Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). CCM1 and CCM3 form the CCM Signal Complex (CSC) by binding to CCM2. Both CCM1 and CCM2 exhibit cellular heterogeneity through multiple alternative spliced isoforms, where exons from the same gene combine in diverse ways, leading to varied mRNA transcripts. Additionally, both demonstrate nucleocytoplasmic shuttling between the nucleus and cytoplasm, suggesting their potential role in gene expression regulation as transcription factors (TFs). Due to the accumulated data indicating the cellular localization of CSC proteins in the nucleus and their interaction with progesterone receptors, which serve dual roles as both cellular signaling components and TFs, a question has arisen regarding whether CCMs could also function in both capacities like progesterone receptors. METHODS: To investigate this potential, we employed our proprietary deep-learning (DL)-based algorithm, specifically utilizing a biased-Support Vector Machine (SVM) model, to explore the plausible cellular function of any of the CSC proteins, particularly focusing on CCM gene isoforms with nucleocytoplasmic shuttling, acting as TFs in gene expression regulation. RESULTS: Through a comparative DL-based predictive analysis, we have effectively discerned a collective of 11 isoforms across all CCM proteins (CCM1-3). Additionally, we have substantiated the TF functionality of 8 isoforms derived from CCM1 and CCM2 proteins, marking the inaugural identification of CCM isoforms in the role of TFs. CONCLUSIONS: This groundbreaking discovery directly challenges the prevailing paradigm, which predominantly emphasizes the involvement of CSC solely in endothelial cellular functions amid various potential cellular signal cascades during angiogenesis.

Multi-Resolution Image Segmentation Based on a Cascaded U-ADenseNet for the Liver and Tumors.

The liver is an irreplaceable organ in the human body, maintaining life activities and metabolism. Malignant tumors of the liver have a high mortality rate at present. Computer-aided segmentation of the liver and tumors has significant effects on clinical diagnosis and treatment. There are still many challenges in the segmentation of the liver and liver tumors simultaneously, such as, on the one hand, that convolutional kernels with fixed geometric structures do not match complex, irregularly shaped targets; on the other, pooling during convolution results in a loss of spatial contextual information of images. In this work, we designed a cascaded U-ADenseNet with coarse-to-fine processing for addressing the above issues of fully automatic segmentation. This work contributes multi-resolution input images and multi-layered channel attention combined with atrous spatial pyramid pooling densely connected in the fine segmentation. The proposed model was evaluated by a public dataset of the Liver Tumor Segmentation Challenge (LiTS). Our approach attained competitive liver and tumor segmentation scores that exceeded other methods across a wide range of metrics.

MRI and PET image fusion using the nonparametric density model and the theory of variable-weight.

Medical image fusion is important in the field of clinical diagnosis because it can improve the availability of information contained in images. Magnetic Resonance Imaging (MRI) provides excellent anatomical details as well as functional information on regional changes in physiology, hemodynamics, and tissue composition. In contrast, although the spatial resolution of Positron Emission Tomography (PET) provides is lower than that an MRI, PET is capable of depicting the tissue's molecular and pathological activities that are not available from MRI. Fusion of MRI and PET may allow us to combine the advantages of both imaging modalities and achieve more precise localization and characterization of abnormalities. Previous image fusion algorithms, based on the estimation theory, assume that all distortions follow Gaussian distribution and are therefore susceptible to the model mismatch problem. To overcome this mismatch problem, we propose a new image fusion method with multi-resolution and nonparametric density models (MRNDM). The RGB space registered from the source multi-modal medical images is first transformed into a generalized intensity-hue-saturation space (GIHS), and then is decomposed into the low- and high-frequency components using the non-subsampled contourlet transform (NSCT). Two different fusion rules, which are based on the nonparametric density model and the theory of variable-weight, are developed and used to fuse low- and high-frequency coefficients. The fused images are constructed by performing the inverse of the NSCT operation with all composite coefficients. Our experimental results demonstrate that the quality of images fused from PET and MRI brain images using our proposed method MRNDM is higher than that of those fused using six previous fusion methods.