Search results for: MERS

Authors: Raham El-Kahlout, Hadi Yassin, Asmaa Athani, Marwan Abou Madi, Gheyath Nasrallah

Abstract:

Background: Since its first isolation in September 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has diffused across 27 countries infecting more than two thousand individuals with a high case fatality rate. MERS-CoV–specific antibodies are widely found in Dromedary camel along with viral shedding of similar viruses detected in human at same region, suggesting that MERS epidemiology may be central role by camel. Interestingly, MERS-CoV has also been also reported to be asymptomatic or to cause influenza-like mild illnesses. Therefore, in a country like Qatar (bordered Saudi Arabia), where camels are widely spread, serological surveys are important to explore the role of camels in MERS-CoV transmission. However, widespread strategic serological surveillances of MERS-CoV among populations, particularly in endemic country, are infrequent. In the absence of clear epidemiological view, cross-sectional MERS antibody surveillances in human populations are of global concern. Method: We performed a comparative serological screening of 4719 healthy blood donors, 135 baseline case contacts (high risk individual), and four MERS confirmed patients (by PCR) for the presence of anti-MERS IgG. Initially, samples were screened using Euroimmune anti- MERS-CoV IgG ELISA kit, the only commercial kit available in the market and recommended by the CDC as a screening kit. To confirm ELISA test results, farther serological testing was performed for all borderline and positive samples using two assays; the anti MERS-CoV IgG and IgM Euroimmune indirect immunofluorescent test (IIFT) and pseudoviral particle neutralizing assay (PPNA). Additionally, to test cross reactivity of anti-MERS-CoV antibody with other family members of coronavirus, borderline and positive samples were tested for the presence of the of IgG antibody of the following viruses; SARS, HCoV-229E, HKU1 using the Euroimmune IIFT for SARS and HCoV-229E and ELISA for HKU1. Results: In all of 4858 screened 15 samples [10 donors (0.21%, 10/4719), 1 case contact (0.77 %, 1/130), 3 patients (75%, 3/4)] anti-MERS IgG reactive/borderline samples were seen in ELISA. However, only 7 (0.14%) of them gave positive with in IIFT and only 3 (0.06%) was confirmed by the specific anti-MERS PPNA. One of the interesting findings was, a donor, who was selected in the control group as a negative anti-MERS IgG ELISA, yield reactive for anti-MERS IgM IIFT and was confirmed with the PPNA. Further, our preliminary results showed that there was a strong cross reactivity between anti- MERS-COV IgG with both HCoV-229E or anti-HKU1 IgG, yet, no cross reactivity of SARS were found. Conclusions: Our findings suggest that MERS-CoV is not heavily circulated among the population of Qatar and this is also indicated by low number of confirmed cases (only 18) since 2012. Additionally, the presence of antibody of other pathogenic human coronavirus may cause false positive results of both ELISA and IIFT, which stress the need for more evaluation studies for the available serological assays. Conclusion: this study provides an insight about the epidemiological view for MERS-CoV in Qatar population. It also provides a performance evaluation for the available serologic tests for MERS-CoV in a view of serologic status to other human coronaviruses.

Keywords: seroprevalence, MERS-CoV, healthy individuals, Qatar

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Authors: G. Tamilpavai, C. Vishnuppriya

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Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.

Keywords: bioinformatics, cancer motif, DNA, k-mers, Levenshtein distance, SOM

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Authors: Monika Sogani, Zainab Syed, Adrian C. Fisher

