Numerous components of the immune system, including inflammatory mediators, immune cells and cytokines, have a profound modulatory effect on the homeostatic regulation and regenerative activity of endogenous stem cells and progenitor cells. Thus, understanding how the immune system interacts with stem/progenitor cells could build the foundation to design novel and more effective regenerative therapies. Indeed, utilizing and controlling immune system components may be one of the most effective approaches to promote tissue regeneration. In this review, we first summarize the effects of various immune cell types on endogenous stem/progenitor cells, focusing on the tissue healing context. Then, we present interesting regenerative strategies that control or mimic the effect of immune components on stem/progenitor cells, in order to enhance the regenerative capacity of endogenous and transplanted stem cells. We highlight the potential clinical translation of such approaches for multiple tissues and organ systems, as these novel regenerative strategies could considerably improve or eventually substitute stem cell-based therapies. Overall, harnessing the power of the cross-talk between the immune system and stem/progenitor cells holds great potential for the development of novel and effective regenerative therapies.

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Hydrogels are three-dimensional polymeric network, capable of entrapping substantial amounts of fluids. Hydrogels are formed due to physical or chemical cross-linking in different synthetic and natural polymers. Recently, hydrogels have been receiving much attention for biomedical applications due to their innate structure and compositional similarities to the extracellular matrix. Hydrogels fabricated from naturally derived materials provide an advantage for biomedical applications due to their innate cellular interactions and cellular-mediated biodegradation. Synthetic materials have the advantage of greater tunability when it comes to the properties of hydrogels. There has been considerable progress in recent years in addressing the clinical and pharmacological limitations of hydrogels for biomedical applications. The primary objective of this article is to review the classification of hydrogels based on their physical and chemical characteristics. It also reviews the technologies adopted for hydrogel fabrication and the different applications of hydrogels in the modern era.

Background Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. The identification of differentially expressed genes (DEGs) across affected brain regions can provide new insights into the mechanisms of AD. Method Our study aims to identify potential biomarkers of AD across brain regions using gene expression and network‐based approaches. The gene expression data were downloaded from Gene Expression Omnibus (GEO) for the series GSE5281. The series comprises expression data from 6 brain regions Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX). Differentially expressed genes (DEGs) were identified using GeneSpring GX 12.6.1 software. A protein‐protein interaction network (PPIN) was constructed from high throughput experiments using Biosogenet. A subnetwork comprising common DEGs and their first neighbors was extracted from the complex PPIN, and the network centralities, including degree and betweenness, were calculated using NetworkAnalyzer plugin in Cytoscape 3.7.1. Result Our results identified 4748 non‐redundant DEGs, of which 1493 and 3255 constitute the up and down‐regulated genes, respectively. A total of 124 common DEGs were identified across more than four brain regions. The subnetwork comprised 5723 and 140625 nodes and edges, respectively. Topological analysis of the subnetwork identified 474/148 Hub/Bottleneck genes and 146 Hub‐Bottleneck genes. Two HB genes EGFR and FYN and two H genes NOTCH2NL and SRRM2 were identified as up‐regulated across four brain regions. Six HB genes CUL3, COPS5, HSP90AB1, YWHAZ, YWHAB, and CDC42, and two H genes SNCA, TUBA4A were identified down‐regulated across five brain regions. Furthermore, four HB genes UBC, CUL1, C1QBP, and UBQLN1 and 10 H genes TUBB, GAPDH, SSX2IP, AP2M1, PSMA1, SKP1, TERF2IP, ATP5A1, CCT7, and NDUFA4 are found to be down‐regulated across four brain‐region. Of these, SNCA, GAPDH, UBQLN1 were known to be associated with AD. Conclusion The identification of AD biomarkers across different brain regions integrating differential gene expression study and network‐based approach may provide new insights into the mechanisms of AD.