3D Bioprinting for Vascularization.

In the world of clinic treatments, 3D-printed tissue constructs have emerged as a less invasive treatment method for various ailments. Printing processes, scaffold and scaffold free materials, cells used, and imaging for analysis are all factors that must be observed in order to develop successful 3D tissue constructs for clinical applications. However, current research in 3D bioprinting model development lacks diverse methods of successful vascularization as a result of issues with scaling, size, and variations in printing method. This study analyzes the methods of printing, bioinks used, and analysis techniques in 3D bioprinting for vascularization. These methods are discussed and evaluated to determine the most optimal strategies of 3D bioprinting for successful vascularization. Integrating stem and endothelial cells in prints, selecting the type of bioink according to its physical properties, and choosing a printing method according to physical properties of the desired printed tissue are steps that will aid in the successful development of a bioprinted tissue and its vascularization.

Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas.

OBJECTIVE: Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts. METHODS: Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher's exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features. RESULTS: Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among "mutation unknown" samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor's underlying driver mutation. CONCLUSIONS: Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.