Mahmoud ElHefnawi

Prof. Mahmoud ElHefnawi

orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 October 2022 in Science
Verified
Journal Article
Closed access
HTHouriiyah Tegally
Houriiyah Tegally
JSJames Emmanuel San
James Emmanuel San
MCMatthew Cotten
Matthew Cotten
MMMonika Moir
Monika Moir
BTBryan Tegomoh
Bryan Tegomoh
EWEduan Wilkinson
Eduan Wilkinson
Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern—particularly Alpha, Beta, Delta, and Omicron—on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

22 October 2021 in Science
Verified
Journal Article
Open access
EWEduan Wilkinson
Eduan Wilkinson
Marta GiovanettiMarta Giovanetti
Marta Giovanetti
HTHouriiyah Tegally
Houriiyah Tegally
JSJames Emmanuel San
James Emmanuel San
RLRichard Lessells
Richard Lessells
TOTulio De Oliveira
Tulio De Oliveira
SARS-CoV-2 across Africa: The impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been hard to track in African countries, largely because of patchy data. Wilkinson et al . curated viral genomes collected in 2021 from several countries across the continent. Outbreaks during 2020 in each African country were initiated by imported cases, mostly from Europe. As the pandemic developed, case numbers in African countries were likely many times higher than reported, and subsequent waves of the pandemic appear to have been more severe. Consequently, high-transmission variants have emerged that have spread within the continent, and African countries must be included in global control efforts. —CA

4 April 2017 in IEEE/ACM Transactions on Computational Biology and Bioinformatics
Verified
Journal Article
Closed access
SHSomaya Hashem
Somaya Hashem
GEGamal Esmat
Gamal Esmat
WEWafaa Elakel
Wafaa Elakel
SHShahira Habashy
Shahira Habashy
SRSafaa Abdel Raouf
Safaa Abdel Raouf
MEMahmoud ElHefnawi
Mahmoud ElHefnawi
Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets – one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multilinear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3% and 84.4%. Conclusions: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.

16 December 2015 in Bioinformatics
Verified
Journal Article
Closed access
PDF file
FEFatma-Elzahraa Eid
Fatma-Elzahraa Eid
MEMahmoud ElHefnawi
Mahmoud ElHefnawi
LHLenwood S. Heath
Lenwood S. Heath
Motivation: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins, and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization.

1 November 2020 in Computer Methods and Programs in Biomedicine
Verified
Journal Article
Closed access
SHSomaya Hashem
Somaya Hashem
MEMahmoud ElHefnawi
Mahmoud ElHefnawi
SHShahira Habashy
Shahira Habashy
MEMohamed El-Adawy
Mohamed El-Adawy
GEGamal Esmat
Gamal Esmat
HSHend Ibrahim Shousha
Hend Ibrahim Shousha

9 September 2021 in Heliyon
Verified
Journal Article
Open access
AMAmal Mahmoud
Amal Mahmoud
Ahmed MostafaAhmed Mostafa
Ahmed Mostafa
AAAhmed A. Al-Karmalawy
Ahmed A. Al-Karmalawy
AZAhmad Zidan
Ahmad Zidan
HAHamada S. Abulkhair
Hamada S. Abulkhair
MAMohamed A. Ali
Mohamed A. Ali
Drug repurposing is an important approach to the assignment of already approved drugs for new indications. This technique bypasses some steps in the traditional drug approval system, which saves time and lives in the case of pandemics. Direct acting antivirals (DAAs) have repeatedly repurposed from treating one virus to another. In this study, 16 FDA-approved hepatitis C virus (HCV) DAA drugs were studied to explore their activities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) human and viral targets. Among the 16 HCV DAA drugs, telaprevir has shown the best in silico evidence to work on both indirect human targets (cathepsin L [CTSL] and human angiotensin-converting enzyme 2 [hACE2] receptor) and direct viral targets (main protease [Mpro]). Moreover, the docked poses of telaprevir inside both hACE2 and Mpro were subjected to additional molecular dynamics simulations monitored by calculating the binding free energy using MM-GBSA. In vitro analysis of telaprevir showed inhibition of SARS-CoV-2 replication in cell culture (IC50 = 11.552 μM, CC50 = 60.865 μM, and selectivity index = 5.27). Accordingly, based on the in silico studies and supported by the presented in vitro analysis, we suggest that telaprevir may be considered for therapeutic development against SARS-CoV-2.