Rationale:Primary signet ring cell carcinoma of the uterine cervix is extremely rare and the clinical characteristics and prognosis are not well known and there are no specific guidelines for treatment.Patient concerns:A 43-year-old woman was referred to our hospital for abnormal uterine bleeding lasting 1 month.Diagnoses:Histological examination revealed a signet ring cell carcinoma of the uterine cervix. After evaluation of extragenital origin, the patient was diagnosed International Federation of Gynecology and Obstetrics stage IIIC1 primary signet ring cell carcinoma or the uterine cervix.Intervention:The patient was prescribed concomitant chemo-radiation followed by intracavitary brachytherapy.Outcomes:She showed no evidence of disease after treatment but, it recurred after 7 months of last treatment.Lessons:Different approaches to diagnosis and treatment of this rare disease are needed and molecular pathological studies related to the onset of the disease are required.
Background Cervical cancer is the third most common gynecological malignancy. Conventional treatment options are known to be ineffective for the majority of patients with advanced or recurrent cervical cancer. Therefore, novel therapeutic agents for cervical cancer are necessary. In this study, the effects of CKD-602 in cervical cancer were investigated. Methods Three established human, immortalized, cervical cancer cell lines (CaSki, HeLa and SiHa) were used in this study. Following treatment with CKD-602, apoptosis was quantified using fluorescein isothiocyanate Annexin V-FITC and propidium iodide (PI) detection kit and cell cycle analysis was analyzed using fluorescence activated cell sorting (FACS). Transwell chambers were used for invasion assays. Western blot assay was performed to analyze proteomics. CaSki cells were subcutaneously injected into BALB/c-nude mice and cervical cancer xenograft model was established to elucidate the antitumor effect of CKD-602 in vivo. Results Treatment with CKD-602 induced apoptosis and increased expression of the enzyme PARP, cleaved PARP, and BAX. In addition, expression of phosphorylated p53 increased. Cell cycle arrest at G2/M phase and inhibition of invasion were detected after treatment with CKD-602. A significant decrease in cervical cancer tumor volume was observed in this in vivo model, following treatment with CKD-602. Conclusions This is the first report of CKD-602 having an antitumor effect in cervical cancer in both an in vitro and in vivo models. The results of this study indicate that CKD-602 may be a novel potential drug, targeting cervical cancer, providing new opportunities in the development of new therapeutic strategies. Electronic supplementary material The online version of this article (10.1186/s10020-019-0089-y) contains supplementary material, which is available to authorized users.
This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.
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