Continuous cerebral blood flow monitoring: What should we do with these extra numbers?

NeoDoppler is a noninvasive monitoring device that can be attached to a patient's head to provide real-time continuous cerebral Doppler evaluation. A feasibility study shows that it can be used in operating theatres during anaesthesia to potentially guide haemodynamic management. We discuss the impact of this new device and which further research would be necessary to find its role in clinical practice.

Identification of Modulators of the C. elegans Aryl Hydrocarbon Receptor and Characterization of Transcriptomic and Metabolic AhR-1 Profiles.

The Aryl hydrocarbon Receptor (AhR) is a xenobiotic sensor in vertebrates, regulating the metabolism of its own ligands. However, no ligand has been identified to date for any AhR in invertebrates. In C. elegans, the AhR ortholog, AHR-1, displays physiological functions. Therefore, we compared the transcriptomic and metabolic profiles of worms expressing AHR-1 or not and investigated the putative panel of chemical AHR-1 modulators. The metabolomic profiling indicated a role for AHR-1 in amino acids, carbohydrates, and fatty acids metabolism. The transcriptional profiling in neurons expressing AHR-1, identified 95 down-regulated genes and 76 up-regulated genes associated with neuronal and metabolic functions in the nervous system. A gene reporter system allowed us to identify several AHR-1 modulators including bacterial, dietary, or environmental compounds. These results shed new light on the biological functions of AHR-1 in C. elegans and perspectives on the evolution of the AhR functions across species.

Safety of general anaesthetics on the developing brain: are we there yet?

Thirty years ago, neurotoxicity induced by general anaesthetics in the developing brain of rodents was observed. In both laboratory-based and clinical studies, many conflicting results have been published over the years, with initial data confirming both histopathological and neurodevelopmental deleterious effects after exposure to general anaesthetics. In more recent years, animal studies using non-human primates and new human cohorts have identified some specific deleterious effects on neurocognition. A clearer pattern of neurotoxicity seems connected to exposure to repeated general anaesthesia. The biochemistry involved in this neurotoxicity has been explored, showing differential effects of anaesthetic drugs between the developing and developed brains. In this narrative review, we start with a comprehensive description of the initial concerning results that led to recommend that any non-essential surgery should be postponed after the age of 3 yr and that research into this subject should be stepped up. We then focus on the neurophysiology of the developing brain under general anaesthesia, explore the biochemistry of the observed neurotoxicity, before summarising the main scientific and clinical reports investigating this issue. We finally discuss the GAS trial, the importance of its results, and some potential limitations that should not undermine their clinical relevance. We finally suggest some key points that could be shared with parents, and a potential research path to investigate the biochemical effects of general anaesthesia, opening up perspectives to understand the neurocognitive effects of repetitive exposures, especially in at-risk children.

Statistical analysis in metabolic phenotyping.

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.

Metabolomics: The Stethoscope for the Twenty-First Century.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

IL-7 Restores T Lymphocyte Immunometabolic Failure in Septic Shock Patients through mTOR Activation.

T lymphocyte alterations are central to sepsis pathophysiology, whereas related mechanisms remain poorly understood. We hypothesized that metabolic alterations could play a role in sepsis-induced T lymphocyte dysfunction. Samples from septic shock patients were obtained at day 3 and compared with those from healthy donors. T cell metabolic status was evaluated in the basal condition and after T cell stimulation. We observed that basal metabolic content measured in lymphocytes by nuclear magnetic resonance spectroscopy was altered in septic patients. Basal ATP concentration, oxidative phosphorylation (OXPHOS), and glycolysis pathways in T cells were decreased as well. After stimulation, T lymphocytes from patients failed to induce glycolysis, OXPHOS, ATP production, GLUT1 expression, glucose entry, and proliferation to similar levels as controls. This was associated with significantly altered mTOR, but not Akt or HIF-1α, activation and only minor AMPKα phosphorylation dysfunction. IL-7 treatment improved mTOR activation, GLUT1 expression, and glucose entry in septic patients' T lymphocytes, leading to their enhanced proliferation. mTOR activation was central to this process, because rapamycin systematically inhibited the beneficial effect of recombinant human IL-7. We demonstrate the central role of immunometabolism and, in particular, mTOR alterations in the pathophysiology of sepsis-induced T cell alterations. Our results support the rationale for targeting metabolism in sepsis with recombinant human IL-7 as a treatment option.

