A novel approach to the analysis of human growth

Gliozzi, Antonio; Guiot, Caterina; Delsanto, Pier Paolo; Iordache, D.

doi:10.1186/1742-4682-9-17

Cited by 14 publications

(14 citation statements)

References 47 publications

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“…Growth is described, according to [ 11 ] and the parameters listed in Table 2 , using an algorithm which assumes that, starting from an initial height y 0 , (actually corresponding to 3-months stature) a gompertzian [ 14 ] lengthening with growth rate a 0 and carrying capacity related parameter k 0 occurs.…”

Section: Methodsmentioning

confidence: 99%

“…In order to model the growth from early childhood to maturity, alternative approaches have been proposed [ 10 ]. Gliozzi A et al [ 11 ] have recently presented an alternative method for the fitting and modelling of human HGC and individual (longitudinal) growth curves, based on the formalism of the Phenomenological Universalities (PUN) [ 12 ], already used in the analysis of several datasets of great relevance for growth and to verify the influence of the different parameters.…”

Section: Introductionmentioning

confidence: 99%

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Growth impairment after TBI of leukemia survivors children: a model- based investigation

Galletto

Gliozzi

Nucera

et al. 2014

Theor Biol Med Model

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BackgroundChildren receiving Total Body Irradiation (TBI) in preparation for Hematopoietic Stem Cell Transplantation (HSCT) are at risk for Growth Hormone Deficiency (GHD), which sometimes severely compromises their Final Height (FH). To better represent the impact of such therapies on growth we apply a mathematical model, which accounts both for the gompertzian-like growth trend and the hormone-related ‘spurts’, and evaluate how the parameter values estimated on the children undergoing TBI differ from those of the matched normal population.Methods25 patients long-term childhood lymphoblastic and myeloid acute leukaemia survivors followed at Pediatric Onco-Hematology, Stem Cell Transplantation and Cellular Therapy Division, Regina Margherita Children’s Hospital (Turin, Italy) were retrospectively analysed for assessing the influence of TBI on their longitudinal growth and for validating a new method to estimate the GH therapy effects. Six were treated with GH therapy after a GHD diagnosis.ResultsWe show that when TBI was performed before puberty overall growth and pubertal duration were significantly impaired, but such growth limitations were completely reverted in the small sample (6 over 25) of children who underwent GH replacement therapies.ConclusionSince in principle the model could account for any additional growth ‘spurt’ induced by therapy, it may become a useful ‘simulation’ tool for paediatricians for comparing the predicted therapy effectiveness depending on its timing and dosage.Electronic supplementary materialThe online version of this article (doi:10.1186/1742-4682-11-44) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Growth impairment after TBI of leukemia survivors children: a model- based investigation

Galletto

Gliozzi

Nucera

et al. 2014

Theor Biol Med Model

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“…PUN was successfully applied to describe the tumor multi-passage transplant in mice [6] , external stresses limiting tumor invasiveness [18] , multi-cellular tumor spheroids [19] as well as to simulate the response to selected therapies [20] . Applications to other growth phenomena, such as human height from birth to maturity [21] show that the model may easily include "growth spurts"provided a "piece-wise" formulation is used. In this setting, each time interval is characterized by its own specific parameter values.…”

Section: The Modelmentioning

confidence: 99%

A two-clones tumor model: Spontaneous growth and response to treatment

Stura

Venturino

Guiot

2016

Mathematical Biosciences

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The paper aims at providing a general theoretical frame bridging the macroscopic growth law with the complex heterogeneous structure of real tumors. We apply the "Phenomenological Universality" approach to model the growth of cancer cells accounting for "populations", which are defined not as biologically pre-defined cellular ensemble but as groups of cells behaving homogeneously with respect to their position (e.g. primary or metastatic tumor), growth characteristics, response to treatment, etc. Populations may mutually interact, limit each other their growth or even mutate into another population. To keep the description as simple and manageable as possible only two populations are considered, but the extension to a multiplicity of cell populations is straightforward. Our findings indicate that the eradication of the metastatic population is much more critical in the presence of mutations, either spontaneous or therapy-induced. Furthermore, a treatment that eradicates only the primary tumor, having a low kill rate on the metastases, is ultimately not successful but promotes a "growth spurt" in the latter.

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“…Human physical growth is continuous, dynamic and complex biological and developmental processes which are characterized by various growth phases [6][7][8]. The usual cross-sectional data analysis might not describe and consider such dynamic nature of growth [9][10][11][12]. Thus, alternative growth models have been widely applied to longitudinal data settings to capture and map theoretical and practical meanings of individual and population level growth variation, [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

Debeko¹

2018

AJTAS

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The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant's growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.

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A novel approach to the analysis of human growth

Cited by 14 publications

References 47 publications

Growth impairment after TBI of leukemia survivors children: a model- based investigation

Growth impairment after TBI of leukemia survivors children: a model- based investigation

A two-clones tumor model: Spontaneous growth and response to treatment

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

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