The transfer constant K is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K . Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K and plasma flow F , vessel permeability PS and extraction rate EF under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K based on the vascular transit time. K is primarily associated with F and EF respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F only serve as secondary constraints for which K can be further associated with EF and PS in the slow and rapid transit states, respectively.
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http://dx.doi.org/10.1088/1361-6560/aa70c9 | DOI Listing |
BMC Biol
January 2025
Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China.
Background: Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.
View Article and Find Full Text PDFJ Biol Chem
December 2024
Department of Biochemistry, University of Zurich, Zurich, Switzerland.
Most processes of life are the result of polyvalent interactions between macromolecules, often of heterogeneous types and sizes. Frequently, the times associated with these interactions are prohibitively long for interrogation using atomistic simulations. Here, we study the recognition of N6-methylated adenine (mA) in RNA by the reader domain YTHDC1, a prototypical, cognate pair that challenges simulations through its composition and required timescales.
View Article and Find Full Text PDFCell Biochem Biophys
December 2024
Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China.
The purpose of this research was to investigate the main active components, potential targets of action, and pharmacological mechanisms of Erhuang Quzhi Formula (EHQZF) against NAFLD using network pharmacology, molecular docking, and experimental validation. The main active chemical components of EHQZF and the potential targets for treating NAFLD were extracted and analyzed. The PPI network diagram of "Traditional Chinese Medicine-Active Ingredients-Core Targets" was constructed and the GO, KEGG, and molecular docking analysis were carried out.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
June 2024
Gansu University of Chinese Medicine Lanzhou 730000, China Gansu Key Laboratory of Excavation and Innovative Transformation of Traditional Chinese Medicine, Gansu Engineering Laboratory of Creation and Manufacturing of New Traditional Chinese Medicine Products Lanzhou 730000, China.
Metabolites
May 2024
Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA.
A major limitation of most metabolomics datasets is the sparsity of pathway annotations for detected metabolites. It is common for less than half of the identified metabolites in these datasets to have a known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a "pathway category", as defined by a metabolic knowledge base like KEGG.
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