@article { ISI:000326207600024, title = {Mechanism of action of novel synthetic dodecapeptides against Candida albicans}, journal = {Biochimica Et Biophysica Acta-General Subjects}, volume = {1830}, number = {11}, year = {2013}, month = {NOV}, pages = {5193-5203}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

Background: Three de novo designed low molecular weight cationic peptides (IJ2, IJ3 and IJ4) containing an unnatural amino acid alpha,beta-didehydrophenylalanine (APhe) exhibited potent antifungal activity against fluconazole (FLC) sensitive and resistant clinical isolates of Candida albicans as well as non-albi cans and other yeast and filamentous pathogenic fungi. In the present study, their synthesis, susceptibility of different fungi and the mechanism of anti-candidal action have been elucidated. Methods: The antimicrobial peptides (AMPs) were synthesized by solid-phase method and checked for antifungal activity against different yeasts and fungi by broth microdilution method. Anti-candidal mode of action of the peptides was investigated through detecting membrane permeabilization by confocal microscopy, Reactive Oxygen Species (ROS) generation by fluorometry, apoptosis and necrosis by flow cytometry and cell wall damage using Scanning and Transmission Electron Microscopy. Results and conclusions: The MIC of the peptides against C. albicans and other yeast and filamentous fungal pathogens ranged between 3.91 and 250 mu M. All three peptides exhibited effect on multiple targets in C. albi cans including disruption of cell wall structures, compromised cell membrane permeability leading to their enhanced entry into the cells, accumulation of ROS and induction of apoptosis. The peptides also showed synergistic effect when used in combination with fluconazole (FLC) and caspofungin (CAS) against C. albi cans. General significance: The study suggests that the AMPs alone or in combination with conventional antifungals hold promise for the control of fungal pathogens, and need to be further explored for treatment of fungal infections. (C) 2013 Elsevier By. All rights reserved.

}, keywords = {Antifungal peptide, Antimicrobial peptide (AMP), Apoptosis, Candida albicans, Reactive Oxygen Species (ROS)}, issn = {0304-4165}, doi = {10.1016/j.bbagen.2013.07.016}, author = {Maurya, Indresh Kumar and Thota, Chaitanya Kumar and Sharma, Jyotsna and Tupe, Santosh Genba and Chaudhary, Preeti and Singh, Manoj Kumar and Thakur, Indu Shekhar and Deshpande, Mukund V. and Prasad, Rajendra and Chauhan, Virander Singh} } @article {47024, title = {Magnetic and luminescent multifunctional nanohybrid: Fe3O4@CaF2:Tb3+: a facile synthesis and characterization}, journal = {Indian Journal of Pure \& Applied Physics}, volume = {58}, year = {2020}, month = {JAN}, pages = {31-35}, type = {Article}, abstract = {

In the present study, bi-functional hybrid nanomaterial has been synthesized and characterized via facile method and characterized. The synthesized nanomaterial shows both magnetic and luminescent properties which are confirmed by VSM (vibrating sample magnetometer), UV-Vis spectra and the photoluminescence emission spectra. For the magnetic phase, i.e., nanocrystalline magnetite, Fe3O4 is used as the core which is then functionalized using polyethylene glycol (PEG) and for the luminescent phase, polyethylenimine (PEI) functionalized CaF2 doped with Tb3+ is used as the emitter. Wherein, both PEG and PEI serve the dual purpose of functionalization as well as stabilization by steric repulsion. The structure and morphology of the synthesized bifunctional hybrid nanomaterial are studied with the help of scanning electron microscopy (SEM) and X-ray powder diffraction.

}, keywords = {Biocompatible, CaF2, Co-precipitation, Fe3O4, Luminescent nanoparticle, Magnetic nanoparticles, Nanohybrid, Tb3+ doping}, issn = {0019-5596}, author = {Vats, Monika and Kumar, Rakesh and Sharma, Jyotsna and Luwang, Meitram Niraj} } @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 = {

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.

}, 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} }