Background: MicroRNA 375 (miR-375) is expressed in the pituitary gland, but its functions and the related mechanisms have not been studied. Results: miR-375 mediates the signaling pathway of CRF regulating POMC expression by targeting MAP3K8. Conclusion: miR-375 negatively regulates POMC expression and related hormone secretion. Significance: These new data suggest that miRNAs play important roles in regulating pituitary hormone secretion.

Platinum-based chemotherapy agents are widely used in the treatment of various solid malignancies. However, their efficacy is limited by drug resistance. Recent studies suggest that copper efflux transporters, which are encoded by ATP7A and ATP7B, play an important role in platinum drug resistance. Over-expressions of ATP7A and ATP7B are observed in multiple cancers. Moreover, their expressions are associated with cancer prognosis and treatment outcomes of platinum-based chemotherapy. In our review, we highlight the roles of ATP7A/7B in platinum drug resistance and cancer progression. We also discuss the possible mechanisms of platinum drug resistance mediated by ATP7A/7B and provide novel strategies for overcoming resistance. This review may be helpful for understanding the roles of ATP7A and ATP7B in platinum drug resistance. © 2018 IUBMB Life, 70(3):183-191, 2018.

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.

There is an increased demand for efficient biomarkers for the diagnosis of non-small cell lung cancer (NSCLC). This study aimed to evaluate plasma levels of TrxR activity in a large population to confirm its validity and efficacy in NSCLC diagnosis. Blood samples were obtained from 1922 participants (638 cases of NSCLC, 555 cases of benign lung diseases (BLDs) and 729 sex- and age-matched healthy controls). The plasma levels of TrxR activity in patients with NSCLC (15.66 ± 11.44 U/ml) were significantly higher (P < 0.01) than in patients with BLDs (6.27 ± 3.78 U/ml) or healthy controls (2.05 ± 1.86 U/ml). The critical value of plasma TrxR activity levels for diagnosis of NSCLC was set at 10.18 U/ml, with a sensitivity of 71.6% and a specificity of 91.9%. The combination of NSE, CEA, CA19-9, Cyfra21-1, and TrxR was more effective for NSCLC diagnosis (sensitivity and specificity in the training set: 85.6%, 90.2%; validation set: 86.2%, 92.4%) than was each biomarker individually (P < 0.001). TrxR can also efficiently distinguish the metastatic status of the tumor, and it can further differentiate between various histological differentiations. Together, plasma TrxR activity was identified as a convenient, non-invasive, and efficient biomarker for the diagnosis of NSCLCs, particularly for discriminating between metastatic and non-metastatic tumors, or for histologic differentiation.

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