A population pharmacodynamic Markov mixed-effects model for determining remimazolam-induced sedation when co-administered with fentanyl in procedural sedation.

The clinical effects of remimazolam (an investigational, ultra-short acting benzodiazepine being studied in procedural sedation) were measured using the Modified Observer's Assessment of Awareness/Sedation Scale (MOAA/S). The objective of this analysis was to develop a population pharmacokinetic/pharmacodynamic model to describe remimazolam-induced sedation with fentanyl over time in procedural sedation. MOAA/S from 10 clinical phase I-III trials were pooled for analysis, where data were collected after administration of placebo or remimazolam with or without concomitant fentanyl. A Markov model described transition states for 35,356 MOAA/S-time observations from 1071 subjects. Effect-compartment models of remimazolam and fentanyl linked plasma concentrations to the Markov model, and drug effects were described using a synergistic maximum effect (Emax ) model. Simulations were performed to identify the optimal remimazolam-fentanyl combination doses in procedural sedation. Fentanyl showed synergistic effects with remimazolam in sedation. Increasing age was related to longer recovery from sedation. Patients with body mass index greater than 25 kg/m2 had ~30% higher rates of distribution from plasma to the effect site (keo), indicating a slightly faster onset of sedation. Simulations showed that remimazolam 5 mg was more appropriate than 4 or 6 mg when administered with fentanyl 50 µg. The model and simulations support that a combination of remimazolam 5 mg with fentanyl 50 µg is an appropriate dosing regimen and the dose of remimazolam does not need to be changed in elderly patients, but some elderly patients may have a longer duration of sedation.

Multivariate MR biomarkers better predict cognitive dysfunction in mouse models of Alzheimer's disease.

To understand multifactorial conditions such as Alzheimer's disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between pathology affected brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD, while being raised in uniform environments and with known genotype alterations. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI traditional voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included hippocampus, olfactory areas, entorhinal cortex and cerebellum, as well as the frontal association area. The properties of these regions, extracted from each of the imaging markers, were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning, allowing the detection of associations between context, location, and responses. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, expected to play a role in memory dysfunction, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our cross-sectional results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.

Small Animal Multivariate Brain Analysis (SAMBA) - a High Throughput Pipeline with a Validation Framework.

While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase throughput and reproducibility of quantitative small animal brain studies, we have developed a publicly shared, high throughput VBA pipeline in a high-performance computing environment, called SAMBA. The increased computational efficiency allowed large multidimensional arrays to be processed in 1-3 days-a task that previously took ~1 month. To quantify the variability and reliability of preclinical VBA in rodent models, we propose a validation framework consisting of morphological phantoms, and four metrics. This addresses several sources that impact VBA results, including registration and template construction strategies. We have used this framework to inform the VBA workflow parameters in a VBA study for a mouse model of epilepsy. We also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community. The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging, thus facilitating the creation and communication of robust results.

Imaging Nutrient Distribution in the Rhizosphere Using FTIR Imaging.

Symbiotic associations in the rhizosphere between plants and microorganisms lead to efficient changes in the distribution of nutrients that promote growth and development for each organism involved. Understanding these nutrient fluxes provides insight into the molecular dynamics involved in nutrient transport from one organism to the other. To study such a nutrient flow, a new application of Fourier transform infrared imaging (FTIRI) was developed that entailed growing Populus tremulodes seedlings on a thin, nutrient-enriched Phytagel matrix that allows pixel to pixel measurement of the distribution of nutrients, in particular, nitrate, in the rhizosphere. The FTIR spectra collected from ammonium nitrate in the matrix indicated the greatest changes in the spectra at 1340 cm-1 due to the asymmetric stretching vibrations of nitrate. For quantification of the nitrate concentration in the rhizosphere of experimental plants, a calibration curve was generated that gave the nitrate concentration at each pixel in the chemical image. These images of the poplar rhizosphere showed evidence for symbiotic sharing of nutrients between the plant and the fungi, Laccaria bicolor, where the nitrate concentration was five times higher near mycorrhizal roots than further out into the rhizosphere. This suggested that nitrates are acquired and transported from the media toward the plant root by the fungi. Similarly, the sucrose used in the growth media as a carbon source was depleted around the fungi, suggesting its uptake and consumption by the system. This study is the first of its kind to visualize and quantify the nutrient availability associated with mycorrhizal interactions, indicating that FTIRI has the ability to monitor nutrient changes with other microorganisms in the rhizosphere as a key step for understanding nutrient flow processes in more diverse biological systems.