@article { ISI:000258981000008, title = {Synthesis and characterization of novel polybenzimidazoles bearing pendant phenoxyamine groups}, journal = {Journal of Polymer Science Part A-Polymer Chemistry}, volume = {46}, number = {17}, year = {2008}, month = {SEP}, pages = {5776-5793}, publisher = {JOHN WILEY \& SONS INC}, type = {Article}, address = {111 RIVER ST, HOBOKEN, NJ 07030 USA}, abstract = {

A novel aromatic diacid, 3, 5-dicarboxyl-4{\textquoteright}-amino diphenyl ether, containing pendant phenoxy amine group was synthesized. Homo- and co-polybenzimidazoles containing different content of pendant phenoxyamine groups were synthesized by condensation of 3,3{\textquoteright}-diaminobenzidine with this acid and a mixture of this acid and isophthalic acid in different ratio in polyphosphoric acid. Copolybenzimidazoles with structural variations were also synthesized based on this acid and pyridine dicarboxylic acid, terephthalic acid, adipic acid, or sebacic acid. The polymers have good solubility in polar aprotic solvents and strong acids and they form tough flexible films by solution casting. The polymers were characterized by different instrumental techniques (FTIR, TGA, DSC, XRD, etc.) and for solvent solubility, mechanical properties, inherent viscosity, and proton conductivity. The inherent viscosities of the polymers vary in the range of 0.62-1.52 dL/g. They have high thermal stability up to 475506 degrees C (IDT) in nitrogen, high glass transition temperatures (T-g) ranging from 313 to 435 degrees C and good tensile strength ranging from 58 to 125 MPa. Proton conductivity of homo polymer is 3.72 x 10(-3) S/cm at 25 degrees C and 2.45 x 10(-2) S/cm at 200 degrees C (c) 2008 Wiley Periodicals, Inc.

}, keywords = {Characterization, copolybenzimidazoles, High performance polymers, high temperature materials, pendant phenoxyamine group, polybenzimidazoles, synthesis}, issn = {0887-624X}, doi = {10.1002/pola.22892}, author = {Kulkarni, Mahesh and Potrekar, Ravindra and Kulkarni, R. A. and Vernekar, S. P.} } @article { ISI:000279958200023, title = {Nitrophenoxy groups containing polybenzimidazoles as polymer electrolytes for fuel cells: synthesis and characterization}, journal = {Journal of Applied Polymer Science}, volume = {117}, number = {6}, year = {2010}, month = {SEP}, pages = {3282-3292}, publisher = {JOHN WILEY \& SONS INC}, address = {111 RIVER ST, HOBOKEN, NJ 07030 USA}, abstract = {

Polybenzimidazoles containing different contents of pendant nitrophenoxy groups were prepared by condensation of 3,3{\textquoteright}-diamino-benzidine with a mixture of 3,5-dicarboxyl-4{\textquoteright}-nitro diphenyl ether and isophthalic acid (IPA) in different ratios in polyphosphoric acid. The polymers are soluble in polar aprotic solvents, they have inherent viscosities in the range of 0.75-1.10 dL g(-1) and they form tough and transparent films on solution casting. They have good thermal stability with initial decomposition temperature ranging from 380 to 416 degrees C in nitrogen, good tensile strength ranging from 56 to 65 MPa and reasonably good oxidative stability. Phosphoric acid uptake of these polymers is low compared with PBI and membranes doped with phosphoric acid exhibit good proton conductivity in the range of 6.6x 10(-3) to 1.9x 10(-2) S/cm at 25 degrees C and 1.2x 10(-2) to 4.9x 10(-2) S/cm at 175 degrees C, compared with 3.9x 10(-3) S/cm at 25 degrees C and 3.2x 10(-2) S/cm at 175 degrees C for PBI. These membranes are suitable for applications as polymer electrolyte for fuel cell and presumably for gas separation at high temperature. (C) 2010 Wiley Periodicals, Inc. J Appl Polym Sci 117: 3282-3292, 2010

