02279nas a2200265   4500008004100000022001400041245009900055210006900154260006800223300001400291490000700305520131400312653002101626653002501647653003001672653003101702653003101733653002301764653001401787100002101801700002301822700002101845700002101866856012601887       2008                        eng d  a0887-624X00aSynthesis and characterization of novel polybenzimidazoles bearing pendant phenoxyamine groups0 aSynthesis and characterization of novel polybenzimidazoles beari  a111 RIVER ST, HOBOKEN, NJ 07030 USAbJOHN WILEY & SONS INCcSEP  a5776-57930 v463 a<p>A novel aromatic diacid, 3, 5-dicarboxyl-4'-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'-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.</p>10aCharacterization10acopolybenzimidazoles10aHigh performance polymers10ahigh temperature materials10apendant phenoxyamine group10apolybenzimidazoles10asynthesis1 aKulkarni, Mahesh1 aPotrekar, Ravindra1 aKulkarni, R., A.1 aVernekar, S., P.  uhttps://library.ncl.res.in/content/synthesis-and-characterization-novel-polybenzimidazoles-bearing-pendant-phenoxyamine-002154nas a2200253   4500008004100000022001400041245012500055210006900180260006800249300001400317490000800331520122500339653002101564653001401585653003101599653002301630653002401653653001401677100002101691700002301712700002101735700002101756856012301777       2010                        eng d  a0021-899500aNitrophenoxy groups containing polybenzimidazoles as polymer electrolytes for fuel cells: synthesis and characterization0 aNitrophenoxy groups containing polybenzimidazoles as polymer ele  a111 RIVER ST, HOBOKEN, NJ 07030 USAbJOHN WILEY & SONS INCcSEP  a3282-32920 v1173 a<p>Polybenzimidazoles containing different contents of pendant nitrophenoxy groups were prepared by condensation of 3,3'-diamino-benzidine with a mixture of 3,5-dicarboxyl-4'-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</p>10aCharacterization10amembranes10apendant nitrophenoxy group10apolybenzimidazoles10aproton conductivity10asynthesis1 aKulkarni, Mahesh1 aPotrekar, Ravindra1 aKulkarni, R., A.1 aVernekar, S., P.  uhttps://library.ncl.res.in/content/nitrophenoxy-groups-containing-polybenzimidazoles-polymer-electrolytes-fuel-cells-006307nas a2201765   4500008004100000022001400041245009500055210006900150260000900219300001100228490000700239520113100246100001901377700001901396700002101415700002001436700001701456700002601473700002101499700002301520700001501543700002701558700003401585700002101619700002801640700001701668700001701685700002801702700003001730700002401760700001801784700002301802700001901825700002601844700002601870700003301896700001801929700001801947700002401965700003001989700002702019700002702046700001702073700002602090700002102116700002702137700002402164700003502188700002502223700002802248700002702276700002102303700001802324700002002342700002902362700001602391700001802407700002002425700002002445700001902465700002402484700001902508700002302527700002202550700002402572700001702596700002202613700002102635700002302656700002702679700002202706700001902728700001802747700001802765700001802783700002102801700002002822700002002842700002502862700002102887700001902908700002802927700002502955700001802980700001902998700002203017700002903039700002403068700002603092700002203118700001603140700002403156700002803180700002103208700002203229700002603251700001903277700002703296700001903323700002103342700002303363700002203386700001603408700002703424700001803451700001703469700001503486700001903501700002203520700002003542700002103562700002703583700002303610700002403633700001803657700002103675700002403696700002703720700001803747700002503765700002103790700002103811700003203832700002503864700002203889700002203911700001803933700002503951700001903976700002103995700002004016700002004036700002104056700002204077700002904099700002204128700002304150700003204173700002404205700002904229700002304258700001804281700002104299700002504320700001804345700002004363700001804383700002304401856011704424       