An Update on Sepsis Biomarkers

Noncontact 3D Bioprinting of Proteinaceous Microarrays for Highly Sensitive Immunofluorescence Detection within Clinical Samples

Immunofluorescence assays are extensively used for the detection of disease-associated biomarkers within patient samples for direct diagnosis. Unfortunately, these 2D microarrays suffer from low repeatability and fail to attain the low limits of detection (LODs) required to accurately discern disease progression for clinical monitoring. While three-dimensional microarrays with increased biorecognition molecule density stand to circumvent these limitations, their viscous component materials are not compatible with current microarray fabrication protocols. Herein, we introduce a platform for 3D microarray bioprinting, wherein a two-step printing approach enables the high-throughput fabrication of immunosorbent hydrogels. The hydrogels are composed entirely of cross-linked proteins decorated with clinically relevant capture antibodies. Compared to two-dimensional microarrays, these proteinaceous microarrays offer 3-fold increases in signal intensity. When tested with clinically relevant biomarkers, ultrasensitive single-plex and multiplex detection of interleukin-6 (LOD 0.3 pg/mL) and tumor necrosis factor receptor 1 (LOD 1 pg/mL) is observed. When challenged with clinical samples, these hydrogel microarrays consistently discern elevated levels of interleukin-6 in blood plasma derived from patients with systemic blood infections. Given their easy-to-implement, high-throughput fabrication, and ultrasensitive detection, these three-dimensional microarrays will enable better clinical monitoring of disease progression, yielding improved patient outcomes.

Biomarkers as Predictors of Mortality in Sepsis and Septic Shock for Patients Admitted to Emergency Department: Who Is the Winner? A Prospective Study

Background/Objectives: Sepsis and septic shock remain significant contributors to high early mortality rates among patients admitted to the emergency department (ED). The objective of this study was to identify among newer biomarkers those with the highest sensitivity in early mortality prediction. Methods: This prospective, unicentric, observational study enrolled 47 adult patients admitted to the ED between November 2020 and December 2022. This study monitored the kinetics of the older and newer biomarkers, including azurocidin (AZU1), soluble triggering receptor expressed on myeloid cells (sTREM), soluble urokinase-type plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hsCRP), procalcitonin (PCT), and interleukin-6 (IL-6), and their capacity in predicting mortality. Results: SuPAR showed the most significant predictive utility for early prognosis of mortality in the ED, with an area under the curve (AUC) of 0.813 (95% CI: 0.672 to 0.912), a cutoff value > 8168 ng/mL, sensitivity of 75%, and specificity of 81.48% (p < 0.001). IL-6 and PCT showed comparable prognostic accuracy, whereas hsCRP and AZU1 demonstrated lower predictive performance. Conclusions: In our study, suPAR, IL-6, and PCT showed good predictive value for short-term mortality in sepsis and septic shock patients.

The Role of Monocyte Distribution Width in the Early Prediction of Sepsis in Patients Undergoing Cardiovascular Surgery: A Cross-Sectional Study

Background and Objectives: This is the first study to examine the role of monocyte distribution width (MDW) in predicting sepsis after cardiovascular surgery. Methods: This study included 43 consecutive patients who had undergone cardiovascular surgery between July 2021 and July 2022. All patients were examined at the following three time points (TPs): preoperative period (TP1), postoperative at 24 h (TP2), and discharge (TP3). SOFA score, leukocyte count, neutrophil-to-lymphocyte ratio (NLR), MDW, C-reactive protein (CRP), and procalcitonin (PCT) levels were tested at each TPs. The Sepsis-3 criteria were used to diagnose patients with sepsis. Results: The mean values of all variables (leukocyte count, NLR, MDW, CRP, and PCT levels) were significantly higher at TP2 and TP3 than at TP1 (p < 0.05). All these values were significantly higher at TP2 than at TP3 (p < 0.05). Patients with sepsis had significantly higher mean values for leukocyte count, NLR, MDW, CRP, and PCT levels than those without sepsis (p < 0.05). There was a significant correlation between MDW and inflammatory markers (CRP, PCT, and NLR) during the three time periods (p < 0.05). According to the ROC analysis, the optimal MDW cutoff value with the highest sensitivity and specificity for predicting sepsis in the postoperative period was 20.5. Conclusions: Our findings indicate that elevated MDW levels may be a valuable predictor of sepsis in patients following cardiovascular surgery.

