Abstract: A machine learning approach is herein provided for preparing a model for assessing the progression of a lung disease, comprises receiving a first set of segmented images of lungs from different patients with the lung disease. The first set of images is segmented and used to train the model. The trained model is applied to a set of unsegmented images to generate a second set of segmented images. The model is updated with the second set of segmented segmentation. From the model, at least one result associated with progression of the lung disease is outputted.

Abstract: Disclosed is a process for the production of sodium hyaluronate with a molecular weight of between 60 and 2400 kDa and low polydispersity (1.4 Mw/Mn), which comprises: a) a step of fermentation of Streptococcus equi subsp. Zooepidemicus CNCM 1-4645 in a suitable culture medium; b) a step of ultrafiltration of the cell-free filtered solution; by concentrating and diafiltering the solution under differential pressure conditions (?P) of 1.0-5.0 bar(g) and transmembrane pressure (TMP) of 0.5-4 bar(g).

Abstract: Disclosed is a process for the production of sodium hyaluronate with a molecular weight of between 60 and 2400 kDa and low polydispersity (1.4 Mw/Mn), which comprises: a) a step of fermentation of Streptococcus equi subsp. zooepidemicus CNCM 1-4645 in a suitable culture medium; b) a step of ultrafiltration of the cell-free filtered solution, by concentrating and diafiltering the solution under differential pressure conditions (?P) of 1.0-5.0 barg and transmembrane pressure (TMP) of 0.5-4 barg.