Search

UNSTRUCTURED The proliferation and increasing maturity of biometric monitoring technologies allows clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of clinical settings. This includes capturing meaningful aspects of health in daily living and in a more granular and objective manner compared to traditional tools in clinical settings. Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures and are called upon to provide input into study planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. This paper discusses statistical considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that statisticians and data scientists may encounter while dealing with data from biometric monitoring technologies.

Objective. The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as ‘spikes’, in people with epilepsy. Approach. A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen’s kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results. Main results. We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise < 300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG. Significance. These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

UNSTRUCTURED The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Background Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. Objective We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. Methods We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). Results Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. Conclusions We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

BACKGROUND Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. OBJECTIVE We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. METHODS We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). RESULTS Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. CONCLUSIONS We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports. CLINICALTRIAL

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks'') for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

IntroductionCystic Fibrosis (CF) is a disease without a primary cure that requires lifelong care and is characterized by pulmonary exacerbations (PEx). Wearable devices could provide a way for long-term monitoring of disease progression and early signs of PEx to intervene as early as possible, thereby improving long-term outcomes.MethodsIn-hospital feasibility study (n = 26) to 1) assess the ability of Byteflies Sensor Dot to collect relevant cardiorespiratory data in people with CF and its compatibility with clinical workflows, 2) identify candidate digital biomarkers, and 3) collect user feedback from patients and healthcare providers.ResultsCollected sample-level biopotential, bioimpedance and actigraphy data were of high quality. Sensor Dot heart rate (HR) correlated with hospital HR, whereas respiratory rate (RR) did not. HR and RR were associated with CF severity, and HR and coughing with PEx. Willingness to use the device was very high with CF patients and study coordinators considered the device easy-to-use.ConclusionDetermining if a wearable is fit-for-purpose is a long and multidisciplinary process that requires involvement from all stakeholders as early as possible in the development process. Our pilot identified interesting correlations between cardiorespiratory parameters as measured by the wearable, and CF severity and PEx. Together with the usability data, this will inform the next steps in the clinical development process.

Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

Competition for the amino acid arginine by endothelial nitric-oxide synthase (NOS3) and (pro-)inflammatory NO-synthase (NOS2) during endotoxemia appears essential in the derangement of the microcirculatory flow. This study investigated the role of NOS2 and NOS3 combined with/without citrulline supplementation on the NO-production and microcirculation during endotoxemia. Wildtype (C57BL6/N background, control, n = 36), Nos2-deficient, (n = 40), Nos3-deficient (n = 39) and Nos2/Nos3-deficient mice (n = 42) received a continuous intravenous LPS infusion alone (200 μg total, 18 h) or combined with L-citrulline (37.5 mg, last 6 h). The intestinal microcirculatory flow was measured by side-stream dark field (SDF)-imaging. The jejunal intracellular NO production was quantified by in vivo NO-spin trapping combined with electron spin-resonance (ESR) spectrometry. Amino-acid concentrations were measured by high-performance liquid chromatography (HPLC). LPS infusion decreased plasma arginine concentration in control and Nos3−/− compared to Nos2−/− mice. Jejunal NO production and the microcirculation were significantly decreased in control and Nos2−/− mice after LPS infusion. No beneficial effects of L-citrulline supplementation on microcirculatory flow were found in Nos3−/− or Nos2−/−/Nos3−/− mice. This study confirms that L-citrulline supplementation enhances de novo arginine synthesis and NO production in mice during endotoxemia with a functional NOS3-enzyme (control and Nos2−/− mice), as this beneficial effect was absent in Nos3−/− or Nos2−/−/Nos3−/− mice.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Data in the intensive care unit (ICU) can be broadly categorized as “phenotypic” and “physiologic.” Examples of “phenotypic” data include demographics, age, sex, laboratory values, and physician and nursing notes. Examples of “physiologic” data include common vital signs (blood pressure, heart rate, respiratory rate, core temperature) and other parameters generated from bedside monitoring devices (intracranial pressure, electroencephalogram). Most ICUs offer continuous 24/7 monitoring of these physiologic data (both numeric and waveforms) but lack the capability for data collection, integration, and analysis. The ability to do all of this in real time is virtually impossible. Analytical tools are also typically limited to averages and trends, while recent studies have demonstrated that complex systems analysis may provide greater insight into the dynamics of critical illness. This chapter will review various types of data in the ICU and methods of analysis including complex systems analysis and “patient state” tracking in the ICU.

Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

Sepsis is one of the leading causes of death around the world. The failure of clinical trials to treat sepsis demonstrates that the molecular mechanisms are multiple and are still insufficiently understood.To clarify the long disputed hierarchical contribution of several central inflammatory mediators (IL-1β, IL-18, caspase [CASP] 7, CASP1, and CASP11) in septic shock and to explore their therapeutic potential.LPS- and tumor necrosis factor (TNF)-induced lethal shock, and cecal ligation and puncture (CLP) were performed in genetically or pharmacologically targeted mice. Body temperature and survival were monitored closely, and plasma was analyzed for several markers of cellular disintegration and inflammation.Interestingly, deficiency of both IL-1β and IL-18 additively prevented LPS-induced mortality. The detrimental role of IL-1β and IL-18 was confirmed in mice subjected to a lethal dose of TNF, or to a lethal CLP procedure. Although their upstream activator, CASP1, and its amplifier, CASP11, are considered potential therapeutic targets because of their crucial involvement in endotoxin-induced toxicity, CASP11- or CASP1/11-deficient mice were not, or hardly, protected against a lethal TNF or CLP challenge. In line with our results obtained in genetically deficient mice, only the combined neutralization of IL-1 and IL-18, using the IL-1 receptor antagonist anakinra and anti-IL-18 antibodies, conferred complete protection against endotoxin-induced lethality.Our data point toward the therapeutic potential of neutralizing IL-1 and IL-18 simultaneously in sepsis, rather than inhibiting the upstream inflammatory caspases.

Aims Septic shock is the leading cause of death in intensive care units worldwide, resulting from a progressive systemic inflammatory reaction causing cardiovascular and organ failure. Nitric oxide (NO) is a potent vasodilator and inhibition of NO synthases (NOS) can increase blood pressure in septic shock. However, NOS inhibition does not improve outcome, on the contrary, and certain NO donors may even provide protection. In addition, NOS produce superoxide in case of substrate or cofactor deficiency or oxidation. We hypothesized that excessive systemic iNOS-derived NO production is insufficient to trigger cardiovascular failure and shock. Methods and results We found that the systemic injection with various synthetic Toll-like receptor-2 (TLR2), TLR3, or TLR9 agonists triggered systemic NO production identical to that of lipopolysaccharide (LPS) or tumour necrosis factor. In contrast to the latter, however, these agonists did not cause hypothermia or any other signs of discomfort or morbidity, and inflammatory cytokine production was low. TLR2 stimulation with the triacylated lipopeptide Pam3CSK4 not only caused identical NO levels in circulation, but also identical iNOS expression patterns as LPS. Nevertheless, Pam3CSK4 did not cause hypotension, bradycardia, reduced blood flow, or inadequate tissue perfusion in the kidney or the liver. Conclusion We demonstrate that excessive iNOS-derived NO in circulation is not necessarily linked to concomitant cardiovascular collapse, morbidity, or mortality. As such, our data indicate that the central role of iNOS-derived NO in inflammation-associated cardiovascular failure may be overestimated.

SummaryEngagement of tumor necrosis factor receptor 1 signals two diametrically opposed pathways: survival-inflammation and cell death. An additional switch decides, depending on the cellular context, between caspase-dependent apoptosis and RIP kinase (RIPK)-mediated necrosis, also termed necroptosis. We explored the contribution of both cell death pathways in TNF-induced systemic inflammatory response syndrome (SIRS). Deletion of apoptotic executioner caspases (caspase-3 or -7) or inflammatory caspase-1 had no impact on lethal SIRS. However, deletion of RIPK3 conferred complete protection against lethal SIRS and reduced the amounts of circulating damage-associated molecular patterns. Pretreatment with the RIPK1 kinase inhibitor, necrostatin-1, provided a similar effect. These results suggest that RIPK1-RIPK3-mediated cellular damage by necrosis drives mortality during TNF-induced SIRS. RIPK3 deficiency also protected against cecal ligation and puncture, underscoring the clinical relevance of RIPK kinase inhibition in sepsis and identifying components of the necroptotic pathway that are potential therapeutic targets for treatment of SIRS and sepsis.

Enhanced arginase-induced arginine consumption is believed to play a key role in the pathogenesis of sickle cell disease-induced end organ failure. Enhancement of arginine availability with l-arginine supplementation exhibited less consistent results, however, l-citrulline, the precursor of l-arginine, may be a promising alternative. In this study, we determined the effects of l-citrulline compared to l-arginine supplementation on arginine-nitric oxide (NO) metabolism, arginine availability and microcirculation in a murine model with acutely-enhanced arginase activity. The effects were measured in six groups of mice (n = 8 each) injected intraperitoneally with sterile saline or arginase (1000 IE/mouse) with or without being separately injected with l-citrulline or l-arginine 1 h prior to assessment of the microcirculation with side stream dark-field (SDF)-imaging or in vivo NO-production with electron spin resonance (ESR) spectroscopy. Arginase injection caused a decrease in plasma and tissue arginine concentrations. l-arginine and l-citrulline supplementation both enhanced plasma and tissue arginine concentrations in arginase-injected mice. However, only the citrulline supplementation increased NO production and improved microcirculatory flow in arginase-injected mice. In conclusion, the present study provides for the first time in vivo experimental evidence that l-citrulline, and not l-arginine supplementation, improves the end organ microcirculation during conditions with acute arginase-induced arginine deficiency by increasing the NO concentration in tissues.

