02434nas a2200289   4500008004100000022001400041245013600055210006900191260000900260300000800269490000700277520149200284653001601776653001201792653002001804653001701824653001501841653001401856100001501870700001801885700002001903700002101923700002801944700002301972700002601995856012302021       2025                        eng d  a0301-485100aComprehensive in vitro and in silico assessment of eugenol glycoconjugates against azole and amphotericin B resistant Rhizopus spp.0 aComprehensive in vitro and in silico assessment of eugenol glyco  cJUN   a5890 v523 a<p>
	Background Rhizopus spp. is a major cause of mucormycosis, a severe infectious disease with high morbidity and mortality. Treatment is challenging due to rising antifungal resistance. Glycosylation is a crucial technique for enhancing the properties of phenolic compounds like eugenol. The present study aims to examine the antifungal efficacy of eugenol glycoconjugates against azole and amphotericin B-resistant Rhizopus isolates. Methods and results Out of 50 soil samples, 12 isolates belonging to Mucorales were obtained, of which 7 were identified as Rhizopus spp. via 18S ITS sequencing. Antifungal susceptibility testing (AST) revealed that all Rhizopus isolates were resistant to amphotericin B (MIC &gt; 1 mu g/mL). Most isolates also showed resistance towards posaconazole (MIC &gt; 1 mu g/mL) and itraconazole (MIC &gt; 2 mu g/mL). AST of eugenol glycoconjugate (coded 6g) showed efficacy against resistant Rhizopus isolates, with MIC values ranging from 6.25 mu g/mL to 25 mu g/mL. Flow cytometry confirmed its fungicidal activity, correlating with MIC data. Compound 6g significantly reduced conidial germination within 24 h and exhibited no cytotoxicity on A549 lung cancer cells. In-silico analysis revealed a negative binding affinity of compound 6g for the spore coat protein CotH3, which could be a potential antifungal target. Conclusion Compound 6g could be an potential antifungal molecule against resistant Rhizopus spp, which requires further studies.</p>
10aAntifungals10aEugenol10aGlycoconjugates10aMucormycosis10aResistance10aVirulence1 aSen, Pooja1 aGupta, Lovely1 aChauhan, Aastha1 aGoswami, Lakshmi1 aBhattacharya, Asish, K.1 aSengupta, Abhishek1 aVijayaraghavan, Pooja  uhttps://library.ncl.res.in/content/comprehensive-vitro-and-silico-assessment-eugenol-glycoconjugates-against-azole-and03451nas a2200433   4500008004100000245014700041210006900188260000900257300000800266490000700274520201300281653002002294653003402314653001702348653002202365653001702387653001502404100001802419700004002437700001702477700002302494700001702517700001702534700002402551700001302575700002002588700002602608700002602634700002102660700003202681700002302713700002302736700001902759700002502778700003102803700002402834700002702858856013202885       2025                        eng d00aPrecision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus0 aPrecision integrated identification of predictive firsttrimester  cNOV   a4340 v243 a<p>
	Background and aimGestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. MethodsAmong 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (&lt; 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. ResultsA total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance. ConclusionThis study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.</p>
10aFirst trimester10aGestational diabetes mellitus10aIndian women10aMass spectrometry10aMetabolomics10aPrediction1 aSharma, Sapna1 aSubrahmanyam, Yalamanchili, Venkata1 aGupta, Payal1 aVadivel, Sangeetha1 aDeepa, Mohan1 aTandon, Ansh1 aSreedevi, Sreekumar1 aRam, Uma1 aNarad, Priyanka1 aParmar, Dharmeshkumar1 aAnjana, Ranjit, Mohan1 aRaghunathan, Anu1 aBalasubramanyam, Muthuswamy1 aMohan, Viswanathan1 aSengupta, Abhishek1 aAdamski, Jerzy1 aSaravanan, Ponnusamy1 aPanchagnula, Venkateswarlu1 aUsharani, Dandamudi1 aGokulakrishnan, Kuppan  uhttps://library.ncl.res.in/content/precision-integrated-identification-predictive-first-trimester-metabolomics-signatures-early