<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Immanuel, Selva Rupa Christinal</style></author><author><style face="normal" font="default" size="100%">Ghanate, Avinash D.</style></author><author><style face="normal" font="default" size="100%">Parmar, Dharmeshkumar S.</style></author><author><style face="normal" font="default" size="100%">Yadav, Ritu</style></author><author><style face="normal" font="default" size="100%">Uthup, Riya</style></author><author><style face="normal" font="default" size="100%">Panchagnula, Venkateswarlu</style></author><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated genetic and metabolic landscapes predict vulnerabilities of temozolomide resistant glioblastoma cells</style></title><secondary-title><style face="normal" font="default" size="100%">npj Systems Biology and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Metabolic reprogramming and its molecular underpinnings are critical to unravel the duality of cancer cell function and chemo-resistance. Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC50 value of 5 nM, three orders of magnitude lower than for U87MG that exhibited an IC50 value of 1.8 mM was thus identified using our integrated systems-based approach.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
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</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Asokan, Mangaiarkarasi S.</style></author><author><style face="normal" font="default" size="100%">Joan, Roshni Florina</style></author><author><style face="normal" font="default" size="100%">Babji, Sudhir</style></author><author><style face="normal" font="default" size="100%">Dayma, Girish</style></author><author><style face="normal" font="default" size="100%">Nadukkandy, Prajitha</style></author><author><style face="normal" font="default" size="100%">Subrahmanyam, Vinutha</style></author><author><style face="normal" font="default" size="100%">Pandey, Archana</style></author><author><style face="normal" font="default" size="100%">Malagi, Girish</style></author><author><style face="normal" font="default" size="100%">Arya, Pooja</style></author><author><style face="normal" font="default" size="100%">Mahajan, Vibhuti</style></author><author><style face="normal" font="default" size="100%">Bhavikatti, Jayateerth</style></author><author><style face="normal" font="default" size="100%">Pawar, Ketakee</style></author><author><style face="normal" font="default" size="100%">Thorat, Aishwarya</style></author><author><style face="normal" font="default" size="100%">Shah, Priyanki</style></author><author><style face="normal" font="default" size="100%">Goud, Ramakrishna B.</style></author><author><style face="normal" font="default" size="100%">Roy, Bishnudeo</style></author><author><style face="normal" font="default" size="100%">Rajukutty, Shon</style></author><author><style face="normal" font="default" size="100%">Immanuel, Sushil</style></author><author><style face="normal" font="default" size="100%">Agarwal,Dhiraj</style></author><author><style face="normal" font="default" size="100%">Saha, Sankhanil</style></author><author><style face="normal" font="default" size="100%">Shivaraj, Akshatha</style></author><author><style face="normal" font="default" size="100%">Panikulam, Patricia</style></author><author><style face="normal" font="default" size="100%">Shome, Rajeshwari</style></author><author><style face="normal" font="default" size="100%">Gulzar, Shah-E-Jahan</style></author><author><style face="normal" font="default" size="100%">Sharma, Anusmrithi U.</style></author><author><style face="normal" font="default" size="100%">Naik, Ajinkya</style></author><author><style face="normal" font="default" size="100%">Talashi, Shruti</style></author><author><style face="normal" font="default" size="100%">Belekar, Madhuri</style></author><author><style face="normal" font="default" size="100%">Yadav, Ritu</style></author><author><style face="normal" font="default" size="100%">Khude, Poornima</style></author><author><style face="normal" font="default" size="100%">V, Mamatha</style></author><author><style face="normal" font="default" size="100%">Shivalingaiah, Sudarshan</style></author><author><style face="normal" font="default" size="100%">Deshmukh, Urmila</style></author><author><style face="normal" font="default" size="100%">Bhise, Chinmayee</style></author><author><style face="normal" font="default" size="100%">Joshi, Manjiri</style></author><author><style face="normal" font="default" size="100%">Inbaraj, Leeberk Raja</style></author><author><style face="normal" font="default" size="100%">Chandrasingh, Sindhulina</style></author><author><style face="normal" font="default" size="100%">Ghose, Aurnab</style></author><author><style face="normal" font="default" size="100%">Jamora, Colin</style></author><author><style face="normal" font="default" size="100%">Karumbati, Anandi S.