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Pollution of estrogenic compounds has caught the attention of researchers as the slight increase of estrogens in the water bodies has a significant impact on the aquatic system. They belong to a class of endocrine disrupting compounds (EDCs) and are able to mimic hormones or interfere with the action of endogenous hormones. The microbial electrochemical remediation system (MERS) is employed here for exploiting an electrophototrophic bacterium for evaluating the capacity of biodegradation of ethinylestradiol hormone (EE2) under anaerobic conditions with power generation. MERS using electro-phototrophic bacterium offers a tailored solution of wastewater treatment in a developing country like India which has a huge solar potential. It is a clean energy generating technology as they require only sunlight, water, nutrients, and carbon dioxide to operate. Its main feature that makes it superior over other technologies is that the main fuel for this MERS is sunlight which is indefinitely present. When grown in light with organic compounds, these photosynthetic bacteria generate ATP by cyclic photophosphorylation and use carbon compounds to make cell biomass (photoheterotrophic growth). These cells showed EE2 degradation and were able to generate hydrogen as part of the process of nitrogen fixation. The two designs of MERS were studied, and a maximum of 88.45% decrease in EE2 was seen in a total period of 14 days in the better design. This research provides a better insight into microbial electricity generation and self-sustaining wastewater treatment facilities. Such new models of waste treatment aiming waste to energy generation needs to be followed and implemented for building a resource efficient and sustainable economy.

Keywords: endocrine disrupting compounds, ethinylestradiol, microbial electrochemical remediation systems, wastewater treatment

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Mohammad J. J. Taha, Mohammad T. Abuawwad, Warda A. Alrubasy, Shams Khalid Sameer, Taleb Alsafi, Yaqeen Al-Bustanji, Luai Abu-Ismail, Abdulqadir J. Nashwan

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Viral pandemics often take the world by storm, urging the medical community to prioritize the most evident systemic manifestations, often causing ocular manifestations to go unnoticed. This literature review aims to highlight the ocular complications of monkeypox, SARS-CoV-2, MERS, ebola, H1N1, and zika viruses as the most recent viral pandemics. Since the emergence of the newly resurfacing monkeypox and the novel SARS-CoV-2, research aiming to uncover the effects of these pandemics began right away. Moreover, it also discusses the ocular complications of the vaccines and treatments that were used in the scope of the viral pandemics. To add, this work discussed the role of the eye as an important route of viral transmission, and thereafter, the American Academy of Ophthalmology (AAO) recommendations to reduce the incidence of viral transmission were mentioned. Finally, this paper aims to outline a platform for researchers who are interested in further investigating eye-related viral manifestations.

Keywords: ophthalmology, monkeypox, ebola, zika, MERS, H1N1, influenza, COVID-19

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Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

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When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

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Authors: Howaida I. Abd-Alla, Omnia Kutkat, Yassmin Moatasim, Magda T. Ibrahim, Marwa A. Mostafa, Mohamed GabAllah, Mounir M. El-Safty

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Artemisia judaica (known shih-balady), Azadirachta indica and Sophora tomentosa (known yellow necklace pod) are members of available medicinal plants well-known for their traditional medical use in Egypt which suggests that they probably harbor broad-spectrum antiviral, immunostimulatory and anti-inflammatory functions. Their ethyl acetate-dichloromethane (1:1, v/v) extracts were evaluated for the potential anti-Middle East respiratory syndrome-related coronavirus (anti-MERS-CoV) activity. Their cytotoxic activity was tested in Vero-E6 cells using 3-(4,-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method with minor modification. The plot of percentage cytotoxicity for each extract concentration has calculated the concentration which exhibited 50% cytotoxic concentration (TC50). A plaque reduction assay was employed using safe dose of extract to evaluate its effect on virus propagation. The highest inhibition percentage was recorded for the yellow necklace pod, followed by Shih-balady. The possible mode of action of virus inhibition was studied at three different levels viral replication, viral adsorption and virucidal activity. The necklace pod leaves have induced virucidal effects and direct effects on the replication of virus. Phytochemical investigation of the promising necklace pod led to the isolation and structure determination of nine compounds. The structure of each compound was determined by a variety of spectroscopic methods. Compounds 4-O-methyl sorbitol 1, 8-methoxy daidzin 6 and 6-methoxy apigenin-7-O-β-D-glucopyranoside 8 were isolated for the first time from the Sophora genus and the other six compounds were the first time that they were isolated from this species according to available works of literature. Generally, the highest anti-CoV 2 activity of S. tomentosa was associated with the crude ethanolic extract, indicating the possibility of synergy among the antiviral phytochemical constituents (1-9).