2021                        eng d  a2050-084X00aInsights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV20 aInsights from a pan India Seroepidemiological survey PhenomeIndi  cAPR   ae665370 v103 a<p>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, &lt;0.0001), use of public transport (1.79, 1.43-2.24, &lt;0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, &lt;0.0001), and B blood group (1.36, 1.15-1.61, 0.001).</p>1 aNaushin, Salwa1 aSardana, Viren1 aUjjainiya, Rajat1 aBhatheja, Nitin1 aKutum, Rintu1 aBhaskar, Akash, Kumar1 aPradhan, Shalini1 aPrakash, Satyartha1 aKhan, Raju1 aRawat, Birendra, Singh1 aTallapaka, Karthik, Bharadwaj1 aAnumalla, Mahesh1 aChandak, Giriraj, Ratan1 aLahiri, Amit1 aKar, Susanta1 aMulay, Shrikant, Ramesh1 aMugale, Madhav, Nilakanth1 aSrivastava, Mrigank1 aKhan, Shaziya1 aSrivastava, Anjali1 aTomar, Bhawana1 aVeerapandian, Murugan1 aVenkatachalam, Ganesh1 aVijayakumar, Selvamani, Raja1 aAgarwal, Ajay1 aGupta, Dinesh1 aHalami, Prakash, M.1 aPeddha, Muthukumar, Serva1 aSundaram, Gopinath, M.1 aVeeranna, Ravindra, P.1 aPal, Anirban1 aAgarwal, Vinay, Kumar1 aMaurya, Anil, Ku1 aSingh, Ranvijay, Kumar1 aRaman, Ashok, Kumar1 aAnandasadagopan, Suresh, Kumar1 aKaruppanan, Parimala1 aVenkatesan, Subramanian1 aSardana, Harish, Kumar1 aKothari, Anamika1 aJain, Rishabh1 aThakur, Anupama1 aParihar, Devendra, Singh1 aSaifi, Anas1 aKaur, Jasleen1 aKumar, Virendra1 aMishra, Avinash1 aGogeri, Iranna1 aRayasam, Geethavani1 aSingh, Praveen1 aChakraborty, Rahul1 aChaturvedi, Gaura1 aKarunakar, Pinreddy1 aYadav, Rohit1 aSinghmar, Sunanda1 aSingh, Dayanidhi1 aSarkar, Sharmistha1 aBhattacharya, Purbasha1 aAcharya, Sundaram1 aSingh, Vandana1 aVerma, Shweta1 aSoni, Drishti1 aSeth, Surabhi1 aVashisht, Sakshi1 aThakran, Sarita1 aFatima, Firdaus1 aSingh, Akash, Pratap1 aSharma, Akanksha1 aSharma, Babita1 aSubramanian, Manikandan1 aPadwad, Yogendra, S.1 aHallan, Vipin1 aPatial, Vikram1 aSingh, Damanpreet1 aTripude, Narendra, Vijay1 aChakrabarti, Partha1 aMaity, Sujay, Krishna1 aGanguly, Dipyaman1 aSarkar, Jit1 aRamakrishna, Sistla1 aKumar, Balthu, Narender1 aKumar, Kiran, A.1 aGandhi, Sumit, G.1 aJamwal, Piyush, Singh1 aChouhan, Rekha1 aJamwal, Vijay, Lakshmi1 aKapoor, Nitika1 aGhosh, Debashish1 aThakkar, Ghanshyam1 aSubudhi, Umakanta1 aSen, Pradip1 aChaudhury, Saumya, Ray1 aKumar, Rashmi1 aGupta, Pawan1 aTuli, Amit1 aSharma, Deepak1 aRinge, Rajesh, P.1 aAmarnarayan, D.1 aKulkarni, Mahesh1 aShanmugam, Dhansekaran1 aDharne, Mahesh, S.1 aDastager, Sayed, G.1 aJoshi, Rakesh1 aPatil, Amita, P.1 aMahajan, Sachin, N.1 aKhan, Abujunaid, Habib1 aWagh, Vasudev1 aYadav, Rakesh, Kumar1 aKhilari, Ajinkya1 aBhadange, Mayuri1 aChaurasiya, Arvindkumar, H.1 aKulsange, Shabda, E.1 aKhairnar, Krishna1 aParanjape, Shilpa1 aKalita, Jatin1 aSastry, Narahari, G.1 aPhukan, Tridip1 aManna, Prasenjit1 aRomi, Wahengbam1 aBharali, Pankaj1 aOzah, Dibyajyoti1 aSahu, Ravi, Kumar1 aBabu, Elapavalooru, Vssk1 aSukumaran, Rajeev1 aNair, Aiswarya, R.1 aValappil, Prajeesh, Kooloth1 aPuthiyamadam, Anoop1 aVelayudhanpillai, Adarsh1 aChodankar, Kalpana1 aDamare, Samir1 aMadhavi, Yennapu1 aAggarwal, Ved, Varun1 aDahiya, Sumit1 aAgrawal, Anurag1 aDash, Debasis1 aSengupta, Shantanu  uhttps://library.ncl.res.in/content/insights-pan-india-sero-epidemiological-survey-phenome-india-cohort-sars-cov201874nas a2200337   4500008004100000022001400041245012900055210006900184260001100253300001100264490000700275520074300282653002601025653002001051653003101071653004201102653001501144100001701159700001901176700001901195700002101214700002001235700001701255700002701272700002001299700002401319700001801343700001701361700002701378856013101405       2021                        eng d  a2234-943X00aPlasma-derived extracellular vesicles reveal galectin-3 binding protein as potential biomarker for early detection of glioma0 aPlasmaderived extracellular vesicles reveal galectin3 binding pr  cNOV 26  a7787540 v113 a{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 III10ablood-based biomarker10aearly detection10agalectin-3 binding protein10aplasma-derived extracellular vesicles10aproteomics1 aRana, Rashmi1 aChauhan, Kirti1 aGautam, Poonam1 aKulkarni, Mahesh1 aBanarjee, Reema1 aChugh, Parul1 aChhabra, Satnam, Singh1 aAcharya, Rajesh1 aKalra, Samir, Kumar1 aGupta, Anshul1 aJain, Sunila1 aGanguly, Nirmal, Kumar  uhttps://library.ncl.res.in/content/plasma-derived-extracellular-vesicles-reveal-galectin-3-binding-protein-potential-biomarker02151nas a2200241   4500008004100000245011400041210006900155260000800224300001200232490000600244520132800250100001701578700002101595700001701616700002001633700001901653700002501672700001701697700002201714700002001736700002101756856013201777       2022                        eng d00aBehavioral and proteomic studies reveal methylglyoxal activate pathways associated with alzheimer’s disease0 aBehavioral and proteomic studies reveal methylglyoxal activate p  cDEC  a65–750 v63 a<p>
	<span style="color: rgb(0, 0, 0); font-family: georgia, serif; font-size: 17.008px; background-color: rgb(244, 244, 244);">Diabetes is one of the major risk factors for Alzheimer’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.</span></p>
1 aPatil, Gouri1 aKulsange, Shabda1 aKazi, Rubina1 aChirmade, Tejas1 aKale, Vaikhari1 aMote, Chandrashekhar1 aAswar, Manoj1 aKoratkar, Santosh1 aAgawane, Sachin1 aKulkarni, Mahesh  uhttps://library.ncl.res.in/content/behavioral-and-proteomic-studies-reveal-methylglyoxal-activate-pathways-associated-alzheimer07622nas a2201957   4500008004100000022001400041245016800055210006900223260000800292300001100300490000800311520199500319653001102314653001202325653001302337653002102350653001402371653002102385653001502406100001902421700002102440700002302461700001902484700001902503700002002522700002402542700001902566700001802585700002002603700001502623700002702638700003402665700002102699700001702720700001702737700001902754700002402773700003002797700001902827700001802846700002102864700001402885700002502899700001702924700001702941700001202958700002002970700001902990700001703009700002103026700002603047700002603073700001903099700001803118700002403136700003003160700002703190700001703217700002703234700003503261700002503296700002403321700002303345700002803368700002703396700002703423700002103450700002903471700001903500700001603519700001703535700001803552700001603570700001803586700001703604700002003621700001903641700002403660700002103684700001903705700001803724700002203742700002203764700002403786700002603810700002203836700002403858700002803882700002103910700001803931700001703949700002603966700001603992700001704008700001904025700002204044700001804066700001604084700001904100700002704119700002704146700002404173700001704197700002104214700002304235700001804258700002504276700002804301700001904329700002204348700002204370700001604392700001804408700002004426700001704446700001904463700001504482700002704497700003004524700001604554700001204570700001804582700002804600700002204628700002104650700002704671700002004698700001804718700002104736700001804757700002004775700002004795700001704815700001904832700001604851700001904867700001604886700002004902700002204922700002104944700001804965700001904983700002105002700002005023700002005043700002105063700002005084700002205104700003105126700001905157700002705176700003505203700002605238700002405264700002405288700003205312700003905344700002305383700001805406700002405424700002505448700002005473700002305493700001805516856013005534       