07622nas 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

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.

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-whole04148nas a2200589 4500008004100000022001400041245012400055210006900179260000800248300001400256490000700270520236200277653001302639653001002652653001202662653003602674653002302710653003402733100002102767700001702788700002102805700002102826700002102847700001902868700001602887700002302903700001702926700002702943700001902970700001902989700002103008700001803029700002003047700001903067700001903086700002103105700002203126700001903148700002203167700001903189700001803208700001903226700002303245700001803268700002003286700002403306700002603330700002803356700002103384700002603405856012703431 2023 eng d a1876-034100aTale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India0 aTale of two waves Delineating diverse genomic and transmission l cAUG a1290-13000 v163 a

Background: Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data.Methods: A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a ``band of five'' outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5).Results: The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM. Conclusions: The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implica-tions for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.& COPY; 2023 Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

10aCOVID-1910aDelta10aOmicron10aSARS-CoV-2 genomic surveillance10aVariant of concern10aWhole Genome Sequencing (WGS)1 aNiveditha, Divya1 aKhan, Soumen1 aKhilari, Ajinkya1 aNadkarni, Sanica1 aBhalerao, Unnati1 aKadam, Pradnya1 aYadav, Ritu1 aKanekar, Jugal, B.1 aShah, Nikita1 aLikhitkar, Bhagyashree1 aSawant, Rutuja1 aThakur, Shikha1 aTupekar, Manisha1 aNagar, Dhriti1 aRao, Anjani, G.1 aJagtap, Rutuja1 aJogi, Shraddha1 aBelekar, Madhuri1 aPathak, Maitreyee1 aShah, Priyanki1 aRanade, Shatakshi1 aPhadke, Nikhil1 aDas, Rashmita1 aJoshi, Suvarna1 aKaryakarte, Rajesh1 aGhose, Aurnab1 aKadoo, Narendra1 aShashidhara, L., S.1 aMonteiro, Joy, Merwin1 aShanmugam, Dhanasekaran1 aRaghunathan, Anu1 aKarmodiya, Krishanpal uhttps://library.ncl.res.in/content/tale-two-waves-delineating-diverse-genomic-and-transmission-landscapes-driving-covid-1902305nas a2200313 4500008004100000245009800041210006900139260000800208300001200216490000700228520125200235653002901487653002301516653002701539653003101566653002301597100001901620700002101639700002701660700001901687700002501706700002401731700002101755700002001776700002701796700001801823700002801841856012201869 2025 eng d00aDetection and variant characterization of lumpy skin disease virus from dairy cattle in India0 aDetection and variant characterization of lumpy skin disease vir cDEC aveaf0900 v113 a

The spread of a severe and often fatal form of lumpy skin disease (LSD) in cattle and water buffaloes has caused widespread mortality and morbidity of these animals in India. To track and understand the genetic changes occurring in the virus and to enable routine surveillance of the virus, multiplexed polymerase chain reaction (PCR) and sequencing methods were developed and validated in this study. Multiplexed nested PCR for LSD virus (LSDV) detection was optimized using skin lesion swabs and nasal samples collected from symptomatic and asymptomatic animals. For genotyping, overlapping PCRs to amplify the entire LSDV genome were developed and tested on field samples collected from the Maharashtra and Odisha states of India. Analysis of LSDV genomes from 41 field samples collected in 2022 and 2023 revealed the presence of highly conserved novel mutations. Phylogenetic analysis shows that a distinct genotype of LSDV has spread across India, which warrants genomic surveillance of the virus in the coming years to track the evolution and transmission of the virus. The non-invasive sample collection, detection, and genotyping methods described in this study can facilitate large-scale surveillance of LSDV in dairy animals.

10agenotyping by sequencing10alumpy skin disease10amultiplexed nested PCR10aOxford Nanopore technology10avirus surveillance1 aBajpai, Manali1 aKhilari, Ajinkya1 aLikhitkar, Bhagyashree1 aMusale, Pankaj1 aJadhav, Santoshkumar1 aDhanikachalam, Velu1 aKakramkar, Payal1 aBhave, Kaustubh1 aSwaminathan, Marimuthu1 aJoshi, Sachin1 aShanmugam, Dhanasekaran uhttps://library.ncl.res.in/content/detection-and-variant-characterization-lumpy-skin-disease-virus-dairy-cattle-india