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This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.

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Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

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Although it has been known that the tumor microenvironment affects angiogenesis, the precise mechanism remains unclear. In this study, we simulated the microenvironment of human esophageal squamous cell carcinoma (ESCC) by tumor conditioned medium (TCM) to assess the influence on normal endothelial cells (NECs). We found that the TCM-induced NECs showed enhanced angiogenic properties, such as migration, invasion and tube formation. Moreover, the TCM-induced NECs expressed tumor endothelial cells (TECs) markers at higher levels, which indicated that TCM probably promoted tumor angiogenesis by coercing NECs to change toward TECs. The microarray gene expression analysis indicated that TCM induced great changes in the genome of NECs and altered many regulatory networks, especially c-MYC and JAK/STAT3 signaling pathway. More importantly, we investigated the anti-angiogenic effect of metformin, and found that metformin abrogated the ESCC microenvironment-induced transition of NECs toward TECs by inhibiting JAK/STAT3/c-MYC signaling pathway. Furthermore, we verified the anti-angiogenic activity of metformin in vivo by a human ESCC patient-derived xenograft (PDX) mouse model for the first time. Taken together, our research provides a novel mechanism for the anti-angiogenic effect of metformin, and sets an experimental basis for the development of new anti-angiogenic drugs by blocking the transition of NECs toward TECs, which possibly open new avenues for targeted treatment of cancer.

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