RESUMEN
INTRODUCTION: Heat is a kinetic process whereby energy flows from between two systems, hot-to-cold objects. In oro-dental implantology, conductive heat transfer/(or thermal stress) is a complex physical phenomenon to analyze and consider in treatment planning. Hence, ample research has attempted to measure heat-production to avoid over-heating during bone-cutting and drilling for titanium (Ti) implant-site preparation and insertion, thereby preventing/minimizing early (as well as delayed) implant-related complications and failure. OBJECTIVE: Given the low bone-thermal conductivity whereby heat generated by osteotomies is not effectively dissipated and tends to remain within the surrounding tissue (peri-implant), increasing the possibility of thermal-injury, this work attempts to obtain an exact analytical solution of the heat equation under exponential thermal-stress, modeling transient heat transfer and temperature changes in Ti implants (fixtures) upon hot-liquid oral intake. MATERIALS AND METHODS: We, via an ex vivo-based model, investigated the impact of the (a) material, (b) location point along implant length, and (c) exposure time of the thermal load on localized temperature changes. RESULTS: Despite its simplicity, the presented solution contains all the physics and reproduces the key features obtained in previous numerical analyses studies. To the best of our knowledge, this is the first introduction of the intrinsic time, a "proper" time that characterizes the geometry of the dental implant fixture, where we show, mathematically and graphically, how the interplay between "proper" time and exposure time influences temperature changes in Ti implants, under the suitable initial and boundary conditions. This fills the current gap in the literature by obtaining a simplified yet exact analytical solution, assuming an exponential thermal load model relevant to cold/hot beverage or food intake. CONCLUSIONS: This work aspires to accurately complement the overall clinical diagnostic and treatment plan for enhanced bone-implant interface, implant stability, and success rates, whether for immediate or delayed loading strategies.
RESUMEN
Predicting pharmacokinetics, based on the theory of dynamic systems, for an administered drug (whether intravenously, orally, intramuscularly, etc.), is an industrial and clinical challenge. Often, mathematical modeling of pharmacokinetics is preformed using only a measured concentration time profile of a drug administered in plasma and/or in blood. Yet, in dynamic systems, mathematical modeling (linear) uses both a mathematically described drug administration and a mathematically described body response to the administered drug. In the present work, we compare several mathematical models well known in the literature for simulating controlled drug release kinetics using available experimental data sets obtained in real systems with different drugs and nanosized carriers. We employed the χ 2 minimization method and concluded that the Korsmeyer-Peppas model (or power-law model) provides the best ï¬t, in all cases (the minimum value of χ 2 per degree of freedom; χ min 2/d.o.f. = 1.4183, with 2 free parameters or m = 2). Hence, (i) better understanding of the exact mass transport mechanisms involved in drugs release and (ii) quantitative prediction of drugs release can be computed and simulated. We anticipate that this work will help devise optimal pharmacokinetic and dynamic release systems, with measured variable properties, at nanoscale, characterized to target specific diseases and conditions.
RESUMEN
In the present work, we model single-cell movement as a random walk in an external potential observed within the extreme dumping limit, which we define herein as the extreme nonuniform behavior observed for cell responses and cell-to-cell communications. Starting from the Newton-Langevin equation of motion, we solve the corresponding Fokker-Planck equation to compute higher moments of the displacement of the cell, and then we build certain quantities that can be measurable experimentally. We show that, each time, the dynamics depend on the external force applied, leading to predictions distinct from the standard results of a free Brownian particle. Our findings demonstrate that cell migration viewed as a stochastic process is still compatible with biological and experimental observations without the need to rely on more complicated or sophisticated models proposed previously in the literature.