment for DMD with nonsense mutations (nmDMD), in patients in the Middle East., Methods: This retrospective longitudinal study assessed the outcomes of seven boys with nmDMD who received treatment with ataluren and follow-up at a single center since 2016., Results: The median patient age at treatment initiation was 8.04 years (range: 3.3-9.92), and the median duration of exposure was 3.95 years (interquartile range = 4.42 years). Five patients were still ambulatory at the last follow-up. Ataluren was more effective in individuals with baseline 6-min walking distance (6MWD) ≥300 m, as these patients had smaller declines in 6MWD and North Star Ambulatory Assessment scores. Pulmonary function was well preserved in all patients, with no patients having forced vital capacity <60% at their last follow-up. Six patients maintained normal cardiac function, whereas one patient developed heart failure before starting ataluren treatment., Conclusions: Our results demonstrated both the efficacy and safety of ataluren. Early initiation of ataluren treatment delayed the loss of ambulation and cardiorespiratory milestones.

Abstract: Endogenous endophthalmitis is an intraocular inflammation caused by the hematogenous spread of microorganisms from distant foci. Streptococcus mitis is a low-virulence organism that inhabits the oral cavity; however, it has rarely been reported to cause endogenous endophthalmitis. In this case report, we present a 63-year-old woman with bilateral asymmetrical endogenous endophthalmitis and a severely affected right eye, with light perception vision, hypotony, and severe anterior and posterior segment inflammation. The left eye exhibited intraretinal infectious infiltrates and minimal vitritis. However, the cause of the S. mitis bacteremia was unclear. Considering the patient's recent dental implant procedure, we hypothesized that the bacteria may have entered the bloodstream through the oral cavity. The patient received intravitreal and systemic antibiotics and underwent pars plana vitrectomy for the right eye. In conclusion, we present a case of endogenous endophthalmitis, presumed following a dental implant procedure. S. mitis may invade the bloodstream following an uncomplicated dental procedure, leading to bilateral endogenous endophthalmitis. Early detection, prompt management with systemic and intravitreal antibiotics, and early vitrectomy may potentially preserve the patient's globe and vision., (Copyright © 2024 Copyright: © 2024 Annals of African Medicine.)

Purpose: To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system., Materials and Methods: Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05)., Results: Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p < 0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations., Conclusion: This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra-low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15 mm is recommended, while a voxel size of 0.45 mm should be avoided., (© 2024 The Authors. Journal of Prosthodontics published by Wiley Periodicals LLC on behalf of American College of Prosthodontists.)

Background: Violence against children constitutes a significant public health problem globally., Aim: To document and compare media reports of violence against children before and during COVID-19, and measures taken by countries to address such violence., Methods: This comparative review covered news reports of violence against children from 1 January to 30 June of 2019 and 2020 in the WHO Eastern Mediterranean Region countries. A total of 823 articles published in Arabic and English, covering incidents, initiatives, opinions and views on all types of violence among children under 18 years of age were sourced using search engines and platforms and reviewed. News on incidents was analysed quantitatively while news on initiatives and opinions was analysed qualitatively., Results: Some 40.3% of the news reports was on incidents, followed by interviews or opinions (31.5%) and initiatives (28.2%). There were 1129 reports of violence against children from 1 January to 30 June of 2019 and 1880 for the same period in 2020. Reports of physical violence increased from 34% in 2019 to 40% in 2020, while reports of sexual violence decreased from 45% in 2019 to 37% in 2020. Views and opinion reports showed 0.4-1.1% alignment with the 7 INSPIRE strategies., Conclusion: The COVID-19 pandemic affected the incidence and reporting of violence against children across the region. It is essential to provide accurate and sensitive media coverage for incidences of violence against children so that survivors and at-risk children can receive adequate support and ensure that communities can tackle it appropriately., (Copyright: © Authors 2024; Licensee: World Health Organization. EMHJ is an open access journal. All papers published in EMHJ are available under the Creative Commons Attribution Non-Commercial ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo).)

