Transplantation Induces Profound Changes in the Transcriptional Asset of Hematopoietic Stem Cells: Identification of Specific Signatures Using Machine Learning Techniques

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis

Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.

Analysis of Peripheral Blood Mononuclear Cells Gene Expression Highlights the Role of Extracellular Vesicles in the Immune Response following Hematopoietic Stem Cell Transplantation in Children

Hematopoietic stem cell transplantation (HSCT) is an effective treatment method used in many neoplastic and non-neoplastic diseases that affect the bone marrow, blood cells, and immune system. The procedure is associated with a risk of adverse events, mostly related to the immune response after transplantation. The aim of our research was to identify genes, processes and cellular entities involved in the variety of changes occurring after allogeneic HSCT in children by performing a whole genome expression assessment together with pathway enrichment analysis. We conducted a prospective study of 27 patients (aged 1.5–18 years) qualified for allogenic HSCT. Blood samples were obtained before HSCT and 6 months after the procedure. Microarrays were used to analyze gene expressions in peripheral blood mononuclear cells. This was followed by Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein–protein interaction (PPI) analysis using bioinformatic tools. We found 139 differentially expressed genes (DEGs) of which 91 were upregulated and 48 were downregulated. “Blood microparticle”, “extracellular exosome”, “B-cell receptor signaling pathway”, “complement activation” and “antigen binding” were among GO terms found to be significantly enriched. The PPI analysis identified 16 hub genes. Our results provide insight into a broad spectrum of epigenetic changes that occur after HSCT. In particular, they further highlight the importance of extracellular vesicles (exosomes and microparticles) in the post-HSCT immune response.

Visualization of individual cell division history in complex tissues using iCOUNT

The division potential of individual stem cells and the molecular consequences of successive rounds of proliferation remain largely unknown. Here, we developed an inducible cell division counter (iCOUNT) that reports cell division events in human and mouse tissues in vitro and in vivo. Analyzing cell division histories of neural stem/progenitor cells (NSPCs) in the developing and adult brain, we show that iCOUNT can provide novel insights into stem cell behavior. Further, we use single-cell RNA sequencing (scRNA-seq) of iCOUNT-labeled NSPCs and their progenies from the developing mouse cortex and forebrain-regionalized human organoids to identify functionally relevant molecular pathways that are commonly regulated between mouse and human cells, depending on individual cell division histories. Thus, we developed a tool to characterize the molecular consequences of repeated cell divisions of stem cells that allows an analysis of the cellular principles underlying tissue formation, homeostasis, and repair.

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.