Sepsis Biomarkers: Advancements and Clinical Applications-A Narrative Review

Sepsis is now defined as a life-threatening syndrome of organ dysfunction triggered by a dysregulated host response to infection, posing significant challenges in critical care. The main objective of this review is to evaluate the potential of emerging biomarkers for early diagnosis and accurate prognosis in sepsis management, which are pivotal for enhancing patient outcomes. Despite advances in supportive care, traditional biomarkers like C-reactive protein and procalcitonin have limitations, and recent studies have identified novel biomarkers with increased sensitivity and specificity, including circular RNAs, HOXA distal transcript antisense RNA, microRNA-486-5p, protein C, triiodothyronine, and prokineticin 2. These emerging biomarkers hold promising potential for the early detection and prognostication of sepsis. They play a crucial role not only in diagnosis but also in guiding antibiotic therapy and evaluating treatment effectiveness. The introduction of point-of-care testing technologies has brought about a paradigm shift in biomarker application, enabling swift and real-time patient evaluation. Despite these advancements, challenges persist, notably concerning biomarker variability and the lack of standardized thresholds. This review summarizes the latest advancements in sepsis biomarker research, spotlighting the progress and clinical implications. It emphasizes the significance of multi-biomarker strategies and the feasibility of personalized medicine in sepsis management. Further verification of biomarkers on a large scale and their integration into clinical practice are advocated to maximize their efficacy in future sepsis treatment.

Can We Improve Mortality Prediction in Patients with Sepsis in the Emergency Department?

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED.

Unraveling the Peroxidase Activity in Peroxiredoxins: A Comprehensive Review of Mechanisms, Functions, and Biological Significance

Peroxiredoxins (Prxs) are members of the antioxidant enzymes necessary for every living object in the three domains of life and play critical roles in controlling peroxide levels in cells. This comprehensive literature review aims to elucidate the peroxidase activity of Prxs, examining their roles and significance for organisms across various taxa. Ironically, the primary role of the Prxs is the peroxidase activity, which comprises the reduction of hydrogen peroxide and other organic hydroperoxides and decreases the risk of oxidative damage in the cells. The above enzymatic activity occurs through the reversible oxidation-reduction catalyzed by cysteine residues in the active site by forming sulfenic acid and reduction by intracellular reductants. Structurally and functionally, Prxs function as dimers or decamers and show different catalytic patterns according to their subfamilies or cellular compartments. Compared to the mechanisms of the other two subgroups of Prxs, including 2-Cys Prxs and atypical Prxs, the 1-Cys Prxs have monomer-dimer switch folding coupled with catalytic activity. In addition to their peroxidase activity, which is widely known, Prxs are becoming acknowledged to be involved in other signaling processes, including redox signaling and apoptosis. This aversion to oxidative stress and regulation by the cellular redox state places them at the heart of adaptive cellular responses to changes in the environment or manifestations of diseases. In conclusion, based on the data obtained and on furthering the knowledge of Prxs' structure and function, these enzymes may be classified as a diverse yet essential family of proteins that can effectively protect cells from the adverse effects of oxidative stress due to peroxidase activity. This indicates secondary interactions, summarized as peroxide detoxification or regulatory signaling, and identifies their applicability in multiple biological pathways. Such knowledge is valuable for enhancing the general comprehension of essential cellular functions and disclosing further therapeutic approaches to the diseases caused by the increased production of reactive oxygen species.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

Predictive value of CD86 for the occurrence of sepsis (Sepsis-3) in patients with infection

This prospective observational study explored the predictive value of CD86 in the early diagnosis of sepsis in the emergency department. The primary endpoint was the factors associated with a diagnosis of sepsis. The secondary endpoint was the factors associated with mortality among patients with sepsis. It enrolled inpatients with infection or high clinical suspicion of infection in the emergency department of a tertiary Hospital between September 2019 and June 2021. The patients were divided into the sepsis and non-sepsis groups according to the Sepsis-3 standard. The non-sepsis group included 56 patients, and the sepsis group included 65 patients (19 of whom ultimately died). The multivariable analysis showed that CD86% (odds ratio [OR] = 1.22, 95% confidence interval [CI]: 1.04-1.44, P = 0.015), platelet count (OR = 0.99, 95%CI: 0.986-0.997, P = 0.001), interleukin-10 (OR = 1.01, 95%CI: 1.004-1.025, P = 0.009), and procalcitonin (OR = 1.17, 95%CI: 1.01-1.37, P = 0.043) were independent risk factors for sepsis, while human leukocyte antigen (HLA%) (OR = 0.96, 05%CI: 0.935-0.995, P = 0.022), respiratory rate (OR = 1.16, 95%CI: 1.03-1.30, P = 0.014), and platelet count (OR = 1.01, 95%CI: 1.002-1.016, P = 0.016) were independent risk factors for death in patients with sepsis. The model for sepsis (CD86%, platelets, interleukin-10, and procalcitonin) and the model for death (HLA%, respiratory rate, and platelets) had an area under the curve (AUC) of 0.870 and 0.843, respectively. CD86% in the first 24 h after admission for acute infection was independently associated with the occurrence of sepsis in the emergency department.