Keywords: coronavirus, MERS-CoV, mode of action, necklace pod, shih-balady

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Authors: Yaw-Ling Lin

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A de Bruijn graph of order k is a graph whose vertices representing all length-k sequences with edges joining pairs of vertices whose sequences have maximum possible overlap (length k−1). Every Hamiltonian cycle of this graph defines a distinct, minimum length de Bruijn sequence containing all k-mers exactly once. A Kautz sequence is the minimal generating sequence so as the sequence of minimal length that produces all possible length-k sequences with the restriction that every two consecutive alphabets in the sequences must be different. A collection of de Bruijn/Kautz sequences are orthogonal if any two sequences are of maximally differ in sequence composition; that is, the maximum length of their common substring is k. In this paper, we discuss how such a collection of (maximal) orthogonal de Bruijn/Kautz sequences can be made and use the algorithm to build up a web application service for the synthesized DNA and other related biomolecular sequences.

Keywords: biomolecular sequence synthesis, de Bruijn sequences, Eulerian cycle, Hamiltonian cycle, Kautz sequences, orthogonal sequences

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Authors: Young Kwon Yang, In Sung Kang, Jung Ha Hwang, Jin Chul Park

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The present study is a foundational study for performance improvements on isolation wards to prevent proliferation of secondary infection of infectious diseases such as SARS, H1N1, and MERS inside hospitals. Accordingly, the present study conducted an analysis of the effect of sealing mechanisms and filling of openings on ensuring air tightness performance in isolation wards as well as simulation on air currents in improved isolation wards. The study method is as follows. First, previous studies on aerial infection type and mechanism were reviewed, and the review results were utilized as basic data of analysis on simulation of air current. Second, national and international legislations and regulations in relation to isolation wards as well as case studies on developed nations were investigated in order to identify the problems in isolation wards in Korea and improvement plans. Third, construction and facility plans were compared and analyzed between general and isolation wards focusing on large general hospitals in Korea, thereby conducting comparison and analysis on the performance and effects of air-tightness of general and isolation wards through CFD simulations. The study results showed that isolation wards had better air-tightness performance than that of general wards.

Keywords: AII Room, air-borne infection, CFD, computational fluid dynamics

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Authors: Hanjun Zhao, Ke Zhang, Bojian Zheng

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Background: So far, there is no universal antiviral agent which can inhibit multiple viral infections. More and more drug-resistant viral strains emerge after the antiviral drug application for treatment. Defensins are the front line of host innate immunity and have broad spectrum antibacterial and antiviral effects. However, there is limited data to show if these defensins have good antiviral activity in vivo and what the antiviral mechanism is. Subjects: To investigate a peptide with widespread antivirus activity in vitro and in vivo and illustrate the antiviral mechanism. Methods: Antiviral peptide library designed from mouse beta defensins was synthesized by the company. Recombinant beta defensin was obtained from E. coli. Antiviral activity in vitro was assayed by plaque assay, qPCR. Antiviral activity in vivo was detected by animal challenge with 2009 pandemic H1N1 influenza A virus. The antiviral mechanism was assayed by western blot, ELISA, and qPCR. Conclusions: We identify a new peptide which has widespread effects against multiple viruses (H1N1, H5N1, H7N9, MERS-CoV) in vitro and has efficient antivirus activity in vivo. This peptide inhibits viral entry into target cells and subsequently blocks viral replication. The in vivo study of the antiviral peptide against other viral infections and the investigation of its more detail antiviral mechanism are ongoing.

Keywords: antiviral peptide, defensin, Influenza A virus, mechanism

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Authors: G. Tamilpavai, C. Vishnuppriya

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Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system.