}, keywords = {Characterization, membranes, pendant nitrophenoxy group, polybenzimidazoles, proton conductivity, synthesis}, issn = {0021-8995}, doi = {10.1002/app.30279}, author = {Kulkarni, Mahesh and Potrekar, Ravindra and Kulkarni, R. A. and Vernekar, S. P.} } @article {47390, title = {Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2}, journal = {eLife}, volume = {10}, year = {2021}, month = {APR }, pages = {e66537}, type = {Article}, abstract = {

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14\%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95\% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10\% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95\% confidence interval, p value: 2.23, 1.92-2.59, \<0.0001), use of public transport (1.79, 1.43-2.24, \<0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, \<0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

}, issn = {2050-084X}, doi = {10.7554/eLife.66537}, author = {Naushin, Salwa and Sardana, Viren and Ujjainiya, Rajat and Bhatheja, Nitin and Kutum, Rintu and Bhaskar, Akash Kumar and Pradhan, Shalini and Prakash, Satyartha and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Chandak, Giriraj Ratan and Lahiri, Amit and Kar, Susanta and Mulay, Shrikant Ramesh and Mugale, Madhav Nilakanth and Srivastava, Mrigank and Khan, Shaziya and Srivastava, Anjali and Tomar, Bhawana and Veerapandian, Murugan and Venkatachalam, Ganesh and Vijayakumar, Selvamani Raja and Agarwal, Ajay and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Sundaram, Gopinath M. and Veeranna, Ravindra P. and Pal, Anirban and Agarwal, Vinay Kumar and Maurya, Anil Ku and Singh, Ranvijay Kumar and Raman, Ashok Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Venkatesan, Subramanian and Sardana, Harish Kumar and Kothari, Anamika and Jain, Rishabh and Thakur, Anupama and Parihar, Devendra Singh and Saifi, Anas and Kaur, Jasleen and Kumar, Virendra and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Singh, Praveen and Chakraborty, Rahul and Chaturvedi, Gaura and Karunakar, Pinreddy and Yadav, Rohit and Singhmar, Sunanda and Singh, Dayanidhi and Sarkar, Sharmistha and Bhattacharya, Purbasha and Acharya, Sundaram and Singh, Vandana and Verma, Shweta and Soni, Drishti and Seth, Surabhi and Vashisht, Sakshi and Thakran, Sarita and Fatima, Firdaus and Singh, Akash Pratap and Sharma, Akanksha and Sharma, Babita and Subramanian, Manikandan and Padwad, Yogendra S. and Hallan, Vipin and Patial, Vikram and Singh, Damanpreet and Tripude, Narendra Vijay and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sarkar, Jit and Ramakrishna, Sistla and Kumar, Balthu Narender and Kumar, Kiran A. and Gandhi, Sumit G. and Jamwal, Piyush Singh and Chouhan, Rekha and Jamwal, Vijay Lakshmi and Kapoor, Nitika and Ghosh, Debashish and Thakkar, Ghanshyam and Subudhi, Umakanta and Sen, Pradip and Chaudhury, Saumya Ray and Kumar, Rashmi and Gupta, Pawan and Tuli, Amit and Sharma, Deepak and Ringe, Rajesh P. and Amarnarayan, D. and Kulkarni, Mahesh and Shanmugam, Dhansekaran and Dharne, Mahesh S. and Dastager, Sayed G. and Joshi, Rakesh and Patil, Amita P. and Mahajan, Sachin N. and Khan, Abujunaid Habib and Wagh, Vasudev and Yadav, Rakesh Kumar and Khilari, Ajinkya and Bhadange, Mayuri and Chaurasiya, Arvindkumar H. and Kulsange, Shabda E. and Khairnar, Krishna and Paranjape, Shilpa and Kalita, Jatin and Sastry, Narahari G. and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, Ravi Kumar and Babu, Elapavalooru Vssk and Sukumaran, Rajeev and Nair, Aiswarya R. and Valappil, Prajeesh Kooloth and Puthiyamadam, Anoop and Velayudhanpillai, Adarsh and Chodankar, Kalpana and Damare, Samir and Madhavi, Yennapu and Aggarwal, Ved Varun and Dahiya, Sumit and Agrawal, Anurag and Dash, Debasis and Sengupta, Shantanu} } @article {47710, title = {Plasma-derived extracellular vesicles reveal galectin-3 binding protein as potential biomarker for early detection of glioma}, journal = {Frontiers in Oncology}, volume = {11}, year = {2021}, month = {NOV 26}, pages = {778754}, type = {Article}, abstract = {{Gliomas are the most common type of the malignant brain tumor, which arise from glial cells. They make up about 40\% of all primary brain tumors and around 70\% of all primary malignant brain tumors. They can occur anywhere in the central nervous system (CNS) and have a poor prognosis. The average survival of glioma patients is approximately 6-15 months with poor aspects of life. In this edge, identification of proteins secreted by cancer cells is of special interest because it may provide a better understanding of tumor progression and provide early diagnosis of the diseases. Extracellular vesicles (EVs) were isolated from pooled plasma of healthy controls (n=03) and patients with different grades of glioma (Grade I or II or III}, keywords = {blood-based biomarker, early detection, galectin-3 binding protein, plasma-derived extracellular vesicles, proteomics}, issn = {2234-943X}, doi = {10.3389/fonc.2021.778754}, author = {Rana, Rashmi and Chauhan, Kirti and Gautam, Poonam and Kulkarni, Mahesh and Banarjee, Reema and Chugh, Parul and Chhabra, Satnam Singh and Acharya, Rajesh and Kalra, Samir Kumar and Gupta, Anshul and Jain, Sunila and Ganguly, Nirmal Kumar} } @article {48419, title = {Behavioral and proteomic studies reveal methylglyoxal activate pathways associated with alzheimer{\textquoteright}s disease}, journal = {ACS Pharmacology \& Translational Science}, volume = {6}, year = {2022}, month = {DEC}, pages = {65{\textendash}75}, type = {Article}, abstract = {

