Development of Janus hydrogel microcapsules as novel biomaterials-stem cells construct for 3D co-culture applications

Co-cultures of two or more cell types and biodegradable biomaterials of natural origin have been successfully combined to recreate tissue microenvironments. Segregated co-cultures are preferred over conventional mixed ones in order to better control the degree of homotypic and heterotypic interactions. Hydrogel-based systems in particular, have gained much attention to mimic tissue-specific microenvironments and they can be microengineered by innovative bottom-up approaches such as microfluidics. In this study, we developed bi-compartmentalized (Janus) hydrogel microcapsules of methacrylated hyaluronic acid (MeHA)/methacrylated-chitosan (MeCht) blended with marine-origin collagen by droplet-based microfluidics co-flow. Human adipose stem cells (hASCs) and microvascular endothelial cells (hMVECs) were co-encapsulated to create platforms of study relevant for vascularized bone tissue engineering. A specially designed Janus-droplet generator chip was used to fabricate the microcapsules (<250â μm units) and Janus-gradient co-cultures of hASCs: hMVECs were generated in various ratios (90:10; 75:25; 50:50; 25:75; 10:90), through an automated microfluidic flow controller (Elveflow microfluidics system). Such monodisperse 3D co-culture systems were optimized regarding cell number and culture media specific for concomitant maintenance of both phenotypes to establish effective cell-cell (homotypic and heterotypic) and cell-materials interactions. Cellular parameters such as viability, matrix deposition, mineralization and hMVECs re-organization in tube-like structures, were enhanced by blending MeHA/MeCht with marine-origin collagen and increasing hASCs: hMVECs co-culture gradient had significant impact on it. Such Janus hybrid hydrogel microcapsules can be used as a platform to investigate biomaterials interactions with distinct combined cell populations.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Localização de conteúdos: experiência de estágio na Bloomidea

Relatório de estágio de mestrado em Tradução e Comunicação Multilingue