High-Spatial-Resolution Benchtop X-ray Fluorescence Imaging through Bragg-Diffraction-Based Focusing with Bent Mosaic Graphite Crystals: A Simulation Study.

X-ray fluorescence imaging (XFI) can localize diagnostic or theranostic entities utilizing nanoparticle (NP)-based probes at high resolution in vivo, in vitro, and ex vivo. However, small-animal benchtop XFI systems demonstrating high spatial resolution (variable from sub-millimeter to millimeter range) in vivo are still limited to lighter elements (i.e., atomic number Z≤45). This study investigates the feasibility of focusing hard X-rays from solid-target tubes using ellipsoidal lens systems composed of mosaic graphite crystals with the aim of enabling high-resolution in vivo XFI applications with mid-Z (42≤Z≤64) elements. Monte Carlo simulations are performed to characterize the proposed focusing-optics concept and provide quantitative predictions of the XFI sensitivity, in silico tumor-bearing mice models loaded with palladium (Pd) and barium (Ba) NPs. Based on simulation results, the minimum detectable total mass of PdNPs per scan position is expected to be on the order of a few hundred nanograms under in vivo conform conditions. PdNP masses as low as 150 ng to 50 ng could be detectable with a resolution of 600 µm when imaging abdominal tumor lesions across a range of low-dose (0.8 µGy) to high-dose (8 µGy) exposure scenarios. The proposed focusing-optics concept presents a potential step toward realizing XFI with conventional X-ray tubes for high-resolution applications involving interesting NP formulations.

Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.

Purpose: Earlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients. Methods: 317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics. Results: A correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration. Conclusion: The machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations.

Background: Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD. Methods: Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis. Results: A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, pFDR=0.0006), 3-methylpyridine (p = 0.0002, pFDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, pFDR = 0.0174), valeric acid (p = 0.005, pFDR = 0.0162) and isoprene (p = 0.038, pFDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96). Conclusion: According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.

Reduced phagocytosis, ROS production and enhanced apoptosis of leukocytes upon alcohol drinking in healthy volunteers.

BACKGROUND: Alcohol drinking is associated with a serious risk of developing health problems as well as with a large number of traumatic injuries. Although chronic alcohol misuse is known to contribute to severe inflammatory complications, the effects of an acute alcohol misuse are still unclear. Here, the impact of acute alcohol drinking on leukocyte counts and their cellular functions were studied. METHODS: Twenty-two healthy volunteers (12 female, 10 male) received a predefined amount of a whiskey-cola mixed drink (40% v/v), at intervals of 20 min, over 4 h to achieve a blood alcohol concentration of 1‰. Blood samples were taken before drinking T0, 2 h (T2), 4 h (T4), 6 h (T6), 24 h (T24) and 48 h (T48) after starting drinking alcohol. Leukocytes, monocytes and granulocyte counts and their functions regarding the production of reactive oxidative species (ROS), phagocytosis and apoptosis were analyzed by flow cytometry. RESULTS: Total leukocyte counts significantly increased at T2 and T4, while granulocyte and monocyte counts decreased at T4 and T6 vs. T0. Monocytes increased significantly at T24 and T48 vs. T0. While the total number of ROS-producing leukocytes and notably granulocytes significantly increased, in parallel, the intracellular ROS intensity decreased at T2 and T6. The numbers of ROS-positive monocytes have shown a delayed modulation of ROS, with a significant reduction in the total number of ROS-producing cells at T48 and a significantly reduced intracellular ROS-intensity at T24. Phagocyting capacity of leukocytes significantly decreased at T4 and T6. In general leukocytes, and notably granulocytes demonstrated significantly increased early (T2), while monocyte exerted significantly increased late apoptosis (T24 and T48). CONCLUSIONS: Alcohol drinking immediately impacts leukocyte functions, while the impact on monocytes occurs at even later time points. Thus, even in young healthy subjects, alcohol drinking induces immunological changes that are associated with diminished functions of innate immune cells that persist for days.

Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina.

BACKGROUND: The green microalga Dunaliella salina accumulates a high proportion of ß-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data. RESULTS: The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina. CONCLUSIONS: The reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.

Transformation of remnant algal biomass to 5-HMF and levulinic acid: influence of a biphasic solvent system.

The primary commercial product from the green microalgae Dunaliella salina is ß-carotene. After extracting the lipophilic fraction containing this red-orange pigment, an algal residue remains. As the carotenogenesis is induced by light stress with simultaneous nitrogen depletion, the protein content is low and the remnant is comprised largely of storage carbohydrates. In this work, we transformed the defatted remnant directly to the platform chemicals, 5-hydroxy methyl furfural (5-HMF) and levulinic acid (LA), without previous purification or any pretreatment. The batch experiments were carried out in an autoclave under biphasic solvent conditions at 453 K for 1 h using acidic ZSM-5 zeolite as a heterogeneous catalyst. Mixtures of methyl isobutyl ketone (MIBK/H2O) or tetrahydrofuran (THF/H2O/NaCl) with water were used to create the biphasic reactor conditions. The biphasic reaction mixtures helped to increase the 5-HMF yield and simultaneously mitigated the formation of insoluble humins. The carbon yields of 5-HMF and of LA in the MIBK/H2O biphasic system without NaCl were 13.9% and 3.7%, respectively. The highest carbon yield of 5-HMF (34.4%) was achieved by adding NaCl to the reaction mixture containing THF/H2O. The experimentally measured partition ratios of 5-HMF between the two liquid phases were compared to the predictions calculated by the computational method COSMO-RS, which is a quantum chemistry-based method to predict the thermodynamic equilibria of liquid mixtures and the solubilities. The COSMO-RS predicted partition ratios of 5-HMF were in line with the experimentally measured ones.

Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae.

BACKGROUND: Photosynthetic organisms can be used for renewable and sustainable production of fuels and high-value compounds from natural resources. Costs for design and operation of large-scale algae cultivation systems can be reduced if data from laboratory scale cultivations are combined with detailed mathematical models to evaluate and optimize the process. RESULTS: In this work we present a flexible modeling formulation for accumulation of high-value storage molecules in microalgae that provides quantitative predictions under various light and nutrient conditions. The modeling approach is based on dynamic flux balance analysis (DFBA) and includes regulatory models to predict the accumulation of pigment molecules. The accuracy of the model predictions is validated through independent experimental data followed by a subsequent model-based fed-batch optimization. In our experimentally validated fed-batch optimization study we increase biomass and [Formula: see text]-carotene density by factors of about 2.5 and 2.1, respectively. CONCLUSIONS: The analysis shows that a model-based approach can be used to develop and significantly improve biotechnological processes for biofuels and pigments.

A dynamic growth model of Dunaliella salina: parameter identification and profile likelihood analysis.

In this work, a photoautotrophic growth model incorporating light and nutrient effects on growth and pigmentation of Dunaliella salina was formulated. The model equations were taken from literature and modified according to the experimental setup with special emphasis on model reduction. The proposed model has been evaluated with experimental data of D. salina cultivated in a flat-plate photobioreactor under stressed and non-stressed conditions. Simulation results show that the model can represent the experimental data accurately. The identifiability of the model parameters was studied using the profile likelihood method. This analysis revealed that three model parameters are practically non-identifiable. However, some of these non-identifiabilities can be resolved by model reduction and additional measurements. As a conclusion, our results suggest that the proposed model equations result in a predictive growth model for D. salina.