A Fully Integrated Microfluidic Device with Immobilized Dyes for Simultaneous Detection of Cell-Free DNA and Histones from Plasma Using Dehydrated Agarose Gates

Sepsis, a life-threatening condition resulting from a failing host response to infection, causes millions of deaths annually, necessitating rapid and simple prognostic assessments. A variety of genomic and proteomic biomarkers have been developed for sepsis. For example, it has been shown that the level of plasma cell-free DNA (cfDNA) and circulating histones increases considerably during sepsis, and they are linked with sepsis severity and mortality. Developing a diagnostic tool that is capable of assessing such diverse biomarkers is challenging as the detection methodology is quite different for each. Here, a fully integrated microfluidic device capable of detecting a genomic biomarker (cfDNA) and a proteomic biomarker (total circulating histones) using a common detection platform has been demonstrated. The microfluidic device utilizes dehydrated agarose gates loaded with pH-specific agarose to electrophoretically trap cfDNA and histones at their respective isoelectric points. It also incorporates fluorescent dyes within the device, eliminating the need for off-chip sample preparation and allowing the direct testing of plasma samples without the need for labeling DNA and histones with fluorescent dyes beforehand. Xurography, which is a low-cost and rapid method for fabrication of microfluidics, is used in all the fabrication steps. Experimental results demonstrate the effective accumulation and separation of cfDNA and histones in the agarose gates in a total processing time of 20 min, employing 10 and 30 Volts for cfDNA and histone accumulation and detection, respectively. The device can potentially be used to distinguish between the survivors and non-survivors of sepsis. The integration of the detection of both biomarkers into a single device and dye immobilization enhances its clinical utility for rapid point-of-care assessment of sepsis prognosis.

Pediatric sepsis inflammatory blood biomarkers that correlate with clinical variables and severity of illness scores

BACKGROUND

Sepsis is a dysregulated systemic inflammatory response triggered by infection, resulting in organ dysfunction. A major challenge in clinical pediatrics is to identify sepsis early and then quickly intervene to reduce morbidity and mortality. As blood biomarkers hold promise as early sepsis diagnostic tools, we aimed to measure a large number of blood inflammatory biomarkers from pediatric sepsis patients to determine their predictive ability, as well as their correlations with clinical variables and illness severity scores.

METHODS

Pediatric patients that met sepsis criteria were enrolled, and clinical data and blood samples were collected. Fifty-eight inflammatory plasma biomarker concentrations were determined using immunoassays. The data were analyzed with both conventional statistics and machine learning.

RESULTS

Twenty sepsis patients were enrolled (median age 13 years), with infectious pathogens identified in 75%. Vasopressors were administered to 85% of patients, while 55% received invasive ventilation and 20% were ventilated non-invasively. A total of 24 inflammatory biomarkers were significantly different between sepsis patients and age/sex-matched healthy controls. Nine biomarkers (IL-6, IL-8, MCP-1, M-CSF, IL-1RA, hyaluronan, HSP70, MMP3, and MMP10) yielded AUC parameters > 0.9 (95% CIs: 0.837-1.000; p < 0.001). Boruta feature reduction yielded 6 critical biomarkers with their relative importance: IL-8 (12.2%), MCP-1 (11.6%), HSP70 (11.6%), hyaluronan (11.5%), M-CSF (11.5%), and IL-6 (11.5%); combinations of 2 biomarkers yielded AUC values of 1.00 (95% CI: 1.00-1.00; p < 0.001). Specific biomarkers strongly correlated with illness severity scoring, as well as other clinical variables. IL-3 specifically distinguished bacterial versus viral infection (p < 0.005).

