Estimation of Parameters and Stability Analysis of Coronavirus Pandemic

Despite ranking amongst the highest in medical systems in Africa and spending a substantial amount on health sector than other African nations, Algeria suffered a major blow in the first wave of the Covid-19 pandemic. Vaccine hesitancy also affected the country adversely in subsequent waves of the disease. This study estimates the number of Covid-19 cases for Algeria in January 2022 using two numerical methods Multi-step Differential Transform Method (MsDTM) and Repeated MsDTM. Stability analysis of the pandemic for the country has also been discussed in the paper.

Analysis and Prediction of Covid-19 Spread Using Numerical Method

In this paper, we have discussed the impact of Coronavirus variants in a phase of 2021-22 along with a previous phase of 2020-21 in Italy. We analyse and compare the Covid-19 scenario in Italy for the period from October 04, 2020 to January 16, 2021 with a period from October 04, 2021 to January 16, 2022. For this study, we have used repeated multi-step differential transform method (RMsDTM). Also, we have predicted the number of active cases for 10 days following the period of study.

Heart Transplantation (ht) from Active and Recently Active Covid19 Donors

HT centers may avoid donors with Covid19 (Cov19) infection due to uncertain risk of virus transmission and possibility of virus mediated myocardial injury. We investigated Cov19 donor utilization, transplant characteristics and early post HT outcomes in the U.S. Between May 2020-June 2022, n=27,862 donors in UNOS had data available on Cov19 NAT tests and organ disposition. Since donors may get Cov19 testing multiple times prior to organ retrieval, additional data on multiple Cov19 NAT was requested and analyzed. Donors were classified Cov19-donors if NAT+ at any time during terminal hospitalization, and subclassified as Active Cov19(A-Cov19) if NAT+ at organ procurement and 'Recently Active Cov19' (rA-Cov19) if NAT+ initially but NAT negative prior to organ retrieval. HT outcomes using Cov19 and nonCov19 donors were compared by Kaplan Meier (KM) and Cox hazards ratio (HR). Prior to organ retrieval, 27,862 donors had 60,699 Cov19 NAT tests done. Of these, n=1445 were Cov19 donors, n=125 indeterminate and n=26,292 nonCov19. Of Cov19 donors, n=1017 were A-Cov19 and n=428 rA-Cov19. 309 HTs used hearts from Cov19 donors and 239 (n=150 A-Cov19, n=89 rA-Cov19) met study criteria. Compared to nonCov19, Cov19 donors used for adult HT were younger [30(23-37) vs 32(25-40)yrs] and mostly male (80.3% vs 72.1%), p<0.05. Otherwise, HTs from Cov19 and nonCov19 donors were similar in recipient age, race, etiology, UNOS status, BMI, LVAD, ECMO use;and donor LVEF, and DCD status. HTs from Cov19 and nonCov19 donors had similar survival up to 3 months [CoxHR=1.23(0.63-2.39), p=0.54, adjusted for baseline characteristics, Fig1A]. Survival was also statistically similar in A-Cov19 and rA-Cov19 donor HT cohorts [CoxHR=1.47(0.40-5.48), p=0.56, Fig1B]. HTs from Cov19 donors increased from n=5 in May-Dec 2020 to n=207 in Jan-June 2022, p<0.05 for trend. Data on Cov19 treatment was not available. In the largest analysis to date, HTs from selective Cov19 donors had acceptable early outcomes. Longer follow up is needed. [ABSTRACT FROM AUTHOR] Copyright of Journal of Heart & Lung Transplantation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Covid-19 cases in Morocco: A comparative analysis

Covid-19 is a highly infectious disease caused by novel Corona virus SARS-CoV-2, affecting the whole world. In this paper, we introduce and apply two iterative methods, RMsDTM and R2KM, to obtain approximate values of Covid-19 cases in Morocco. We also compare the approximations of both methods and see that the solution of RMsDTM is more accurate. © 2022, Erdal Karapinar. All rights reserved.

