Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Chronic spontanous urticaria following anti-covid 19 vaccine: Is there a link?

Background: Urticarial reactions following Covid-19 vaccine were rarely reported and have a short self-limited resolution. However only one case of chronic spontaneous urticaria (CSU) after mRNA vaccine was observed (1). Herein, we describe an original case series of patients who exhibited a CSU after Sars-Cov- 2 vaccination. Method(s): It was a retrospective case series of patients referred to the department of Clinical pharmacology of the University of Monastir for exploration of urticaria after Covid-19 vaccination., between January 2021 and January 2022. Result(s): Eight patients (8 F /5M) were included in this study. The median patient age was 36.5 years. None of them had a medical history of CSU. Urticaria was reported in 4 patients following mRNA vaccine (BNT162b2 and Moderna). Viral vector vaccine (Oxford/ AstraZeneca) was offended in 2 cases and inactivated virus vaccine (Sinovac, CoronaVac) was reported in 2 others cases. The mean time interval between vaccination and the onset of urticaria was 28.5 hours. The first shot of vaccine was the mostly offended dose (n = 6). Urticaria was associated with angioedema in 5 patients after Oxford/AstraZenecavaccine (n = 2) and following mRNA vaccine (n = 2). One case of urticaria was associated with angioedema and dyspnea after the CoronaVac administration. Blood tests showed polynuclear leucocytosis in 37% of patients. Positive anti-thyroperoxidase antibodies, and elevated polyclonal hypergammaglobulinemia were present in one patient 3 months after receiving BNT162b2 vaccine. Total serum IgE were high in 25% of patients following BNT162b2 and CoronaVac. All patients required antihistamines and 4 cases required intravenous betametasone. The median time to symptom resolution was 3 days but urticaria rapidly reccured throughout the entire body inspite the regular use of full dose of antihistamine. Intradermal test for the vaccine excipient as well as the offended Covid-19 vaccine was carried out in 5 patients, and were negative in all of them.Currently, all patients still has the pruritic rash daily. Conclusion(s): These cutaneous reactions seem to be particularly prolonged despite the use of symptomatic drugs, as compared with of drug induced-urticaria. Consequently, careful monitoring of urticaria over an extended period of time is needed.

COVID-19 vaccines safety: Spontaneous notification data

Introduction (contexte de la recherche): Although the SARS-CoV-2 vaccines have undergone preclinical tests and clinical trials evaluating their efficacy and safety, few data have been reported in the post-licensure real-world setting. Objectif: We aimed to assess the safety of COVID-19 vaccines among a Tunisian Pharmacovigilance database. Methodes: An exhaustive observational study including all adverse events following COVID-19 vaccination notified to the pharmacovigilance unit of the Monastir Hospital. Resultats: A total of 336 events were collected. The patients' sex ratio was 1.4. Elderly patients (>= 65-years-old) represented 54% of cases. The most common adverse reaction was fatigue and fever (30%, respectively), followed by headache (21%), muscle soreness (15%) and localized pain at the injection site (13%). Skin eruptions accounted for 14% of the reported events and anaphylaxis was noted in 0.6% of cases. The most reported events were mild. However, 15 patients developed serious events [anaphylaxis (seven cases), thrombotic reactions (seven cases), and thrombocytopenia (one case)]. The event was fatal in five patients. Pfizer-BioNTech vaccine was implicated in 65% of reported events, followed by AstraZeneca vaccine (11%). Pfizer-BioNTech vaccine was implicated in 9/15 serious events. Fatal events were observed with Pfizer-BioNTech vaccine in three cases, and AstraZeneca and CoronaVac vaccines in one case each. Conclusion(s): Our study implies that the COVID-19 vaccines have a favorable safety profile due to the low incidence of self-reported adverse reactions. Further large-scale studies are needed to confirm the safety of COVID-19 vaccines.Copyright © 2023