Keywords: clustering, k-mers, longest common subsequence, SOM

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Authors: Manal Kamel, Sara Maher, Hanan El Baz, Faten Salah, Omar Sayyouh, Zeinab Demerdash

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In the recent COVID 19 pandemic, experts of public health have emphasized testing, tracking infected people, and tracing their contacts as an effective strategy to reduce the spread of the virus. Development of rapid and sensitive diagnostic assays to replace reverse transcription polymerase chain reaction (RT-PCR) is mandatory..Our innovative test strip relying on the application of nanoparticles conjugated to recombinant nanobodies for SARS-COV-2 spike protein (S1) & angiotensin-converting enzyme 2 (that is responsible for the virus entry into host cells) for rapid detection of SARS-COV-2 spike protein (S1) in saliva or sputum specimens. Comparative tests with RT-PCR will be held to estimate the significant effect of using COVID 19 nanobodies for the first time in the development of lateral flow test strip. The SARS-CoV-2 S1 (3 ng of recombinant proteins) was detected by our developed LFIA in saliva specimen of COVID-19 Patients No cross-reaction was detected with Middle East respiratory syndrome coronavirus (MERS-CoV) or SARS- CoV antigens..Our developed system revealed 96 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% sensitivity and specificity for nasopharyngeal swabs. providing a reliable alternative for the painful and uncomfortable nasopharyngeal swab process and the complexes, time consuming PCR test. An increase in testing compliances to be expected.

Keywords: COVID 19, diagnosis, LFIA, nanobodies, ACE2

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Authors: Abu Salim Mustafa

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Rv3873 is a relatively large size protein (371 amino acids in length) and its gene is located in the immunodominant genomic region of difference (RD)1 that is present in the genome of Mycobacterium tuberculosis but deleted from the genomes of all the vaccine strains of Bacillus Calmette Guerin (BCG) and most other mycobacteria. However, when tested for cellular immune responses using peripheral blood mononuclear cells from tuberculosis patients and BCG-vaccinated healthy subjects, this protein was found to be a major stimulator of cell mediated immune responses in both groups of subjects. In order to further identify the sequence of immunodominant epitopes and explore their Human Leukocyte Antigen (HLA)-restriction for epitope recognition, 24 peptides (25-mers overlapping with the neighboring peptides by 10 residues) covering the sequence of Rv3873 were synthesized chemically using fluorenylmethyloxycarbonyl chemistry and tested in cell mediated immune responses. The results of these experiments helped in the identification of an immunodominant peptide P9 that was recognized by people expressing varying HLA-DR types. Furthermore, it was also predicted to be a promiscuous binder with multiple epitopes for binding to HLA-DR, HLA-DP and HLA-DQ alleles of HLA-class II molecules that present antigens to T helper cells, and to HLA-class I molecules that present antigens to T cytotoxic cells. In addition, the evaluation of peptide P9 using an immunogenicity predictor server yielded a high score (0.94), which indicated a greater probability of this peptide to elicit a protective cellular immune response. In conclusion, P9, a peptide with multiple epitopes and ability to bind several HLA class I and class II molecules for presentation to cells of the cellular immune response, may be useful as a peptide-based vaccine against tuberculosis.

Keywords: mycobacterium tuberculosis, PPE68, peptides, vaccine

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Nermeen Elbeltagy, Helmy abd Elsatar, Sara Hassan, Mohamed Darwish

