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1.
Sci Rep ; 9(1): 18658, 2019 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-31796857

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

2.
Sci Rep ; 9(1): 9729, 2019 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-31278284

RESUMO

Single nucleotide polymorphisms (SNPs) in TLR genes may serve as a crucial marker for early susceptibility of various cancers including cervical cancer. The present study was therefore designed to ascertain the role of TLR4 and TLR9 SNPs and haplotypes to hrHPV infection and cervical cancer susceptibility. The study included 110 cervical cancer biopsies and 141 cervical smears from age-matched healthy controls of Gujarati ethnicity of Western India. hrHPV 16 and 18 were detected using Real-time PCR. Eight SNPs, four each in TLR4 and TLR9 were analyzed using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism and Allele-Specific PCR. HPV 16 and 18 were detected in 68% cervical cancer cases. TLR4 rs4986790, rs1927911 and TLR9 rs187084 showed association with HPV 16/18 infection. CC and CT genotypes of TLR4 rs11536889 and rs1927911 respectively, and TC, CC genotypes of TLR9 rs187084, as well as minor alleles of TLR4 rs4986790 and TLR9 rs187084, were associated with the increased risk of cervical cancer. Stage-wise analysis revealed TLR9 rs187084 and rs352140 to be associated with early-stage cancer. TLR4 haplotype GTAC and TLR9 haplotype GATC were associated with the increased risk of cervical cancer while TLR4 haplotype GCAG was associated with the decreased risk. TLR4 haplotype GCAG and TLR9 haplotype GATC showed association with increased susceptibility to hrHPV infection. In conclusion, the present study revealed association of TLR4 and TLR9 polymorphisms and haplotypes with hrHPV infection and cervical cancer risk. Further evaluation of a larger sample size covering diverse ethnic populations globally is warranted.


Assuntos
Papillomavirus Humano 16/genética , Papillomavirus Humano 18/genética , Infecções por Papillomavirus/genética , Receptor 4 Toll-Like/genética , Receptor Toll-Like 9/genética , Neoplasias do Colo do Útero/virologia , Adulto , Estudos de Casos e Controles , Feminino , Estudos de Associação Genética , Predisposição Genética para Doença , Genótipo , Haplótipos , Humanos , Pessoa de Meia-Idade , Polimorfismo de Fragmento de Restrição , Polimorfismo de Nucleotídeo Único , Neoplasias do Colo do Útero/genética
3.
J Obstet Gynaecol India ; 68(4): 276-282, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30065542

RESUMO

BACKGROUND: Attempting vaginal birth after cesarean section (VBAC) places women at an increased risk of complications. Trial of labor after cesarean (TOLAC) calculators aim to predict the chance of successful vaginal birth after cesarean (VBAC) based on the patient's preexisting demographic and clinical factors. OBJECTIVE: To assess the rate of successful TOLAC using two calculators: FLAMM and the Grobman calculator, and to compare the performance of the two calculators in the successful prediction of VBAC. METHODS: Prospective cohort study in subjects with previous one caesarean section using well-defined inclusion and exclusion criteria. RESULTS: A total of 280 subjects with previous one cesarean section were enrolled. One hundred thirty-nine subjects consented for TOLAC, 90 (67%) underwent successful trial of vaginal birth, and 49 (32.8) required cesarean section. Cervical dilatation (p < 0.0001) and effacement (p < 0.0001), and any prior vaginal delivery (p < 0.02) were significantly associated with a successful outcome. At a cutoff score of 5, the sensitivity of the FLAMM score was 72% and specificity was 76%. For the Grobman calculator, the best sensitivity (69%) and specificity (67%) were seen at a cutoff score of 85%. CONCLUSION: Both prediction models, the FLAMM and the "close to delivery" nomogram, recommended by Grobman et al. are easy to use and could successfully estimate the chances of vaginal birth in previous caesarean, in this small cohort. The decision for women opting for TOLAC can be individualized, and patient-specific chances of success can be predicted by the use of these prediction models.

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