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Concentration-QTc (C-QTc) modeling is being increasingly used in phase 1 studies. For studies without a placebo arm (single arm studies), the scientific whitepaper by Garnett et al. ( https://doi.org/10.1007/s10928-017-9558-5 ) states that time-matched baseline adjustments may minimize the effect of diurnal variation in QTc intervals, and categorical time effects are not needed in the model. However, how diurnal variations can be accounted for when only pre-dose baselines are available is unclear. This research investigates whether including categorical time effects in the model can adjust diurnal variation in single arm studies with pre-dose baselines, where QTc prolongation is evaluated at a concentration of interest based on ΔQTc at 24 h and ΔΔQTc (a model-derived difference in ΔQTc from concentration zero). To understand the operating characteristics for the models with and without categorical time effects, simulations were conducted under various scenarios considering oncology early phase studies. When the C-QTc relationship is linear, models without categorical time effects provided biased estimates for model parameters and inflated or decreased false negative rates (FNRs) depending on the pattern of diurnal variations in QTc intervals, whereas models with categorical time effects caused no biases and controlled the FNRs. For non-linear C-QTc relationships, ΔΔQTc estimations made using the model with categorical time effects were not robust. Thus, for single arm studies where only pre-dose baselines are available, we recommend collecting QTc measurements at 24 h and estimating ΔQTc at a concentration of interest at 24 h using the C-QTc model with categorical time effects.
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http://dx.doi.org/10.1007/s10928-021-09737-0 | DOI Listing |
Cell Mol Biol (Noisy-le-grand)
September 2025
Department of Biology, College of Education for Pure Sciences, University of Kerbala, Kerbala, Iraq.
Gastric cancer is one of the causes of deaths related to cancer across the globe and both genetic and environmental factors are the most prominent. Causes of its pathogenesis. This paper researches the expression of the C-FOS gene.
View Article and Find Full Text PDFFunct Integr Genomics
September 2025
Zhengzhou Research Base, State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University/Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Zhengzhou, China.
In this study, a comprehensive genome-wide identification and analysis of the aldo-keto reductase (AKR) gene family was performed to explore the role of Gossypium hirsutumAKR40 under salt stress in cotton. A total of 249 AKR genes were identified with uneven distribution on the chromosomes in four cotton species. The diversity and evolutionary relationship of the cotton AKR gene family was identified using physio-chemical analysis, phylogenetic tree construction, conserved motif analysis, chromosomal localization, prediction of cis-acting elements, and calculation of evolutionary selection pressure under 300 mM NaCl stress.
View Article and Find Full Text PDFJ Dev Behav Pediatr
September 2025
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Objective: We sought to measure whether receipt of an enhanced 18-month well-baby visit with use of a developmental screening tool versus a routine 18-month well-baby visit (which typically involves developmental surveillance without screening) is associated with time to identification of developmental delays.
Method: We conducted a cohort study of children (17-22 months) in Ontario who received an 18-month well-baby visit (March 2020‒March 2022), followed to September 2022 using linked health administrative datasets. Visits were categorized as enhanced (n = 83,554) or routine (n = 15,723).
J Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
View Article and Find Full Text PDFBlood Neoplasia
November 2025
Section of Hematology/Oncology, The University of Chicago, Chicago, IL.
Modern multiple myeloma treatment enables deep and sustained responses, necessitating assessment of minimal residual disease (MRD) in the bone marrow to refine response categorization. Recently, mass spectrometry (MS)-based methods have emerged as highly sensitive tools for measuring MRD in the peripheral blood. However, the role specific MS techniques play in response categorization has yet to be established.
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