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Accurate prediction of blood toxin concentration during and after dialysis will greatly contribute to the determination of dialysis treatment conditions. Conventional models, namely single-compartment model and two-compartment model, have advantages and disadvantages in terms of accuracy and practical application. In this study, we attempted to derive the mathematical model that predicts blood toxin concentrations during and after dialysis, which has both accuracy and practicality. To propose the accurate model, a new two-compartment model was mathematically derived by adapting volume-averaging theory to the mass transfer around peripheral tissues. Subsequently, to propose a practical model for predicting the blood toxin concentration during dialysis, an analytical solution expressed as algebraic expression was derived by adopting variable transformation. Furthermore, the other analytical solution that predicts rebound phenomena after dialysis was also derived through similar steps. The comparisons with the clinical data revealed that the proposed analytical solutions can reproduce the behavior of the measured blood urea concentration during and after dialysis. The analytical solutions proposed as algebraic expressions will allow a doctor to estimate the blood toxin concentration of a patient during and after dialysis. The proposed analytical solutions may be useful to consider the treatment conditions for dialysis, including the rebound phenomenon.

Tritium is one of the factors of internal exposure of the humans both in occupational and public environments. It enters the body mainly as tritiated water through inhalation, with food, drinks and through the skin; part of tritium gradually transforms into the metabolised organically bound tritium as a result of biochemical reactions. The purpose of this study was to evaluate organically bound tritium contribution to the effective dose of an adult using the biokinetic model and real dosimetric data. The data of long term monitoring from 6 studies with 17 workers or volunteers following single intake of tritiated water in the body were selected from 9 publications (1968-1997). Three two-compartment models of tritium biokinetics were used in this study: recurrent model with gradual transformation of tritium from tritiated water into organically bound tritium and tritiated water excretion; model with instant transformation of tritium into organically bound tritium and tritiated water excretion; model with instant transformation of tritium into organically bound tritium and both tritiated water and organically bound tritium excretion (according to ICRP). The ICRP model doesn’t properly reflect the real tritiated water metabolism in the human body: second exponent of the tritiated water content in the body water and accumulation of the organically bound tritium fraction are absent. The organically bound tritium fraction composes 3% of tritiated water in ICRP model. It is significantly higher compared to two other models (0,4% and 0,8%). According to the first model the contribution of OBT fraction to the mean dose varied from 1,8 to 4,6% for individuals; mean value was 3,0 ± 0,9%. According to the second model the contribution of organically bound tritium fraction was slightly higher: 3,6 ± 1,1%, according to the ICRP model – 9%. The dynamic of excretion of tritium with urine can be described with double-exponential curves and provides the basis for two-compartment modeling. The recurrent model with tritiated water excretion was more adjusted to human physiology. Contribution of organically bound tritium to effective dose can be somewhat higher than that to absorbed dose defined in this work. The presented dose assessment system can be used when specified individual absorbed dose reconstruction in tissues is necessary following accidental intake of large tritium activities.

INTRODUCTION Fractional exhaled nitric oxide (FENO) is a non-invasive marker of airway inflammation. Measuring FENO at several flow rates enables the calculation of flow independent NO-parameters (alveolar NO concentration (CANO), bronchial flux of NO (JawNO), bronchial mucosal NO concentration (CawNO) and bronchial wall NO diffusion capacity (DawNO)) that are capable of partitioning the source and release mechanism of NO from the lower respiratory tract. However, the current literature on repeatability and normal variation of the NO-parameters is deficient, and this information is needed to develop the method towards clinical use. METHODS We calculated NO-parameters in 28 healthy subjects using two different mathematical methods and used three different study protocols to investigate: (i) repeatability of two consecutive measurements of NO-parameters, (ii) within-day variation of the NO-parameters over one working day and (iii) day-to-day variation of the NO-parameters between consecutive days during course of a working week. RESULTS JawNO was the most repeatable among the NO-parameters, whereas DawNO and CawNO were notably least repeatable. CANO was higher during the second consecutive measurement (1.22 versus 1.57 ppb, p = 0.017). Both investigated mathematical methods yielded equally repeatable results. JawNO was slightly higher in the afternoon compared to morning (716 versus 881 pl/s, p = 0.01), but other parameters showed no diurnal variation. Upper 95% limit for the day-to-day difference in the parameters in healthy subjects was about 1.2 ppb in CANO, 400 pl/s in JawNO, 92 ppb in CawNO and 16 pl/s/ppb in DawNO. CONCLUSIONS This is the first study assessing short-time repeatability of the NO-parameters. Repeatability of the NO-parameters was good and day-to-day variation in NO-parameters was quite low. We recommend scheduling FENO-measurements at the same time of day, if possible, and in clinical use variation in NO-parameters above the normal limits found in this study suggest changes in the disease's activity.