Covid-19 vaccines safety: Spontaneous notification data

Background: Although the SARS-CoV- 2 vaccines have undergone preclinical tests and clinical trials evaluating their efficacy and safety, few data have been reported in the post-licensure real-world setting. We aimed to assess the safety of Covid-19 vaccines among a Tunisian Pharmacovigilance database. Method(s): An exhaustive observational study including all adverse events following Covid-19 vaccination notified to the pharmacovigilance unit of the Monastir Hospital from April to December 2021. Result(s): A total of 336 events were collected. The patients' sex ratio was 1.4. Elderly patients (>=65 years old) represented 54% of cases. The most common adverse reaction was fatigue and fever (30%, respectively), followed by headache (21%), muscle soreness (15%) and localized pain at the injection site (13%). Skin eruptions accounted for 14% of the reported events and anaphylaxis was noted in 0.6% of cases. The most reported events were mild. However, 15 patients developed serious events (anaphylaxis (seven cases), (thrombotic reactions (seven cases), and thrombocytopenia (one case)). The event was fatal in five patients. Pfizer-BioNTech vaccine was implicated in 65% of reported events, followed by AstraZeneca vaccine (11%). Pfizer-BioNTech vaccine was implicated in 9/15 serious events. Fatal events were observed with Pfizer-BioNTech vaccine in three cases, and AstraZeneca and CoronaVac vaccines in one case each. Conclusion(s): Our study implies that the Covid-19 vaccines have a favorable safety profile due to the low incidence of self-reported adverse reactions. Further large-scale studies are needed to confirm the safety of Covid-19 vaccines.

Acute pericarditis after COVID-19 vaccination

Case report Acute pericarditis is a sudden inflammation of the pericardium with many etiologies. A wide variety of infectious agents, systemic or endocrine diseases, and drugs can cause it. We report the case of a woman who developed pericarditis after receiving the COVID-19- Pfizer- BioNTech vaccine. A 36-year- old female patient, presenting five days after the first dose of COVID-19- Pfizer- BioNTech vaccine with tingling chest pain, increased by deep inspiration, for which she consulted cardiology. The ECG showed a PQ segment elevation in DII. The biological tests revealed a biological inflammatory syndrome (CRP = 176 mg/L). The cardiac ultrasound showed a pericardial effusion of moderate size. Thus, the diagnosis of acute pericarditis was retained. The etiological investigation in search of an infectious, neoplastic, endocrine or autoimmune cause was negative. The patient was put on anti-inflammatory drugs with good clinical, biological and echographic evolution 15 days later. The course of action was to contraindicate the mRNA platform vaccines (Pfizer-BioNTech and Moderna). We report in the present work an original case of acute pericarditis most probably induced by the anti-COVID- 19- Pfizer- BioNTech vaccine. The role of the latter was evoked by the delay between the administration of the vaccine and the onset of the disease, which was highly suggestive (typically within the first 7 days), and the potential provider of this type of vaccine, given that the mRNA platform vaccines (Pfizer-BioNTech and Moderna) are associated with an additional risk of 2.1 and 6.1 respectively for the occurrence of acute pericarditis.

Syndrome Babouin induit par le vaccin anti-Covid-19 (CoronaVac®) : premier cas rapporté

Déclaration de liens d’intérêts: Les auteurs n’ont pas précisé leurs éventuels liens d’intérêts.

An accurate neural network algorithm to diagnose Covid-19 from CT images

Background: A new coronavirus appeared in late December 2019 in Wuhan, China. He was named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus is responsible for Covid-19, the name given to the disease associated with it. It spreads worldwide, infecting more than a million people and killing more than 70 miles. The rapid and accurate diagnosis of suspected Covid-19 cases plays a crucial role in medical treatment and timely quarantine. Materials and Methods: In order to counter the Covid-19 pandemic, we have developed a method for the automatic detection of Covid -19, from 2D computed tomography (CT) chest images. It is a supervised software system based on the ANN (Artificial Neural Network) algorithm. Pulmonary CT images were collected from multiple international datasets, with a total of 395 images: 70% were used for training and 30% were used for testing. For each patient, the lungs were segmented using simple thresholding. Then, the segmented lungs were fed into a neural network to predict the probability of SARS-CoV-2 infectious. Results: The internal validation achieved a total accuracy of 97.5% with a specificity of 96.6 % and a 100 % sensitivity. Conclusion: These results demonstrate the proof-ofprinciple for using artificial intelligence to extract radiological features for timely and accurate Covid-19 diagnosis.