Pemetrexed (PEM) is an effective chemotherapeutic drug used for the treatment of clinical non-small-cell lung cancer (NSCLC) and is reported to induce severe hepatotoxicity. Exploring potential drugs which could counteract the side effects of PEM is of great clinical interest. Here, we aim to examine the beneficial effects of Montelukast, a novel anti-asthma drug, against PEM-induced cytotoxicity in hepatocytes, and to explore the underlying mechanism. We found that Montelukast reduces cytotoxicity of PEM in hepatocytes, confirmed by its increasing cell viability and reducing lactate dehydrogenase (LDH) release. In addition, Montelukast attenuated PEM-induced oxidative stress by reducing mitochondrial reactive oxygen species (ROS), increasing reduced glutathione (GSH), and downregulating NADPH oxidase 4 (NOX-4) expression. Importantly, Montelukast suppressed PEM-induced activation of the nucleotide oligomerization domain-like receptor protein 3 (NLRP3) inflammasome and mitigated endoplasmic reticulum (ER) stress by reducing NLRP3, growth arrest, and DNA damage-inducible protein 34 (GADD34), CEBP-homologous protein (CHOP), and also blocking the eukaryotic initiation factor 2 (eIF-2α)/activating transcription factor 4 (ATF4) signaling pathway. Lastly, we found that Montelukast inhibited the transcriptional activity of nuclear factor kappa-B (NF-κB). Montelukast exerted a protective action against PEM-induced cytotoxicity in hepatocytes by mitigating ER stress and NLRP3 activation.
The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly picked from the patients’ images as the independent test set to test its searching ability on suspicious tumor regions. Furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. Efficient-Det has demonstrated remarkable progress in general image recognition tasks with specific advantages of locating and identifying tumor areas simultaneously. Compared to the manual method (mean accuracy: 95.3% and 60 s per image) and traditional feature engineering method (mean accuracy: 90% and 15 s per image), our Efficient-Det has the capability of providing a competitive (mean accuracy: 92.6%) and fast (0.06 s per image) classification result. The deployment of Efficient-Det in our local breast cancer discrimination task exhibits specific applicability within real clinical workflows.
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