Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many ...purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands.
Secukinumab, an anti-interleukin-17A monoclonal antibody, improved the signs and symptoms of ankylosing spondylitis (AS) in two phase 3 studies (MEASURE 1 and MEASURE 2). Here, we present 52-week ...results from the MEASURE 3 study assessing the efficacy and safety of secukinumab 300 and 150 mg subcutaneous maintenance dosing, following an intravenous loading regimen.
A total of 226 patients were randomized to intravenous secukinumab 10 mg/kg (baseline, weeks 2 and 4) followed by subcutaneous secukinumab 300 mg (IV-300 mg) or 150 mg (IV-150 mg) every 4 weeks, or matched placebo. Patients in the placebo group were re-randomized to subcutaneous secukinumab at a dose of 300 or 150 mg at week 16. The primary endpoint was the Assessment of SpondyloArthritis international Society criteria for 20% improvement (ASAS20) response rate at week 16 in the IV-300 mg or IV-150 mg versus placebo. Other endpoints assessed through week 52 included improvements in ASAS40, ASAS 5/6, Bath Ankylosing Spondylitis Disease Activity Index, and ASAS partial remission responses, as well as the change from baseline in high-sensitivity C-reactive protein levels. Statistical analyses followed a predefined hierarchical hypothesis testing strategy to adjust for multiplicity of testing, with non-responder imputation used for binary variables and mixed-model repeated measures for continuous variables.
The primary efficacy endpoint was met; the ASAS20 response rate was significantly greater at week 16 in the IV-300 mg (60.5%; P < 0.01) and IV-150 mg (58.1%; P < 0.05) groups versus placebo (36.8%). All secondary endpoints were met at week 16, except ASAS partial remission in the IV-150 mg group. Improvements achieved with secukinumab in all clinical endpoints at week 16 were also sustained at week 52. Infections, including candidiasis, were more common with secukinumab than with placebo during the placebo-controlled period. During the entire treatment period, pooled incidence rates of Candida infections and grade 3-4 neutropenia were 1.8% for both of these adverse events in secukinumab-treated patients.
Secukinumab (300 mg and 150 mg dose groups) provided rapid, significant and sustained improvement through 52 weeks in the signs and symptoms of patients with AS. The safety profile was consistent with previous reports, with no new or unexpected findings.
ClinicalTrials.gov, NCT02008916 . Registered on 8 December 2013. EUDRACT 2013-001090-24. Registered on 24 October 2013). The study was not retrospectively registered.
Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a ...critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.
‘Long COVID’ syndrome Taribagil, Priyal; Creer, Dean; Tahir, Hasan
BMJ case reports,
04/2021, Letnik:
14, Številka:
4
Journal Article
Recenzirano
Odprti dostop
SARS-CoV-2 has resulted in a global pandemic and an unprecedented public health crisis. Recent literature suggests the emergence of a novel syndrome known as ‘long COVID’, a term used to describe a ...diverse set of symptoms that persist after a minimum of 4 weeks from the onset of a diagnosed COVID-19 infection. Common symptoms include persistent breathlessness, fatigue and cough. Other symptoms reported include chest pain, palpitations, neurological and cognitive deficits, rashes, and gastrointestinal dysfunction. We present a complex case of a previously well 28-year-old woman who was diagnosed with COVID-19. After resolution of her acute symptoms, she continued to experience retrosternal discomfort, shortness of breath, poor memory and severe myalgia. Investigations yielded no significant findings. Given no alternative diagnosis, she was diagnosed with ‘long COVID’.
Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based ...Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.
The outbreak of the COVID-19 virus has causedwidespread panic and global initiatives are geared towards treatmentand limiting its spread. With technological advancements,several mechanisms and mobile ...applications have been developedthat attempt to trace the physical contact made by a personwith someone who has been tested COVID-19 positive. Whiledesigning these apps, user’s privacy has been an afterthoughtand has resulted in mass violations of privacy of the public andthe patients. A total of 32 countries have designed apps andrely on them as a strategy to flatten the pandemic curve. Alongwith lack of privacy, these methodologies are centralized, wherethey are fully controlled by the government and the healthcareproviders. Owing to these and many other concerns, peopleare hesitant in the adoption of these technologies. This paperpresents a detailed analysis of user tracking apps belongingto 32 countries, thus demonstrating that they collect personaldata and are a gross violation of user privacy. This paperpresents a novel architecture for the efficient, effective andprivacy-preserving contact tracing of COVID-19 patients usingblockchain. The proposed architecture preserves the privacy ofindividuals and their contact history by encrypting all the dataspecific to an individual using a privacy-preserving Homomorphicencryption scheme and storing it on a permissioned blockchainnetwork. The contacts made with a COVID-19 positive patientare identified by performing search queries directly over theHomomorphic encrypted data stored in the blocks. Therefore,only those contacts that are suspected to be COVID-19 positivemay be decrypted by the healthcare professional or governmentfor further contact tracing/ diagnosis and COVID-19 testing;thereby leading to enhanced privacy. KCI Citation Count: 1
Neonatal mortality comprises 40% of total under-5 mortality, globally. Kangaroo mother care (KMC) is one of the most cost-effective interventions to reduce neonatal mortality. KMC does not require ...highend equipment, intensive care facilities or technical knowledge. A recent meta-analysis reported that KMC may reduce neonatal mortality in preterm and low birth weight neonates up to 36%. A review of enablers and barriers of KMC suggests that KMC can be integrated in maternal health care system by giving awareness, involving family and giving ownership of the intervention to the community. If supported with minimal incentives it would reduce the cost of health care substantially, reduce patient burden on hospitals by reducing hospital stay in postnatal period. It will reduce financial burden, time strain and help eliminate social taboos regarding preterm and low birth weight neonates. Hospital and community based KMC interventions should be tested in Pakistan .
Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online ...consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient’s critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM’s homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.
Abstract
The study aimed to identify the level of production and marketing risks facing tomato growers in the governorates of Halabja and Sulaymani, As well as determining the discrepancy between the ...independent variables studied for the respondents and the production and marketing risks, The data was collected using a questionnaire and the method of personal interview by taking a multi-stage random sample by selecting four regions famous for tomato cultivation in Halabja and Sulaymani governorates with (2050) farmers. Then, in the second stage, a proportional stratified random sample (16%) of the research population was taken. The sample size was (328) farmers. The results showed that the level of production and marketing risks facing tomato growers is high and tends to be medium. The result showed that there is no discrepancy in the opinions of farmers about the production and marketing risks facing tomato growers according to the research variables (Age, Size of farm holding, Type of farm holding, and Contact with the Agricultural Extension Worker), Conversely, disparities arise in relation to the research variables (Educational level, Years of agricultural experience, Volume of tomato production during the year, Involvement in training programs, Sources of agricultural information). Therefore, the researchers recommend intensifying specialized Involvement in training programs in the field of tomato production, marketing, and distributing production and marketing requirements to farmers at reasonable prices or supported by the government.
Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that ...are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.
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