For close to 2 years, we have witnessed the impacts of the SARS-CoV-2 pandemic on research at several different levels. Among the list, limited access to laboratory-based training for undergraduate students prevented this cohort from gaining exposure to the realities of a research laboratory at a critical time in training when they may have found motivation in this area as a career. COVID exposed a weakness in our training pipeline; an extreme dependency on face-to-face training that threatened to create a void in the research talent needed to replenish the scientific community every year. In the classroom, we witnessed a revolution of e-learning based approaches that could be rapidly implemented based on existing footprints. Out of necessity, our laboratory developed and implemented an e-learning model of an undergraduate peer mentor network that provides a knowledge and experience exchange platform between students with different levels of research experience. Implementation of the platform was to aid students with gaining knowledge in multiple aspects of scientific research and hands-on work in a research laboratory. The collaboration between the students of the network was aimed at not only advancing the theoretical and practical research experience, but also at developing feedback implementation and practicing "soft skills" critical for teamwork and leadership. Herein, we present an overview of the model along with survey responses of the students participating in the peer mentor network. We have found that peer delivery of practical benchwork both via scientific presentations and visualized experiments, reduced the time of training and the amount of staff assistance needed when students returned to the bench. Furthermore, this model accelerated student independence in laboratory work and increased research interest overall. In summary, the model of a peer mentor network has the potential to serve as a training platform and as a customized tool, supplementing research laboratory training at the undergraduate level beyond the pandemic., (© 2023 The Authors. Biochemistry and Molecular Biology Education published by Wiley Periodicals LLC on behalf of International Union of Biochemistry and Molecular Biology.)

Glioblastoma is one of the most aggressive types of cancer with success of therapy being hampered by the existence of treatment resistant populations of stem-like Tumour Initiating Cells (TICs) and poor blood-brain barrier drug penetration. Therapies capable of effectively targeting the TIC population are in high demand. Here, we synthesize spherical diketopyrrolopyrrole-based Conjugated Polymer Nanoparticles (CPNs) with an average diameter of 109 nm. CPNs were designed to include fluorescein-conjugated Hyaluronic Acid (HA), a ligand for the CD44 receptor present on one population of TICs. We demonstrate blood-brain barrier permeability of this system and concentration and cell cycle phase-dependent selective uptake of HA-CPNs in CD44 positive GBM-patient derived cultures. Interestingly, we found that uptake alone regulated the levels and signaling activity of the CD44 receptor, decreasing stemness, invasive properties and proliferation of the CD44-TIC populations in vitro and in a patient-derived xenograft zebrafish model. This work proposes a novel, CPN- based, and surface moiety-driven selective way of targeting of TIC populations in brain cancer., (© 2022. The Author(s).)

What Is Known and Objectives: Migraine is a disabling disorder that affects individuals of all ages. To date, there are multiple limitations to using guidelines-recommended treatments and preventive therapies. The goal of this review was to provide a comprehensive clinical review of the safety, efficacy and prescribing information of the emerging calcitonin gene-related peptide (CGRP) antagonists. Agents in this new pharmacologic class were approved by the US Food and Drug Administration (FDA) for the treatment of acute migraine attack pain and the management of episodic and chronic migraine., Methods: A total of 12 randomized, placebo-controlled clinical trials were identified and included in the review utilizing databases such as clinicaltrial.gov, PubMed and EMBASE. The trials collectively evaluated six CGRP antagonists starting from the orally administered CGRPs such as rimegepant and ubrogepant, to the quarterly IV administered CGRP such as eptinezumab, and the monthly/quarterly subcutaneously administered agents such as erenumab, fremanezumab and galcanezumab., Results and Discussion: All agents displayed significant efficacy compared with placebo, measured by reduction in mean monthly migraine days (MMD). In addition, CGRP antagonists displayed a great tolerability profile with few adverse effects. These medications were neither associated with any cardiovascular-related adverse effects, nor do they currently have specific contraindications to pre-existing cardiovascular conditions. This can present a safe alternative to a wide range of patients who cannot be appropriately treated with first-line treatments such as triptans. No treatment-related death was reported in any of the clinical trials outlined and discussed., What Is New and Conclusion: Calcitonin gene-related peptide antagonists are safe and efficacious medications both in treating acute migraine headache pain and the management of episodic and chronic migraine. Head-to-head comparative studies with current guideline-recommended treatments are needed. However, CGRP antagonists are promising agents that present an alternative solution for patients living with migraine., (© 2021 John Wiley & Sons Ltd.)

