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Our aim is to establish a model for predicting the aggressiveness of pathological subtypes of multiple primary invasive lung adenocarcinomas based on CT radiomics to provide a reference for preoperative planning in affected patients. Clinical data and CT images of patients who were diagnosed as having multiple primary invasive lung adenocarcinomas through postoperative pathological analysis from January 2016 to December 2020 in the Third Affiliated Hospital of Kunming Medical University were retrospectively analyzed. 3D Slicer software were used to perform the focal segmentation and feature extraction of the CT images. Five classification learners were employed to establish models for predicting the aggressiveness of pathological subtypes of the lung adenocarcinomas, and evaluate the performance of the prediction model based on the area under the Area Under the subject operating characteristic Curve (AUC). 204 patients were enrolled, surgical intervention was applied to 408 nodules. Of them, 36.8% nodules were Acinar-type.The analysis of the CT radiomics-based aggressiveness prediction model demonstrated that the training group showed that the AUC values of logistic regression (LR), random forest (RF), decision tree(DT), support vector machine(SVM), and adaptive boosting(AdaBoost) models were all within 0.7–1, and the testing group showed that the AUC values of the LR and RF models were all within 0.7–0.9. Our results indicate that acinar-type is the main pathological subtype of multiple primary invasive lung adenocarcinomas. LR and RF models presented a certain level of accuracy performance in predicting the aggressiveness of pathological subtypes of multiple primary invasive lung adenocarcinomas and thus facilitate preoperative planning in cancer patients.

Background: Lung cancer is the most common malignant tumor with the highest mortality all over the world. Among it, Lung Adenocarcinoma owns the highest proportion of pathological classification.Yet, the fuction of MCM4 in the occurrence of Lung Adenocarcinoma is still unknown.In this research, we attempted to disscuss the potential role of MCM4 in LUAD through bioinformatics analysis and hoped to provide a new idea for clinical treatment.Methods: The data of Lung Adenocarcinoma in TCGA and GEO were downloaded.The correlation between MCM4 and immune infiltration were performed by R software packages .Enrichment analysis were underwent to analyze the functions of MCM4. Univariate and multivariate Cox regression analysis were utilized to determine whether MCM4 could be act as an independent prognostic factor for Lung Adenocarcinoma.Lastly,the expression of MCM4 was validated in clinical tissues by immunohistochemistry (IHC), real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), and western blotting (WB).Results: The expression of MCM4 was up-regulated in 13 tumors and was significantly correlated with age, gender, stage, T, N and smoke history. Furthermore, the Kaplan-Meier survival curves pointed that LUAD samples with lower MCM4 expression had longer Overall survival (OS) and the Disease-free survival (DFS) in 4 datasets. Univariate Cox regression analysis showed that the p value of MCM4 expression was less than 0.05(P＜0.05) and it appeared in the results of multivariate Cox regression analysis(HR≥1), which indicated that MCM4 can be used as an independent prognostic factor for LUAD.Enrichment Analysis suggested that MCM4 was correlated with the multiple biological functions and involved in vital pathways in GO and KEGG. Furthermore, the MCM4 expression was correlated with immune cells infiltration.Finally,the experimental results were consistent with the bioinformatic analysis.Conclusion: MCM4 might serve as a prognostic biomarker and correlate with immune infiltration in LUAD.

Background: Persisters make biofilm infections difficult to cure, the low content of persisters in biofilm and the difficulty of obtaining them are outstanding problems in research, finding a reliable method to obtain a sufficient amount of persisters is the key to research. Methods: In this study, C.albicans strain ATCC10231 and S.epidermidis ATCC35984 were used to culture mixed biofilms on the surface of a titanium metal coupon. Tetracycline, gentamicin, 5-fluorocytosine(5-Fc), caspofungin, rifampicin, fluconazole and carbonyl cyanide m-chlorophenylhydrazone (CCCP) were used to induce the dormancy of biofilms to produce persisters. Ciprofloxacin and amphotericin B were used to kill the non dormant bacteria, and then the persisters were screened. Results:The study showed that dormancy induced by tetracycline and 5-Fc could significantly increase the persister levels of S.epidermidis and C.albicans, respectively. After the induced persisters were re-cultured, there was no significant change in antibiotic sensitivity compared with before induction.Scanning electron microscope(SEM) observations showed that after dormancy induction, the surface morphology of C.albicans in the mixed biofilm showed convex changes, and the volume of S.epidermidis decreased. Confocal laser scanning microscope (CLSM)observation showed that the mixed biofilm with high content of persisters did not dissolve under the killing of antibiotics, and the biofilm with low content of persisters dissolved in large area. Conclusions: Induction of dormancy is a reliable method for the formation of persistent bacteria in C.albicans-S.epidermidis mixed biofilm.