Objective: In this study, we aimed to examine the effect of MAN on human lung cancer and reveal the underlying molecular mechanism.
Methods: MTT assay was conducted to measure cell viability. Annexin V-FITC/PI staining was used to detect cell apoptosis. Confocal microscope was performed to determine the formation of autophagosomes and autolysosomes. Flow cytometry was performed to quantify cell death. Western blotting was used to determine the related-signaling pathway.
Results: In the present study, we demonstrated for the first time that MAN inhibitd cell proliferation and induced cell apoptosis in human non-small-cell lung carcinoma (NSCLC) cells. We found that MAN treatment dysregulated mitochondrial function and led to mitochondrial apoptosis in A549 and PC9 cells. Meanwhile, MAN enhanced autophagy flux by the increase of autophagosome formation, the fusion of autophagsomes and lysosomes and lysosomal function. Moreover, mTOR signaling pathway, a classical pathway regualting autophagy, was inhibited by MAN in a time- and dose-dependent mannner, resulting in autophagy induction. Interestingly, autophagy inhibition by CQ or Atg5 knockdown attenuated cell apoptosis by MAN, indicating that autophagy serves as cell death. Furthermore, autophagy-mediated cell death by MAN can be blocked by reactive oxygen species (ROS) scavenger NAC, indicating that ROS accumulation is the inducing factor of apoptosis and autophagy. In summary, we revealed the molecular mechanism of MAN against lung cancer through apoptosis and autophagy, suggesting that MAN might be a novel therapeutic agent for NSCLC treatment.
METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.
MATERIALS AND METHODS: We retrospectively enrolled patients who underwent the procedure from January 2018 to April 2022. Under real time ultrasound (Hitachi Medical ProSound F37), thoracic lesions adjacent to the chest wall were sampled with a full-core biopsy needle (CT Core Single Action Biopsy Device, 18G × 15 cm, Vigeo, Italy). Chest x-ray was performed 30 minutes post procedure ruling out pneumothorax. Patients were discharged home 1-2 hours post biopsy. Data was analysed using Microsoft Excel 2010 and Statistical Package for Social Science (SPSS) Version 26.
RESULTS: A total of 18 patients (14 males, 4 females) underwent USLB for lung tumours. Biopsies were histologically deemed adequate with an overall diagnostic yield of 77.8% (14/18). A total of 57% were positive for thoracic malignancy (21% squamous cell carcinoma, 21% adenocarcinoma, 15% small cell carcinoma) and another 43% were positive for extra thoracic malignancy (1 hepatocellular carcinoma, 2 DLBCL, 1 Hodgkin's lymphoma, 1 seminoma, 1 thymoma). Four patients had inconclusive results but managed to get positive results from surgical or lymph node biopsy (thymoma and adenocarcinoma). Statistical analysis showed more than two passes are needed to achieve a positive HPE yield (p value<0.05). There were nil complications to all the cases done.
CONCLUSIONS: USLB can safely and effectively be performed by trained pulmonologists with excellent accuracy and low complication rate in outpatients.