Established and novel biomarkers of sepsis

Bioelectronic Sensor with Magnetic Modulation to Quantify Phagocytic Activity of Blood Cells Employing Machine Learning

Microbial infections result in activating an immune response in the human body, which triggers inflammatory pathways resulting in recognition and subsequent killing of the pathogens. Quantifying the blood cells' natural ability to kill pathogens, i.e., phagocytosis, is critical to demonstrating the effectiveness of an individual's response in combating pathogens. Current laboratory processes and equipment that can be used to monitor phagocytic activity are costly and time-consuming and require significant technical expertise to run such assays. Here, we design and develop a novel biosensing platform capable of quantifying the phagocytic ability of blood cells. The sensor design is composed of electronic sensing and magnetic modulation sub-systems that work in conjunction to monitor phagocytic activity in microfluidic channels. The phagocytes internalize the IgG-coated magnetic beads, and when infused into the sensor, their speed will be modulated using the quadrupole magnetic field configuration as they pass through microfluidic channels where microfabricated electrodes are placed. The electronic sensor will generate the voltage pulse for each passage of the phagocyte, whose distinct features are correlative to the phagocytic activity. We experimentally tested this device using 17 blood samples collected from patients at Robert Wood Johnson Medical Hospital. Further, we developed artificial neural networks (ANN) to improve the accuracy of the phagocytic activity detection. ANN model detected the phagocytic activity with 88.2% accuracy. This novel sensing platform can potentially be used to triage high risk patients and develop personalized theranostics for the septic patients in the future.

Profiling single-cell level phagocytic activity distribution with blood lactate levels

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked as a part of immunological profile in infected patients' diagnosis, as the understanding of phagocytic capabilities in disease states is incomplete. In this work, we have evaluated for the first time the relationship between blood lactate level and the neutrophil phagocytic activity at a single-cell level. Blood samples (N = 19) were grouped on the basis of their blood lactate levels i.e., below (control) or above 2 mmol L⁻¹ (high-risk) (i.e., 2 mmol L⁻¹ is a common clinical lactate threshold used for patients' triage). Neutrophils were isolated from whole blood and then incubated with fluorescent IgG coated beads for 40 minutes, and the ability of each neutrophil to internalize beads was quantified. Single-cell phagocytic activity analysis has shown interesting findings such as: (i) a single neutrophil was able to internalize up to 7 beads, (ii) for a control group, 39.76% cells didn't internalize any beads, while for a high-risk group, 30.65% cells didn't show any phagocytic activity, (iii) similarly, 30.46% cells internalize only 1 bead in a control group, while for a high-risk group the activity is slightly higher with only 31.73% cells showing single bead internalization, and (iv) 7 bead internalization activity was much higher for samples in a high-risk group (0.6% cells) compared to a control group (0.17% cells). We used multiple statistical tests to compare these differences. For a two-tailed T-test, we used the mean phagocytic activity of the cells (i.e., the average number of beads internalized by cells) isolated from the blood samples in the two groups (1.14 vs. 1.35) and found the p-value to be 0.08. We also used principal component analysis (PCA) on this high dimensional phagocytic activity distribution data and performed dimension reduction. However, the first 3 principal components didn't show a clear distinction between groups. Next, we developed machine learning models using artificial neural networks (ANNs) to differentiate between the distribution of phagocytic activity in neutrophil populations of the two groups. Our models yielded area under curve (AUC) values below 0.7 for receiver operator characteristic curves. Although our study highlighted interesting phagocytic activity findings at a single cell level, it further highlights the need for integration of an individual patient's medical record to get more personalized insights into individual phagocytic activity in the future.

Investigating the relationship between neutrophil phagocytic activity and blood lactate levels by employing single-cell data.

Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in Systemic Lupus Erythematosus

Objectives

Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE.

Design

Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.

Setting

Department of Rheumatology of the Second Hospital of Shanxi Medical University.

Participants

SLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.

Outcome

The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established.

Results

Both PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794–0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8–10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE.

Conclusions

The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.

CD123 thioaptamer protects against sepsis via the blockade between IL-3/CD123 in a cecal ligation and puncture rat model

Sepsis is one of the most common causes of death in ICU and especially is a harmful and a life-threatened disease to pediatrics in the world. It has been demonstrated that IL-3 plays an essential role in the processing of sepsis and the inhibition of IL-3 may alleviate sepsis progress. In our previous study, we selected a novel CD123 aptamer successfully which could inhibit the interaction of CD123 and IL-3. The aim of this study is to explore the protection ability of the first thioaptamer SS30 against sepsis in a cecal ligation and puncture (CLP) rat model. Serum IL-3 level of sepsis patients was assessed by ELISA. CLP rat model was applied in all experimental groups. CD123 thioaptamer SS30 and CD123 antibody were used to block the recognition between IL-3 and CD123. Body weight, temperature, blood gas, MAP, and serum cytokines of four grouped rats were assessed. Flow cytometry was utilized to evaluate JAK2 and STAT5 proteins. After the administration of SS30 or CD123 antibody, the rats in SS30 and CD123 antibody group had lower cytokines values(lactate, TNF-α, IL-1β, and IL-6), whereas exhibited higher value of core temperature, MAP, PO₂/FiO₂, and ETCO₂ than those in the CLP group. The expression level of phosphorylated JAK2 and STAT5 was declined and the survival rate of rats was increased. In addition, the protection ability of SS30 was better than CD123 antibody. Therefore, CD123 thioaptamer SS30 could reduce mortality by down-regulating the phosphorylated JAK2/STAT5 signaling pathway, and reduce serum cytokines which involving in sepsis development in CLP rat model.

