Search

Background and Objective: Using graph theory, Electronic Health Records (EHRs) can be represented graphically to exploit the relational dependencies of the multiple information formats to improve Machine Learning (ML) prediction models. In this systematic qualitative review, we explore the question: How are graphs used on EHRs, to predict diagnosis and health outcomes?, Methodology: The search strategy identified studies that used patient-level graph representations of EHRs to utilise ML to predict health outcomes and diagnoses. We conducted our search on MEDLINE, Web of Science and Scopus., Results: 832 studies were identified by the search strategy, of which 27 studies were selected for data extraction. Following data extraction, 18 studies used ML with patient-level graph-based representations of EHRs to predict health outcomes and diagnoses. Models ranged from traditional ML to neural network-based models. MIMIC-III was the most used dataset (n = 6, where n is the number of occurrences), followed by National Health Insurance Research Database (NHIRD) (n = 4) and eICU Collaborative Research Database (eICU) (n = 4). The most predicted health outcomes were mortality (n = 9; 21%), hospital readmission (n = 9; 21%), and treatment success (n = 4; 9%). Model performances ranged across outcomes, mortality prediction (Area Under the Receiver Operating Characteristic (AUROC): 72.1 - 91.6; Area Under Precision-Recall Curve (AUPRC): 34.8 - 81.3) and readmission prediction (AUROC: 63.7 - 85.8; AUPRC 39.86 - 84.7). Only one paper had a low Risk of Bias (RoB) that applied to our research question (4%)., Conclusion: Graph-based representations using EHRs, for individual health outcomes and diagnoses requires further research before we can see the results applied clinically. The use of graph representations appears to improve EHR representation and predictive performance compared to baseline ML methods in multiple fields of medicine., Competing Interests: Declaration of competing interest No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

With an escalating incidence of breast cancer cases all over the world and the deleterious psychological impact that mastectomy has on patients along with several limitations of the currently applied modalities, it's plausible to seek unconventional approaches to encounter such a burgeoning issue. Breast tissue engineering may allow that chance via providing more personalized solutions which are able to regenerate, mimicking natural tissues also facing the witnessed limitations. This review is dedicated to explore the utilization of adipose tissue-derived mesenchymal stem cells for breast tissue regeneration among postmastectomy cases focusing on biomaterials and cellular aspects in terms of harvesting, isolation, differentiation and new tissue formation as well as scaffolds types, properties, material-host interaction and an in vitro breast tissue modeling.

Cornea tissue is in high demand by tissue donation centres globally, and thus tissue engineering cornea, which is the main topic of corneal translational medicine, can serve as a limitless alternative to a donated human cornea tissue. Tissue engineering aims to produce solutions to the challenges associated with conventional cornea tissue, including transplantation and use of human amniotic membrane (HAM), which have issues with storage and immune rejection in patients. Accordingly, by carefully selecting biomaterials and fabrication methods to produce these therapeutic tissues, the demand for cornea tissue can be met, with an improved healing outcome for recipients with less associated harmful risks. In this review paper, we aim to present the recent advancements in the research and clinical applications of cornea tissue, applications including biomaterial selection, fabrication methods, scaffold structure, cellular response to these scaffolds, and future advancements of these techniques.

Polymers and scaffolds are the most significant tools in regenerative medicine. Urogenital disorders are an important group of diseases that greatly affect the patient's life expectancy and quality. Reconstruction of urogenital defects is one of the current challenges in regenerative medicine. Regenerative medicine, as well as tissue engineering, may offer suitable approaches, while the tools needed are appropriate materials and cells. Autologous urothelial cells obtained from biopsy, bone marrow-derived stem cells, adipose stem cells and urine-derived stem cells that expressed mesenchymal cell markers are the cells that mainly used. In addition, two main types of biomaterials mainly exist; synthetic polymers and composite scaffolds that are biodegradable polymers with controllable properties and naturally derived biomaterials such as extracellular matrix components and acellular tissue matrices. In this review, we present and evaluate the most appropriate and suitable scaffolds (naturally derived and synthetic polymers) and cells applied in urogenital reconstruction., (© 2020 The Authors. Journal of Biomedical Materials Research Part A published by Wiley Periodicals, Inc.)