Corneal stem cells niche and homeostasis impacts in regenerative medicine; concise review.

The limbal stem cells niche (LSCN) is an optimal microenvironment that provides the limbal epithelial stem cells (LESCs) and strictly regulates their proliferation and differentiation. Disturbing the LSCN homeostasis can lead to limbal stem cell dysfunction (LSCD) and subsequent ocular surface aberrations, such as corneal stromal inflammation, persistent epithelial defects, corneal neovascularisation, lymphangiogenesis, corneal opacification, and conjunctivalization. As ocular surface disorders are considered the second main cause of blindness, it becomes crucial to explore different therapeutic strategies for restoring the functions of the LSCN. A major limitation of corneal transplantation is the current shortage of donor tissue to meet the requirements worldwide. In this context, it becomes mandatory to find an alternative regenerative medicine, such as using cultured limbal epithelial/stromal stem cells, inducing the production of corneal like cells by using other sources of stem cells, and using tissue engineering methods aiming to produce the three-dimensional (3D) printed cornea. Limbal epithelial stem cells have been considered the magic potion for eye treatment. Epithelial and stromal stem cells in the limbal niche hold the responsibility of replenishing the corneal epithelium. These stem cells are being used for transplantation to maintain corneal epithelial integrity and ultimately sustain optimal vision. In this review, we summarised the characteristics of the LSCN and their current and future roles in restoring corneal homeostasis in eyes with LSCD.

Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors.

BACKGROUND: To develop a deep learning (DL) method based on multiphase, contrast-enhanced (CE) magnetic resonance imaging (MRI) to distinguish Liver Imaging Reporting and Data System (LI-RADS) grade 3 (LR-3) liver tumors from combined higher-grades 4 and 5 (LR-4/LR-5) tumors for hepatocellular carcinoma (HCC) diagnosis. METHODS: A total of 89 untreated LI-RADS-graded liver tumors (35 LR-3, 14 LR-4, and 40 LR-5) were identified based on the radiology MRI interpretation reports. Multiphase 3D T1-weighted gradient echo imaging was acquired at six time points: pre-contrast, four phases immediately post-contrast, and one hepatobiliary phase after intravenous injection of gadoxetate disodium. Image co-registration was performed across all phases on the center tumor slice to correct motion. A rectangular tumor box centered on the tumor area was drawn to extract subset tumor images for each imaging phase, which were used as the inputs to a convolutional neural network (CNN). The pre-trained AlexNet CNN model underwent transfer learning using liver MRI data for LI-RADS tumor grade classification. The output probability number closer to 1 or 0 indicated a higher possibility of being combined LR-4/LR-5 tumor or LR-3 tumor, respectively. Five-fold cross validation was used for training (60% dataset), validation (20%) and testing processes (20%). RESULTS: The DL CNN model for LI-RADS grading using inputs of multiphase liver MRI data acquired at three time points (pre-contrast, arterial, and washout phase) achieved a high accuracy of 0.90, sensitivity of 1.0, precision of 0.835, and AUC of 0.95 with reference to the expert human radiologist report. The CNN output of probability provided radiologists a confidence level of the model's grading for each liver lesion. CONCLUSIONS: An AlexNet CNN model for LI-RADS grading of liver lesions provided diagnostic performance comparable to radiologists and offered valuable clinical guidance for differentiating intermediate LR-3 liver lesions from more-likely malignant LR-4/LR-5 lesions in HCC diagnosis.