By the ejection fraction global (EFg) statements concerning the remaining function of the myocardium in acute myocardial infarction and thus individually concerning the prognosis (classification of risk groups) become possible. For the valuation of the dynamics of the EFg in a period up to 6 months after an acute myocardial infarction the EFg was multifariously controlled. Only patients with first myocardial infarction in localization on the anterior wall and Q-wave showed a significant dynamics of the EFg between the measurements acute and third week as well as acute and 6th month (absolutely 5.2%). --In re-infarction/Q-wave this could be confirmed also for the localization of the posterior wall in the period acute till third week. For the localization on anterior and posterior wall a dynamics of the EFg could also be calculated for the period acute and 6th month. Thereby the absolute increase of the EFg was between 4.0 and 4.6%. The dynamics of the EFg in the region of the anterior wall was 5.2% for the first infarction and only 4% for the reinfarction. Thus it is below the dynamics of the EFg in an effective thrombolytic therapy.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Computer-aided drug design approaches for the identification of potent inhibitors targeting elongation factor G of Mycobacterium tuberculosis.
J Mol Graph Model
January 2025
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160062, Punjab, India. Electronic address:
Elongation factor G (EF-G) is essential for protein synthesis in Mycobacterium tuberculosis (Mtb), positioning it as a promising target for anti-tubercular drug development. This study employs Structure-Based Drug Design (SBDD) to identify potential small molecule inhibitors that specifically target EF-G. Initially, binding hotspots on EF-G were pinpointed, and the binding modes of various compounds were analyzed.
View Article and Find Full Text PDFMachine Learning Mapping Approach for Computing Spin Relaxation Dynamics.
J Phys Chem Lett
January 2025
Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.
In this work, a machine learning mapping approach for predicting the properties of atomistic systems is reported. Within this approach, the atomic orbital overlap, density, or Kohn-Sham (KS) Fock matrix elements obtained at a low level of theory such as extended tight-binding have been used as input features to predict the electric field gradient (EFG) tensors at a higher level of theory such as those obtained with hybrid functionals. It is shown that the machine-learning-predicted EFG tensors can be used to compute spin relaxation rates of several ions in aqueous solutions.
View Article and Find Full Text PDFWe present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPMs), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between theoretical material structure models and the interpretation of their experimental SS-NMR spectra.
View Article and Find Full Text PDFTracking Li atoms in real-time with ultra-fast NMR simulations.
Faraday Discuss
January 2025
Fritz-Haber Institute of the Max Planck Society, Berlin, Germany.
We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material LiPS (LPS) by calculating quadrupolar frequencies of Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes.
View Article and Find Full Text PDFFirst-principles NMR of oxide glasses boosted by machine learning.
Faraday Discuss
January 2025
Université Paris-Saclay, CEA, CNRS, NIMBE, 91191 Gif-sur-Yvette cedex, France.
Solid-state NMR has established itself as a cutting-edge spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progress. First-principles calculations of NMR properties combined with molecular-dynamics (MD) simulations provides a powerful complementary approach for the interpretation of NMR data, although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!