@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}
}
@article {49913,
	title = {Interplay between genotoxic stress and STING activation in cellular senescence and inflammatory responses},
	journal = {International Immunopharmacology},
	volume = {164},
	year = {2025},
	month = {OCT},
	pages = {115371},
	type = {Article},
	abstract = {<p>
	STING pathway is activated by endogenous or exogenous DNA damage and is known to trigger cell-intrinsic innate immunity. In this study, we demonstrated that the Peharmaline analog NDS101781 is a potent genotoxic molecule to trigger cellular senescence via innate immune-responsive STING activation. We found NDS101781 consistently modulated the expression of DDR markers including gamma-H2AX, Rad51, PARP1, ATM and MRE11 in breast cancer cells with concomitant amplification in the hallmarks of senescence along with STING signaling mediators which is intricately involved in NDS101781-mediated senescence activation as evidenced by significant reduction in the senescent population in si-TMEM173-transfected cells. In vitro findings proclaimed that STING activation by NDS101781 is crucial for p21-mediated senescence augmentation, a process regulated by ATM and p53 via a pathway independent of cGAS. Although STING is activated by both canonical and non-canonical manner, our mechanistic findings indicated that ATM played a crucial role in early activation of NDS101781 driven STING signaling via p53 activation and stimulation of pTBK1, NF-kappa B, and p-IRF3, through a non-canonical cascade in cGAS-independent mechanism. The results also indicated that interference of canonical and non-canonical STING activation, responsible for NF-kappa B stimulation leading to IL-6 generation. Intriguingly, the inhibition of ATM diminished senescence hallmarks; however, suppression of ATM as well as p21 neutralization triggered apoptotic cascade and thus regulating the SASP factors. However, transient knockdown of p21 moderately instigated the apoptotic mediators underscoring that NDS101781 mediated senescence induction delayed programmed cell death under intact p21 conditions. Moreover, pharmacokinetics of NDS101781 confirmed its excellent half-life in a preclinical model and in vivo studies confirmed that NDS101781 significantly inhibited tumor growth in a syngeneic aggressive 4T1-p53 breast cancer model.</p>
},
	keywords = {ATM, IL-6, Peharmaline, Senescence, STING},
	issn = {1567-5769},
	doi = {10.1016/j.intimp.2025.115371},
	author = {Dash, Anshurekha and Kulkarni, Akshay S. and Irshad, Faisal and Masal, Dattatraya P. and Manhas, Diksha and Nandi, Utpal and Reddy, D. Srinivasa and Goswami, Anindya}
}
