Sepsis is a potentially fatal physiological state caused by an imbalance in the body's immune response to an infection and is one of the most common causes for deaths in the non‐coronary intensive care unit worldwide. In this article, the state of art on sepsis is presented in a manner that facilitates easy comprehension also for the non‐medical researchers by introducing sepsis, its causes, extent and comparison of diagnostic techniques (conventional labeled as well as label‐free detection). The article also provides a comprehensive discussion on sepsis biomarkers, to help researchers from multi‐disciplinary domain in developing devices and ideas to complement the existing sepsis diagnosis systems for quick and premature detection of the physiological condition and reduce mortality by means of early treatments.more » « less
- Award ID(s):
- NSF-PAR ID:
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- MEDICAL DEVICES & SENSORS
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis.more » « less
Numerous models are available for the preclinical study of sepsis, and they fall into one of three general categories: (1) administration of exogenous toxins (e.g., lipopolysaccharide, zymosan), (2) virulent bacterial or viral challenge, and (3) host barrier disruption, e.g., cecal ligation and puncture (CLP) or colon ascendens stent peritonitis (CASP). Of the murine models used to study the pathophysiology of sepsis, CLP combines tissue necrosis and polymicrobial sepsis secondary to autologous fecal leakage, as well as hemodynamic and biochemical responses similar to those seen in septic humans. Further, a transient numerical reduction of multiple immune cell types, followed by development of prolonged immunoparalysis, occurs in CLP‐induced sepsis just as in humans. Use of the CLP model has led to a vast expansion in knowledge regarding the intricate physiological and cellular changes that occur during and after a septic event. This updated article details the steps necessary to perform this survival surgical technique, as well as some of the obstacles that may arise when evaluating the sepsis‐induced changes within the immune system. It also provides representative monoclonal antibody (mAb) panels for multiparameter flow cytometric analysis of the murine immune system in the septic host. © 2020 Wiley Periodicals LLC.
Basic Protocol: Cecal ligation and puncture in the mouse
Sepsis is responsible for the highest economic and mortality burden in critical care settings around the world, prompting the World Health Organization in 2018 to designate it as a global health priority. Despite its high universal prevalence and mortality rate, a disproportionately low amount of sponsored research funding is directed toward diagnosis and treatment of sepsis, when early treatment has been shown to significantly improve survival. Additionally, current technologies and methods are inadequate to provide an accurate and timely diagnosis of septic patients in multiple clinical environments. For improved patient outcomes, a comprehensive immunological evaluation is critical which is comprised of both traditional testing and quantifying recently proposed biomarkers for sepsis. There is an urgent need to develop novel point‐of‐care, low‐cost systems which can accurately stratify patients. These point‐of‐critical‐care sensors should adopt a multiplexed approach utilizing multimodal sensing for heterogenous biomarker detection. For effective multiplexing, the sensors must satisfy criteria including rapid sample to result delivery, low sample volumes for clinical sample sparring, and reduced costs per test. A compendium of currently developed multiplexed micro and nano (M/N)‐based diagnostic technologies for potential applications toward sepsis are presented. We have also explored the various biomarkers targeted for sepsis including immune cell morphology changes, circulating proteins, small molecules, and presence of infectious pathogens. An overview of different M/N detection mechanisms are also provided, along with recent advances in related nanotechnologies which have shown improved patient outcomes and perspectives on what future successful technologies may encompass.
This article is categorized under:
Diagnostic Tools > Biosensing
This article presents assays that allow induction and measurement of activation of different inflammasomes in mouse macrophages, human peripheral blood mononuclear cell (PBMC) cultures, and mouse peritonitis and endotoxic shock models. Basic Protocol 1 describes how to prime the inflammasome in mouse macrophages with different Toll‐like receptor agonists and TNF‐α; how to induce NLRP1, NLRP3, NLRC4, and AIM2 inflammasome activation by their corresponding stimuli; and how to measure inflammasome activation‐mediated maturation of interleukin (IL)‐1β and IL‐18 and pyroptosis. Since the well‐established agonists for NLRP1 are inconsistent between mice and humans, Basic Protocol 2 describes how to activate the NLRP1 inflammasome in human PBMCs. Basic Protocol 3 describes how to purify, crosslink, and detect the apoptosis‐associated speck‐like protein containing a CARD (ASC) pyroptosome. Formation of the ASC pyroptosome is a signature of inflammasome activation. A limitation of ASC pyroptosome detection is the requirement of a relatively large cell number. Alternate Protocol 1 is provided to stain ASC pyroptosomes using an anti‐ASC antibody and to measure ASC specks by fluorescence microscopy in a single cell. Intraperitoneal injection of lipopolysaccharides (LPS) and inflammasome agonists will induce peritonitis, which is seen as an elevation of IL‐1β and other proinflammatory cytokines and an infiltration of neutrophils and inflammatory monocytes. Basic Protocol 4 describes how to induce NLRP3 inflammasome activation and peritonitis by priming mice with LPS and subsequently challenging them with monosodium urate (MSU). The method for measuring cytokines in serum and through peritoneal lavage is also described. Finally, Alternate Protocol 2 describes how to induce noncanonical NLRP3 inflammasome activation by high‐dose LPS challenge in a sepsis model. © 2020 Wiley Periodicals LLC.
Basic Protocol 1: Priming and activation of inflammasomes in mouse macrophages Basic Protocol 2: Activation of human NLRP1 inflammasome by DPP8/9 inhibitor talabostat Basic Protocol 3: Purification and detection of ASC pyroptosome Alternate Protocol 1: Detection of ASC speck by immunofluorescence staining Basic Protocol 4: Activation of canonical NLRP3 inflammasome in mice by intraperitoneal delivery of MSU crystals Alternate Protocol 2: Activation of noncanonical NLRP3 inflammasome in mice by intraperitoneal delivery of LPS
Sepsis is a deadly condition lacking a specific treatment despite decades of research. This has prompted the exploration of new approaches, with extracellular vesicles (EVs) emerging as a focal area. EVs are nanosized, cell‐derived particles that transport bioactive components (i.e., proteins, DNA, and RNA) between cells, enabling both normal physiological functions and disease progression depending on context. In particular, EVs have been identified as critical mediators of sepsis pathophysiology. However, EVs are also thought to constitute the biologically active component of cell‐based therapies and have demonstrated anti‐inflammatory, anti‐apoptotic, and immunomodulatory effects in sepsis models. The dual nature of EVs in sepsis is explored here, discussing their endogenous roles and highlighting their therapeutic properties and potential. Related to the latter component, prior studies involving EVs from mesenchymal stem/stromal cells (MSCs) and other sources are discussed and emerging producer cells that could play important roles in future EV‐based sepsis therapies are identified. Further, how methodologies could impact therapeutic development toward sepsis treatment to enhance and control EV potency is described.