Sepsis is associated with high mortality rates as a result of the development of multiple organ failure (MOF). In MOF the competition for the amino acid arginine between the microcirculation and the inflammatory response is important. The endothelial nitric-oxide synthase (eNOS) uses arginine to produce nitric oxide (NO), which results in microcirculatory vasodilatation, whereas arginase and the inflammatory nitric-oxide synthase (iNOS) use arginine in the inflammatory response to produce acute phase proteins and to stimulate the host response. Enhanced arginine consumption combined with decreased arginine de novo synthesis from citrulline results in arginine deficiency for eNOS, eNOS uncoupling, and a decreased organ perfusion.

Objective: Early detection and start of appropriate treatment are highly correlated with survival of sepsis and septic shock, but the currently available predictive tools are not sensitive enough to identify patients at risk. Design: Linear (time and frequency domain) and nonlinear (unifractal and multiscale complexity dynamics) measures of beat-to-beat interval variability were analyzed in two mouse models of inflammatory shock to determine if they are sensitive enough to predict outcome. Setting: University research laboratory. Subjects: Blood pressure transmitter-implanted female C57BL/6J mice. Interventions: IV administration of tumor necrosis factor (n = 11) or lipopolysaccharide (n = 14). Measurements and Main Results: Contrary to linear indices of variability, unifractal dynamics, and absolute heart rate or blood pressure, quantification of complex beat-to-beat dynamics using multiscale entropy was able to predict survival outcome starting as early as 40 minutes after induction of inflammatory shock. Based on these results, a new and clinically relevant index of multiscale entropy was developed that scores the key features of a multiscale entropy profile. Contrary to multiscale entropy, multiscale entropy scoring can be followed as a function of time to monitor disease progression with limited loss of information. Conclusions: Analysis of multiscale complexity of beat-to-beat dynamics at high temporal resolution has potential as a sensitive prognostic tool with translational power that can predict survival outcome in systemic inflammatory conditions such as sepsis and septic shock.

In PNAS, Seok et al. advocate against relying on mouse models to study human inflammatory diseases, because genomic responses in human and mouse leukocytes correlate poorly (1). We fully agree with the conclusion of the authors that it is incorrect to believe “that molecular results from current mouse models developed to mimic human diseases translate directly to human conditions” (1). Several mouse studies—although yielding solid and interesting results—have been the victim of overinterpretation; for example, by extrapolating successful pretreatment in mice to therapeutic treatment in men. However, this aspect does not render these results useless. In the field of inflammation, valuable human therapies have been derived from mouse studies (e.g., anti-TNF treatment for rheumatoid arthritis and inflammatory bowel disease). In the field of sepsis, failure of the phase III clinical trial inhibiting NO synthases, which had to be terminated because of excess mortality, was actually predicted in assorted murine shock models (2).

Despite extensive research, the mortality rate of patients with sepsis-induced acute kidney injury (AKI) is unacceptably high, especially in the elderly. Current sepsis models have difficulties in reproducing AKI. This study aimed to develop a novel, clinically relevant mouse model for sepsis-induced AKI by uterine ligation and inoculation of bacteria. In addition, the age dependency of the severity of sepsis and sepsis-induced AKI was studied by validating this model in three different age categories.Experimental animal investigation.University research laboratory.Young (12-14 wks), aged (46-48 wks), and old (70-72 wks) C57BL/6 female mice were used as models for adolescent, adult premenopausal, and elderly postmenopausal women, respectively.Uterine ligation and inoculation with 10 colony forming unit Escherichia coli or saline (sham) was performed; in vivo imaging with a luminescent Escherichia coli strain documented the course of infection.All mice had established Escherichia coli sepsis at 48 hrs postinfection, with higher mortality rate in old (43%) compared to aged (23%) or young (9%) mice. Infected mice had elevated serum or plasma cytokine, chemokine (tumor necrosis factor, interleukin-6, keratinocyte-derived chemokine, monocyte chemoattractant protein 1, and interleukin-10), and NOx concentrations compared to sham mice. AKI was confirmed by renal histology. Serum creatinine concentrations at 48 hrs increased with age (mean ± SEM; controls 0.18 ± 0.03 mg/dL, young 0.28 ± 0.03 mg/dL, aged 0.38 ± 0.05 mg/dL, and old 0.44 ± 0.06 mg/dL).The uterine ligation and inoculation model for sepsis-induced AKI starts from a real infectious focus and shows an age-dependent severity of septic AKI that resembles AKI in humans.