Diabetes is one of the major risk factors for Alzheimer{\textquoteright}s disease (AD) development. The role of elevated levels of glucose, methylglyoxal (MGO), and advanced glycation end products (AGEs) in the pathogenesis of AD is not well understood. In this pursuit, we studied the role of methylglyoxal in the pathogenesis of AD in rat models. The elevated plus-maze (EPM) behavioral study indicated that MGO induces anxiety. Treatment of telmisartan (RAGE expression inhibitor) and aminoguanidine (MGO quencher) attenuated MGO induced anxiety. Further, hippocampal proteomics demonstrated that MGO treated rats differentially regulate proteins involved in calcium homeostasis, mitochondrial functioning, and apoptosis, which may affect neurotransmission and neuronal plasticity. The hippocampal tau phosphorylation level was increased in MGO treated rats, which was reduced in the presence of aminoguanidine and telmisartan. The plasma fructosamine level was increased upon MGO treatment. Hippocampal histochemistry showed vascular degeneration and neuronal loss upon MGO treatment. This study provides mechanistic insight into the role of MGO in the diabetes-associated development of AD.

}, doi = {10.1021/acsptsci.2c00143}, author = {Patil, Gouri and Kulsange, Shabda and Kazi, Rubina and Chirmade, Tejas and Kale, Vaikhari and Mote, Chandrashekhar and Aswar, Manoj and Koratkar, Santosh and Agawane, Sachin and Kulkarni, 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 = {

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} } @article {48319, title = {Comprehensive molecular and clinical investigation of approved Anti-HCV drugs repurposing against SARS-CoV-2 infection: a glaring gap between benchside and bedside medicine}, journal = {Vaccines}, volume = {11}, year = {2023}, month = {MAR}, pages = {515}, type = {Article}, abstract = {

