ABSTRACT
Purpose/Objectives: <Most used lower respiratory tract models consist of cell monolayers which lack of tissue and organ level response and of in-vivo phenotype. Ex-vivo lung tissues have short viability and limited availability. Lung organoids, which recapitulates better the 3D cellular complex structures, architecture, and in-vivo function, fail to reach maturity even after 85 -185 days of culture. Therefore, these models have a limited use to study fetal lung diseases. Other lung models, consist of only one structure of the lower track, such as bronchial tubes or alveoli, but fail to recapitulate the whole organ structure. In this work, cell microenvironment was used to promote the self-organization of epithelial and mesenchymal cells into macro-structures, aiming to mimic the whole and adult lower respiratory tract model> Methodology: <Different parts of the microenvironment were considered to create a compliant matrix. Alginate-Gelatin hydrogels were used for 3D encapsulation of mesenchymal origin cells. This hydrogel provided a stiffness like the one on the lung. Base membrane zone proteins were used to induce the attachment and guidance of epithelial cells into 3D structures. The interactions between both cell types, further guided them into lung fate. The morphology of resulting organoids was analyzed using immunostaining and confocal microscopy, LSM710, with the purpose of evaluate polarization, protein markers, and different cell populations. Quantitative PCR was performed to evaluate and compare the expression of lung fate genes with traditional cell monocultures.> Results: <The engineered microenvironment and protocol development done in this work resulted in macro-scale structures, in which branching morphogenesis occurred at day 21. Different structures were identified in the organoid including bronchial tube, bronchioles, and alveoli. Polarization of the organoids was confirmed by visualization of E-cadherin, and ZO-1. Expression of Surfactant Protein B and C into the organoids confirmed the presence of alveolar type II cells, which are only present in the later development stage. Surfactant Protein B, Transmembrane protease, serine 2, TMPRSS-2, and Angiotensin-converting enzyme 2, ACE2 were found to be significantly higher expressed into the organoids in comparison with traditional epithelial cells monolayers.> Conclusion/Significance: <Growth factors are normally used to induce the fate of stem cells into lung organoids;however, these fail to reach maturity. Here, we developed a new methodology to induce the formation of the organoids based on the cell microenvironment. The resulting organoids require less time for development. The initial stage of adult cells can be modulated through culture conditions induce a 3D structure like the adult lung. As such, these organoids have the potential to be used for modeling adult diseases and to develop specific models from patient cells, which is one step forward to personalized medicine. SFTPB is one of the main proteins which facilitates the breathing process. Its high expression into our model may indicate that breathing occurs into our lung organoids. The higher expression of TMPRSS-2 and ACE2 into the organoids has a major significance in the field of virology since both proteins are the mainly entrance of SARS-CoV-2, and influenza H1N1.>.
ABSTRACT
PURPOSE: Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. METHODS: A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. RESULTS: 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. CONCLUSION: This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.