Insights into Gastrointestinal Virome: Etiology and Public Exposure

Recycled wastewater is widely used owing to the potential shortage of water resources for drinking purposes, recreational activities, and irrigation. However, gut microbiomes of both human beings and animals negatively affect this water quality. Wastewater contamination is continuously monitored, using fecal contamination indicators or microbial source tracking approaches, to oppose arising enteric infections. Viral gastroenteritis is considered a principal manifestation of waterborne pathogenic virome-mediated infections, which are mainly transmitted via the fecal-oral route. Furthermore, acquired enteric viromes are the common cause of infantile acute diarrhea. Moreover, public exposure to wastewater via wastewater discharge or treated wastewater reuse has led to a significant surge of public health concerns. In this review, we discussed the etiology of waterborne enteric viromes, notably gastrointestinal virus infections, and public exposure to municipal wastewater. Conclusively, the early human virome is affected mainly by birth mode, dietary behavior, and maternal health, and could provide a signature of disease incidence, however, more virome diversification is acquired in adulthood. A multi-phase treatment approach offered an effective means for the elimination of wastewater reuse mediated public risks. The insights highlighted in this paper offer essential information for defining probable etiologies and assessing risks related to exposure to discharged or reused wastewater.

Automated image classification of chest X-rays of COVID-19 using deep transfer learning.

INTRODUCTION: In December 2019, the city of Wuhan, located in the Hubei province of China became the epicentre of an outbreak of a pandemic called COVID-19 by the World Health Organisation. The detection of this virus by rRTPCR (Real-Time Reverse Transcription-Polymerase Chain Reaction) tests reported high false negative rate. The manifestations of CXR (Chest X-Ray) images contained salient features of the virus. The objective of this paper is to establish the application of an early automated screening model that uses low computational power coupled with raw radiology images to assist the physicians and radiologists in the early detection and isolation of potential positive COVID-19 patients, to stop the rapid spread of the virus in vulnerable countries with limited hospital capacities and low doctor to patient ratio in order to prevent the escalating death rates. MATERIALS AND METHODS: Our database consists of 447 and 447 CXR images of COVID-19 and Nofindings respectively, a total of 894 CXR images. They were then divided into 4 parts namely training, validation, testing and local/Aligarh dataset. The 4th (local/Aligarh) folder of the dataset was created to retest the diagnostics efficacy of our model on a developing nation such as India (Images from J.N.M.C., Aligarh, Uttar Pradesh, India). We used an Artificial Intelligence technique called CNN (Convolutional Neural Network). The architecture based on CNN used was MobileNet. MobileNet makes it faster than the ordinary convolutional model, while substantially decreasing the computational cost. RESULTS: The experimental results of our model show an accuracy of 96.33%. The F1-score is 93% and 96% for the 1st testing and 2nd testing (local/Aligarh) datasets (Tables 3.3 and 3.4). The false negative (FN) value, for the validation dataset is 6 (Fig. 3.6), for the testing dataset is 0 (Fig. 3.7) and that for the local/Aligarh dataset is 2 . The recall/sensitivity of the classifier is 93% and 96% for the 1st testing and 2nd testing (local/Aligarh) datasets (Tables 3.3 and 3.4). The recall/sensitivity for the detection of specifically COVID-19 (+) for the testing dataset is 88% and for the locally acquired dataset from India is 100%. The False Negative Rate (FNR) is 12% for the testing dataset and 0% for the locally acquired dataset (local/Aligarh). The execution time for the model to predict the input images and classify them is less than 0.1 s. DISCUSSION AND CONCLUSION: The false negative rate is much lower than the standard rRT-PCR tests and even 0% on the locally acquired dataset. This suggests that the established model with end-to-end structure and deep learning technique can be employed to assist radiologists in validating their initial screenings of Chest X-Ray images of COVID-19 in developed and developing nations. Further research is needed to test the model to make it more robust, employ it on multiclass classification and also try sensitise it to identify new strains of COVID-19. This model might help cultivate tele-radiology.

A novel mathematical model for COVID-19 with remedial strategies.

Coronavirus (COVID-19) outbreak from Wuhan, Hubei province in China and spread out all over the World. In this work, a new mathematical model is proposed. The model consists the system of ODEs. The developed model describes the transmission pathways by employing non constant transmission rates with respect to the conditions of environment and epidemiology. There are many mathematical models purposed by many scientists. In this model, " α E " and " α I ", transmission coefficients of the exposed cases to susceptible and infectious cases to susceptible respectively, are included. " δ " as a governmental action and restriction against the spread of coronavirus is also introduced. The RK method of order four (RK4) is employed to solve the model equations. The results are presented for four countries i.e., Pakistan, Italy, Japan, and Spain etc. The parametric study is also performed to validate the proposed model.

Molecular adaptive evolution of SARS-COV-2 spike protein in Saudi Arabia.

The sequences of SARS-CoV-2 spike (S) from Saudi Arabia along with SARS-CoV and bat SARS-like CoVs were obtained. Positive selection analysis and secondary structure investigation of spike sequences were performed. Adaptive molecular evolution was observed in SARS-CoV-2 displayed by positive selection pressure at N-terminal domain (NTD; codons 41, 163, 174 and 218), Receptor binding domain (RBD; codons 378 and 404) and S1/S2 Cleavage site (codon 690). Furthermore, the spike protein secondary structure depicted by the homo-trimer structure showed a high similarity between Saudi SARS-CoV-2 isolate and the parental strain (bat SL-COVZC45). Despite the high similarity depicted in the spike sequence model alignment, it displayed a significant difference when each chain was treated solely owing to 7 motif differences in the three composing chains. In addition, SARS-CoV-2 S trimer model uncovered the presence of N-acetyl glucosamine ligands. Eventually, 3C-like proteinase cleavage site was observed in S2 domain could be used as a site for drug discovery. Genetics and molecular evolutionary facts are useful for assessment of evolution, host adaptation and epidemic patterns ultimately helpful for adaptation of control strategies.