CONCLUSIONS

Specific inflammatory biomarkers were identified as markers of pediatric sepsis and strongly correlated to both clinical variables and sepsis severity.

No Significant Differences in Presepsin Levels According to the Causative Microorganism of Bloodstream Infection

BACKGROUND

CD14 recognizes lipopolysaccharide (LPS), and presepsin is a fragment of soluble CD14. Still, it remains uncertain whether Gram-negative bacteria induce higher presepsin levels than other microorganisms. To address this question, this study aimed to analyze presepsin levels based on microorganisms isolated in blood cultures.

MATERIALS AND METHODS

This study was a single-center study comprising suspected sepsis patients enrolled from July 2020 to September 2020. A total of 95 patients with a single isolate confirmed in blood culture were analyzed to evaluate if there are any differences in presepsin levels according to microbial isolates. Plasma presepsin level was measured using PATHFAST assay kit and analyzer (LSI Medience Corporation, Tokyo, Japan).

RESULTS

There were 26 Gram-positive bacteremia, 65 Gram-negative bacteremia, and 3 fungemia patients with median presepsin levels of 869, 1,439, and 11,951 pg/mL, respectively. Besides, one case of algaemia demonstrated a presepsin level of 1,231 pg/mL. Our results showed no statistically significant difference in presepsin levels among patients with Gram-positive bacteremia, Gram-negative bacteremia, and fungemia. Furthermore, presepsin levels did not differ significantly among bloodstream infections caused by bacteria that were isolated from at least three different patients. In particular, Gram-positive bacteria such as Staphylococcus aureus and Enterococcus faecalis were able to induce presepsin levels comparable to those induced by Gram-negative bacteria.

CONCLUSION

We demonstrated that there were no significant differences in plasma presepsin levels according to microbial isolates in blood culture. The major cause of the variability in presepsin levels during bloodstream infection might be the immunogenicity of each microorganism rather than the presence of LPS in the microorganism.

Establishing reference intervals for soluble urokinase plasminogen activator receptor in Northern European adults

Objectives

Soluble urokinase plasminogen activator receptor (suPAR) may have untapped potential in clinical diagnostics. Previous studies determined reference intervals using an enzyme-linked immunoassay, but there is a need for reference intervals using a faster assay if the analysis is to be used in emergency medicine. The current study aims to determine reference intervals for suPAR using a fully automated particle-enhanced turbidimetric immunoassay (PETIA) according to the Clinical and Laboratory Standards Institute guideline A28-A3c.

Design and methods

Blood samples were prospectively collected from Danish blood donors. Plasma suPAR was analyzed on the cobas 8000 module c502 in an open channel using a PETIA. Sex-partitioned reference intervals were determined using a parametric quantile approach.

Results

The study included 241 participants-123 females and 118 males. The common reference interval for suPAR was 1.56-4.11 ng/mL (95% confidence intervals (CI) for the lower and upper limits were 1.56-1.63 and 3.81-4.47, respectively). The reference interval for females was 1.59-4.65 ng/mL (95% CIs 1.48-1.70 and 4.09-5.48, respectively) and for males, 1.56-3.59 ng/mL (95% CIs 1.47-1.65 and 3.31-3.93, respectively).

Conclusions

Our results support using sex-partitioned reference intervals for suPAR and provide a basis for future studies using the PETIA method.

Evaluation of circulating levels of miR-135a and miR-193 in patients with sepsis

BACKGROUND

Sepsis is a life-threatening condition where early diagnosis and prognostic awareness provide guidance for selecting the appropriate treatment strategies. A wide variety of biomarker-based studies in clinical medicine provide new insights into personalized medicine for sepsis patients. MiRNAs are endogenous non-coding RNA molecules that have been acting as great potential diagnostic, prognostic and therapeutic biomarkers in various diseases.

METHODS AND RESULTS

In the present study, the expression levels of two selected miRNAs, including miR-135a and miR-193, were evaluated for their prognostic potential in patients with sepsis. The circulating levels of miRNAs were quantified by quantitative PCR (qPCR) in patients with sepsis (n = 100) and age- and sex-matched healthy controls (n = 100). Statistical findings confirmed the valuable prognostic potential of miR-135a in patients with sepsis, while no significant difference was found between the miR-193 expression level in the patients with sepsis and the controls.

