Perfusion MR Imaging of Liver: Principles and Clinical Applications

Perfusion imaging techniques provide quantitative characterization of tissue microvasculature. Perfusion MR of liver is particularly challenging because of dual afferent flow, need for large organ high-resolution coverage, and significant movement with respiration. The most common MR technique used for quantifying liver perfusion is dynamic contrast-enhanced MR imaging. Here, the authors describe the various perfusion MR models of the liver, the basic concepts behind implementing a perfusion acquisition, and clinical results that have been obtained using these models.

A compartment model to predict in vitro finite dose absorption of chemicals by human skin

Dermal uptake is an important and complex exposure route for a wide range of chemicals. Dermal exposure can occur due to occupational settings, pharmaceutical applications, environmental contamination, or consumer product use. The large range of both chemicals and scenarios of interest makes it difficult to perform generalizable experiments, creating a need for a generic model to simulate various scenarios. In this study, a model consisting of a series of four well-mixed compartments, representing the source solution (vehicle), stratum corneum, viable tissue, and receptor fluid, was developed for predicting dermal absorption. The model considers experimental conditions including small applied doses as well as evaporation of the vehicle and chemical. To evaluate the model assumptions, we compare model predictions for a set of 26 chemicals to finite dose in-vitro experiments from a single laboratory using steady-state permeability coefficient and equilibrium partition coefficient data derived from in-vitro experiments of infinite dose exposures to these same chemicals from a different laboratory. We find that the model accurately predicts, to within an order of magnitude, total absorption after 24 h for 19 of these chemicals. In combination with key information on experimental conditions, the model is generalizable and can advance efficient assessment of dermal exposure for chemical risk assessment.

An Integrative Biology Approach to Quantify the Biodistribution of Azidohomoalanine In Vivo

Background

Identification and quantitation of newly synthesized proteins (NSPs) are critical to understanding protein dynamics in development and disease. Probing the nascent proteome can be achieved using non-canonical amino acids (ncAAs) to selectively label the NSPs utilizing endogenous translation machinery, which can then be quantitated with mass spectrometry. We have previously demonstrated that labeling the in vivo murine proteome is feasible via injection of azidohomoalanine (Aha), an ncAA and methionine (Met) analog, without the need for Met depletion. Aha labeling can address biological questions wherein temporal protein dynamics are significant. However, accessing this temporal resolution requires a more complete understanding of Aha distribution kinetics in tissues.

Results

To address these gaps, we created a deterministic, compartmental model of the kinetic transport and incorporation of Aha in mice. Model results demonstrate the ability to predict Aha distribution and protein labeling in a variety of tissues and dosing paradigms. To establish the suitability of the method for in vivo studies, we investigated the impact of Aha administration on normal physiology by analyzing plasma and liver metabolomes following various Aha dosing regimens. We show that Aha administration induces minimal metabolic alterations in mice.

Conclusions

Our results demonstrate that we can reproducibly predict protein labeling and that the administration of this analog does not significantly alter in vivo physiology over the course of our experimental study. We expect this model to be a useful tool to guide future experiments utilizing this technique to study proteomic responses to stimuli.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00760-4.

Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models

Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent these diseases from spreading, may also negatively impact society, leading to jobs losses, mental health problems, and increased inequalities, making crucial the prioritization of riskier areas when applying these protocols. The modeling of mobility data derived from contact-tracing data can be used to forecast infectious trajectories and help design strategies for prevention and control. In this work, we propose a new spatial-stochastic model that allows us to characterize the temporally varying spatial risk better than existing methods. We demonstrate the use of the proposed model by simulating an epidemic in the city of Valencia, Spain, and comparing it with a contact tracing-based stochastic compartment reference model. The results show that, by accounting for the spatial risk values in the model, the peak of infected individuals, as well as the overall number of infected cases, are reduced. Therefore, adding a spatial risk component into compartment models may give finer control over the epidemic dynamics, which might help the people in charge to make better decisions.

Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

A compartment modeling approach to reconstruct and analyze gender and age-grouped CoViD-19 Italian data for decision-making strategies

BACKGROUND

Available national public data are often too incomplete and noisy to be used directly to interpret the evolution of epidemics over time, which is essential for making timely and appropriate decisions. The use of compartment models can be a worthwhile and attractive approach to address this problem. The present study proposes a model compartmentalized by sex and age groups that allows for more complete information on the evolution of the CoViD-19 pandemic in Italy.

