Introduction: Heart failure (HF) is a common and potentially fatal condition. Cardiovascular research has focused on medical therapy for HF. Theoretical modelling could enable simulation and evaluation of the effectiveness of medications. Furthermore, the models could also help predict patients' cardiac response to the treatment which will be valuable for clinical decision-making.
Methods: This study presents a fast parameters estimation algorithm for constructing a cardiovascular model for medicine evaluation. The outcome of HF treatment is assessed by hemodynamic parameters and a comprehensive index furnished by the model. Angiotensin-converting enzyme inhibitors (ACEIs) were used as a model drug in this study.
Results: Our simulation results showed different treatment responses to enalapril and lisinopril, which are both ACEI drugs. A dose-effect was also observed in the model simulation.
Conclusions: Our results agreed well with the findings from clinical trials and previous literature, suggesting the validity of the model.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471471 | PMC |
http://dx.doi.org/10.1155/2012/608637 | DOI Listing |
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
Microbiol Spectr
January 2025
College of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
The rumen microbiota plays a vital role in the nutrient metabolism affecting the growth of velvet antler. However, the fermentation patterns and dynamics of the rumen microbiota across growth stages of velvet antler remain largely unexplored. Here, we employed an fermentation approach to assess fermentation parameters and microbial composition in the rumen liquid of sika deer during the early growth (EG), metaphase growth (MG), and fast growth (FG) phases .
View Article and Find Full Text PDFmSphere
January 2025
Animal nutrition and feed science, College of Animal Science and Technology, Shihezi University, Shihezi, China.
This study aimed to investigate the effects of and on the chemical composition, fermentation characteristics, bacterial communities, and predicted metabolic pathways of whole-plant triticale silage (). Fresh triticale harvested at the milk stage was ensiled in sterile distilled water (CON), (ST), (LP), and a combination of and (LS) for 3, 7, 15, and 30 days. During ensiling, the pH and water-soluble carbohydrate (WSC) content in the inoculated groups was significantly lower than those in the CON group ( < 0.
View Article and Find Full Text PDFNat Prod Res
January 2025
Department of Pharmacognosy and Medicinal Plants, Faculty of Pharmacy, Al-Azhar University (Boys), Cairo, Egypt.
The herbal extracts of four traditional plants; namely leaves, fruits leaves, and seeds, were identified for their main constituents using UHPLC/QTOF-MS/MS. Then, a pharmacology-based analysis and molecular docking verification were established targeting the evaluation of each individual herbal extract for their antidiabetic/anti-obesity potential besides their safety. Streptozotocin-induced diabetic rats were used to evaluate antiobesity and insulinotropic effects against insulin (10 U/Kg, IP) and metformin (100 mg/Kg, per oral) as standard regimens.
View Article and Find Full Text PDFJ Appl Crystallogr
January 2024
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving measurement efficiency is active learning (AL), in which the next measurement configurations are selected on the basis of information gained from the partial data collected so far.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!