</style></author><author><style face="normal" font="default" size="100%">Sundaramurthy, Varadharajan</style></author><author><style face="normal" font="default" size="100%">Johnson, Avita</style></author><author><style face="normal" font="default" size="100%">Ramesh, Naveen</style></author><author><style face="normal" font="default" size="100%">Chetan, Nirutha</style></author><author><style face="normal" font="default" size="100%">Parthiban, Chaitra</style></author><author><style face="normal" font="default" size="100%">Ahmed, Asma</style></author><author><style face="normal" font="default" size="100%">Rakshit, Srabanti</style></author><author><style face="normal" font="default" size="100%">Adiga, Vasista</style></author><author><style face="normal" font="default" size="100%">D'souza, George</style></author><author><style face="normal" font="default" size="100%">Rale, Vinay</style></author><author><style face="normal" font="default" size="100%">George, Carolin Elizabeth</style></author><author><style face="normal" font="default" size="100%">John, Jacob</style></author><author><style face="normal" font="default" size="100%">Kawade, Anand</style></author><author><style face="normal" font="default" size="100%">Chaturvedi, Akanksha</style></author><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author><author><style face="normal" font="default" size="100%">Dias, Mary</style></author><author><style face="normal" font="default" size="100%">Bhosale, Anand</style></author><author><style face="normal" font="default" size="100%">Raghu, Padinjat</style></author><author><style face="normal" font="default" size="100%">Shashidhara, L. S.</style></author><author><style face="normal" font="default" size="100%">yakarnam, Annapurna V.</style></author><author><style face="normal" font="default" size="100%">Bal, Vineeta</style></author><author><style face="normal" font="default" size="100%">Kang, Gagandeep</style></author><author><style face="normal" font="default" size="100%">Mayor, Satyajit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Immunogenicity of SARS-CoV-2 vaccines BBV152 (COVAXIN®) and ChAdOx1 nCoV-19 (COVISHIELD™) in seronegative and seropositive individuals in India: a multicentre, nonrandomised observational study</style></title><secondary-title><style face="normal" font="default" size="100%">Lancet Regional Health - Southeast Asia</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAR</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;box-sizing: inherit; line-height: 1.5; margin: 1.2rem 0px; color: rgb(33, 33, 33); font-family: BlinkMacSystemFont, -apple-system, &amp;quot;Segoe UI&amp;quot;, Roboto, Oxygen, Ubuntu, Cantarell, &amp;quot;Fira Sans&amp;quot;, &amp;quot;Droid Sans&amp;quot;, &amp;quot;Helvetica Neue&amp;quot;, sans-serif; font-size: 16px;&quot;&gt;
	&lt;strong class=&quot;sub-title&quot; style=&quot;box-sizing: inherit;&quot;&gt;Background:&amp;nbsp;&lt;/strong&gt;There are limited global data on head-to-head comparisons of vaccine platforms assessing both humoral and cellular immune responses, stratified by pre-vaccination serostatus. The COVID-19 vaccination drive for the Indian population in the age group 18-45 years began in April 2021 when seropositivity rates in the general population were rising due to the delta wave of COVID-19 pandemic during April-May 2021.&lt;/p&gt;
&lt;p style=&quot;box-sizing: inherit; line-height: 1.5; margin: 1.2rem 0px; color: rgb(33, 33, 33); font-family: BlinkMacSystemFont, -apple-system, &amp;quot;Segoe UI&amp;quot;, Roboto, Oxygen, Ubuntu, Cantarell, &amp;quot;Fira Sans&amp;quot;, &amp;quot;Droid Sans&amp;quot;, &amp;quot;Helvetica Neue&amp;quot;, sans-serif; font-size: 16px;&quot;&gt;
	&lt;strong class=&quot;sub-title&quot; style=&quot;box-sizing: inherit;&quot;&gt;Methods:&amp;nbsp;&lt;/strong&gt;Between June 30, 2021, and Jan 28, 2022, we enrolled 691 participants in the age group 18-45 years across four clinical sites in India. In this non-randomised and laboratory blinded study, participants received either two doses of Covaxin® (4 weeks apart) or two doses of Covishield™ (12 weeks apart) as per the national vaccination policy. The primary outcome was the seroconversion rate and the geometric mean titre (GMT) of antibodies against the SARS-CoV-2 spike and nucleocapsid proteins post two doses. The secondary outcome was the frequency of cellular immune responses pre- and post-vaccination.&lt;/p&gt;
&lt;p style=&quot;box-sizing: inherit; line-height: 1.5; margin: 1.2rem 0px; color: rgb(33, 33, 33); font-family: BlinkMacSystemFont, -apple-system, &amp;quot;Segoe UI&amp;quot;, Roboto, Oxygen, Ubuntu, Cantarell, &amp;quot;Fira Sans&amp;quot;, &amp;quot;Droid Sans&amp;quot;, &amp;quot;Helvetica Neue&amp;quot;, sans-serif; font-size: 16px;&quot;&gt;