MATERIAL AND METHODS

Italian public data on CoViD-19 were pre-treated with a 7-day moving average filter to reduce noise. A time-varying susceptible-infected-recovered-deceased (SIRD) model distributed by age and sex groups was then proposed. Recovered and infected individuals distributed by groups were reconstructed through the SIRD model, which was also used to simulate and identify optimal scenarios of pandemic containment by vaccination. The simulation started from realistic initial conditions based on the SIRD model parameters, estimated from filtered and reconstructed Italian data, at different pandemic times and phases. The following three objective functions, accounting for total infections, total deaths, and total quality-adjusted life years (QALYs) lost, were minimized by optimizing the percentages of vaccinated individuals in five different age groups.

RESULTS

The developed SIRD model clearly highlighted those pandemic phases in which younger people, who had more contacts and lower mortality, infected older people, characterized by a significantly higher mortality, especially in males. Optimizing vaccination strategies yielded different results depending on the cost function used. As expected, to reduce total deaths, the suggested strategy was to vaccinate the older age groups, whatever the baseline scenario. In contrast, for QALYs lost and total infections, the optimal vaccine solutions strongly depended on the initial pandemic conditions: during phases of high virus diffusion, the model suggested to vaccinate mainly younger groups with a higher contact rate.

CONCLUSION

Because of the poor quality and insufficient availability of stratified public pandemic data, ad hoc information filtering and reconstruction procedures proved essential. The time-varying SIRD model, stratified by age and sex groups, provided insights and additional information on the dynamics of CoViD-19 infection in Italy, also supporting decision making for containment strategies such as vaccination.

Similarities and differences of 137Cs distributions in the marine environments of the Baltic and Black seas and off the Fukushima Dai-ichi nuclear power plant in model assessments

The compartment model POSEIDON-R with an embedded food web model was used to assess ¹³⁷Cs distributions in the Baltic and Black seas and off the Pacific coast of Japan during 1945-2020 due to the weapon testing and accidents at the Chernobyl and Fukushima Dai-ichi nuclear power plants. The results of simulations conducted with generic parameters agreed well with measurements of ¹³⁷Cs concentrations in the water, bottom sediments, and in fish. In the Black and Baltic seas, salinity variations affected the transfer of ¹³⁷Cs through the food web. The contamination of pelagic fish followed the water contamination with some delay, whereas demersal fish depuration was found to be related to decreasing ¹³⁷Cs concentrations in the upper sediment layer. On the Pacific shelf off Japan, intensive currents and eddies caused the simulated depuration rates in fish to be one-two orders of magnitude larger than those in the semi-enclosed Black and Baltic seas.

N-[11C]Methyl-AMD3465 PET as a Tool for In Vivo Measurement of Chemokine Receptor 4 (CXCR4) Occupancy by Therapeutic Drugs

Purpose

Chemokine receptor 4 (CXCR4) is overexpressed in many cancers and a potential drug target. We have recently developed the tracer N-[¹¹C]methyl-AMD3465 for imaging of CXCR4 expression by positron emission tomography (PET). We investigated the pharmacokinetics of N-[¹¹C]methyl-AMD3465 in rats bearing a C6 tumor and assessed whether the CXCR4 occupancy by the drug Plerixafor® can be measured with this PET tracer.

Procedure

A subcutaneous C6 tumor was grown in Wistar rats. Dynamic N-[¹¹C]methyl-AMD3465 PET scans with arterial blood sampling was performed in control rats and rats pretreated with Plerixafor® (30 mg/kg, s.c). The distribution volume (V_T) of the tracer was estimated by compartment modeling with a two-tissue reversible compartment model (2TRCM) and by Logan graphical analysis. The non-displaceable binding potential (BP_ND) was estimated with the 2TRCM. Next, CXCR4 receptor occupancy of different doses of the drug Plerixafor® (0.5–60 mg/kg) was investigated.

