Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics

C/EBP beta-LIP induces cancer-type metabolic reprogramming by regulating the let-7/LIN28B circuit in mice

The transcription factors LAP1, LAP2 and LIP are derived from the Cebpb-mRNA through the use of alternative start codons. High LIP expression has been associated with human cancer and increased cancer incidence in mice. However, how LIP contributes to cellular transformation is poorly understood. Here we present that LIP induces aerobic glycolysis and mitochondrial respiration reminiscent of cancer metabolism. We show that LIP-induced metabolic programming is dependent on the RNA-binding protein LIN28B, a translational regulator of glycolytic and mitochondrial enzymes with known oncogenic function. LIP activates LIN28B through repression of the let-7 microRNA family that targets the Lin28b-mRNA. Transgenic mice overexpressing LIP have reduced levels of let-7 and increased LIN28B expression, which is associated with metabolic reprogramming as shown in primary bone marrow cells, and with hyperplasia in the skin. This study establishes LIP as an inducer of cancer-type metabolic reprogramming and as a regulator of the let-7/LIN28B regulatory circuit

The novel compound Sul-121 inhibits airway inflammation and hyperresponsiveness in experimental models of chronic obstructive pulmonary disease

COPD is characterized by persistent airflow limitation, neutrophilia and oxidative stress from endogenous and exogenous insults. Current COPD therapy involving anticholinergics, beta(2)-adrenoceptor agonists and/or corticosteroids, do not specifically target oxidative stress, nor do they reduce chronic pulmonary inflammation and disease progression in all patients. Here, we explore the effects of Sul-121, a novel compound with anti-oxidative capacity, on hyperresponsiveness (AHR) and inflammation in experimental models of COPD. Using a guinea pig model of lipopolysaccharide (LPS)-induced neutrophilia, we demonstrated that Sul-121 inhalation dose-dependently prevented LPS-induced airway neutrophilia (up to similar to 60%) and AHR (up to similar to 90%). Non-cartilaginous airways neutrophilia was inversely correlated with blood H2S, and LPS-induced attenuation of blood H2S (similar to 60%) was prevented by Sul-121. Concomitantly, Sul-121 prevented LPS-induced production of the oxidative stress marker, malondialdehyde by similar to 80%. In immortalized human airway smooth muscle (ASM) cells, Sul-121 dose-dependently prevented cigarette smoke extract-induced IL-8 release parallel with inhibition of nuclear translocation of the NF-kappa B subunit, p65 (each similar to 90%). Sul-121 also diminished cellular reactive oxygen species production in ASM cells, and inhibited nuclear translocation of the anti-oxidative response regulator, Nrf2. Our data show that Sul-121 effectively inhibits airway inflammation and AHR in experimental COPD models, prospectively through inhibition of oxidative stress

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems

C/EBPβ-LIP induces cancer-type metabolic reprogramming by regulating the let-7/LIN28B circuit in mice

The transcription factors LAP1, LAP2 and LIP are derived from the Cebpb-mRNA through the use of alternative start codons. High LIP expression has been associated with human cancer and increased cancer incidence in mice. However, how LIP contributes to cellular transformation is poorly understood. Here we present that LIP induces aerobic glycolysis and mitochondrial respiration reminiscent of cancer metabolism. We show that LIP-induced metabolic programming is dependent on the RNA-binding protein LIN28B, a translational regulator of glycolytic and mitochondrial enzymes with known oncogenic function. LIP activates LIN28B through repression of the let-7 microRNA family that targets the Lin28b-mRNA. Transgenic mice overexpressing LIP have reduced levels of let-7 and increased LIN28B expression, which is associated with metabolic reprogramming as shown in primary bone marrow cells, and with hyperplasia in the skin. This study establishes LIP as an inducer of cancer-type metabolic reprogramming and as a regulator of the let-7/LIN28B regulatory circuit