This article describes a quantitative metric for coculture pattern fidelity and its use in the assessment of bioprinting systems. Increasingly, bioprinting is used to create in vitro cell and tissue models for the purpose of studying cell behavior and cell-cell interaction. To create meaningful models, a bioprinting system must be able to place cells in biologically relevant patterns with sufficient fidelity. A metric for assessing fidelity would be valuable for tuning experimental processes and parameters within a bioprinting system and for comparing performance between different systems. Toward this end, the "bioprinting fidelity index" (BFI), a metric which rates a bioprinted patterned coculture with a single number based on the proportions of correctly placed cells, is proposed. Additionally, a mathematical model of drop-on-demand printing is introduced, which predicts an upper bound on the BFI based on drop placement statistics. A proof-of-concept study was conducted in which patterned cocultures of D1 and 4T07 cells were produced in two different demonstration patterns. The BFI for the patterned cocultures was calculated and compared to the printing model fidelity prediction. The printing model successfully predicted the best BFI observed in the samples, and the BFI showed quantitatively that post-processing techniques negatively impacted the final fidelity of the samples. The BFI provides a principled method for comparing printing and post-processing methods.
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http://dx.doi.org/10.1111/j.1525-1594.2012.01460.x | DOI Listing |
Pediatr Res
January 2025
Department of Physiology, University of Helsinki, Helsinki, Finland.
Background: To study how early gross motor development links to concurrent prelinguistic and social development.
Methods: We recruited a population-based longitudinal sample of 107 infants between 6 and 21 months of age. Gross motor performance was quantified using novel wearable technology for at-home recordings of infants' spontaneous activity.
Phys Med Biol
January 2025
Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, California, 90095, UNITED STATES.
Objective: The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.
Approach: A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel).
Invest Radiol
January 2025
From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).
Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, Maryland, USA.
Introduction: The plasma proteome's mediating or moderating roles in the association between poor cardiovascular health (CVH) and brain white matter (WM) microstructural integrity are largely unknown.
Methods: Data from 3953 UK Biobank participants were used (40-70 years, 2006-2010), with a neuroimaging visit between 2014 and 2021. Poor CVH was determined using Life's Essential 8 (LE8) and reversing standardized z-scores (LE8 ).
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