De novo design of anti-variant COVID vaccine with T-cell memory (preprint)

Recent studies have shown the efficacy of hybrid COVID-19 vaccines using wild-type nucleocapsid (N) and Spike (S) protein. We upgrade this strategy by one step further using clinically proven spike protein (but with delta and post-delta omicron mutations) and nucleocapsid peptides conferring T-cell immunity. As per the latest research nucleocapsid peptides are perfect immunological replacement of nucleocapsid protein. Therefore, peptide linking strategy is pursued (economic for cellular biosynthesis than whole protein). One envelope peptide with potent T-cell response is also chosen. This peptide is also functionally indispensable for the virus. All these peptides were clustered in our designed cytoplasmic domain separated by non-immunogenic helical linkers. We also propose the idea of introduction of any T-cell peptide similar to other Human Corona Viruses (HuCoV) in these linker regions whenever required. In addition to COVID, the same approach can be applied for any emergency or even long-term unsolved outbreaks of Influenza, Dengue and West Nile Virus etc. In this era of novelty as presented by subunit and nucleic acid vaccines, multiepitope strategies like this can help to combat multiple diseases successfully in real time to give hope for better future.

Pneumonia Classification Using Few-Shot Learning with Visual Explanations

Deep learning models have demonstrated state of the art performance in varied domains, however there is still room for improvement when it comes to learning new concepts from little data. Learning relevant features from a few training samples remains a challenge in machine learning applications. In this study, we propose an automated approach for the classification of Viral, Bacterial, and Fungal pneumonia using chest X-rays on a publicly available dataset. We employ distance learning based Siamese Networks with visual explanations for pneumonia detection. Our results demonstrate remarkable improvement in performance over conventional deep convolutional models with just a few training samples. We exhibit the powerful generalization capability of our model which once trained, effectively predicts new unseen data in the test set. Furthermore, we also illustrate the effectiveness of our model by classifying diseases from the same genus like COVID-19 and SARS. © 2022, Springer Nature Switzerland AG.

Automated detection of COVID-19 on a small dataset of chest CT images using metric learning

Coronavirus disease has caused unprecedented chaos across the globe causing potentially fatal pneumonia, since the beginning of 2020. Researchers from different communities are working in conjunction with front-line doctors and policy-makers to better understand the disease. The key to prevent the spread is a rapid diagnosis, prioritized isolation, and fastidious contact tracing. Recent studies have confirmed the presence of underlying patterns on chest CT for patients with COVID-19. We present a completely automated framework to detect COVID-19 using chest CT scans, only needing a small number of training samples. We present a few-shot learning technique based on the Triplet network in comparison to the conventional deep learning techniques which require a substantial amount of training examples. We used 140 chest CT images for training and the rest for testing from a total of 2482 images for both COVID-19 and non-COVID-19 cases from a publicly available dataset. The model trained with chest CT images achieves an AUC of 0.94, separates the two classes into distinct clusters;thereby giving correct prediction accuracy on the evaluation dataset.

Facial colliculus syndrome with inflammatory cranial neuritis in a patient with covid 19 with mucormycosis superinfection

Features of unilateral cranial nerve 7th lower motor neuron palsy and same sided 6th nerve involvement without hemiplegia or hemiparesis is a rare clinical entity in itself, which is referred to as Facial Colliculus Syndrome. This presentation was noticed in a patient of Covid 19 positive status and mucormycosis not on prolonged steroid treatment. This is a case report of an 80-year old female patient who was admitted to the medical ward of our hospital with presenting complaints of breathlessness and fever of 5 days duration. Later during course of the disease on followup in hospital she developed left sided 6th and 7th cranial nerve involvement. This case report highlights the case of an elderly female patient presenting with cranial nerve involvement with mucormycosis with covid 19 superinfection on follow-up after home quarantine.

CTO: A community-based clinical trial ontology and its applications in PubChemRDF and SCAIView

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed. Copyright © 2020 for this paper by its authors.