	&lt;strong class=&quot;sub-title&quot; style=&quot;box-sizing: inherit;&quot;&gt;Findings:&amp;nbsp;&lt;/strong&gt;When compared to pre-vaccination baseline, both vaccines elicited statistically significant seroconversion and binding antibody levels in both seronegative and seropositive individuals. In the per-protocol cohort, Covishield™ elicited higher antibody responses than Covaxin® as measured by seroconversion rate (98.3% vs 74.4%, p &amp;lt; 0.0001 in seronegative individuals; 91.7% vs 66.9%, p &amp;lt; 0.0001 in seropositive individuals) as well as by anti-spike antibody levels against the ancestral strain (GMT 1272.1 vs 75.4 binding antibody units/ml [BAU/ml], p &amp;lt; 0.0001 in seronegative individuals; 2089.07 vs 585.7 BAU/ml, p &amp;lt; 0.0001 in seropositive individuals). As participants at all clinical sites were not recruited at the same time, site-specific immunogenicity was impacted by the timing of vaccination relative to the delta and omicron waves. Surrogate neutralising antibody responses against variants-of-concern including delta and omicron was higher in Covishield™ recipients than in Covaxin® recipients; and in seropositive than in seronegative individuals after both vaccination and asymptomatic infection (omicron variant). T cell responses are reported from only one of the four site cohorts where the vaccination schedule preceded the omicron wave. In seronegative individuals, Covishield™ elicited both CD4+ and CD8+ spike-specific cytokine-producing T cells whereas Covaxin® elicited mainly CD4+ spike-specific T cells. Neither vaccine showed significant post-vaccination expansion of spike-specific T cells in seropositive individuals.&lt;/p&gt;
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	Foreign&lt;/p&gt;
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	5&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Belekar, Madhuri</style></author><author><style face="normal" font="default" size="100%">Kavatalkar, Vijendra</style></author><author><style face="normal" font="default" size="100%">Yadav, Ritu</style></author><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated analysis of mitochondrial ETC inhibition reveals genotype-specific heterogeneity of drug response in glioblastoma</style></title><secondary-title><style face="normal" font="default" size="100%">Biochemical and Biophysical Research Communications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Drug dose response</style></keyword><keyword><style  face="normal" font="default" size="100%">Electron transport chain</style></keyword><keyword><style  face="normal" font="default" size="100%">Glioblastoma</style></keyword><keyword><style  face="normal" font="default" size="100%">IC50 value</style></keyword><keyword><style  face="normal" font="default" size="100%">Instantaneous inhibitory potential</style></keyword><keyword><style  face="normal" font="default" size="100%">Mitochondrial genome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">787</style></volume><pages><style face="normal" font="default" size="100%">152798</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Glioblastoma (GBM) is among the most aggressive brain cancers, driven by genetic diversity and resistance to therapy. Mitochondrial metabolism-and in particular the electron transport chain (ETC)-has emerged as both a key weakness and a source of variable drug response. To investigate this, we integrated constraint-based metabolic modeling (CBM), high-resolution drug profiling, and genomic sequencing across three GBM cell models: LN229, U87MG, and neurospheres (NSP). Modeling predicted distinct ETC vulnerabilities, which were confirmed experimentally using inhibitors against Complexes I-V. Sensitivity to rotenone varied sharply: NSP cells were most vulnerable (IC50 = 0.007 mu M), LN229 showed intermediate sensitivity (0.021 mu M), and U87MG remained highly resistant (1.816 mu M). Across inhibitors, LN229 consistently showed steep dose-response slopes, U87MG maintained flat curves, and NSP displayed selective weaknesses. By incorporating slope (m) and Instantaneous Inhibitory Potential (IIP), median-effect analysis captured dynamic drug-response behaviour's that IC50 values alone overlooked. Genomic sequencing revealed striking differences in mutational burden: U87MG and NSP carried 354 and 307 single nucleotide polymorphisms (SNPs), respectively, compared with 141 in LN229. Several non-synonymous mutations were directly linked to altered drug sensitivity, including L194S, Y50 N, and L46V in LN229; S456L, A466T, and Y629F in U87MG; and the NSP-specific R159Q. Notably, mutations near catalytic sites correlated with changes in slope and IIP, providing mechanistic insight into therapeutic response. Together, these results show how genetic variation reshapes ETC function and drug sensitivity in GBM, offering a predictive framework for mutation-informed, personalized therapy.&lt;/p&gt;
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	2.2&lt;/p&gt;
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