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Abstract Objective This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma. Materials and methods We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram’s predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared. Results In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS. Conclusion The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.

Enzymes are important raw material for the detergent industry. In order to develop enzymes for detergents, Bacillus licheniformis D5, a strain with high alkaline protease production, was screened in this study. The alkaline protease production was optimized using response surface methodology (RSM). The optimized fermentation medium (w/v) consisted of soybean cake flour (4.0%), peanut cake flour (2.69%), beef paste (0.95%), maltodextrin (4.0%), glucose (2.23%), KCl (0.1%), KH2PO4 (0.2%), K2HPO4·12H2O (0.40%), CaCl2 (0.04%), MgSO4·7H2O (0.04%), Tween-80 (0.05%) and serine (0.05%). The fermentation level increased by 2.12-fold compared with the control. The culture temperature was 33°C, and the initial pH was 10. The fed-batch fermentation was performed in a 30 L fermenter. The fermentation level was increased by 47.4% (from 43,587 U/mL to 60,830 U/mL), and the fermentation time was shortened from 78 h to 54 h. The results demonstrated significantly improved fermentation level, reduced fermentation cost, simplified large-scale production of alkaline protease, and thereby promote application of alkaline protease in detergent industry. [ABSTRACT FROM AUTHOR]