Application of Advanced Nanomaterials for Kidney Failure Treatment and Regeneration

The implementation of nanomedicine not only provides enhanced drug solubility and reduced off-target adverse effects, but also offers novel theranostic approaches in clinical practice. The increasing number of studies on the application of nanomaterials in kidney therapies has provided hope in a more efficient strategy for the treatment of renal diseases. The combination of biotechnology, material science and nanotechnology has rapidly gained momentum in the realm of therapeutic medicine. The establishment of the bedrock of this emerging field has been initiated and an exponential progress is observed which might significantly improve the quality of human life. In this context, several approaches based on nanomaterials have been applied in the treatment and regeneration of renal tissue. The presented review article in detail describes novel strategies for renal failure treatment with the use of various nanomaterials (including carbon nanotubes, nanofibrous membranes), mesenchymal stem cells-derived nanovesicles, and nanomaterial-based adsorbents and membranes that are used in wearable blood purification systems and synthetic kidneys

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Abstract Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy