We explored the independent risk factors associated with cases of spontaneous abortion in infertile patients treated with human-assisted reproductive technology (ART) and established a smooth curve fit and perform a threshold effect analysis can provide guidance and a valuable reference for predicting the probability of spontaneous abortion. This was a retrospective cohort study of 16,097 patients successfully conceived with ART in Shangqiu First People's Hospital from June 2013 to December 2018. Overall, 2,378 (14.77%) had an abortion and 13,719 (85.23%) did not have an abortion. Multivariate logistic regression analysis showed that female age (OR 1.050; 95% CI 1.032–1.069; P < 0.001), male age (OR 1.100; 95% CI 1.086–1.115; P < 0.001), follicular-stimulating hormone (OR 1.049; 95% CI 1.022–1.076; P < 0.001), anti-Mullerian hormone (OR 0.893; 95% CI 0.862–0.925; P < 0.001) and the number of fetuses at pregnancy diagnosis were independent factors associated with spontaneous abortion. The threshold effect analysis found that when female age > 32 years (cut-off point) old, age and the risk of spontaneous abortion were positively correlated. When follicular-stimulating hormone > 6.1 IU/L (cut-off point), follicular-stimulating hormone was positively correlated with the occurrence of spontaneous abortion, When anti-Mullerian hormone ≤ 3.1 ng/mL (cut-off point), anti-Mullerian hormone was negatively correlated with the occurrence of spontaneous abortion and there was a linear positive correlation between antral Follicle Counting and live birth. In addition, the older the male age, the higher the incidence of abortion. The smooth curve fit and threshold effect analyses can provide a more detailed estimate of the probability of spontaneous abortion for pregnant couples.
BackgroundEstrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects.MethodsHerein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.ResultsThe chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists.ConclusionThese results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.
ObjectiveThe objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) and to establish a nomogram model evaluate the probability of OHSS in PCOS patients.MethodsWe retrospectively analyzed clinical data from 4,351 patients with PCOS receiving IVF/ICSI in our reproductive medical center. The clinical cases were randomly divided into a modeling group (3,231 cases) and a verification group (1,120 cases) according to a ratio of about 3:1. The independent risk factors correlation with the occurrence of OHSS was identified by logistic regression analysis. Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of OHSS in PCOS patients, and the predictive accuracy of the model was measured using the area under the receiver operating curve (AUC).ResultsUnivariate and multivariate logistic regression analyses showed that FSH (OR, 0.901; 95% CI, 0.847–0.958; P<0.001), AMH (OR, 1.259; 95% CI, 1.206–1.315; P<0.001), E2 value on the day of hCG injection (OR, 1.122; 95% CI, 1.021–1.253; P<0.001), total dosage of Gn used (OR, 1.010; 95% CI, 1.002–1.016; P=0.041), and follicle number on the day of hCG injection (OR, 0.134; 95% CI, 1.020–1.261; P=0.020) are the independent risk factors for OHSS in PCOS patients. The AUC of the modeling group is 0.827 (95% CI, 0.795–0.859), and the AUC of the verification group is 0.757 (95% CI, 0.733–0.782).ConclusionThe newly established nomogram model has proven to be a novel tool that can effectively, easily, and intuitively predict the probability of OHSS in the patients with PCOS, by which the clinician can set up a better clinical management strategies for conducting a precise personal therapy.
Cervical cancer is a serious public health problem and is associated with high cancer-related mortality in females worldwide. The expression of IL17A can increase the migration and invasiveness of cervical cancer cells by activating the NF-κB signal pathway. Single-nucleotide polymorphisms (SNPs) can alter gene function and protein expression. We examined the association between two IL17A SNPs (rs2275913 and rs3748067) and the risk of cervical cancer. We also investigated the interaction between IL17A -174G/C and -572C/G mutations and environmental factors. Our 1:2 matched case-control study included 185 cervical cancer patients and 370 healthy controls. The IL17A rs2275913 and rs3748067 SNPs were genotyped by polymerase chain reaction-restriction fragment length polymorphism. Using logistic regression analysis, we found that individuals harboring the TT genotype of IL17A rs3748067 had an increased risk of cervical cancer compared with those carrying the CC genotype, and the adjusted OR (95%CI) was 6.29 (2.30-19.81). Moreover, individuals carrying the T allele of IL17A rs3748067 were more susceptible to cervical cancer than those with the C allele, and the adjusted OR (95%CI) was 2.31 (1.53-3.50). No significant interaction was observed between the IL17A rs2275913 polymorphism and cervical cancer risk. In conclusion, our study suggests that the IL17A rs3748067 polymorphism is independently associated with the risk of cervical cancer, and has a relationship with human papillomavirus infection with regard to the risk of cervical cancer.
Asthenozoospermia (AZS) is a severe form of male infertility with no clear pathogenesis, despite numerous research efforts, there is no consensus on this. This study was to investigate the expression of gene-associated with retinoid-interferon-induced mortality 19 (GRIM-19) in the sperm of patients with asthenozoospermia and the regulation of GC-2 spd cell proliferation, apoptosis and migration. We analyzed the sperm samples from 82 asthenozoospermia and normal patients were collected in the First People's Hospital of Shangqiu and the First Affiliated Hospital of Zhengzhou University. Immunofluorescence, western blots and RT-qPCR analyses were used to verify the expressions of GRIM-19. MTT assays were used to assess cell proliferations, flow cytometry was performed to assess cell apoptosis, wound‑healing was performed to measure cell migration. Immunofluorescence showed that GRIM-19 is predominantly expressed in the sperm mid-piece, the mRNA expressions of GRIM-19 in sperms of the asthenozoospermia group were significantly low, relative to the normal group (OR 0.266; 95% CI = 0.081–0.868; P = 0.028). The protein expressions of GRIM-19 in sperms of the asthenozoospermia group were significantly lower than that of the normal group as well (GRIM-19/GAPDH: 0.827 ± 0.063 vs 0.458 ± 0.033; P < 0.001). GRIM-19 overexpression promotes GC-2 spd cell proliferation and migration and reduces apoptosis, while GRIM-19-silenced reduces GC-2 spd cell proliferation and migration and increased apoptosis. GRIM-19 is closely related to the occurrence of asthenozoospermia and promotes GC-2 spd cell proliferation and migration and reduces apoptosis.
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