CONCLUSIONS

Circulating levels of miRNA-135a can serve a the prognostic biomarker for patients with sepsis. These findings highlight the importance of miRNAs as signatures in the personalized managements of sepsis.

Global trends in research on endothelial cells and sepsis between 2002 and 2022: A systematic bibliometric analysis

Sepsis is a systemic syndrome involving physiological, pathological, and biochemical abnormalities precipitated by infection and is a major global public health problem. Endothelial cells (ECs) dysfunction is a major contributor to sepsis-induced multiple organ failure. This bibliometric analysis aimed to identify and characterize the status, evolution of the field, and new research trends of ECs and sepsis over the past 20 years. For this analysis, the Web of Science Core Collection database was searched to identify relevant publications on ECs in sepsis published between January 1, 2002, and December 31, 2022. Microsoft Excel 2021, VOSviewer software, CiteSpace software, and the online analysis platform of literature metrology (http://bibliometric.com) were used to visualize the trends of publications' countries/regions, institutions, authors, journals, and keywords. In total, 4200 articles were identified and screened, primarily originating from 86 countries/regions and 3489 institutions. The USA was the leading contributor to this research field, providing 1501 articles (35.74 %). Harvard University's scientists were the most prolific, with 129 articles. Overall, 21,944 authors were identified, among whom Bae Jong Sup was the most prolific, contributing 129 publications. Additionally, Levi Marcel was the most frequently co-cited author, appearing 538 times. The journals that published the most articles were SHOCK, CRITICAL CARE MEDICINE, and PLOS ONE, accounting for 10.79 % of the total. The current emerging hotspots are concentrated on "endothelial glycocalyx," "NLRP3 inflammasome," "extracellular vesicle," "biomarkers," and "COVID-19," among others. In conclusion, this study provides a comprehensive overview of the scientific productivity and emerging research trends in the field of ECs in sepsis. The evidence supporting the significant role of ECs in both physiological and pathological responses to sepsis is continuously growing. More in-depth studies of the molecular mechanisms underlying sepsis-induced endothelial dysfunction and EC-targeted therapies are warranted in the future.

Identification and analysis of genes associated with the severity and prognosis of sepsis

BACKGROUND

With rapid progression, severe illness and high fatality rate, sepsis has become an acute and critical condition that seriously threatens human life and health.

OBJECTIVE

To detect miR-210 and miR-494 expression in patients with sepsis and their relationship with severity and prognosis.

METHODS

A total of 165 sepsis patients participated, including 105 patients with septic non-shock and 60 patients with septic shock. 53 sepsis patients died in 28 days, and 112 patients survived. The clinical information of all sepsis patients was retrospectively searched and reviewed. Based on the status of 28-day survival, they were categorized into survival group and death group. The expression levels in each group were compared on the first, third and seventh day. The ROC curve was applied to know the expression level of plasma miR-210 and miR-494 to predict the death.

RESULTS

The two miRNAs expression of the septic shock group were significantly higher than that in sepsis non-shock group on the first, third and seventh day (all were P< 0.05). The ROC curve found that the AUC combined to predict the death on the third day was the largest, which was 0.925 (95%CI: 0.864-0.983). The sensitivity and specificity were 94.6% and 86.3%, respectively.

CONCLUSION

The increased expression levels of plasma miR-210 and miR-494 are closely relevant to the severity and prognosis of sepsis patients. Combining the two items on the third day can predict the death of sepsis patients.

Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential.

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis. This study included three-time points in the sepsis recovery cohort (N = 8) and healthy controls (N = 20). Morphological features and spatial distribution within cells were compared among the patients' statuses. We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology. Correlation between the morphological features and clinical indices were analysed. Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups. The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100% with only a few cells, and a strong correlation between the morphological features and clinical indices was observed. Our study highlights the potential of three-dimensional label-free CD8 + T cell morphology as a promising biomarker for sepsis. This approach is rapid, requires a minimum amount of blood samples, and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.

