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Related Concept Videos

Pharmacokinetics in Pediatric Patients: Drug Distribution01:17

Pharmacokinetics in Pediatric Patients: Drug Distribution

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Drug distribution in the pediatric population exhibits unique challenges and considerations due to the physiological differences between children, particularly neonates and infants, and adults. A crucial aspect of pediatric pharmacology is understanding how these differences impact the pharmacokinetics of various drugs, necessitating age-specific dosing strategies to ensure efficacy and safety.Neonates and infants have a higher total body water content, ~75%–90% of their body weight,...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Temperature-Dependent Growth of Brook TroutThe growth of brook trout is closely influenced by water temperature. Experimental data demonstrate how trout weight changes over a 24-day period in response to varying water temperatures. At lower temperatures, such as 15.5 degrees Celsius, brook trout show significant weight gain. However, as the temperature increases, the amount of weight gained steadily decreases. At the highest temperature measured, 24.4 degrees Celsius, trout experience a net...
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Understanding the physiological differences in the pediatric population is crucial for effective pharmacotherapy. Neonates, infants, and children exhibit significant variations in gastric pH, gastric emptying time, intestinal transit time, and biliary function. These variations profoundly affect oral drug absorption, necessitating a nuanced approach to pediatric dosing.Neonates present with a unique physiological profile, having a gastric pH greater than 4 and faster and more irregular gastric...
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Related Experiment Video

Updated: Mar 23, 2026

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect
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Characterizing and Forecasting Individual Weight Changes in Term Neonates.

Mélanie Wilbaux1, Severin Kasser2, Sven Wellmann2

  • 1Division of Pediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland.

The Journal of Pediatrics
|April 4, 2016
PubMed
Summary
This summary is machine-generated.

A new mathematical model accurately forecasts infant weight changes in the first week of life. This tool helps monitor healthy, breastfed newborns and identify influencing factors.

Keywords:
babychildnewbornspopulation approachweight loss

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Area of Science:

  • Neonatal physiology
  • Mathematical modeling
  • Pediatric health

Background:

  • Accurate monitoring of neonatal weight is crucial for assessing infant health.
  • Existing methods for tracking infant weight changes can be imprecise.
  • Understanding factors influencing neonatal weight gain is essential for early intervention.

Purpose of the Study:

  • To develop a mathematical, semimechanistic model for physiological weight changes in term neonates.
  • To identify and quantify maternal and neonatal factors affecting infant weight changes.
  • To create an online tool for forecasting individual neonatal weight changes during the first week of life.

Main Methods:

  • Utilized longitudinal weight data from 1335 healthy, exclusively breastfed term neonates.
  • Applied nonlinear mixed-effects modeling to characterize weight changes.
  • Employed a stepwise forward selection-backward deletion approach for covariate testing and external validation on 300 neonates.

Main Results:

  • Developed a semimechanistic model describing weight changes based on gain and loss rates.
  • Identified significant effects of gestational age and mother's age on birth weight and weight gain rate.
  • Achieved excellent predictive performance in external validation (bias = 0.011%, precision = 0.52%) and forecasting (bias = -0.74%, precision = 1.54%) with minimal initial measurements.

Conclusions:

  • The semimechanistic model accurately characterizes weight changes in healthy, breastfed neonates.
  • A user-friendly online tool is available for forecasting and monitoring individual infant weight changes.
  • Future plans include external validation and expansion to include preterm neonates.