Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport.

The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

Genome-scale metabolic network reconstruction of model animals as a platform for translational research.

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

EANM practical guidance on uncertainty analysis for molecular radiotherapy absorbed dose calculations.

A framework is proposed for modelling the uncertainty in the measurement processes constituting the dosimetry chain that are involved in internal absorbed dose calculations. The starting point is the basic model for absorbed dose in a site of interest as the product of the cumulated activity and a dose factor. In turn, the cumulated activity is given by the area under a time-activity curve derived from a time sequence of activity values. Each activity value is obtained in terms of a count rate, a calibration factor and a recovery coefficient (a correction for partial volume effects). The method to determine the recovery coefficient and the dose factor, both of which are dependent on the size of the volume of interest (VOI), are described. Consideration is given to propagating estimates of the quantities concerned and their associated uncertainties through the dosimetry chain to obtain an estimate of mean absorbed dose in the VOI and its associated uncertainty. This approach is demonstrated in a clinical example.

Cancer Risk Assessment of Airborne PAHs Based on in Vitro Mixture Potency Factors.

Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are common environmental pollutants associated with adverse human health effects including cancer. However, the risk of exposure to mixtures is difficult to estimate, and risk assessment by whole mixture potency evaluations has been suggested. To facilitate this, reliable in vitro based testing systems are necessary. Here, we investigated if activation of DNA damage signaling in vitro could be an endpoint for developing whole mixture potency factors (MPFs) for airborne PAHs. Activation of DNA damage signaling was assessed by phosphorylation of Chk1 and H2AX using Western blotting. To validate the in vitro approach, potency factors were determined for seven individual PAHs which were in very good agreement with established potency factors based on cancer data in vivo. Applying the method using Stockholm air PAH samples indicated MPFs with orders of magnitude higher carcinogenic potency than predicted by established in vivo-based potency factors. Applying the MPFs in cancer risk assessment suggested that 45.4 (6% of all) cancer cases per year in Stockholm are due to airborne PAHs. Applying established models resulted in <1 cancer case per year, which is far from expected levels. We conclude that our in vitro based approach for establishing MPFs could be a novel method to assess whole mixture samples of airborne PAHs to improve health risk assessment.

Scattering and polarization properties of the scarab beetle Cyphochilus insulanus cuticle.

Optical properties of natural photonic structures can inspire material developments in diversified areas, such as the spectral design of surfaces for camouflage. Here, reflectance, scattering, and polarization properties of the cuticle of the scarab beetle Cyphochilus insulanus are studied with spectral directional hemispherical reflectance, bidirectional reflection distribution function (BRDF) measurements, and Mueller-matrix spectroscopic ellipsometry (MMSE). At normal incidence, a reflectance (0.6-0.75) is found in the spectral range of 400-1600 nm and a weaker reflectance <0.2 in the UV range as well as for wavelengths >1600 nm. A whiteness of W=42 is observed for mainly the elytra of the beetle. Chitin is a major constituent of the insect cuticle which is verified by the close similarity of the measured IR spectrum to that of α-chitin. The BRDF signal shows close-to-Lambertian properties of the beetle for visible light at small angles of incidence. From the MMSE measurement it is found that the beetles appear as dielectric reflectors reflecting linearly polarized light at oblique incidence with low gloss and a low degree of polarization. The measured beetle properties are properties that can be beneficial in a camouflage material.

A Deep-Learning-Based Partial-Volume Correction Method for Quantitative 177Lu SPECT/CT Imaging.