Measurement of Urinary Gc-Globulin by a Fluorescence ELISA Technique: Method Validation and Clinical Evaluation in Septic Patients-A Pilot Study

A major complication of sepsis is the development of acute kidney injury (AKI). In case of acute tubular damage, Gc-globulin, a known serum sepsis marker is increasingly filtrated into the urine therefore, urinary Gc-globulin (u-Gc) levels may predict septic AKI. We developed and validated a competitive fluorescence ELISA method for u-Gc measurement. Serum and urine samples from septic patients were collected in three consecutive days (T1, T2, T3) and data were compared to controls. Intra- and interassay imprecisions were CV < 14% and CV < 20%, respectively, with a recovery close to 100%. Controls and septic patients differed (p < 0.001) in their u-Gc/u-creatinine levels at admission (T1, median: 0.51 vs. 79.1 µg/mmol), T2 (median: 0.51 vs. 57.8 µg/mmol) and T3 (median: 0.51 vs. 55.6 µg/mmol). Septic patients with AKI expressed higher u-Gc/u-creatinine values than those without AKI at T1 (median: 23.6 vs. 136.5 µg/mmol, p < 0.01) and T3 (median: 34.4 vs. 75.8 µg/mmol, p < 0.05). AKI-2 stage patients exhibited more increased u-Gc/u-creatinine levels at T1 (median: 207.1 vs. 53.3 µg/mmol, p < 0.05) than AKI-1 stage individuals. Moderate correlations (p < 0.001) were observed between u-Gc/u-creatinine and se-urea, se-creatinine, se-hsCRP, WBC, u-total protein, u-albumin, u-orosomucoid/u-creatinine, and u-Cystatin C/u-creatinine levels. U-Gc testing may have a predictive value for AKI in septic patients.

Chemo Markers as Biomarkers in Septic Shock: A Comprehensive Review of Their Utility and Clinical Applications

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, often leading to septic shock. Early diagnosis and prompt intervention are crucial for improving patient outcomes. Chemo markers, which are measurable biological substances associated with the pathophysiology of septic shock, have emerged as potential biomarkers for the identification, risk stratification, and management of this condition. This comprehensive review aims to thoroughly evaluate the utility and clinical applications of chemo markers in septic shock. The review begins by discussing the criteria for ideal chemo markers, including specificity, sensitivity, dynamic range, stability, non-invasiveness, and prognostic value. These characteristics ensure accurate diagnosis, early detection, effective monitoring, and prediction of clinical outcomes. Furthermore, the review explores the role of chemo markers in monitoring treatment response and disease progression, highlighting their ability to serve as objective indicators for assessing the effectiveness of interventions and making timely adjustments in management strategies. Moreover, the prognostic value of chemo markers in predicting outcomes is discussed, emphasizing their association with mortality, hospital stays, and the development of complications. Integration of chemo markers into prognostic models or scoring systems enhances risk stratification and informs therapeutic decisions. The review also delves into recent advances in chemo marker research and technology, emphasizing the potential for discovering novel chemo markers with enhanced diagnostic and prognostic capabilities. It highlights the use of high-throughput proteomics, genomics, and transcriptomics in identifying specific molecular signatures associated with septic shock. This contributes to a deeper understanding of the complex immune and inflammatory responses involved. In conclusion, chemo markers have emerged as valuable biomarkers in septic shock, offering potential utility in diagnosis, risk stratification, treatment monitoring, and prediction of outcomes. Continued research, validation, and integration into clinical practice are necessary to fully realize their potential in improving patient care and outcomes in septic shock.

Advances in Rodent Experimental Models of Sepsis

In the development of therapeutic strategies for human diseases, preclinical experimental models have a key role. However, the preclinical immunomodulatory therapies developed using rodent sepsis were not successful in human clinical trials. Sepsis is characterized by a dysregulated inflammation and redox imbalance triggered by infection. Human sepsis is simulated in experimental models using methods that trigger inflammation or infection in the host animals, most often mice or rats. It remains unknown whether the characteristics of the host species, the methods used to induce sepsis, or the molecular processes focused upon need to be revisited in the development of treatment methods that will succeed in human clinical trials. Our goal in this review is to provide a survey of existing experimental models of sepsis, including the use of humanized mice and dirty mice, and to show how these models reflect the clinical course of sepsis. We will discuss the strengths and limitations of these models and present recent advances in this subject area. We maintain that rodent models continue to have an irreplaceable role in studies toward discovering treatment methods for human sepsis.

