An artificial intelligence deep learning platform achieves high diagnostic accuracy for Covid-19 pneumonia by reading chest X-ray images

The coronavirus disease of 2019 (Covid-19) causes deadly lung infections (pneumonia). Accurate clinical diagnosis of Covid-19 is essential for guiding treatment. Covid-19 RNA test does not reflect clinical features and severity of the disease. Pneumonia in Covid-19 patients could be caused by non-Covid-19 organisms and distinguishing Covid-19 pneumonia from non-Covid-19 pneumonia is critical. Chest X-ray detects pneumonia, but a high diagnostic accuracy is difficult to achieve. We develop an artificial intelligence-based (AI) deep learning method with a high diagnostic accuracy for Covid-19 pneumonia. We analyzed 10,182 chest X-ray images of healthy individuals, bacterial pneumonia. and viral pneumonia (Covid-19 and non-Covid-19) to build and test AI models. Among viral pneumonia, diagnostic accuracy for Covid-19 reaches 99.95%. High diagnostic accuracy is also achieved for distinguishing Covid-19 pneumonia from bacterial pneumonia (99.85% accuracy) or normal lung images (100% accuracy). Our AI models are accurate for clinical diagnosis of Covid-19 pneumonia by reading solely chest X-ray images

Exploiting Unique Biological Features of Leukemia Stem Cells for Therapeutic Benefit

Cancer stem cells play a critical role in disease initiation and insensitivity to chemotherapy in numerous hematologic malignancies and some solid tumors, and these stem cells need to be eradicated to achieve a cure. Key to successful targeting of cancer stem cells is to identify and functionally test critical target genes and to fully understand their associated molecular network in these stem cells. Human chronic myeloid leukemia (CML) is well accepted as one of the typical types of hematopoietic malignancies that are derived from leukemia stem cells (LSCs), serving as an excellent model disease for understanding the biology of LSCs and developing effective, selective, and curative strategies through targeting LSCs. Here, we discuss LSCs in CML with a focus on identification of unique biological features of these stem cells to emphasize the feasibility and significance of specific targeting of LSCs while sparing normal stem cell counterparts in leukemia therapy

A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data

This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples

Robust dynamic classifier selection for remote sensing image classification

Dynamic classifier selection (DCS) is a classification technique that, for each new sample to be classified, selects and uses the most competent classifier among a set of available ones. We here propose a novel DCS model (R-DCS) based on the robustness of its prediction: the extent to which the classifier can be altered without changing its prediction. In order to define and compute this robustness, we adopt methods from the theory of imprecise probabilities. Additionally, two selection strategies for R-DCS model are presented and are applied on remote sensing images. The experiment results demonstrate that our model successfully incorporates uncertainty with respect to the model parameters without losing the performance

Novel oral transforming growth factor-beta signaling inhibitor EW-7197 eradicates CML-initiating cells

Recent strategies for treating CML patients have focused on investigating new combinations of tyrosine kinase inhibitors (TKIs) as well as identifying novel translational research agents that can eradicate CML leukemia-initiating cells (CML-LICs). However, little is known about the therapeutic benefits such CML-LIC targeting therapies might bring to CML patients. In this study, we investigated the therapeutic potential of EW-7197, an orally bioavailable transforming growth factor-beta signaling inhibitor which has recently been approved as an Investigational New Drug (NIH, USA), to suppress CML-LICs in vivo. Compared to TKI treatment alone, administration of TKI plus EW-7197 to CML-affected mice significantly delayed disease relapse and prolonged survival. Notably, combined treatment with EW-7197 plus TKI was effective in eliminating CML-LICs even if they expressed the TKI-resistant T315I mutant BCR-ABL1 oncogene. Collectively, these results indicate that EW-7197 may be a promising candidate for a new therapeutic that can greatly benefit CML patients by working in combination with TKIs to eradicate CML-LICs

Scd1 plays a tumor-suppressive role in survival of leukemia stem cells and the development of chronic myeloid leukemia

Chronic myeloid leukemia (CML) is derived from a stem cell, and it is widely accepted that the existence of leukemia stem cells (LSCs) is one of the major reasons for the relapse of CML treated with kinase inhibitors. Key to eradicating LSCs is to identify genes that play a critical role in survival regulation of these stem cells. Using BCR-ABL-induced CML mouse model, here we show that expression of the stearoyl-CoA desaturase 1 (Scd1) gene is downregulated in LSCs and that Scd1 plays a tumor-suppressive role in LSCs with no effect on the function of normal hematopoietic stem cells. Deletion of Scd1 causes acceleration of CML development and conversely overexpression of Scd1 delays CML development. In addition, using genetic approaches, we show that Pten, p53, and Bcl2 are regulated by Scd1 in LSCs. Furthermore, we find that induction of Scd1 expression by a PPARγ agonist suppresses LSCs and delays CML development. Our results demonstrate a critical role for Scd1 in functional regulation of LSCs, providing a new anti-LSC strategy through enhancing Scd1 activity

Low-density nanoporous iron foams synthesized by sol-gel autocombustion

Nanoporous iron metal foams were synthesized by an improved sol-gel autocombustion method in this report. It has been confirmed to be pure phase iron by X-ray diffraction measurements. The nanoporous characteristics were illustrated through scanning electron microscope and transmission electron microscope images. Very low density and quite large saturation magnetization has been performed in the synthesized samples