Nitric oxide (NO), the first gas known to act as a biological messenger, is one of the most widely studied free radical/gas in medicine, both for its biological function and therapeutic applications. The measurement of endogenous NO in exhaled air is widely used in the evaluation of lung disorders. Partitioning of exhaled nitric oxide (eNO) is of increasing interest because of the additional information about lung pathology and distal lung inflammation that can be obtained. Specifically, measuring exhaled NO at multiple flow rates allows assessment of the flow-independent NO parameters: alveolar NO concentration (CalvNO), bronchial NO flux (JNO), bronchial wall NO concentration (CWNO), and bronchial diffusing capacity of NO (DNO). Several studies have reported that there were different patterns of those parameters in different airway diseases and/or in different severities of the same disease, mostly in asthma. Specifically, while JNO seems to provide the same information as FeNO50, alveolar NO concentration appears to be an independent parameter that is putatively associated with increased distal lung inflammation and more severe disease. However, despite much research interest in partitioning exhaled NO, clinical usefulness has yet to be established.

An in vitro pharmacokinetic/pharmacodynamic perfusion model that simulates a two-compartment open model of serum drug concentration-time profiles following intravenous bolus injection and infusion was developed and mathematically described. In the present apparatus model, flow was kept in a one-way mode to avoid liquid traffic, and the washout effect seen in dilution models was overcome by embedding the tested bacteria in low melting point agarose gel. The validity of the equations and the reproducibility of the apparatus model were ascertained by simulating the concentration-time profiles of cefazolin and fosfomycin by substitution of their pharmacokinetic parameters obtained from humans for the equations. An empirical regimen 1X(q24h) of 1 g with cefazolin administered by intravenous infusion effectively killed a Staphylococcus aureus strain. The same regimen with fosfomycin produced a marked kill-curve with a fosfomycin-susceptible enterohaemorrhagic Escherichia coli O157:H7, whereas considerable regrowth was observed with a resistant strain. These results indicated that the present model was able to provide a convenient and reliable method for evaluating the efficacy of antimicrobial agents administered by intravenous infusion., Microbiology and immunology. 51(5), pp.567-575; 2007

A non-linear mixed-effects model is proposed to assess the impact of acarbose over time on postprandial glycaemia in a single rat. The model is based on two compartments, one representing the entry of glucose in the blood and the other its exit. The rat was submitted to two treatments: ingestion of starch and ingestion of starch plus acarbose. The model showed great suitability, with inferences on the behavior of glucose levels in response to treatments and supplying a richer description than just the area under the curve. The marginal curves for the two treatments are similar during the first moments; however, after reaching the peak of glucose concentration, they progressively became separate due to acarbose treatment and reached the initial levels more quickly. The proposed model, albeit with a single sample unit, showed similar results to those with larger samples; in other words, acarbose significantly attenuates glycaemia after ingestion of starch.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is becoming an indispensable tool to non-invasively study tumor characteristics. However, many different DCE-analysis methods are currently being used. To compare and validate different methods, histology is the gold standard. For this purpose, exact co-localization between histology and MRI images is a prerequisite. In this study a methodology is developed to validate DCE-data with histology with an emphasis on correct registration of DCE-MRI and histological data. A pancreatic tumor was grown in a rat model. The tumor was dissected after MR imaging, embedded in paraffin, and cut into thin slices. These slices were stained with haematoxylin and eosin, digitized and stacked in a 3D volume. Next, the 3D histology was registered to ex-vivo SWI-weighted MR images, which in turn were registered to in-vivo SWI and DCE images to achieve correct co-localization. Semi-quantitative and quantitative parameters were calculated. Preliminary results suggest that both pharmacokinetic and heuristic DCE-parameters can discriminate between vital and non-vital tumor regions. The developed method offers the basis for an accurate spatial correlation between DCE-MRI derived parametric maps and histology, and facilitates the evaluation of different DCE-MRI analysis methods.