Puzzling out the role of MIAT LncRNA in hepatocellular carcinoma.

A non-negligible part of our DNA has been proven to be transcribed into non-protein coding RNA and its intricate involvement in several physiological processes has been highly evidenced. The significant biological role of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) has been variously reported. In the current review, the authors highlight the multifaceted role of myocardial infarction-associated transcript (MIAT), a well-known lncRNA, in hepatocellular carcinoma (HCC). Since its discovery, MIAT has been described as a regulator of carcinogenesis in several malignant tumors and its overexpression predicts poor prognosis in most of them. At the molecular level, MIAT is closely linked to the initiation of metastasis, invasion, cellular migration, and proliferation, as evidenced by several in-vitro and in-vivo models. Thus, MIAT is considered a possible theranostic agent and therapeutic target in several malignancies. In this review, the authors provide a comprehensive overview of the underlying molecular mechanisms of MIAT in terms of its downstream target genes, interaction with other classes of ncRNAs, and potential clinical implications as a diagnostic and/or prognostic biomarker in HCC.

Development of Verapamil Hydrochloride-loaded Biopolymer-based Composite Electrospun Nanofibrous Mats: In vivo Evaluation of Enhanced Burn Wound Healing without Scar Formation.

Introduction: Researchers aim for new heights in wound healing to produce wound dressings with unique features. Natural, synthetic, biodegradable, and biocompatible polymers especially in the nanoscale are being employed to support and provide efficient wound management. Economical and environmentally friendly sustainable wound management alternatives are becoming an urgent issue to meet future needs. Nanofibrous mats possess unique properties for ideal wound healing. They mimic the physical structure of the natural extracellular matrix (ECM), promote hemostasis, and gas permeation. Their interconnected nanoporosity prevents wound dehydration and microbial infiltration. Purpose: To prepare and evaluate a novel verapamil HCl-loaded environmentally friendly composite, with biopolymer-based electrospun nanofibers suitable for application as wound dressings providing adequate wound healing with no scar formation. Methods: Composite nanofibers were prepared by electrospinning of a blend of the natural biocompatible polymers, sodium alginate (SA) or zein (Z) together with polyvinyl alcohol (PVA). Composite nanofibers were characterized in terms of morphology, diameter, drug entrapment efficiency, and release. In vivo study of the therapeutic efficacy of verapamil HCl-loaded nanofibers on a Sprague Dawley rat model with dermal burn wound was investigated in terms of percent wound closure, and presence of scars. Results: Combining PVA with SA or Z improved the electrospinnability and properties of the developed nanofibers. Verapamil HCl-loaded composite nanofibers showed good pharmaceutical attributes favorable for wound healing including, fiber diameter ∼150 nm, high entrapment efficiency (∼80-100%) and biphasic controlled drug release for 24 h. In vivo study demonstrated promising potentials for wound healing without scaring. Conclusion: The developed nanofibrous mats combined the beneficial properties of the biopolymers and verapamil HCl to provide an increased functionality by exploiting the unique advantages of nanofibers in wound healing at a small dose proved to be insufficient in case of the conventional dosage form.

Comprehensive assessment of facial paralysis based on facial animation units.

Quantitative grading and classification of the severity of facial paralysis (FP) are important for selecting the treatment plan and detecting subtle improvement that cannot be detected clinically. To date, none of the available FP grading systems have gained widespread clinical acceptance. The work presented here describes the development and testing of a system for FP grading and assessment which is part of a comprehensive evaluation system for FP. The system is based on the Kinect v2 hardware and the accompanying software SDK 2.0 in extracting the real time facial landmarks and facial animation units (FAUs). The aim of this paper is to describe the development and testing of the FP assessment phase (first phase) of a larger comprehensive evaluation system of FP. The system includes two phases; FP assessment and FP classification. A dataset of 375 records from 13 unilateral FP patients was compiled for this study. The FP assessment includes three separate modules. One module is the symmetry assessment of both facial sides at rest and while performing five voluntary facial movements. Another module is responsible for recognizing the facial movements. The last module assesses the performance of each facial movement for both sides of the face depending on the involved FAUs. The study validates that the FAUs captured using the Kinect sensor can be processed and used to develop an effective tool for the automatic evaluation of FP. The developed FP grading system provides a detailed quantitative report and has significant advantages over the existing grading scales. It is fast, easy to use, user-independent, low cost, quantitative, and automated and hence it is suitable to be used as a clinical tool.

Classification of facial paralysis based on machine learning techniques.

Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.