For a long time nitrite (NO2-) was believed to be an inert metabolite of the endogenous vasodilator NO. Recently, however, nitrite was identified as an important biologic NO reservoir in vasculature and tissues, contributing to hypoxic signaling, vasodilation and cytoprotection after ischemia-reperfusion injury. Reduction of nitrite to NO may occur enzymatically at low pH and oxygen tension by deoxyhemoglobin or deoxymyoglobin, xanthine oxidase, mitochondria or NO synthase. Considering that NO may exert protective effects in inflammatory and septic shock, and that circulating nitrite may function as a source of NO in hypoxic and/or acidic conditions present in ischemic microvasculature of vital organs during shock, we decided to test the protective capacity of nitrite on toxicity associated with inflammatory shock.

Background: MAPK-activated protein kinase 2 (MK2) plays a pivotal role in the cell response to (inflammatory) stress. Among others, MK2 is known to be involved in the regulation of cytokine mRNA metabolism and regulation of actin cytoskeleton dynamics. Previously, MK2-deficient mice were shown to be highly resistant to LPS/D-Galactosamine-induced hepatitis. Additionally, research in various disease models has indicated the kinase as an interesting inhibitory drug target for various acute or chronic inflammatory diseases. Results: We show that in striking contrast to the known resistance of MK2-deficient mice to a challenge with LPS/D-Gal, a low dose of tumor necrosis factor (TNF) causes hyperacute mortality via an oxidative stress driven mechanism. We identified in vivo defects in the stress fiber response in endothelial cells, which could have resulted in reduced resistance of the endothelial barrier to deal with exposure to oxidative stress. In addition, MK2-deficient mice were found to be more sensitive to cecal ligation and puncture-induced sepsis. Conclusions: The capacity of the endothelial barrier to deal with inflammatory and oxidative stress is imperative to allow a regulated immune response and maintain endothelial barrier integrity. Our results indicate that, considering the central role of TNF in pro-inflammatory signaling, therapeutic strategies examining pharmacological inhibition of MK2 should take potentially dangerous side effects at the level of endothelial barrier integrity into account. Research was supported by the agency for Innovation by Science and Technology (IWT); Research Foundation Flanders (FWO); and Ghent University: Concerted Research Actions (GOA)

Sepsis is one of the leading causes of death around the world. The failure of clinical trials to treat sepsis demonstrates that the molecular mechanisms are multiple and still insufficiently understood. The objective is to clarify the long disputed hierarchical contribution of several central inflammatory mediators, namely IL-1β, IL-18, CASP7, CASP1 and CASP11, in septic shock, and to explore their therapeutic potential.

Sepsis and septic shock result from an exacerbated systemic inflammatory reaction to infection. Their incidence is rising, and they have recently become the main cause of death in intensive care units. Septic shock is defined as sepsis accompanied by life-threatening refractory hypotension, for which excessive nitric oxide (NO), produced by inducible NO synthase iNOS, is thought responsible. LPS, a vital outer membrane component of Gram-negative bacteria, mimics most of the septic effects and is widely used as a model for septic shock. TLR4 is the signal-transducing receptor for LPS, evidenced by the resistance of TLR4-deficient C3H/HeJ and C57BL/10ScNJ mice. As expected, we found that TLR4 deficiency precludes LPS-induced cytokine production, independent of the purity of the LPS preparation. However, various conventional LPS preparations induced NO in TLR4-deficient mice to the same level as in control animals, while ultrapure LPS did not, indicating the presence of NO-producing contaminant(s). Nevertheless,despite identical iNOS induction pattern and systemic NO levels, the contaminant does not cause hypotension, hypothermia, or any other sign of morbidity. Using mice deficient for TLR2, TRL3, TLR4, TRL2x4, TLR9, MyD88 or TRIF, we found that the contaminant signals via TLR2 and MyD88. In conclusion, conventional LPS preparations generally used in endotoxic shock research contain TLR2 agonists that induce iNOS and high levels of systemic NO as such, and synergize with LPS towards the production of pro-inflammatory cytokines, morbidity and mortality. Surprisingly, the excessive iNOS-derived systemic NO production induced by impure LPS in TLR4(-/-) is not accompanied by hypotension or morbidity.