Sepsis often is characterized by an acute brain dysfunction, which is associated with increased morbidity and mortality. Its pathophysiology is highly complex, resulting from both inflammatory and noninflammatory processes, which may induce significant alterations in vulnerable areas of the brain. Important mechanisms include excessive microglial activation, impaired cerebral perfusion, blood–brain-barrier dysfunction, and altered neurotransmission. Systemic insults, such as prolonged inflammation, severe hypoxemia, and persistent hyperglycemia also may contribute to aggravate sepsis-induced brain dysfunction or injury. The diagnosis of brain dysfunction in sepsis relies essentially on neurological examination and neurological tests, such as EEG and neuroimaging. A brain MRI should be considered in case of persistent brain dysfunction after control of sepsis and exclusion of major confounding factors. Recent MRI studies suggest that septic shock can be associated with acute cerebrovascular lesions and white matter abnormalities. Currently, the management of brain dysfunction mainly consists of control of sepsis and prevention of all aggravating factors, including metabolic disturbances, drug overdoses, anticholinergic medications, withdrawal syndromes, and Wernicke’s encephalopathy. Modulation of microglial activation, prevention of blood–brain-barrier alterations, and use of antioxidants represent relevant therapeutic targets that may impact significantly on neurologic outcomes. In the future, investigations in patients with sepsis should be undertaken to reduce the duration of brain dysfunction and to study the impact of this reduction on important health outcomes, including functional and cognitive status in survivors.

Peripheral blood dynamics in the 100 days before delivery reveal a transition from pregnancy maintenance to prelabor biology.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2017. Other selected articles can be found online at http://ccforum.com/series/annualupdate2017. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.Originally published in the Annual Update in Intensive Care and Emergency Medicine 2017. The number of authors differs in the two versions due to constraints regarding the number of authors in the Annual Update in Intensive Care and Emergency Medicine. In the Annual Update version of the review, the three senior authors appear in the acknowledgement section. In the Critical Care version, these three senior authors appear as full authors of the manuscript. All authors helped draft and revise the manuscript for critical intellectual content.

Muscle wasting in critically ill patients is associated with negative clinical outcomes. Ultrasound quadriceps femoris muscle assessment may constitute a convenient tool to evaluate muscle wasting. Nevertheless, its reliability remains uncertain. Our primary aim was to study the intra- and inter-observer reliability of this technique. Our secondary aim was to assess the evolution of the quadriceps muscle during the first 3 weeks after ICU admission and its possible association with nutritional intake. This observational study included patients expected to stay more than 7 days in the ICU. Ultrasound quadriceps muscle thickness was measured with a 12 MHz linear transducer, by two trained physicians, on D1, D3, D5, D7 and D21. Two measurements sites were evaluated: on the midpoint or on the two-thirds of the length between the anterior superior iliac spine and the upper border of the patella. Intra and inter-observer reliability was assessed by calculating the intra-class correlation coefficient (ICC). A total of 280 ultrasound quadriceps thickness measurements were performed on 29 critically ill patients. Intra-observer reliability’s ICC was 0.74 [95% CI 0.63; 0.84] at the “midpoint” site and 0.83 [95% CI 0.75; 0.9] at the “two-thirds” site. Inter-observer reliability’s ICC was 0.76 [95% CI, 0.66; 0.86] at the “midpoint” site and 0.81 [95% CI, 0.7; 0.9] at the “two-thirds” site. Quadriceps femoris muscle thickness decreased over 16% within the first week after ICU admission. No correlation was found between muscle loss and caloric (p = 0.96) or protein (p = 0.80) debt over the first week. The assessment by ultrasonography of the quadriceps muscle thickness reveals good intra- and inter-observer reliability and may constitute a promising tool to evaluate the effect of nutritional-based interventions on muscle wasting in critically ill patients. “Committee for the Protection of Human Subjects in Biomedical Research” - Paris Ile de France VI Pitié-Salpêtrière – 10/07/2014. French Data Protection Committee (“Commission Nationale Informatique et Libertés”) - #1771144.

Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex “dendritic-like” aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. Using the knock-in mouse model CX3CR1GFP/+, we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. Our results counteract the classical view of a homogenous regional resting state of the microglial cells within the brain. Microglial cells are distributed in different defined sub-populations that present specific behaviour after pathological challenge, allowing postulating for a cellular and functional specialization. Moreover, this new experimental approach will provide a support not only to neuropathological diagnosis but also to study microglial function in various disease models while reducing the number of animals needed to approach the international ethical statements.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400–35,000 features down to 4–34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl.