Bioluminescence imaging is a noninvasive technique whereby surface weighted images of luminescent probes within animals are used to characterize cell count and function. Traditionally, data are collected over the entire emission spectrum of the source using no filters and are used to evaluate cell count/function over the entire spectrum. Alternatively, multispectral data over several wavelengths can be incorporated to perform tomographic reconstruction of source location and intensity. However, bandpass filters used for multispectral data acquisition have a specific bandwidth, which is ignored in the reconstruction. In this work, ignoring the bandwidth is shown to introduce a dependence of the recovered source intensity on the bandwidth of the filters. A method of accounting for the bandwidth of filters used during multispectral data acquisition is presented and its efficacy in increasing the quantitative accuracy of bioluminescence tomography is demonstrated through simulation and experiment. It is demonstrated that while using filters with a large bandwidth can dramatically decrease the data acquisition time, if not accounted for, errors of up to 200% in quantitative accuracy are introduced in two-dimensional planar imaging, even after normalization. For tomographic imaging, the use of this method to account for filter bandwidth dramatically improves the quantitative accuracy.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996822 | PMC |
| http://dx.doi.org/10.1117/1.JBO.20.9.096001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Head-to-Head Comparison Between Phosphatidylethanol Versus Indirect Alcohol Biomarkers for Diagnosis of MetALD Versus MASLD: A Prospective Study.
Aliment Pharmacol Ther
January 2025
MASLD Research Center, Division of Gastroenterology and Hepatology, University of California at San Diego, La Jolla, California, USA.
Background: The current subclassification of steatotic liver disease (SLD) relies on validated questionnaires, such as Alcohol Use Disorders Identification Test (AUDIT) and Lifetime Drinking History (LDH), which, while useful, are impractical and lack precision for their use in routine clinical practice. Phosphatidylethanol (PEth) is a quantitative, objective alcohol biomarker with high sensitivity and specificity.
Aims: To assess the diagnostic accuracy of PEth for differentiating metabolic dysfunction and alcohol-associated liver disease (MetALD) from metabolic dysfunction-associated steatotic liver disease (MASLD) in a large, population-based, prospective, multiethnic cohort of individuals with overweight or obesity.
Estimation of factorial expressions and its improvement through calibration: A replication and extension of Tversky and Kahneman (1973).
Mem Cognit
January 2025
School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA.
Fifty years ago, Tversky and Kahneman (Cognitive Psychology, 5[2], 207-232, 1973) reported that people's speeded estimations of 8 × 7 × 6 × 5 × 4 × 3 × 2 × 1 were notably higher than their estimations for the equivalent expression in the opposite order, 1 × 2 × 3 × 4 × 5 × 6 × 7 × 8 (Median = 2,250 vs. 512, respectively). On top of this order effect, both groups grossly underestimated the correct value (40,320).
View Article and Find Full Text PDFAn accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF).
Nat Protoc
January 2025
Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker).
View Article and Find Full Text PDFApproximate Hamiltonians from a Linear Vibronic Coupling Model for Solution-Phase Spin Dynamics.
J Chem Theory Comput
January 2025
Department of Chemistry, The University of Manchester, Manchester M13 9PL, U.K.
The linear vibronic coupling (LVC) model is an approach for approximating how a molecular Hamiltonian changes in response to small changes in molecular geometry. The LVC framework thus has the ability to approximate molecular Hamiltonians at low computational expense but with quality approaching multiconfigurational calculations, when the change in geometry compared to the reference calculation used to parametrize it is small. Here, we show how the LVC approach can be used to project approximate spin Hamiltonians of a solvated lanthanide complex along a room-temperature molecular dynamics trajectory.
View Article and Find Full Text PDFEnhancing biosensing of trace tau protein in clinical samples via emergence macroscopic directed aggregation.
Biosens Bioelectron
January 2025
Synthetic Biology Research Center, Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong, 518060, PR China; School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, PR China. Electronic address:
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that poses a significant risk to human health and well-being. The high cost and invasiveness of neuroimaging and cerebrospinal fluid (CSF) analysis underscores the necessity for accessible early screening via blood samples. In this study, we developed an ultrasound-based strategy for emergent macroscopic that enhances the acoustic response enrichment of specific proteins by introducing functionalized microspheres.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!