We previously described flow cytometry-based methods for scoring the incidence of micronucleated reticulocytes (MN-RET) and PIG-A mutant phenotype reticulocytes (MUT RET) in rodent and human blood samples. The current report describes important methodological improvements for human blood analyses, including immunomagnetic enrichment of CD71-positive reticulocytes prior to MN-RET scoring, and procedures for storing frozen blood for later PIG-A analysis. Technical replicate variability in MN-RET and MUT RET frequencies based on blood specimens from 14 subjects, intra-subject variability based on serial blood draws from 6 subjects, and inter-subject variation based on up to 344 subjects age 0 to 73 years were quantified. Inter-subject variation explained most of the variability observed for both endpoints (≥77%), with much lower intra-subject and technical replicate variability. The relatively large degree of inter-subject variation is apparent from mean and standard deviation values for MN-RET (0.15 ± 0.10%) and MUT RET (4.7 ± 5.0 per million, after omission of two extreme outliers). The influences of age and sex on inter-subject variation were investigated, and neither factor affected MN-RET whereas both influenced MUT RET frequency. The lowest MUT RET values were observed for subjects <11 years old, and males had moderately higher frequencies than females. These results indicate that MN-RET and MUT RET are automation-compatible biomarkers of genotoxicity that bridge species of toxicological interest to include human populations. These data will be useful for appropriately designing future human studies that include these biomarkers of genotoxicity, and highlight the need for additional work aimed at identifying the sources of inter-individual variability reported herein.
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http://dx.doi.org/10.1002/em.22393 | DOI Listing |
Front Cardiovasc Med
December 2024
Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States.
Purpose: Evaluate the feasibility of quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame (RAFF2) relaxation times in the human myocardium at 3 T.
Methods: mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wise maps were obtained after three-parameter exponential fitting.
Sensors (Basel)
December 2024
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.
Behav Res Methods
December 2024
Move'N'Brains Lab, Department of Psychology, University of Turin, Via Verdi, 10, 10124, Turin, Italy.
Aside from some common movement regularities, significant inter-individual and inter-trial variation within the same individual exists in motor system output. However, there is still a lack of a robust and widely adopted solution for quantifying the degree of similarity between movements. We therefore developed an innovative approach based on the Procrustes transformation to compute 'motor distance' between pairs of kinematic data.
View Article and Find Full Text PDFObjective: Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.
View Article and Find Full Text PDFComput Biol Med
January 2025
Institute for Digital Medicine, University Hospital Bonn, Venusberg-Campus 1, Bonn, 53127, North Rhine-Westphalia, Germany.
Wearable technology enables the unsupervised recording of electrocardiogram (ECG) signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical approaches and explainability. This work investigates the feasibility of medically explainable anomaly detection through disentangled representational learning of ECGs and personalization to mitigate inter-subject variations.
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