Results

The tumor could be clearly visualized by PET in control animals. Pretreatment with 30 mg/kg Plerixafor® significantly reduced tumor uptake (SUV 0.65 ± 0.08 vs. 0.20 ± 0.01, p < 0.05). N-[¹¹C]Methyl-AMD3465 was slowly metabolized in vivo, with 70 ± 7% of the tracer in plasma still being intact after 60 min. The tracer showed reversible in vivo binding to its receptor. Both 2TRCM modeling and Logan graphical analysis could be used to estimate V_T. Pre-treatment with 30 mg/kg Plerixafor® resulted in a significant reduction in V_T (2TCRM 0.87 ± 0.10 vs. 0.23 ± 0.12, p < 0.05) and BP_ND (1.85 ± 0.14 vs. 0.87 ± 0.12, p < 0.01). Receptor occupancy by Plerixafor® was dose-dependent with an in vivo ED₅₀ of 12.7 ± 4.0 mg/kg. Logan analysis gave comparable results.

Conclusion

N-[¹¹C]Methyl-AMD3465 PET can be used to visualize CXCR4 expression and to calculate receptor occupancy. V_T determined by Logan graphical analysis is a suitable parameter to assess CXCR4 receptor occupancy. This approach can easily be translated to humans and used for early drug development and optimization of drug dosing schedules.

Phosphate Kinetic Models in Hemodialysis: A Systematic Review

BACKGROUND

Understanding phosphate kinetics in dialysis patients is important for the prevention of hyperphosphatemia and related complications. One approach to gain new insights into phosphate behavior is physiologic modeling. Various models that describe and quantify intra- and/or interdialytic phosphate kinetics have been proposed, but there is a dearth of comprehensive comparisons of the available models. The objective of this analysis was to provide a systematic review of existing published models of phosphate metabolism in the setting of maintenance hemodialysis therapy.

STUDY DESIGN

Systematic review.

SETTING & POPULATION

Hemodialysis patients.

SELECTION CRITERIA FOR STUDIES

Studies published in peer-reviewed journals in English about phosphate kinetic modeling in the setting of hemodialysis therapy.

PREDICTOR

Modeling equations from specific reviewed studies.

OUTCOMES

Changes in plasma phosphate or serum phosphate concentrations.

RESULTS

Of 1,964 nonduplicate studies evaluated, 11 were included, comprising 9 different phosphate models with 1-, 2-, 3-, or 4-compartment assumptions. Between 2 and 11 model parameters were included in the models studied. Quality scores of the studies using the Newcastle-Ottawa Scale ranged from 2 to 11 (scale, 0-14). 2 studies were considered low quality, 6 were considered medium quality, and 3 were considered high quality.

LIMITATIONS

Only English-language studies were included.

CONCLUSIONS

Many parameters known to influence phosphate balance are not included in existing phosphate models that do not fully reflect the physiology of phosphate metabolism in the setting of hemodialysis. Moreover, models have not been sufficiently validated for their use as a tool to simulate phosphate kinetics in hemodialysis therapy.

Dispersion and fate of ⁹⁰Sr in the Northwestern Pacific and adjacent seas: global fallout and the Fukushima Dai-ichi accident

The 3D compartment model POSEIDON-R was applied to the Northwestern Pacific and adjacent seas to simulate the transport and fate of (90)Sr in the period 1945-2010 and to perform a radiological assessment on the releases of (90)Sr due to the Fukushima Dai-ichi nuclear accident for the period 2011-2040. The contamination due to runoff of (90)Sr from terrestrial surfaces was taken into account using a generic predictive model. A dynamical food-chain model describes the transfer of (90)Sr to phytoplankton, zooplankton, molluscs, crustaceans, piscivorous and non-piscivorous fishes. Results of the simulations were compared with observation data on (90)Sr for the period 1955-2010 and the budget of (90)Sr activity was estimated. It was found that in the East China Sea and Yellow Sea the riverine influx was 1.5% of the ocean influx and it was important only locally. Calculated concentrations of (90)Sr in water, bottom sediment and marine organisms before and after the Fukushima Dai-ichi accident are in good agreement with available experimental measurements. The concentration of (90)Sr in seawater would return to the background levels within one year after leakages were stopped. The model predicts that the concentration of (90)Sr in fish after the Fukushima Dai-ichi accident shall return to the background concentrations only 2 years later due to the delay of the transfer throughout the food web and specific accumulation of (90)Sr. The contribution of (90)Sr to the maximal dose rate due to the FDNPP accident was three orders of magnitude less than that due to (137)Cs, and thus well below the maximum effective dose limits for the public.