With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Especially for small objects, this so-called partial-volume effect limits the accuracy of activity quantification. Numerous methods for partial-volume correction (PVC) have been proposed, but most methods have the disadvantage of assuming a spatially invariant resolution of the imaging system, which does not hold for SPECT. Furthermore, most methods require a segmentation based on anatomic information. Methods: We introduce DL-PVC, a methodology for PVC of 177Lu SPECT/CT imaging using deep learning (DL). Training was based on a dataset of 10,000 random activity distributions placed in extended cardiac-torso body phantoms. Realistic SPECT acquisitions were created using the SIMIND Monte Carlo simulation program. SPECT reconstructions without and with resolution modeling were performed using the CASToR and STIR reconstruction software, respectively. The pairs of ground-truth activity distributions and simulated SPECT images were used for training various U-Nets. Quantitative analysis of the performance of these U-Nets was based on metrics such as the structural similarity index measure or normalized root-mean-square error, but also on volume activity accuracy, a new metric that describes the fraction of voxels in which the determined activity concentration deviates from the true activity concentration by less than a certain margin. On the basis of this analysis, the optimal parameters for normalization, input size, and network architecture were identified. Results: Our simulation-based analysis revealed that DL-PVC (0.95/7.8%/35.8% for structural similarity index measure/normalized root-mean-square error/volume activity accuracy) outperforms SPECT without PVC (0.89/10.4%/12.1%) and after iterative Yang PVC (0.94/8.6%/15.1%). Additionally, we validated DL-PVC on 177Lu SPECT/CT measurements of 3-dimensionally printed phantoms of different geometries. Although DL-PVC showed activity recovery similar to that of the iterative Yang method, no segmentation was required. In addition, DL-PVC was able to correct other image artifacts such as Gibbs ringing, making it clearly superior at the voxel level. Conclusion: In this work, we demonstrate the added value of DL-PVC for quantitative 177Lu SPECT/CT. Our analysis validates the functionality of DL-PVC and paves the way for future deployment on clinical image data.

Single-cell omics analysis with genome-scale metabolic modeling.

Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.

Kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy based on multiple small VOIs.

PURPOSE: The aim was to investigate the use of multiple small VOIs for kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy. METHOD: The study was based on patient and simulated SPECT images in anthropomorphic geometries. Images were reconstructed using two reconstruction programs (local LundaDose and commercial Hermia) using OS-EM with and without resolution recovery (RR). Five small VOIs were placed to determine the average activity concentration (AC) in each kidney. The study consisted of three steps: (i) determination of the number of iterations for AC convergence based on simulated images; (ii) determination of recovery-coefficients (RCs) for 2 mL VOIs using a separate set of simulated images; (iii) assessment of operator variability in AC estimates for simulated and patient images. Five operators placed the VOIs, using for guidance: a) SPECT/CT with RR, b) SPECT/CT without RR, and c) CT only. For simulated images, time-integrated ACs (TIACs) were evaluated. For patient images, estimated ACs were compared with results of a previous method based on whole-kidney VOIs. RESULTS: Eight iterations and ten subsets were sufficient for both programs and reconstruction settings. Mean RCs (mean ± SD) with RR were 1.03 ± 0.02 (LundaDose) and 1.10 ± 0.03 (Hermia), and without RR 0.91 ± 0.03 (LundaDose) and 0.94 ± 0.03 (Hermia). Most stable and accurate estimates of the AC were obtained using five 2-mL VOIs guided by SPECT/CT with RR, applying them to images without RR, and including an explicit RC for recovery correction. CONCLUSION: The small VOI method based on five 2-mL VOIs was found efficient and sufficiently accurate for kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy.

3D printed non-uniform anthropomorphic phantoms for quantitative SPECT.