2022                        eng d  a0010-482500aMachine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys0 aMachine learningbased approach to determine infection status in   cJUL  a1054190 v1463 a<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>
10aBBV15210aCovaxin10aCOVID-1910aEnsemble methods10aInfection10amachine learning10aSARS-CoV-21 aSingh, Prateek1 aUjjainiya, Rajat1 aPrakash, Satyartha1 aNaushin, Salwa1 aSardana, Viren1 aBhatheja, Nitin1 aSingh, Ajay, Pratap1 aBarman, Joydeb1 aKumar, Kartik1 aGayali, Saurabh1 aKhan, Raju1 aRawat, Birendra, Singh1 aTallapaka, Karthik, Bharadwaj1 aAnumalla, Mahesh1 aLahiri, Amit1 aKar, Susanta1 aBhosale, Vivek1 aSrivastava, Mrigank1 aMugale, Madhav, Nilakanth1 aPandey, C., P.1 aKhan, Shaziya1 aKatiyar, Shivani1 aRaj, Desh1 aIshteyaque, Sharmeen1 aKhanka, Sonu1 aRani, Ankita1 aPromila1 aSharma, Jyotsna1 aSeth, Anuradha1 aDutta, Mukul1 aSaurabh, Nishant1 aVeerapandian, Murugan1 aVenkatachalam, Ganesh1 aBansal, Deepak1 aGupta, Dinesh1 aHalami, Prakash, M.1 aPeddha, Muthukumar, Serva1 aVeeranna, Ravindra, P.1 aPal, Anirban1 aSingh, Ranvijay, Kumar1 aAnandasadagopan, Suresh, Kumar1 aKaruppanan, Parimala1 aRahman, Syed, Nasar1 aSelvakumar, Gopika1 aVenkatesan, Subramanian1 aKarmakar, Malay, Kumar1 aSardana, Harish, Kumar1 aKothari, Anamika1 aParihar, Devendra, Singh1 aThakur, Anupma1 aSaifi, Anas1 aGupta, Naman1 aSingh, Yogita1 aReddu, Ritu1 aGautam, Rizul1 aMishra, Anuj1 aMishra, Avinash1 aGogeri, Iranna1 aRayasam, Geethavani1 aPadwad, Yogendra1 aPatial, Vikram1 aHallan, Vipin1 aSingh, Damanpreet1 aTirpude, Narendra1 aChakrabarti, Partha1 aMaity, Sujay, Krishna1 aGanguly, Dipyaman1 aSistla, Ramakrishna1 aBalthu, Narender, Kumar1 aKumar, Kiran, A.1 aRanjith, Siva1 aKumar, Vijay1 aJamwal, Piyush, Singh1 aWali, Anshu1 aAhmed, Sajad1 aChouhan, Rekha1 aGandhi, Sumit, G.1 aSharma, Nancy1 aRai, Garima1 aIrshad, Faisal1 aJamwal, Vijay, Lakshmi1 aPaddar, Masroor, Ahmad1 aKhan, Sameer, Ullah1 aMalik, Fayaz1 aGhosh, Debashish1 aThakkar, Ghanshyam1 aBarik, S., K.1 aTripathi, Prabhanshu1 aSatija, Yatendra, Kumar1 aMohanty, Sneha1 aKhan, Md, Tauseef1 aSubudhi, Umakanta1 aSen, Pradip1 aKumar, Rashmi1 aBhardwaj, Anshu1 aGupta, Pawan1 aSharma, Deepak1 aTuli, Amit1 aChaudhuri, Saumya, Ray1 aKrishnamurthi, Srinivasan1 aPrakash, L.1 aRao, Ch1 aSingh, B., N.1 aChaurasiya, Arvindkumar1 aChaurasiya, Meera1 aBhadange, Mayuri1 aLikhitkar, Bhagyashree1 aMohite, Sharada1 aPatil, Yogita1 aKulkarni, Mahesh1 aJoshi, Rakesh1 aPandya, Vaibhav1 aMahajan, Sachin1 aPatil, Amita1 aSamson, Rachel1 aVare, Tejas1 aDharne, Mahesh1 aGiri, Ashok1 aMahajan, Sachin1 aParanjape, Shilpa1 aSastry, Narahari1 aKalita, Jatin1 aPhukan, Tridip1 aManna, Prasenjit1 aRomi, Wahengbam1 aBharali, Pankaj1 aOzah, Dibyajyoti1 aSahu, RaviKumar1 aDutta, Prachurjya1 aSingh, Moirangthem, Goutam1 aGogoi, Gayatri1 aTapadar, Yasmin, Begam1 aBabu, Elapavalooru, V. S. S. K1 aSukumaran, Rajeev, K.1 aNair, Aishwarya, R.1 aPuthiyamadam, Anoop1 aValappil, Prajeesh, Kooloth1 aPrasannakumari, Adrash, Velayudhan1 aChodankar, Kalpana1 aDamare, Samir1 aAgrawal, Ved, Varun1 aChaudhary, Kumardeep1 aAgrawal, Anurag1 aSengupta, Shantanu1 aDash, Debasis  uhttps://library.ncl.res.in/content/machine-learning-based-approach-determine-infection-status-recipients-bbv152-covaxin-whole02575nas a2200325   4500008004100000245017700041210006900218260000800287300000800295490000700303520150700310653001401817653001301831653001601844653001501860653001501875653001501890100001901905700002401924700002201948700001701970700002301987700001802010700001802028700001602046700002102062700001802083700002202101856012602123       2023                        eng d00aComprehensive molecular and clinical investigation of approved Anti-HCV drugs repurposing against SARS-CoV-2 infection: a glaring gap between benchside and bedside medicine0 aComprehensive molecular and clinical investigation of approved A  cMAR  a5150 v113 a<p>
