HPV E6/E7 mRNA combined with thin-prep cytology test for the diagnosis of residual/recurrence after loop electrosurgical excision procedure in patients with cervical intraepithelial neoplasia.

To evaluate the diagnostic value of combining HPV E6/E7 mRNA testing with Thin-Prep cytology (TCT) for residual/recurrence detection, a total of 289 patients who underwent loop electrosurgical excision procedure (LEEP) for high-grade cervical lesions were included. Patients were followed up at different time points, and residual/recurrent lesions were confirmed through vaginoscopy. TCT, HPV-DNA, and HPV E6/E7 mRNA tests were conducted. Diagnostic performance, including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, was assessed. Among the patients, 76 cases showed residual lesions/recurrence, while 213 cases showed no residual/recurrence. Positive margins in the cervical-vaginal and cervical canal areas were associated with a higher risk of residual/recurrence. The combined HPV E6/E7 mRNA and TCT test showed higher diagnostic efficacy than individual tests at 6-, 12-, and 24-months follow-up. The combined test consistently demonstrated higher specificity and sensitivity, with significantly larger area under the curve (AUC) values compared to the individual tests.

Association between sensitivity to thyroid hormone indices and osteoporosis in euthyroid patients with type 2 diabetes mellitus.

Background: Thyroid hormones are known to regulate bone metabolism and may influence bone mineral density (BMD), as well as the risk of osteoporosis (OP) and fractures in patients with type 2 diabetes mellitus (T2DM). Recently, sensitivity to thyroid hormone indices has been linked with T2DM and OP independently. However, the relationship between thyroid hormone sensitivity and OP in euthyroid T2DM patients has yet to be investigated. Objectives: The aim of this study was to determine the association between sensitivity to thyroid hormone indices and the risk of OP in euthyroid patients with T2DM. Design: This study employed a retrospective, cross-sectional design and utilized data acquired from the Cangzhou Central Hospital in China between 2019 and 2020. Methods: We retrospectively analyzed the data of 433 patients with T2DM for anthropometric measurements, clinical laboratory test results, and BMD. The thyroid-stimulating hormone index, thyrotroph thyroxine resistance index, and thyroid feedback quantile-based index (TFQI) were calculated to determine thyroid hormone sensitivity. Finally, multivariable logistic regression, generalized additive models, and subgroup analysis were performed to detect the association between sensitivity to thyroid hormone indices and the risk of OP in these patients. Results: We did not observe a statistically significant linear relationship between sensitivity to thyroid hormones indices and OP after covariate adjustment. However, a nonlinear relationship existed between TFQI and the prevalence of OP. The inflection point of the TFQI was at -0.29. The effect sizes (odds ratio) on the left and right of the inflection point were 0.07 [95% confidence interval (CI): 0.01-0.71; p = 0.024] and 2.78 (95% CI: 1.02-7.58; p = 0.046), respectively. This trend was consistent in older female patients with higher body mass index (BMI; 25-30 kg/m2). Conclusion: An approximate U-shaped relationship was observed between sensitivity to thyroid hormone indices and OP risk in euthyroid patients with T2DM with variations in sex, age, and BMI. These findings provide a new perspective to elucidate the role of thyroid hormones in OP, specifically in patients with T2DM.

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study.

Purpose: Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients. Patients and Methods: Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters. Results: In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups. Conclusion: Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

ARFHPV E7 oncogene, lncRNA HOTAIR, miR-331-3p and its target, NRP2, form a negative feedback loop to regulate the apoptosis in the tumorigenesis in HPV positive cervical cancer.

The objective of this study was to explore the role of HOTAIR in the development of cervical cancer, as well as its downstream signaling pathway. We conducted computational analysis, luciferase assay to explore downstream of HOTAIR and miR-331-3p. Real-time PCR and Western blot were carried out to detect the relationship among E7, HOTAIR, miR-331-3p, NRP2, and P53. Finally, MTT assay and flow cytometry analysis were performed to validate the effect of E7 and miR-331-3p on cell apoptosis and proliferation. NRP2 was identified as a virtual target gene of miR-331-3p with a binding site of miR-331-3p, and HOTAIR was directly sponged to miR-331-3p, miR-331-3p reduced luciferase activity of wild-type of NRP2 3'UTR and HOTAIR, but not those of mutant NRP2 3'UTR and HOTAIR. MiR-331-3p down-regulated NRP2 and E7 expression levels, and further promoted cell apoptosis, while inhibited cell proliferation. Cell transfected with HPV16 E7 displayed lower levels of HOTAIR, NRP2 and P53, a higher level of miR-331-3p, over-expression of E7 further repressed cell apoptosis, while improved cell proliferation compared with control. Normal HPV (+) group exhibited a higher miR-331-3p, and lower mRNA levels of HOTAIR and NRP2 than HPV (-) group. According to the result of IHC (immunohistochemistry), we found that NRP2 protein was highly expressed in HPV (-) group compared to that in HPV (+) group. E7-HOTAIR-miR-331-3p-NRP2-E7 formed a regulatory loop, and could be involved in the pathogenesis of cervical cancer.