The heterogeneous nature of the lungs and the range of processes affecting pulmonary drug disposition make prediction of inhaled drugs challenging. These predictions are critical, as the local exposure cannot be measured and current inhalation physiologically based pharmacokinetic (PBPK) models do not capture all necessary features. Utilizing partial differential equations, we present an inhalation PBPK model to describe the heterogeneity in both lung physiology and particle size. The model mechanistically describes important processes, such as deposition, mucociliary clearance, and dissolution. In addition, simplifications are introduced to reduce computational cost without loss of accuracy. Three case studies exemplify how the model can enhance our understanding of pulmonary drug disposition. Specific findings include that most small airways can be targeted by inhalation, and overdosing may eradicate the advantage of inhalation. The presented model can guide the design of inhaled molecules, formulations, as well as clinical trials, providing opportunities to explore regional targeting.
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http://dx.doi.org/10.1002/psp4.12344 | DOI Listing |
Foods
January 2025
College of Biochemical Engineering, Beijing Union University, Beijing 100023, China.
The aroma of yak milk powder is a crucial sensory indicator for evaluating its quality and flavor. Yak milk powders collected from different lactation periods exhibit distinct flavors, but no studies have thoroughly investigated the aroma characteristics and variation patterns of yak milk powders across these periods. This study identified and analyzed the volatile compounds in freeze-dried colostrum powder (YCSP), freeze-dried mature milk powder (YMMP), and freeze-dried ending milk powder (YEMP) using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and multivariate statistical analysis.
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December 2024
Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law's texture feature analysis.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Physical Engineering Faculty, Novosibirsk State Technical University, 630073 Novosibirsk, Russia.
In the development of inflammatory bowel disease (IBD), peritoneal macrophages contribute to the resident intestinal macrophage pool. Previous studies have demonstrated that oral administration of L-fucose exerts an immunomodulatory effect and repolarizes the peritoneal macrophages in vivo in mice. In this study, we analyzed the phenotype and metabolic profile of the peritoneal macrophages from mice, as well as the effect of L-fucose on the metabolic and morphological characteristics of these macrophages in vitro.
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
gRED Computational Sciences, Genentech Inc, South San Francisco, CA, USA.
Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Engineering, Brown University, United States of America; Division of Applied Mathematics, Brown University, United States of America. Electronic address:
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs).
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