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In the current study, we construct growth charts of body surface area (BSA) for adults using the quantile regression (QR) approach and growth charts of different Gaussian Percentiles (-scores) against age. A cross-sectional data consisting of 3,473 individuals aged 5 or more, both males and females were taken from Multan city. Quantile regression (QR) was used to construct BSA growth charts. Growth charts for different -scores were also constructed. For our data set, the mean BSA is 0.48750. The BSA percentiles show a trending higher after the age of 5 until the age of 22, then decrease between age 22 and 35, and then finally increase after age 35. The -score curve increases slightly after age 5 and then proceeds higher until age 22. After age 22 and before 35 it plateaus and then increases slightly after age 35. Since the use of empirical BSA percentiles and -scores with grouped age provides a discrete approximation for the population percentiles and -scores, it is more accurate to use continuous BSA percentile and -score, curves against given ages while using quantile regression and -score approach. Furthermore, this approach can also be adopted to construct many other growth charts for physiological and medical sciences.
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http://dx.doi.org/10.1177/23337214241273189 | DOI Listing |
J Cannabis Res
September 2025
Department of EconomicsMA in Applied Economics, Lebanese American University, P.O. Box: 13-5053, Beirut, Lebanon.
Amidst the global shift toward cannabis legalization, this study examines medical cannabis (MC) sales as an indicator of economic activity and innovation. It explores associations between MC sales, and variables including tobacco use, alcohol consumption, amphetamine, cocaine and cannabis prevalence, and gross domestic product (GDP), using a fixed effects (FE) panel regression model. It also evaluates associations between cannabis legalization and MC sales over time using a dynamic Difference-in-Differences (DiD) approach with multiple time periods.
View Article and Find Full Text PDFPediatr Ann
September 2025
Department of Pediatrics, George Washington University, Washington, DC and.
Routine growth monitoring includes plotting children on World Health Organization or Centers for Disease Control and Prevention charts that have primarily been developed on typical, healthy populations. However, it is advisable to plot children with known genetic conditions on specialized growth curves (SGCs) when they are available. In this review, we highlight the most common genetic conditions for which SGCs are available, clinical reasons to use SGCs based on specific rare diseases, and how these SGCs can be found.
View Article and Find Full Text PDFInt J Pediatr
August 2025
Inherited Metabolic Diseases Program, Department of Pediatrics and Adolescent Medicine, American University of Beirut Medical Center, Beirut, Lebanon.
Scarce data on classical phenylketonuria diagnosis and outcome in low-income Middle Eastern countries is available. The effect of phenylketonuria diet on growth parameters is still controversial. This 15-year retrospective study is aimed at examining the diagnosis, outcome, and growth of classical phenylketonuria patients following a phenylalanine-restricted Mediterranean diet in Lebanon.
View Article and Find Full Text PDFBioinform Adv
August 2025
Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Motivation: Advances in high-throughput technologies have shifted the focus from bulk to single cell or spatial transcriptomic and proteomic analysis of tissues and cell cultures. The resulting increase in gene and/or protein lists leads to the subsequent growth of up- and downregulated pathways lists. This trend creates the need for pathway-network based integration strategies that allow quick exploration of shared and distinct mechanisms across datasets.
View Article and Find Full Text PDFStat Methods Med Res
September 2025
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis.
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