	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.</p>
10aantiviral10aCOVID-1910adaclatasvir10aledipasvir10aSARS-CoV-210asofosbuvir1 aBansode, Sneha1 aSingh, Pawan, Kumar1 aTellis, Meenakshi1 aChugh, Anita1 aDeshmukh, Narendra1 aGupta, Mahesh1 aVerma, Savita1 aGiri, Ashok1 aKulkarni, Mahesh1 aJoshi, Rakesh1 aChaudhary, Dhruva  uhttps://library.ncl.res.in/content/comprehensive-molecular-and-clinical-investigation-approved-anti-hcv-drugs-repurposing02397nas a2200277   4500008004100000022001400041245008800055210006900143260000800212300001100220490000700231520153800238653002501776653001701801653001301818653001201831653001601843653003001859100002301889700001901912700002201931700001501953700002001968700002101988856011002009       2025                        eng d  a1642-431X00aEarly and advanced glycation end product analysis from women with PCOS on metformin0 aEarly and advanced glycation end product analysis from women wit  cMAR  a1009930 v253 a<p>
	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.</p>
10aCarboxymethyl lysine10aFructosamine10afructose10aglucose10aPentosidine10aPolycystic ovary syndrome1 aAmbekar, Aditi, S.1 aNaredi, Nikita1 aMalakar, Dipankar1 aVashum, Y.1 aMisra, Pratibha1 aKulkarni, Mahesh  uhttps://library.ncl.res.in/content/early-and-advanced-glycation-end-product-analysis-women-pcos-metformin01887nas a2200205   4500008004100000022001400041245017700055210006900232260000900301300001600310490000700326520107800333100001901411700001901430700004001449700001901489700002101508700002101529856013101550       2026                        eng d  a2470-134300aEngineering pichia pastoris as a heterologous platform for non-ribosomal peptide biosynthesis of the β-lactam intermediate δ-(L-α-Aminoadipyl)-L-cysteinyl-D-valine (ACV)0 aEngineering pichia pastoris as a heterologous platform for nonri  cJUN   a32038-320480 v113 a<p>
	The production of nonribosomal peptides (NRPs) in non-native hosts remains challenging due to the size and complexity of the biosynthetic enzymes. Here, we report the first successful reconstruction of the delta-(L-alpha-aminoadipyl)-L-cysteinyl-D-valine (ACV) pathway in Pichia pastoris. By introducing codon-optimized versions of the pcbAB and npgA genes directly into the genome, we were able to rebuild the minimal pathway required for ACV synthesis, which we confirmed using LC-MS/MS. Although the starting strain produced only small amounts of ACV (similar to 5 ng/mL), yields increased 10-fold after adaptive evolution (similar to 50 ng/mL) and further improved with precursor supplementation (similar to 60 ng/mL). Transcriptomic analysis showed that the engineered strain reorganized its metabolism to meet the energy and precursor demands of NRPS activity. Overall, this study suggests that P. pastoris can serve as a practical, proof-of-concept host for complex NRP biosynthesis and lays the groundwork for future engineering of beta-lactam pathways.</p>
1 aJogi, Shraddha1 aJagtap, Rutuja1 aSubrahmanyam, Yalamanchili, Venkata1 aSharma, Monika1 aKulkarni, Mahesh1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/engineering-pichia-pastoris-heterologous-platform-non-ribosomal-peptide-biosynthesis-%CE%B2