A Web-based Prediction Model for Early Death in Patients With Metastatic Triple-negative Breast Cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by the absence of expression of estrogen receptor, progesterone receptor, or human epidermal growth factor receptor 2. This subtype of breast cancer is known for its high aggressiveness, high metastatic potential, tendency for recurrence, and poor prognosis. Patients with metastatic TNBC (mTNBC) have a poorer prognosis and a higher likelihood of early death (survival time ≤3 months). Therefore, the development of effective individualized survival prediction tools, such as prediction nomograms and web-based survival calculators, is of great importance for predicting the probability of early death in patients with metastatic TNBC. METHODS: Patients diagnosed with mTNBC in the Surveillance, Epidemiology, and End Results database between 2010 and 2015 were included in the model construction. Univariate and multivariate logistic regression analysis was performed to identify risk factors associated with early death in patients with mTNBC and predictive prognostic nomograms were constructed. The accuracy of the nomograms was verified using receiver operating characteristic curves, and GiViTi Calibration belt plots were used to evaluate the model consistency. The clinical applicability of the nomograms was evaluated using decision curve analysis. On the basis of the predictive prognostic nomograms, a network survival rate calculator was developed for individualized survival prediction in patients with mTNBC. RESULTS: A total of 2230 patients diagnosed with mTNBC were included in the Surveillance, Epidemiology, and End Results database for this study. After strict exclusion criteria, 1428 patients were found to be eligible for the study. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. Independent risk factors for mTNBC, including age, tumor size, brain metastasis, liver metastasis, surgery, and chemotherapy, were identified and integrated to construct the prediction nomogram and survival calculator. Results of receiver operating characteristic curves, calibration curves, and decision curve analysis curves from the training and validation cohort confirmed that the developed nomogram and web-based survival calculator in this study could accurately predict the probability of early death in patients with mTNBC. CONCLUSIONS: In this study, we developed a reliable prediction nomogram and web-based survival calculator for predicting the probability of early death in patients with mTNBC. These tools can assist clinical physicians in identifying high-risk patients and developing personalized treatment plans as early as possible.

An Online Model for Central Lymph Node Metastases in Papillary Thyroid Carcinoma With BRAF V600E Mutation.

BACKGROUND: To construct a predictive model to direct the dissection of the central lymph nodes in papillary thyroid cancer (PTC) with BRAF V600E mutation by identifying the risk variables for central lymph node metastases (CLNM). METHODS: Data from 466 PTC patients with BRAF V600E mutations underwent thyroid surgery was collected and analyzed retrospectively. For these patients, we conducted univariate and multivariate logistic regression analysis to find risk variables for CLNM. To construct a nomogram, the independent predictors were chosen. The calibration, discrimination, and clinical utility of the predictive model were assessed by training and validation data. RESULTS: CLNM was present in 323/466 PTC patients with BRAF V600E mutations. By using univariate and multivariate logistic regression, we discovered that gender, age, tumor size, multifocality, and pathological subtype were all independent predictors of CLNM in PTC patients with BRAF V600E mutations. A predictive nomogram was created by combining these variables. In both training and validation groups, the nomogram demonstrated great calibration capacities. The training and validation groups' areas under the curve (AUC) were 0.772 (specificity 0.694, sensitivity 0.728, 95% CI: 0.7195-0.8247) and 0.731 (specificity 0.778, sensitivity 0.653, 95% CI: 0.6386-0.8232) respectively. According to the nomogram's decision curve analysis (DCA), the nomogram might be beneficial. As well, an online dynamic calculator was developed to make the application of this nomogram easier in the clinic. CONCLUSION: An online nomogram model based on the 5 predictors included gender, age, pathological subtype, multifocality, and tumor size was confirmed to predict CLNM and guide the central lymph nodes dissection in PTC patients with BRAF V600E mutations.

Insight into the structural stability of wild type and mutants of the tobacco etch virus protease with molecular dynamics simulations.

The efficiency and high specificity of tobacco etch virus protease (TEVp) has made it widely used for cleavage of recombinant fusion proteins. However, TEVp suffers from a few intrinsic defects such as self-cleavage, poorly expressed in E. coli and less soluble. So some mutants were designed to improve it, such as S219V, T17S/N68D/I77V and L56V/S135G etc. MD simulations for the WT TEVp and its mutants were performed to explore the underlying dynamic effects of mutations on TEVp. Although the globular domains are fairly conserved, the three mutations have diverse effects on the dynamics properties of TEVp, including the elongation of ß-sheet, conversion of loop to helix and the flexibility of active core. Our present study indicates that the three mutants for TEVp can change their secondary structure and tend to form more helixes and sheets to improve stability. The study also helps us to understand the effects of some mutations on TEVp, provides us insights into the change of them at the atomic level and gives a potential rational method to design an improved protein.