The Effect of the Forging Process on the Microstructure and Mechanical Properties of a New Low-Cost Ti-5Al-1.5Mo-1.8Fe Alloy.

This paper presents results on the microstructure and mechanical properties of a new low-cost titanium alloy Ti-5Al-1.5Mo-1.8Fe after different forging processes. The ß phase transformation temperature of this alloy was 950 °C. In this study, the forging temperatures were designed at 920 °C and 980 °C, and the deformation degree ranged from 20% to 60%, with an interval of 20%. This study investigated the impact of the equiaxed α phase and shape of the lamellar microstructure on the tensile characteristics and fracture toughness of an alloy. The research employed a microstructure analysis and static tensile testing to evaluate the effect of forging temperatures and degree of deformation on the microstructure features. The findings revealed that forging temperatures could modify the microstructure characteristics, and the degree of deformation also affected this microstructure. This study demonstrates that a bimodal structure with an equiaxed α phase can be utilized to balance high strength and high ductility, resulting in better overall mechanical properties.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers.

Objective: To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively. Methods: A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Results: Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. Conclusion: We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.