The limited availability of effective treatment against SARS-CoV-2 infection is a major challenge in managing COVID-19. This scenario has augmented the need for repurposing anti-virals for COVID-19 mitigation. In this report, the anti-SARS-CoV-2 potential of anti-HCV drugs such as daclatasvir (DCV) or ledipasvir (LDP) in combination with sofosbuvir (SOF) was evaluated. The binding mode and higher affinity of these molecules with RNA-dependent-RNA-polymerase of SARS-CoV-2 were apparent by computational analysis. In vitro anti-SARS-CoV-2 activity depicted that SOF/DCV and SOF/LDP combination has IC50 of 1.8 and 2.0 mu M, respectively, comparable to remdesivir, an approved drug for COVID-19. Furthermore, the clinical trial was conducted in 183 mild COVID-19 patients for 14 days to check the efficacy and safety of SOF/DCV and SOF/LDP compared to standard of care (SOC) in a parallel-group, hybrid, individually randomized, controlled clinical study. The primary outcomes of the study suggested no significant difference in negativity after 3, 7 and 14 days in both treatments. None of the patients displayed any worsening in the disease severity, and no mortality was observed in the study. Although, the post hoc exploratory analysis indicated significant normalization of the pulse rate showed in SOF/DCV and SOF/LDP treatment vs. SOC. The current study highlights the limitations of bench side models in predicting the clinical efficacy of drugs that are planned for repurposing.

}, keywords = {antiviral, COVID-19, daclatasvir, ledipasvir, SARS-CoV-2, sofosbuvir}, doi = {10.3390/vaccines11030515}, author = {Bansode, Sneha and Singh, Pawan Kumar and Tellis, Meenakshi and Chugh, Anita and Deshmukh, Narendra and Gupta, Mahesh and Verma, Savita and Giri, Ashok and Kulkarni, Mahesh and Joshi, Rakesh and Chaudhary, Dhruva} } @article {49544, title = {Early and advanced glycation end product analysis from women with PCOS on metformin}, journal = {Reproductive Biology}, volume = {25}, year = {2025}, month = {MAR}, pages = {100993}, type = {Article}, abstract = {

In this cross-sectional study, we have analyzed advanced glycation end products (AGEs) in the plasma and follicular fluid of women with polycystic ovary syndrome (PCOS) taking metformin during in vitro fertilization (IVF) and control women undergoing IVF. Glucose, fructose, fructosamine, carboxymethyl lysine/ arginine (CML/R) proteins, and pentosidine were measured in the plasma and paired follicular fluid. Glycated proteins were characterized by mass spectrometry. Fasting serum glucose and fructosamine were comparable; however, follicular fluid glucose and fructosamine were higher in the PCOS group, and other AGEs remained unaltered. Fructose was lower in both serum and follicular fluid from the PCOS group. A positive correlation between some of these AGEs and sugars estimated was observed. Glucose and fructosamine in the follicular fluid correlated with the antral follicle count. The number of glycated peptides identified in the PCOS group by mass spectrometry was more. Glycated K75, K402 amino acid residues of albumin were detected in the PCOS group only. Additionally, some proteins involved in steroidogenesis and oocyte maturation as well as transporters, and extracellular matrix proteins, were found to be glycated in the PCOS group, which may affect their function. Elevated glucose and fructosamine in the follicular fluid of the PCOS group may contribute to abnormal folliculogenesis. The glycation of albumin should be validated in more samples to be considered as a marker for PCOS diagnosis.

}, keywords = {Carboxymethyl lysine, Fructosamine, fructose, glucose, Pentosidine, Polycystic ovary syndrome}, issn = {1642-431X}, doi = {10.1016/j.repbio.2024.100993}, author = {Ambekar, Aditi S. and Naredi, Nikita and Malakar, Dipankar and Vashum, Y. and Misra, Pratibha and Kulkarni, Mahesh} }