Motivation: 2D fluorescence spectra provide information from intracellular compounds. Fluorophores like trytophan, tyrosine and phenylalanin as well as NADH and flavins make the corresponding measurement systems very important for bioprocess supervision and control. The evaluation is usually based on chemometric modelling using for their calibration procedure off-line measurements of the desired process variables. Due to the data driven approach lots of off-line measurements are required. Here a methodology is presented, which enables to perform a calibration procedure of chemometric models without any further measurement.
Results: The necessary information for the calibration procedure is provided by means of the a priori knowledge about the process, i.e. a mathematical model, whose model parameters are estimated during the calibration procedure, as well as the fact that the substrate should be consumed at the end of the process run. The new methodology for chemometric calibration is applied for a batch cultivation of aerobically grown S. cerevisiae on the glucose Schatzmann medium. As will be presented the chemometric models, which are determined by this method, can be used for prediction during new process runs.
Availability: The MATHLAB routine is free available on request from the authors.
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http://dx.doi.org/10.1093/bioinformatics/19.2.173 | DOI Listing |
Circ Cardiovasc Interv
January 2025
Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, Canada. (A.H., J.J., S.O., K.M., J.A.L., P.B., D.A.W., S.L.S., J.G.W., J.S.).
Background: Transcatheter heart valve (THV) underexpansion after transcatheter aortic valve replacement may be associated with worse outcomes. THV expansion can be assessed fluoroscopically using a pigtail for calibration; however, the accuracy of this technique specific to transcatheter aortic valve replacement is unknown. We assessed the accuracy and reproducibility of a novel fluoroscopic method to assess THV expansion using the THV commissural post for calibration.
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January 2025
Medical Imaging Center, The First Hospital of Kunming, Kunming, China.
Objective: The invasiveness of pituitary neuroendocrine tumor is an important basis for formulating individualized treatment plans and improving the prognosis of patients. Radiomics can predict invasiveness preoperatively. To investigate the value of multiparameter magnetic resonance imaging (mpMRI) radiomics in predicting pituitary neuroendocrine tumor invasion into the cavernous sinus (CS) before surgery.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, Fujian, China.
Background: Depression is being increasingly acknowledged as an important risk factor contributing to coronary heart disease (CHD). Currently, there is no predictive model specifically designed to evaluate the risk of coronary heart disease among individuals with depression. We aim to develop a machine learning (ML) model that will analyze risk factors and forecast the probability of coronary heart disease in individuals suffering from depression.
View Article and Find Full Text PDFFront Pediatr
January 2025
Department of Pediatrics, Dandong Central Hospital, China Medical University, Dandong, China.
Objective: To establish a prediction nomogram for early prediction of neonatal acute respiratory distress syndrome (NARDS).
Methods: This is a retrospective cross-sectional study conducted between January 2021 and December 2023. Clinical characteristics and laboratory results of cases with neonatal pneumonia were compared in terms of presence of NARDS diagnosis based on the Montreux Definition.
EClinicalMedicine
February 2025
Department of Rehabilitation Medicine, Third Affiliated Hospital of Soochow University, Changzhou, China.
Background: Traumatic brain injury (TBI) is a significant public health issue worldwide that affects millions of people every year. Cognitive impairment is one of the most common long-term consequences of TBI, seriously affect the quality of life. We aimed to develop and validate a predictive model for cognitive impairment in TBI patients, with the goal of early identification and support for those at risk of developing cognitive impairment at the time of hospital admission.
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