@article {47134,
	title = {Probiotics in the prophylaxis of COVID-19: something is better than nothing},
	journal = {3 Biotech},
	volume = {11},
	year = {2021},
	month = {JAN},
	type = {Review},
	abstract = {<p>The new viral pandemic of COVID-19 is caused by a novel coronavirus (SARS-CoV-2) that has brought the world at another unprecedented crisis in terms of health and economy. The lack of specific therapeutics necessitates other strategies to prevent the spread of infection caused by this previously unknown viral etiological agent. Recent pieces of evidence have shown an association between COVID-19 disease and intestinal dysbiosis. Probiotics comprise living microbes that upon oral administration benefit human health by reshaping the composition of gut microbiota. The close kinship of the gastrointestinal and respiratory tract suggests why the dysfunction of one may incite illness in others. The emerging studies suggest the capability of probiotics to regulate immune responses in the respiratory system. The efficacy of probiotics has been studied previously on several respiratory tract viral infections. Therefore, the purpose of this review is to comprehend existing information on the gut mediated-pulmonary immunity conferred by probiotic bacteria, in the course of respiratory virus infections and administration as a prophylactic measure in COVID-19 pandemic in managing intestinal dysbiosis as well.</p>
},
	keywords = {Anti-viral, COVID-19, Gut-lung axis, Probiotics, Respiratory tract infection},
	issn = {2190-572X},
	doi = {10.1007/s13205-020-02554-1},
	author = {Gohil, Kushal and Samson, Rachel and Dastager, Syed and Dharne, Mahesh}
}
@article {47624,
	title = {COVID-19 associated mucormycosis: evolving technologies for early and rapid diagnosis},
	journal = {3 Biotech},
	volume = {12},
	year = {2022},
	month = {JAN},
	pages = {6},
	type = {Review},
	abstract = {The post-coronavirus disease (COVID-19) mucormycosis is a deadly addition to the pandemic spectrum. Although it{\textquoteright}s a rare, aggressive, and opportunistic disease, the associated morbidity and mortality are significant. The complex interplay of factors aggravating CAM is uncontrolled diabetes, irrational and excessive use of antibiotics, steroids, and an impaired immune system. Recently, India has been witnessing a rapid surge in the cases of coronavirus disease-associated mucormycosis (CAM), since the second wave of COVID-19. The devastating and lethal implications of CAM had now become a matter of global attention. A delayed diagnosis is often associated with a poor prognosis. Therefore, the rapid and early diagnosis of infection would be life-saving. Prevention and effective management of mucormycosis depend upon its early and accurate diagnosis followed by a multimodal therapeutic approach. The current review summarizes an array of detection methods and highlights certain evolving technologies for early and rapid diagnosis of CAM. Furthermore, several potential management strategies have also been discussed, which would aid in tackling the neglected yet fatal crisis of mucormycosis associated with COVID-19.},
	keywords = {Biosensors, COVID-19, Diagnostics, Mucormycosis, Probiotics},
	issn = {2190-572X},
	doi = {10.1007/s13205-021-03080-4},
	author = {Samson, Rachel and Dharne, Mahesh}
}
@article {48236,
	title = {Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys},
	journal = {Computers in Biology and Medicine},
	volume = {146},
	year = {2022},
	month = {JUL},
	pages = {105419},
	type = {Article},
	abstract = {<p>Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8\% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6\%-80.2\%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.</p>
},
	keywords = {BBV152, Covaxin, COVID-19, Ensemble methods, Infection, machine learning, SARS-CoV-2},
	issn = {0010-4825},
	doi = {10.1016/j.compbiomed.2022.105419},
	author = {Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, V. Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis}
}
