A previously unexplained difference in the resistance to enzymatic hydrolysis of 11-mer Bowman-Birk-type inhibitors of human leukocyte elastase that differ in P1 is found to correlate with the strength of a particular intramolecular hydrogen bond within the inhibitor. This transannular hydrogen bond stabilizes the side chain of the conserved P2 Thr in a 'canonical' +60 degrees -rotamer chi(1) conformation and thereby directs it for a close interaction with the enzyme's catalytic His. As the implications of this NMR analysis are neither limited to this macrocyclic scaffold derived from plant proteins nor to a particular serine protease, we present a unified analysis with inhibitory bacterial depsipeptides of 7-12 residues in length that share key design features for which we propose communal functional explanations.
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http://dx.doi.org/10.1016/j.bmc.2007.03.082 | DOI Listing |
J Biomed Mater Res A
January 2025
Faculty of Materials Science and Engineering, Warsaw University of Technology, Warsaw, Poland.
Bone tissue regeneration can be affected by various architectonical features of 3D porous scaffold, for example, pore size and shape, strut size, curvature, or porosity. However, the design of additively manufactured structures studied so far was based on uniform geometrical figures and unit cell structures, which often do not resemble the natural architecture of cancellous bone. Therefore, the aim of this study was to investigate the effect of architectonical features of additively manufactured (aka 3D printed) titanium scaffolds designed based on microtomographic scans of fragments of human femurs of individuals of different ages on in vitro response of human bone-derived mesenchymal stem cells (hMSC).
View Article and Find Full Text PDFHum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
MDM Policy Pract
January 2025
Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA.
Background: Older adults and Hispanic individuals are increasingly turning to social media platforms to access health-related information. The purpose of this project was to evaluate a social media campaign to disseminate information from decision aids (DAs) on hip and knee osteoarthritis to Spanish-speaking adults.
Methods: A social media marketing team helped create an 8-mo campaign posted across 3 social media platforms to promote visits to a Web site offering free multilingual DAs for treatment of hip or knee osteoarthritis.
Front Neurorobot
December 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Existing image fusion methods primarily focus on complex network structure designs while neglecting the limitations of simple fusion strategies in complex scenarios. To address this issue, this study proposes a new method for infrared and visible image fusion based on a multimodal large language model. The method proposed in this paper fully considers the high demand for semantic information in enhancing image quality as well as the fusion strategies in complex scenes.
View Article and Find Full Text PDFCharacterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges in effectively characterizing the modularity organization of brain networks and capturing varying dFC state patterns.
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