BACKGROUND: A 3D printing grid-based method was developed to construct anthropomorphic phantoms with non-uniform activity distributions, to be used for evaluation of quantitative SPECT images. The aims were to characterize the grid-based method and to evaluate its capability to provide realistically shaped phantoms with non-uniform activity distributions. METHODS: Characterization of the grid structures was performed by printing grid-filled spheres. Evaluation was performed by micro-CT imaging to investigate the printing accuracy and by studying the modulation contrast ([Formula: see text]) in SPECT images for 177Lu and 99mTc as a function of the grid fillable-volume fraction (FVF) determined from weighing. The grid-based technique was applied for the construction of two kidney phantoms and two thyroid phantoms, designed using templates from the XCAT digital phantoms. The kidneys were constructed with a hollow outer container shaped as cortex, an inner grid-based structure representing medulla and a solid section representing pelvis. The thyroids consisted of two lobes printed as grid-based structures, with void hot spots within the lobes. The phantoms were filled with solutions of 177Lu (kidneys) or 99mTc (thyroids) and imaged with SPECT. For verification, Monte Carlo simulations of SPECT imaging were performed for activity distributions corresponding to those of the printed phantoms. Measured and simulated SPECT images were compared qualitatively and quantitatively. RESULTS: Micro-CT images showed that printing inaccuracies were mainly uniform across the grid. The relationships between the FVF from weighing and [Formula: see text] were found to be linear (r = 0.9995 and r = 0.9993 for 177Lu and 99mTc, respectively). The FVF-deviations from the design were up to 15% for thyroids and 4% for kidneys, mainly related to possibilities of cleaning after printing. Measured and simulated SPECT images of kidneys and thyroids exhibited similar activity distributions and quantitative comparisons agreed well, thus verifying the grid-based method. CONCLUSIONS: We find the grid-based technique useful for the provision of 3D printed, realistically shaped, phantoms with non-uniform activity distributions, which can be used for evaluation of different quantitative methods in SPECT imaging.

Position dependence of recovery coefficients in 177Lu-SPECT/CT reconstructions - phantom simulations and measurements.

BACKGROUND: Although the importance of quantitative SPECT has increased tremendously due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation programs to harmonize SPECT imaging. Work is currently underway to develop an accreditation for quantitative 177Lu SPECT/CT. The aim of this study is to verify whether the positioning of the spheres within the phantom has an influence on the recovery and thus needs to be considered in SPECT harmonization. In addition, the effects of these recovery coefficients on a potential partial volume correction as well as absorbed-dose estimates are investigated. METHODS: Using a low-dose CT of a SPECT/CT acquisition, a computerized version of the NEMA body phantom was created using a semi-automatic threshold-based method. Based on the mass-density map, the detector orbit, and the sphere centers, realistic SPECT acquisitions of all possible 720 sphere configurations of both the PET and the SPECT versions of the NEMA Body Phantom were generated using Monte Carlo simulations. SPECT reconstructions with different numbers of updates were performed without (CASToR) and with resolution modeling (STIR). Recovery coefficients were calculated for all permutations, reconstruction methods, and phantoms, and their dependence on the sphere positioning was investigated. Finally, the simulation-based findings were validated using SPECT/CT acquisitions of six different sphere configurations. RESULTS: Our analysis shows that sphere positioning has a significant impact on the recovery for both of the reconstruction methods and the phantom type. Although resolution modeling resulted in significantly higher recovery, the relative variation in recovery within the 720 permutations was even larger. When examining the extreme values of the recovery, reconstructions without resolution modeling were influenced primarily by the sphere position, while with resolution modeling the volume of the two adjacent spheres had a larger influence. The SPECT measurements confirmed these observations, and the recovery curves showed good overall agreement with the simulated data. CONCLUSION: Our study shows that sphere positioning has a significant impact on the recovery obtained in NEMA sphere phantom measurements and should therefore be considered in a future SPECT accreditation. Furthermore, the single-measurement method normally performed for PVC should be reconsidered to account for the position dependency.

A PBPK model for PRRT with [177Lu]Lu-DOTA-TATE: Comparison of model implementations in SAAM II and MATLAB/SimBiology.

Physiologically based pharmacokinetic (PBPK) models offer the ability to simulate and predict the biodistribution of radiopharmaceuticals and have the potential to enable individualised treatment planning in molecular radiotherapy. The objective of this study was to develop and implement a whole-body compartmental PBPK model for peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-DOTA-TATE in SimBiology to allow for more complex analyses. The correctness of the model implementation was ensured by comparing its outputs, such as the time-integrated activity (TIA), with those of a PBPK model implemented in SAAM II software. METHODS: A combined PBPK model for [68Ga]Ga-DOTA-TATE and [177Lu]Lu-DOTA-TATE was developed and implemented in both SAAM II and SimBiology. A retrospective analysis of 12 patients with metastatic neuroendocrine tumours (NETs) was conducted. First, time-activity curves (TACs) and TIAs from the two software were calculated and compared for identical parameter values. Second, pharmacokinetic parameters were fitted to activity concentrations, analysed and compared. RESULTS: The PBPK model implemented in SimBiology produced TIA results comparable to those generated by the model implemented in SAAM II, with a relative deviation of less than 0.5% when using the same input parameters. The relative deviation of the fitted TIAs was less than 5% when model parameter values were fitted to the measured activity concentrations. CONCLUSION: The proposed PBPK model implemented in SimBiology can be used for dosimetry in radioligand therapy and TIA prediction. Its outputs are similar to those generated by the PBPK model implemented in SAAM II, confirming the correctness of the model implementation in SimBiology.

