Growth hormone treatment in pediatrics: What can we improve?

OBJECTIVES

To analyze the age at which treatment with growth hormone (GH) is started in the different indications approved in our country, as well as to assess the response to it and detect points of improvement.

MATERIAL AND METHODS

A descriptive, observational and retrospective study of pediatric patients receiving GH treatment in December 2020 and monitored in the pediatric Endocrinology Unit of a tertiary care hospital.

RESULTS

A total of 111 patients (52 females) were included in the study. The mean age at the start of treatment was 6.6 years old, being delayed in all diagnostic groups with respect to what is approved for each indication. The indication for which they most frequently received treatment was GH deficiency (n = 60, 54%). In this diagnostic group, there is a predominance of males (39 boys vs 21 girls, and a significantly greater increase in height z score (greater height SDS) is observed in those with early start of treatment compared to those who start late (greaterheight SDS 0.93 vs 0.6; P < .05). All diagnostic groups presented a greater height SDS and height velocity. No adverse effects were observed in any patient.

CONCLUSION

GH treatment is effective and safe for the approved indications. The age of initiation of treatment is a point to improve in all indications, especially in SGA patients. For this, good coordination between primary care pediatricians and pediatric endocrinologists is essential, as well as specific training to identify early signs of different pathologies.

Transient hysteresis and inherent stochasticity in gene regulatory networks

Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. We provide a quantitative description of a transient hysteresis phenomenon reconciling experimental evidence of hysteretic behaviour in gene regulatory networks with inherent stochasticity: under sufficiently slow dynamics hysteresis is transient. We quantify this with an estimate of the convergence rate to the equilibrium and introduce a natural landscape capturing system’s evolution that, unlike traditional cell fate potential landscapes, is compatible with coexistence at the microscopic level.

Cell fate commitment is understood in terms of bistable regulatory circuits with hysteresis, but inherent stochasticity in gene expression is incompatible with hysteresis. Here, the authors quantify how, under slow dynamics, the dependency of the non-stationary solutions on the initial state of the cells can lead to transient hysteresis.

Learning control for batch thermal sterilization of canned foods

A control technique based on Reinforcement Learning is proposed for the thermal sterilization of canned foods. The proposed controller has the objective of ensuring a given degree of sterilization during Heating (by providing a minimum temperature inside the cans during a given time) and then a smooth Cooling, avoiding sudden pressure variations. For this, three automatic control valves are manipulated by the controller: a valve that regulates the admission of steam during Heating, and a valve that regulate the admission of air, together with a bleeder valve, during Cooling. As dynamical models of this kind of processes are too complex and involve many uncertainties, controllers based on learning are proposed. Thus, based on the control objectives and the constraints on input and output variables, the proposed controllers learn the most adequate control actions by looking up a certain matrix that contains the state-action mapping, starting from a preselected state-action space. This state-action matrix is constantly updated based on the performance obtained with the applied control actions. Experimental results at laboratory scale show the advantages of the proposed technique for this kind of processes.

Generic parameterization for a pharmacokinetic model to predict Cd concentrations in several tissues of different fish species

In the present work, a set of generic parameters was proposed for a pharmacokinetic model, with the objective of predicting Cd concentration in the tissues of diverse fish species under different environmental conditions. Cd concentrations in a number of tissues of Oncorhynchus mykiss and Cyprinus carpio were estimated by a structurally identifiable multicompartmental model (unique solution). The 13 generic parameters of the model comprised exchange rates, tissue-blood partition coefficients, and weight-corrected elimination rate constants accounting for the routes of water respiration, excretion and egestion. On the other hand, absorption efficiencies from water and food were considered to be condition-specific and estimated for each experiment. These two parameters reflected the differences in fish exposure to diet (food type and metal concentration) or water (water chemistry and bioavailable metal concentration). A data set of 27 experiments of Cd bioaccumulation in fish tissues was compiled for model calibration. The selected dynamics on trout and carp were performed under very different experimental conditions, involving water and/or food exposure, different fish weights and exposure concentrations and the presence/absence of depuration periods. Model predicted, for most compartments and experiments, the tendency of Cd dynamics. However, accumulation in liver and kidney was underestimated in approximately a half of the experiments, due mainly to a rapid metallothionein (MT) sequestration phenomena and subsequent saturation on liver and kidney produced under high exposure concentrations. On the other hand, both generic and condition-specific parameter values were in accordance with the values reported in literature when available. Therefore, the results obtained in this work are an initial step indicating that a generic global input parameter set could be applied to physiology-based pharmacokinetic (PBPK) models for estimating Cd accumulation in fish in different types of scenarios.

Computational procedures for optimal experimental design in biological systems

Mathematical models of complex biological systems, such as metabolic or cell-signalling pathways, usually consist of sets of nonlinear ordinary differential equations which depend on several non-measurable parameters that can be hopefully estimated by fitting the model to experimental data. However, the success of this fitting is largely conditioned by the quantity and quality of data. Optimal experimental design (OED) aims to design the scheme of actuations and measurements which will result in data sets with the maximum amount and/or quality of information for the subsequent model calibration. New methods and computational procedures for OED in the context of biological systems are presented. The OED problem is formulated as a general dynamic optimisation problem where the time-dependent stimuli profiles, the location of sampling times, the duration of the experiments and the initial conditions are regarded as design variables. Its solution is approached using the control vector parameterisation method. Since the resultant nonlinear optimisation problem is in most of the cases non-convex, the use of a robust global nonlinear programming solver is proposed. For the sake of comparing among different experimental schemes, a Monte-Carlo-based identifiability analysis is then suggested. The applicability and advantages of the proposed techniques are illustrated by considering an example related to a cell-signalling pathway.

Computational modeling of entorhinal cortex

Computational modeling provides a means for linking the physiological and anatomical characteristics of entorhinal cortex at a cellular level to the functional role of this region in behavior. We have developed detailed simulations of entorhinal cortical neurons and networks, with an emphasis on the role of acetylcholine in entorhinal cortical function. Computational modeling suggests that when acetylcholine levels are high, this sets appropriate dynamics for the storage of stimuli during performance of delayed matching tasks. In particular, acetylcholine activates a calcium-sensitive nonspecific cation current which provides an intrinsic cellular mechanism which could maintain neuronal activity across a delay period. Simulations demonstrate how this phenomena could underlie entorhinal cortex delay activity as described in previous unit recordings. Acetylcholine also induces theta rhythm oscillations which may be appropriate for timing of afferent input to be encoded in hippocampus and for extraction of individual stored sequences from multiple stored sequences. Lower levels of acetylcholine may allow sharp wave dynamics which can reactivate associations encoded in hippocampus and drive the formation of additional traces in hippocampus and entorhinal cortex during consolidation.

Kinetics of thermal degradation of thiamine and surface colour in canned tuna

The kinetics of thermal degradation of thiamine and surface colour (lightness measured as Hunter L-value) in canned white tuna were determined using an unsteady-state experimental procedure. Kinetic parameters were calculated by weighted non-linear regression considering a first-order kinetic model with a dependence of the kinetic coefficient (D) with temperature of the Thermal Death Time (TDT) type. Mass-average retentions of thiamine were calculated using a mathematical model which takes into account the non-uniform and unsteady distribution of temperature inside the container during thermal processing. The high correlation obtained between the predicted and the observed retention values and the small confidence intervals found for the kinetic parameters indicate a high statistical reliability. The kinetic model thus determined permits the simulation and optimization of the process resulting in a better quality of the final product.