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Introduction: with the inial appearance in Wuhan, China, in December 2019, the coronavirus disease-related respiratory infection (COVID-19) has rapidly spread among people all over the world. The WHO considered it a pandemic in March 2020. The severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreaks have proved that pregnant females as well as their fetuses are exposed to adverse outcomes, including high rates of intensive care unit (ICU) admission and case fatality. Physiological changes occurring during pregnancy such as the increased transverse diameter of the thoracic cage as well as the elevation of the diaphragm can expose the mother to severe infections because of her decreased tolerance for hypoxia. Furthermore, vasodilation and changes in lung capacity can cause mucosal edema and an increase in upper respiratory tract secretions. In addition, the increased susceptibility to infection is enhanced by changes in cellmediated immunity. Aim of the work: to study the effect of COVID-19 on pregnant females admitted to El-Shatby Maternity University Hospital regarding maternal antepartum, intrapartum and postpartum adverse effects on the mothers and their neonates. Method: A retrospective cohort study was done between October 2020 and October 2022. Maternal characteristics and associated health conditions of COVID-19 positive parents were investigated. Also, the severity of their conditions and me of infection (first or second or third trimester)were explored. Cases were diagnosed based on presence of symptoms suggestive of COVID-19, laboratory tests (other than PCR) and radiological findings.all cases were confirmed by positive PCR test results. Results: The most common adverse maternal outcomes were pre-term labor (11.6%) followed by premature rupture of membranes (5.7%), post-partum hemorrhage (5.4%), preeclampsia (5.0%) and placental abrupon (4.3%). One sixth of the neonates of the studied paents were admied to NICUs and 6.5% of them had respiratory distress with no neonatal deaths. The majority of neonates (85.4%) had a birth weight of 2500- 4000g (normal range). Most of the neonates (77.9%) had an APGAR score of equal or more than 7 in 5 minutes. Conclusion: the most common comorbidity that might increase the incidence of COVID-19 before pregnancy were diabetes, cardiac disorders/ chronic hypertension and chronic obstructive lung diseases (non-asthma). During pregnancy, anemia followed by gestational diabetes and pre-eclampsia/gestational hypertension were the most prevalent comorbidity. So, severity of infection can be reduced by good antenatal care.

Keywords: COVID-19, pregnancy outcome, complicated pregnancy., COVID in Egypt

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Authors: Mohanad Elemary, Imose Itua, Rajeswari B. Matam

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Virologists and public health experts have been predicting potential pandemics from coronaviruses for decades. The viruses which caused the SARS and MERS pandemics and the Nipah virus led to many lost lives, but still, the COVID-19 pandemic caused by the SARS-CoV2 virus surprised many scientific communities, experts, and governments with its ease of transmission and its pathogenicity. Governments of various countries reacted by locking down entire populations to their homes to combat the devastation caused by the virus, which led to a loss of livelihood and economic hardship to many individuals and organizations. To revive national economies and support their citizens in resuming their lives, governments focused on the development and use of contact tracing apps as a digital way to track and trace exposure. Google and Apple introduced the Exposure Notification Systems (ENS) framework. Independent organizations and countries also developed different frameworks for contact tracing apps. The efficiency, popularity, and adoption rate of these various apps have been different across countries. In this paper, we present a critical analysis of the different contact tracing apps with respect to their efficiency, adoption rate and general perception, and the governmental strategies and policies, which led to the development of the applications. When it comes to the European countries, each of them followed an individualistic approach to the same problem resulting in different realizations of a similarly functioning application with differing results of use and acceptance. The study conducted an extensive review of existing literature, policies, and reports across multiple disciplines, from which a framework was developed and then validated through interviews with six key stakeholders in the field, including founders and executives in digital health startups and corporates as well as experts from international organizations like The World Health Organization. A framework of best practices and tactics is the result of this research. The framework looks at three main questions regarding the contact tracing apps; how to develop them, how to deploy them, and how to regulate them. The findings are based on the best practices applied by governments across multiple countries, the mistakes they made, and the best practices applied in similar situations in the business world. The findings include multiple strategies when it comes to the development milestone regarding establishing frameworks for cooperation with the private sector and how to design the features and user experience of the app for a transparent, effective, and rapidly adaptable app. For the deployment section, several tactics were discussed regarding communication messages, marketing campaigns, persuasive psychology, and the initial deployment scale strategies. The paper also discusses the data privacy dilemma and how to build for a more sustainable system of health-related data processing and utilization. This is done through principles-based regulations specific for health data to allow for its avail for the public good. This framework offers insights into strategies and tactics that could be implemented as protocols for future public health crises and emergencies whether global or regional.

Keywords: contact tracing apps, COVID-19, digital health applications, exposure notification system

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