Relationship Between Absorbed Dose and Response in Neuroendocrine Tumors Treated with [177Lu]Lu-DOTATATE.

Peptide receptor radionuclide therapy presents the possibility of tracing and quantifying the uptake of the drug in the body and performing dosimetry, potentially allowing individualization of treatment schemes. However, the details of how neuroendocrine tumors (NETs) respond to different absorbed doses are insufficiently known. Here, we investigated the relationship between tumor-absorbed dose and tumor response in a cohort of patients with NETs treated with [177Lu]Lu-DOTATATE. Methods: This was a retrospective study based on 69 tumors in 32 patients treated within a clinical trial. Dosimetry was performed at each cycle of [177Lu]Lu-DOTATATE, rendering 366 individual absorbed dose assessments. Hybrid planar-SPECT/CT imaging using [177Lu]Lu-DOTATATE was used, including quantitative SPECT reconstruction, voxel-based absorbed dose rate calculation, semiautomatic image segmentation, and partial-volume correction. Changes in tumor volume were used to determine tumor response. The volume for each tumor was manually delineated on consecutive CT scans, giving a total of 712 individual tumor volume assessments. Tumors were stratified according to grade. The relationship between absorbed dose and response was investigated using mixed-effects models and logistic regression. Tumors smaller than 4 cm3 were excluded. Results: In grade 2 NETs, a clear relationship between absorbed dose and volume reduction was observed. Our observations suggest a 90% probability of partial tumor response for an accumulated tumor-absorbed dose of at least 135 Gy. Conclusion: Our findings are in accordance with previous observations regarding the relationship between tumor shrinkage and absorbed dose. Moreover, our data suggest an absorbed dose threshold for partial response in grade 2 NETs. These observations provide valuable insights for the design of dosimetry-guided peptide receptor radionuclide therapy schemes.

Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth.

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells' preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

Future trends for patient-specific dosimetry methodology in molecular radiotherapy.

Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.

Monte Carlo investigation of PET [68Ga]Ga-DOTA-TOC activity-administration protocols for consistent image quality.

One example of a PET exam that suffers from noise problems is [68Ga]Ga-DOTA-TOC, where patients are generally administered between 100 and 200 MBq [68Ga]Ga-DOTA-TOC, irrespective of size. However, a fixed activity can result in low signal-to-noise ratios (SNRs) in larger patients. This study aimed to evaluate the impact on image quality with respect to injected activity and patient habitus through Monte Carlo (MC) simulation. Eight anthropomorphic computer phantoms with body mass indices (BMIs) between 19 kg/m2 and 38 kg/m2 and tumours distributed in the liver were simulated using the MC software Gate v8.2 with an activity distribution defined according to [68Ga]Ga-DOTA-TOC standardised uptake values. Three activity-administration protocols were simulated: (i) with a fixed activity of 100 MBq, (ii) with the activity scaled by 2 MBq/kg, and (iii) with the activity scaled by a body size-dependent power-function based on the SNR obtained with (ii). BMI, weight, body surface area, and abdominal circumference were evaluated body size parameters. Images were reconstructed with the CASToR software and evaluated for background SNR and lesion contrast-to-noise ratio (CNR). Large SNR variabilities were obtained with protocols (i) and (ii), while (iii) generated good consistency. Several tumours failed to reach a CNR of 5 for large phantoms with protocol (i), but the CNR was generally improved by (ii) and (iii). An activity scaled by patient habitus generate better image quality consistency, which increases the likelihood that patients receive a similar standard of care.

