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Nuclear morphology plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secretome and surrounding cells remains largely unexplored. In this study, we fabricate implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar-induced nuclear deformation influences cell secretome for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei show enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promotes osteogenic differentiation in neighboring mesenchymal stromal cells (MSCs). In a female mouse model with critical-size cranial defects, nuclear-deformed MSCs on micropillar mPOC/HA implants elevate Col1a2 expression, contributing to bone matrix formation, and drive cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars modulate the secretome of hMSCs, thereby influencing the fate of surrounding cells through matricrine effects. 

Lewis acid catalyzed condensation of pyrrole and 4-fluoro-2,6-dimethylbenzaldehyde followed by chemical oxidation afforded the corresponding chlorin along with the parent porphyrin. The subsequent metalation of the porphyrin-chlorin mixture in the presence of Zn(OAc)₂•2H₂O afforded Zn monoand di-hydroxychlorins in addition to the Zn porphyrin in a one-flask synthesis. This new direct hydroxylation reaction eliminates the need for highly toxic OsO₄and H₂S that are traditionally used for the generation of hydroxy chlorins. In addition to the full characterization of the zinc chlorins, we present cyclic voltammograms, steady-state absorption, and emission profiles of this rarely available class of compounds. Our findings show that Zn mono- and di-hydroxychlorins are stable compounds that possess exceptionally long triplet excited states in solution, making them promising candidates for photodynamic therapy. 

Discrete-event simulation models generate random variates from input distributions and compute outputs according to the simulation logic. The input distributions are typically fitted to finite real-world data and thus are subject to estimation errors that can propagate to the simulation outputs: an issue commonly known as input uncertainty (IU). This paper investigates quantifying IU using the output confidence intervals (CIs) computed from bootstrap quantile estimators. The standard direct bootstrap method has overcoverage due to convolution of the simulation error and IU; however, the brute-force way of washing away the former is computationally demanding. We present two new bootstrap methods to enhance direct resampling in both statistical and computational efficiencies using shrinkage strategies to down-scale the variabilities encapsulated in the CIs. Our asymptotic analysis shows how both approaches produce tight CIs accounting for IU under limited input data and simulation effort along with the simulation sample-size requirements relative to the input data size. We demonstrate performances of the shrinkage strategies with several numerical experiments and investigate the conditions under which each method performs well. We also show advantages of nonparametric approaches over parametric bootstrap when the distribution family is misspecified and over metamodel approaches when the dimension of the distribution parameters is high. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Science Foundation [CAREER CMMI-1834710, CAREER CMMI-2045400, DMS-1854659, and IIS-1849280]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0044 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0044 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ . 

ABSTRACT IntroductionCurrent wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3–8 yr. MethodsChildren (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin’s concordance correlation coefficient (CCC). ResultsMean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. ConclusionsThe PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.