This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Background: It remains essential for patient safety to develop non-invasive diagnostic tools to diagnose non-alcoholic fatty liver rather than invasive techniques. Aim: Our case-control study was to address the value of circulating miRNAs as a potential non-invasive biomarker for the diagnosis of non-alcoholic fatty acid diseases (NAFLD) and monitoring of disease progression. Methods: Routine clinical assessment, laboratory tests, anthropometric study, and liver biopsy results reported for 210 patients with NAFLD (124 patients of simple steatosis (SS) and 86 of non-alcoholic steatohepatitis (NASH)). Apparently matched for age and gender, healthy participants (n= 90) were enrolled as a control group. Serum samples were tested for micro-RNAs (−122, −34a and −99a) by quantitative-PCR. Results: By histopathology, 124 of the NAFLD group were of SS and 86 patients were of NASH. Compared with the control subjects, both mi-RNA-122 and −34a levels were increased in NAFLD (p< 001) and at a cut-off = 1.261, mi-RNA-122 had 92% sensitivity, 85% specificity to differentiate NAFLD from healthy controls, while mi-RNA-99a were significantly decreased in NAFLD patients with an observed decrease in disease severity, and at a cut-off = 0.46, miRNA-99a had 94% sensitivity and 96% specificity to discriminate SS from NASH. Conclusion: The integration of a circulating mi-RNA panel to diagnose NAFLD cases and to discriminate between SS and NASH. Large-scale study is still needed to verify the other mi-RNA profiles and their role in NAFLD pathogenesis and targeting therapy.

Hepatitis C virus (HCV)-specific cell-mediated immunity (CMI) has been reported among exposed individuals without viremia or seroconversion. Limited data are available regarding CMI among at-risk, seronegative, aviremic Egyptian health care workers (HCW), where HCV genotype 4 predominates. We investigated CMI responses among HCW at the National Liver Institute, where over 85% of the patients are HCV infected. We quantified HCV-specific CMI in 52 seronegative aviremic Egyptian HCW using a gamma interferon (IFN-γ) enzyme-linked immunospot assay in response to 7 HCV genotype 4a overlapping 15-mer peptide pools covering most of the viral genome. A positive HCV-specific IFN-γ response was detected in 29 of 52 HCW (55.8%), where 21 (40.4%) had a positive response for two to seven HCV pools and 8 (15.4%) responded to only one pool. The average numbers of IFN-γ total spot-forming cells (SFC) per million peripheral blood mononuclear cells (PBMC) (± standard error of the mean [SEM]) in the 29 responding and 23 nonresponding HCW were 842 ± 141 and 64 ± 15, respectively (P< 0.001). Flow cytometry indicated that both CD4⁺and CD4⁻T cells produced IFN-γ. In summary, more than half of Egyptian HCW demonstrated strong HCV multispecific CMI without viremia or seroconversion, suggesting possible clearance of low HCV exposure(s). These data suggest that detecting anti-HCV and viremia to determine past exposure to HCV can lead to an underestimation of the true disease exposure and that CMI response may contribute to the low degree of chronic HCV infection in these HCW. These findings could have strong implications for planning vaccine studies among populations with a high HCV exposure rate. Further studies are needed to determine whether these responses are protective.

Hepatitis C Virus (HCV) infection is a global health burden particularly in Egypt, where HCV genotype 4a (GT-4a) predominates. The prevention and control of HCV infection will remain a challenge until the development of an effective vaccine that protects against different genotypes. Several HCV GT-1-based vaccines are in different stages of clinical trials, but antigenic differences could make protection against other genotypes problematic. In this regard, data comparing the cell-mediated immune (CMI) response to different HCV genotypes are limited. We aimed to ex vivo investigate whether GT-1-based vaccine may protect against HCV GT-4 infections. This was carried out on samples collected from genotype 4 infected/exposed subjects. The CMI responses of 35 subjects; infected with HCV GT-4/or who had spontaneously-resolved the infection and 10 healthy control subjects; to two sets of seven HCV overlapping 15-mer peptide pools derived from both genotypes; and covering most of the viral proteins; were evaluated. This was carried out using an interferon gamma (IFNγ) enzyme-linked immunospot (ELISpot) assay. Peripheral blood mononuclear cells (PBMC) from 17 subjects (48%) responded to at least one peptide pool derived from GT-1b/GT-4a with 13 subjects responding to peptide pools from both genotypes. A strong correlation was found in the responses to both genotypes (r = 0.82, p<0.001; 95% confidence interval = 0.562–0.933). The average IFNγ total spot forming cells (SFC)/106 PBMC (±SE) from the responding subjects for GT-1b and GT-4a was 216±56 and 199±55, respectively (p = 0.833). Also, there were no significant differences between those who cleared their HCV infection or who remained HCV-RNA positive (p = 0.8). Our data suggest that an effective GT-1b vaccine could protect from GT-4a infection. These data could help in HCV rationale vaccine design and efficacy studies and further our understanding of HCV cross protection against different genotypes.