The role of nCD64 in the diagnosis of neonatal sepsis in preterm newborns

BACKGROUND

Diagnosing neonatal sepsis is difficult, particularly in preterm newborns. A promising method appears to be evaluation of cell surface markers by flow cytometry.

METHODS

This prospective study investigated 217 newborns suspected of having early- or late-onset neonatal sepsis. In all, flow cytometry was used to determine the proportion of CD64-positive neutrophils (nCD64). Based on the clinical course and laboratory test results, newborns were categorized as having proven, possible, clinical or no neonatal sepsis. Subsequently, associations between the categories and nCD64 values were analyzed.

RESULTS

There were significant associations between nCD64 values and the development of sepsis in newborns with both early- or late-onset sepsis.

CONCLUSION

nCD64expression is significantly elevated in preterm newborn with early and late onset sepsis. The results show that nCD64 is a reliable marker for diagnosing neonatal sepsis.

Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit

INTRODUCTION

Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

METHODS

Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

RESULTS

By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

CONCLUSIONS

Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock

OBJECTIVES

To determine whether a nuclear magnetic resonance-based metabolomics approach can be useful for the early diagnosis and prognosis of septic shock in ICUs.

DESIGN

Laboratory-based study.

SETTING

University research laboratory.

SUBJECTS

Serum samples from septic shock patients and ICU controls (ICU patients with systemic inflammatory response syndrome but not suspected of having an infection) were collected within 24 hours of admittance to the ICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

H nuclear magnetic resonance spectra of septic shock and ICU control samples were analyzed and quantified using a targeted profiling approach. By applying multivariate statistical analysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the patient groups and detect specific metabolic patterns. Some of the metabolites were found to have a significant impact on the separation between septic shock and control samples. These metabolites could be interpreted in terms of a biological human response to septic shock and they might serve as a biomarker pattern for septic shock in ICUs. Additionally, nuclear magnetic resonance-based metabolomics was evaluated in order to detect metabolic variation between septic shock survivors and nonsurvivors and to predict patient outcome. The area under the receiver operating characteristic curve indicated an excellent predictive ability for the constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU controls: area under the receiver operating characteristic curve = 0.98; nonsurvivors vs survivors: area under the receiver operating characteristic curve = 1).

CONCLUSIONS

Our results indicate that nuclear magnetic resonance-based metabolic profiling could be used for diagnosis and mortality prediction of septic shock in the ICU.

Presepsin (soluble CD14 subtype) and procalcitonin levels for mortality prediction in sepsis: data from the Albumin Italian Outcome Sepsis trial

Introduction

Sepsis, a leading cause of death in critically ill patients, is the result of complex interactions between the infecting microorganisms and the host responses that influence clinical outcomes. We evaluated the prognostic value of presepsin (sCD14-ST), a novel biomarker of bacterial infection, and compared it with procalcitonin (PCT).

Methods

This is a retrospective, case–control study of a multicenter, randomized clinical trial enrolling patients with severe sepsis or septic shock in ICUs in Italy. We selected 50 survivors and 50 non-survivors at ICU discharge, matched for age, sex and time from sepsis diagnosis to enrollment. Plasma samples were collected 1, 2 and 7 days after enrollment to assay presepsin and PCT. Outcome was assessed 28 and 90 days after enrollment.

Results

Early presepsin (day 1) was higher in decedents (2,269 pg/ml, median (Q1 to Q3), 1,171 to 4,300 pg/ml) than in survivors (1,184 pg/ml (median, 875 to 2,113); P = 0.002), whereas PCT was not different (18.5 μg/L (median 3.4 to 45.2) and 10.8 μg/L (2.7 to 41.9); P = 0.31). The evolution of presepsin levels over time was significantly different in survivors compared to decedents (P for time-survival interaction = 0.03), whereas PCT decreased similarly in the two groups (P = 0.13). Presepsin was the only variable independently associated with ICU and 28-day mortality in Cox models adjusted for clinical characteristics. It showed better prognostic accuracy than PCT in the range of Sequential Organ Failure Assessment score (area under the curve (AUC) from 0.64 to 0.75 vs. AUC 0.53 to 0.65).

Conclusions

In this multicenter clinical trial, we provide the first evidence that presepsin measurements may have useful prognostic information for patients with severe sepsis or septic shock. These preliminary findings suggest that presepsin may be of clinical importance for early risk stratification.

Systemic inflammation in the elderly

Inflammation is an adaptive response to surgery. When the pro-inflammatory responses are unregulated and become over reactive, systemic inflammatory response syndrome may occur. Postoperative systemic inflammation is more common than is generally acknowledged and is observed in about 10-15% of elderly patients undergoing major surgery. Although the vast majority of systemic inflammation is related to infections, other important predisposing risk factors, such as extent of trauma and haemorrhage, should not be overlooked. Increased awareness, modification of risk factors and early recognition are the key elements in the management of systemic inflammation. Prompt resuscitation aiming to correct hypotension, hypovolaemia and tissue hypoxia may improve outcome. Future large prospective observational studies are needed to define the incidence, risk factors and impact of systemic inflammatory syndrome in the elderly surgical patients. A better understanding of the molecular events during the systemic inflammatory response syndrome is required for future development of specific immunotherapy.