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WIREs Nanomed Nanobiotechnol
Impact Factor: 9.182

Point‐of‐critical‐care diagnostics for sepsis enabled by multiplexed micro and nanosensing technologies

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Abstract 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
Bar graph representing incidence of common diseases (in incidence per 100,000 people per year) related to US‐dollars spent for state funded research (per 1 billion USD). As indicated, sepsis is the most common disease yet receives significantly less funding from government research projects
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Theragnostic cycle proposed for sepsis
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Various point‐of‐critical care (POCC) sensors targeting pathogens, including bacteria (a, b), virus' (c), and fungi (d). (a) Sensor employing magnetic particles to purify and culture methicillin‐resistant Staphylococcus aureus (MRSA), Staphylococcus aureus, and Klebsiella pneumoniae, while attributing detection with oligonucleotide‐functionalized quantum dots (QDs) targeting bacterial‐specific DNA fragments (left). Representative emission spectra using the three QDs with their coupled bacteria (middle). Fluorescence intensity from QDs correlates with identifying bacterial concentrations from a sample (right) (Cihalova et al., 2017). (b) A solution circuit chip (SCC) which evaluates bacterial load from a sample on 20 electrochemical sensors. An array of common working, counter, and reference electrodes with the functionalized peptide nuclei acid (PNA) probes (left) which bind with specific regions on bacterial DNA (middle). Ru(NH3)63+ targets the bound DNA phosphate backbone, reducing to Ru(NH3)62+ which undergoes a redox reaction with Fe(CN)63− in solution back to Ru(NH3)63+ and an current is recorded, correlating current strength with bound DNA (right) (Lam et al., 2013). (c) Using gold nanowires and surface‐enhanced Raman spectroscopy (SERRS), exonuclease III digests complementary DNA strands from fungi, resulting in a decreased signal which is SERRS identified (left). Such decreases show identification of specific fungi from the selected samples, with a 100 femtomolar limit of detection (right) (Yoo et al., 2011). (d) A paper‐based influenza/bacteria sensor which uses pH gradients and exploits colorimetric reactions unique with proteins and select enzymatic substrates (left). Varying the pH yields optical peak changes (ΔCIE) and can eliminate nontargeted pathogens in samples as (middle). The colorimetric assay can distinguish between different viral strains as well as their drug resistance (right) (Murdock et al., 2017)
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Micro and nanotechnologies for proteins (a, b, d), small molecules (c). (a) A dual‐layer impedance detection system for interleukin‐6 (IL‐6) and Abelson tyrosine kinase (Abi), where streptavidin‐biotin particle conjugates functionalized with target antibodies bind with captured protein in a microfluidic device (left). As the particles pass over and collect the protein, the particles then flow across an electric field which measures impedance pulses (middle). Total bead counts passing over the electrodes corresponds with control experiments measuring IL‐6 concentration and Abi activity (right) (Mok et al., 2014). (b) A fully integrated localized surface plasmon resonance‐enhanced quantum dots system measuring procalcitonin (PCT) measuring whole blood samples (left). Streptavidin‐functionalized quantum dots target and bind with biotinylated PCT‐targeting antibodies, subsequentially binding with antibodies functionalized on gold nanopillars (middle). PCT concentrations correlate with optical intensity from bound quantum dot (QD) levels (right) (Sun et al., 2020). (c) Label‐free impedance microfluidic biosensor from charge‐linked biomarker reactions to assess PCT, lipopolysaccharide (LPS), and lipoteichoic acid (LTA) from whole blood samples (left). Here, nylon polymer membranes encapsulated three distinct antibody‐coated gold electrodes, where biomarker‐antibody binding generates a coulombic potential measured as impedance change for PCT, LPS, and LTA (middle and right) (Panneer Selvam & Prasad, 2017). (d) A centrifugation‐based measurement system for thrombin using nano‐ and microsized magnetic beads. Detection was determined using both optomagnetic determinations from aptamer‐coated nanobeads (middle channel) juxtaposed with optical imaging of aptamer‐coated microbeads (outer channels) (left and middle left), where aggregation from thrombin determines sample concentration (middle right). Optomagnetic concentration measured from magnetic field pulses, and average size of imaged particle aggregates complete a two‐step thrombin detection assay (right) (Uddin et al., 2016)
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Micro and nanotechnologies for cellular antigen expression (a), mobility (b, c), and stiffness quantification (d). (a) Schematic view of nCD64 quantification neutrophils isolate from whole blood on‐chip. Cells are counted electrically before and after entering the nCD64 capture region, coated with anti‐CD64 antibodies. The differential cell count determines nCD64+ counts (left). Bar and whisker plot for average nCD64 counts for infected patients over time since admitted to intensive care unit (ICU) (middle). Quantitative counts of cells before and after entering capture chamber (right) (Hassan et al., 2017). (b) Optical tracking of neutrophil motility using a microfluidic biochip evaluating whole blood (left). Blood enters the loading chamber (LC) and enters the maze (M) which uses size exclusion in the channel dimensions to filter red blood cells from entering the maze (middle). Occurrences of oscillations, pauses, reverse migration, and average distance traveled in the maze are significantly different between septic and nonseptic blood samples (right) (Ellett et al., 2018). (c) Quantification and visualization of cell migration toward chemokine gradients. Cells loaded in cell traps can migrate toward (chemotaxis) or away from gradient (retro‐taxis) (left). Characterizing chemical gradients in the device using fluorescein labeled dextran. 50 mm channels show a shallower gradient as compared to 6 mm channels. A1 represents the path from buffer channel to cell loading chamber, a2 cell loading chamber and a3 cell‐loading chamber to chemokine reservoir (right) (Boneschansker et al., 2014). (d) Schematic of the stiffness‐dependent cell separation microfluidic device. The diagonal ridges compress cells in succession, with secondary flows in channel results in flow shift of cells proportional to their stiffness (left, middle). Fluorescent scatter plot shows the separation of Hey cells from K562 cells on a stiff outlet (right) (Wang et al., 2013)
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Overview of potential biomarkers for point‐of‐critical care (POCC) sepsis diagnosis
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Representations of sepsis progression through function and management schemes. (a) Current workflow chart for sepsis in critical care settings. Left represents the course of action while right indicates the time expected to perform that action. (b) Sepsis evolution, from systemic inflammatory response syndrome (SIRS) to sepsis to septic shock. Each phase has different patient outcomes (blue) and uses different biomarkers for determination (green). (c) Physiological mechanisms for sepsis, where the body's inflammation response (green) is initially balanced with self‐immunosuppression (red). During sepsis, a cascading effect starts with excessive inflammation (SIRS), followed by compensatory anti‐inflammation response syndrome (CARS), and ends with positive feedback for both conditions (septic shock). At this stage, mortality rates are significantly higher
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