ABSTRACT
BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS: This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS: In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS: The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.
ABSTRACT
Regulation of α-glucosidase (EC 3.2.1.20) and its inhibitors is of great interest to researchers due to its clinical relevance as a target enzyme for the treatment of α-glucosidase-mediated diseases, such as type 2 diabetes mellitus and Pompe disease. In this study, we conducted a phloroglucinol-induced inhibition kinetics assay and performed computational molecular dynamics (MD) simulations to assess binding manner in α-glucosidase. The results showed that phloroglucinol reversibly inhibited α-glucosidase in a dose-dependent but non-competitive manner (Ki=2.07±0.16mM). Interestingly, the maximum peak wavelength and the hydrophobic surface remained unchanged during the inhibition reaction, with computational MD simulations further revealing that phloroglucinol bound in front of the active site pocket rather than in the α-glucosidase active site. Therefore, we speculate that phloroglucinol-specific inhibition is mild and the inhibitor likely binds to a single binding site near but not in the active site. Our study provided insight into the effects and mechanisms associated with a mild inhibitor of α-glucosidase activity and promotes fundamental research and potential applications of inhibitors for treatment of α-glucosidase-mediated clinical disease.