Metabolite alterations in zebrafish embryos exposed to hydroxylated polybrominated diphenyl ethers.

Hydroxylated polybrominated diphenyl ethers (OH-PBDEs) are formed by metabolism from the flame retardants polybrominated diphenyl ethers (PBDEs). In the aquatic environment, they are also produced naturally. OH-PBDEs are known for their potential to disrupt energy metabolism, the endocrine system, and the nervous system. This is the first study focusing on the effects of OH-PBDEs at the metabolite level in vivo. The aim of the current study was to investigate the metabolic effects of exposure to OH-PBDEs using metabolomics, and to identify potential biomarker(s) for energy disruption of OH-PBDEs. Zebrafish (Danio rerio) embryos were exposed to two different concentrations of 6-OH-BDE47 and 6-OH-BDE85 and a mixture of these two compounds. In total, 342 metabolites were annotated and 79 metabolites were affected in at least one exposure. Several affected metabolites, e.g. succinic acid, glutamic acid, glutamine, tyrosine, tryptophan, adenine, and several fatty acids, could be connected to known toxic mechanisms of OH-PBDEs. Several phospholipids were strongly up-regulated with up to a six-fold increase after exposure to 6-OH-BDE47, a scarcely described effect of OH-PBDEs. Based on the observed metabolic effects, a possible connection between disruption of the energy metabolism, neurotoxicity and potential immunotoxicity of OH-PBDEs was suggested. Single compound exposures to 6-OH-BDE47 and 6-OH-BDE85 showed little overlap in the affected metabolites. This shows that compounds of similar chemical structure can induce different metabolic effects, possibly relating to their different toxic mechanisms. There were inter-concentration differences in the metabolic profiles, indicating that the metabolic effects were concentration dependent. After exposure to the mixture of 6-OH-BDE47 and 6-OH-BDE85, a new metabolic profile distinct from the profiles obtained from the single compounds was observed. Succinic acid was up-regulated at the highest, but still environmentally relevant, concentration of 6-OH-BDE47, 6-OH-BDE85, and the mixture. Therefore, succinic acid is suggested as a potential biomarker for energy disruption of OH-PBDEs.

Activity recovery for differently shaped objects in quantitative SPECT.

Objective.The aim was to theoretically and experimentally investigate recovery in SPECT images with objects of different shapes. Furthermore, the accuracy of volume estimation by thresholding was studied for those shapes.Approach.Nine spheres, nine oblate spheroids, and nine prolate spheroids phantom inserts were used, of which the six smaller spheres were part of the NEMA IEC body phantom and the rest of the inserts were 3D-printed. The inserts were filled with99mTc and177Lu. When filled with99mTc, SPECT images were acquired in a Siemens Symbia Intevo Bold gamma camera and when filled with177Lu in a General Electric NM/CT 870 DR gamma camera. The signal rate per activity (SRPA) was determined for all inserts and represented as a function of the volume-to-surface ratio and of the volume-equivalent radius using VOIs defined according to the sphere dimensions and VOIs defined using thresholding. Experimental values were compared with theoretical curves obtained analytically (spheres) or numerically (spheroids), starting from the convolution of a source distribution with a point-spread function. Validation of the activity estimation strategy was performed using four 3D-printed ellipsoids. Lastly, the threshold values necessary to determine the volume of each insert were obtained.Main results.Results showed that SRPA values for the oblate spheroids diverted from the other inserts, when SRPA were represented as a function of the volume-equivalent radius. However, SRPA values for all inserts followed a similar behaviour when represented as a function of the volume-to-surface ratio. Results for ellipsoids were in agreement with those results. For the three types of inserts the volume could be accurately estimated using a threshold method for volumes larger than 25 ml.Significance.Determination of SRPA independently of lesion or organ shape should decrease uncertainties in estimated activities and thereby, in the long term, be beneficial to patient care.