ABSTRACT
Background: Food allergy (FA) has become a major public health concern affecting millions of children and adults worldwide. In Tunisia, published data on FA are scarce. Methods: This study, was intended to fill the gap and estimate the frequency of allergy to different foods in the Sfax region, Tunisia, within self-reported FA. One hundred twenty-five (125) children (56% males, 1-17 years old), and 306 adults (17% males, 18-70 years old) were interviewed using a bilingual questionnaire. Results: The number of self-reported food allergens in this sample was 105; allergens were clustered in 8 foods: fruits, seafood, eggs, milk and dairy, cereals, nuts, vegetables, and peanuts. Cutaneous reactions were the most frequent symptoms, in both children and adults. About 40% of children and 30% of adults had a family history of FA. About 81% of adults and 38% of children are allergic to at least 1 non-food allergen. The most prevalent food allergen was the fruit group in both adults and children, followed by seafood. Most food allergies were mutually exclusive and 90% of individuals have a single FA. The relationship between self-declared FA was modeled using a Bayesian network graphical model in order to estimate conditional probabilities of each FA when other FA is present. Conclusions: Our findings suggest that the prevalence of self-reported FA in Tunisia depends on dietary habits and food availability since the most frequent allergens are from foods that are highly consumed by the Tunisian population.
ABSTRACT
The COVID-19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID-19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re-establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self-sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID-19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self-sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof-of-concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer-indistinguishability, and revocation.
ABSTRACT
Although recognized as effective measures to curb the spread of the COVID19 outbreak, social distancing and home confinement have generated a mental health burden with older adults who are considered to be more vulnerable to psychosocial strains. To date, the application of digital technologies in response to COVID-19 pandemic has been narrowed to public-health needs related to containment and mitigation. However, information and communications technology (ICT)-based initiatives directed toward prediction and prevention of psychosocial support are still limited. Given the power of digital health solutions to allow easy and accurate characterization and intervention for health and disease, as well as to flatten the COVID19 incidence curves in many countries, our ECLB-COVID19 consortium is highlighting the importance of providing innovative ICT-based solutions (ICT-COVID-Companion) to improve elderly physical and mental health, thereby preventing/dampening psychosocial strain during pandemics. Based on innovative approaches (e.g., emotional/social computing, open social platform, interactive coaching, gamification, fitness-tracker, internet of things) and smart digital solutions (smartwatch/smartphone), smart companions must provide safe personalised physical, mental and psychosocial health surveillance. Additionally, by delivering personalised multi-dimension crisis-oriented health recommendations, such innovative crisis-oriented solutions would help (i) facilitate a user's adherence to active and healthy confinement lifestyle (AHCL), (ii) achieve a rapid psychosocial recovery in case of depression issues and (iii) enhance preparedness for eventual future pandemics.
ABSTRACT
In personalized medicine, a challenging task is to identify the most effective treatment for a patient. In oncology, several computational models have been developed to predict the response of drugs to therapy. However, the performance of these models depends on multiple factors. This paper presents a new approach, called Q-Rank, to predict the sensitivity of cell lines to anti-cancer drugs. Q-Rank integrates different prediction algorithms and identifies a suitable algorithm for a given application. Q-Rank is based on reinforcement learning methods to rank prediction algorithms on the basis of relevant features (e.g., omics characterization). The best-ranked algorithm is recommended and used to predict the response of drugs to therapy. Our experimental results indicate that Q-Rank outperforms the integrated models in predicting the sensitivity of cell lines to different drugs.