COVID-19 has transformed the life of human beings and digital twin infrastructure can facilitates working remotely during COVID-19 outbreak by reducing burden on services and infrastructure. Currently, many organizations are installing and developing devices such as thermal cameras, sensors aiming to minimize human contact and so forth, in addition to enforcing social distancing resulting in reducing the risk of transmission. Due to economic reasons, lockdown restrictions are being relaxed/lifted in many countries and Pakistan which is one of the most densely populated countries in the world with a population of 220 + million is no exception. Though, Pakistan contained the first two waves of coronavirus infections reasonably well but the country is struggling to contain the third wave of the spread due to violations of social distancing norms. While our predictions may deviate from official statistics due to lack of mass testing and existence of asymptomatic infections, the described approach predicts the possible actual burden of infection over times. In view of the unique demographics, our data quantify the efficacy of social distancing as an effective measure to forestall the infection. We highlight few areas where digital twins can be created/deployed to provide services and essential facilities to citizens as COVID-19 is expected to have permanent impact on the way we work., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Smart campus is an emerging trend that allows educational institutions to combine smart technologies with physical infrastructure for improved services, decision making, campus sustainability etc. Under the umbrella of smart campus, various solutions have been implemented on campus levels such as smart microgrid, smart classrooms, controlling visual and thermal properties of the buildings, taking students attendance through face recognition/smart cards and so forth. Though these small-scale solutions contribute in parts to the realization of a smart campus, a generic model for smart campus is yet to be established. In this work, we study existing literature and propose a sketch of smart campus based on the smart city concepts. We create a list of smart campus initiatives that can be prioritized as per a university needs and geographical location. The work aims at propagating and providing an insight to the administration of higher educational institutions in evaluating and positioning their existing infrastructure against the smart campus concept. Whilst the vision, available resources and strategic goals of a university may emphasis on a different set of initiatives, in most of the cases, the generic model established in this work for a smart campus remains valid., (© 2020 Elsevier Ltd. All rights reserved.)

Epidemiological models have been used extensively to predict disease spread in large populations. Among these models, Susceptible Infectious Exposed Recovered (SEIR) is considered to be a suitable model for COVID-19 spread predictions. However, SEIR in its classical form is unable to quantify the impact of lockdowns. In this work, we introduce a variable in the SEIR system of equations to study the impact of various degrees of social distancing on the spread of the disease. As a case study, we apply our modified SEIR model on the initial spread data available (till April 9, 2020) for the Kingdom of Saudi Arabia (KSA). Our analysis shows that with no lockdown around 2.1 million people might get infected during the peak of spread around 2 months from the date the lockdown was first enforced in KSA (March 25th). On the other hand, with the Kingdom's current strategy of partial lockdowns, the predicted number of infections can be lowered to 0.4 million by September 2020. We further demonstrate that with a stricter level of lockdowns, the COVID-19 curve can be effectively flattened in KSA., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

This paper aims to assist in the prevention of Chronic Kidney Disease (CKD) by utilizing machine learning techniques to diagnose CKD at an early stage. Kidney diseases are disorders that disrupt the normal function of the kidney. As the percentage of patients affected by CKD is significantly increasing, effective prediction procedures should be considered. In this paper, we focus on applying different machine learning classification algorithms to a dataset of 400 patients and 24 attributes related to diagnosis of chronic kidney disease. The classification techniques used in this study include Artificial Neural Network (ANN) and Support Vector Machine (SVM). To perform experiments, all missing values in the dataset were replaced by the mean of the corresponding attributes. Then, the optimized parameters for the Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques were determined by tuning the parameters and performing several experiments. The final models of the two proposed techniques were developed using the best-obtained parameters and features. The empirical results from the experiments indicated that ANN performed better than SVM, with accuracies of 99.75% and 97.75%, respectively, indicating that the outcome of this study is very promising., (Copyright © 2019 Elsevier Ltd. All rights reserved.)