Persistently Altered Metabolic Phenotype following Perinatal Excitotoxic Brain Injury.

Excitotoxicity plays a key role during insults to the developing brain such as neonatal encephalopathy, stroke, and encephalopathy of prematurity. Such insults affect many thousands of infants each year. Excitotoxicity causes frank lesions due to cell death and gliosis and disturbs normal developmental process, leading to deficits in learning, memory, and social integration that persist into adulthood. Understanding the underlying processes of the acute effects of excitotoxicity and its persistence during brain maturation provides an opportunity to identify mechanistic or diagnostic biomarkers, thus enabling and designing possible therapies. We applied mass spectrometry to provide metabolic profiles of brain tissue and plasma over time following an excitotoxic lesion (intracerebral ibotenate) to the neonatal (postnatal day 5) mouse brain. We found no differences between the plasma from the control (PBS-injected) and excitotoxic (ibotenate-injected) groups over time (on postnatal days 8, 9, 10, and 30). In the brain, we found that variations in amino acids (arginine, glutamine, phenylananine, and proline) and glycerophospholipids were sustaining acute and delayed (tertiary) responses to injury. In particular, the effect of the excitotoxic lesion on the normal profile of development was linked to alterations in a fingerprint of glycerophospolipids and amino acids. Specifically, we identified increases in the amino acids glutamine, proline, serine, threonine, tryptophan, valine, and the sphingolipid SM C26:1, and decreases in the glycerophospholipids, i.e., the arachidonic acid-containing phosphatidylcholine (PC aa) C30:2 and the PC aa C32:3. This study demonstrates that metabolic profiling is a useful approach to identify acute and tertiary effects in an excitotoxic lesion model, and generating a short list of targets with future potential in the hunt for identification, stratification, and possibly therapy.

Power Analysis and Sample Size Determination in Metabolic Phenotyping.

Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY(2) of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography-mass spectrometry data from humans and the model organism C. elegans.

DRESS Syndrome in the ICU: When a Patient Is Treated with Multiple Drugs.

The Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) syndrome is life-threatening. It associates a skin condition with hematological and visceral disorders. The DRESS syndrome diagnosis in the intensive care unit (ICU) is difficult as clinical features are nonspecific. Furthermore, the need to treat patients with multiple drugs usually prevents the identification of the causative drug. We report the case of a patient who developed two bouts of DRESS caused by piperacillin-tazobactam, the first being complicated with a distributive shock. Cases of DRESS occurring inside ICU are seldom reported. However, any intensivist may encounter this situation during his career and should be aware of its diagnostic and management specific aspects.

Sample size calculation in metabolic phenotyping studies.

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi.fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi.fr/redmine/projects/dsd/wiki.

A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer.

Breast cancer (BC) displays a high heterogeneity from histology to prognosis, metastatic evolution and treatment responses. We report here a (1)H NMR-based metabolic phenotyping study aiming at identifying coordinated metabolic serum changes associated with advanced metastatic breast cancer (MBC) in comparison to the localized early disease (EBC). A model discriminating EBC and MBC patients is obtained (n=85: 46 EBC and 39 MBC), and validated with an independent cohort (n=112: 61 EBC and 51 MBC; 89.8% sensitivity, 79.3% specificity). We identify 9 statistically significant metabolites involved in this discrimination: histidine, acetoacetate, glycerol, pyruvate, glycoproteins (N-acetyl), mannose, glutamate and phenylalanine. This work illustrates the strong potential of NMR metabolic phenotyping for the diagnosis, prognosis, and management of cancer patients.