Target product profiles for diagnosis of sepsis: Proposing a new approach for diagnostic innovation

Background & objectives

Sepsis, including neonatal sepsis, remains a prevalent cause of morbidity and mortality in low- and middle-income countries such as India, representing 85 per cent of all sepsis-related deaths globally. Early diagnosis and timely initiation of treatment is challenging due to non-specific clinical manifestations and non-availability of rapid diagnostic tests. There is an urgent need for affordable diagnostics with fast turnaround time catering to the needs of end-users. Target product profiles (TPPs) have been found instrumental in developing 'fit-for-use' diagnostics, thus reducing the time taken to facilitate development and improving diagnosis. Hitherto, no such guidance or criteria has been defined for rapid diagnostics for sepsis/neonatal sepsis. We propose an innovative approach for developing the diagnostics for sepsis screening and diagnosis which can be utilized by diagnostic developers in the country.

Methods

Thr@ee-round Delphi method, including two online surveys and one virtual consultation, was adopted to define criteria for minimum and optimum attributes of TPPs and build consensus on characteristics. Expert panel (n=23) included infectious disease physicians, public health specialists, clinical microbiologists, virologists, researchers/scientists and technology experts/innovators.

Results

We present a three-component product profile for sepsis diagnosis, (i) screening with high sensitivity, (ii) detection of aetiological agent, and (iii) profiling of antimicrobial susceptibility/resistance, in adults and neonates with an option of testing different considerations. An agreement of >75 per cent was achieved for all TPP characteristics by Delphi. These TPPs are tailored to the Indian healthcare settings and can also be extrapolated to other resource-constraint and high-disease burden settings.

Interpretation & conclusions

Diagnostics developed using these TPPs will facilitate utilization of invested resources leading to development of the products that have potential to ease the economic burden on patient and save lives.

Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU

Introduction

Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.

Methods

In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.

Results

We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.

Discussion

In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Cytokine Biomarker Phenotype for Early Prediction and Triage of Sepsis in Blunt Trauma Patients

BACKGROUND

Altered levels of inflammatory markers secondary to severe trauma present a major problem to physicians and are prone to interfering with the clinical identification of sepsis events. This study aimed to establish the profiles of cytokines in trauma patients to characterize the nature of immune responses to sepsis, which might enable early prediction and individualized treatments to be developed for targeted intervention.

METHODS

A 15-plex human cytokine magnetic bead assay system was used to measure analytes in citrated plasma samples. Analysis of the kinetics of these cytokines was performed in 40 patients with severe blunt trauma admitted to our trauma center between March 2016 and February 2017, with an Injury Severity Score (ISS) greater than 20 with regard to sepsis (Sepsis-3) over a 14-d time course.

RESULTS

In total, the levels of six cytokines were altered in trauma patients across the 1-, 3-, 5-, 7-, and 14-d time points. Additionally, IL-6, IL-10, IL-15, macrophage derived chemokine (MDC), GRO, sCD40 L, granulocyte colony-stimulating factor (G-CSF), and fibroblast growth factor (FGF)-2 levels could be used to provide a significant discrimination between sepsis and nonsepsis patients at day 3 and afterward, with an area under the curve (AUC) of up to 0.90 through a combined analysis of the eight biomarkers (P < 0.001). Event-related analysis demonstrated 1.5- to 4-fold serum level changes for these cytokines within 72 h before clinically apparent sepsis.

CONCLUSIONS

Cytokine profiles demonstrate a high discriminatory ability enabling the timely identification of evolving sepsis in trauma patients. These abrupt changes enable sepsis to be detected up to 72 h before clinically overt deterioration. Defining cytokine release patterns that distinguish sepsis risk from trauma patients might enable physicians to initiate timely treatment and reduce mortality. Large prospective studies are needed to validate and operationalize the findings.

TRIAL REGISTRATION

Clinicaltrials, NCT01713205. Registered October 22, 2012, https://register.

CLINICALTRIALS

gov/NCT01713205.

A Pilot Study on Human Circulating System Indicated That Regenerating Islet-Derived Protein 3 Gamma (REG3A) is a Potential Prognostic Biomarker for Sepsis

It is critical to find fast and robust biomarkers for sepsis to reduce the patient's risk for morbidity and mortality. In this work, we compared serum protein expression levels of regenerating islet-derived protein 3 gamma (REG3A) between patients with sepsis and healthy controls and found that serum REG3A protein was significantly elevated in patients with sepsis. In addition, expression level of serum REG3A protein was markedly correlated with the Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, and C-reactive protein levels of patients with sepsis. Serum REG3A protein expression level was also confirmed to have good diagnostic value to differentiate patients with sepsis from healthy controls. Finally, serum REG3A protein expression level was found to have good prognostic value to predict the 28-day survival rate of patients with sepsis. In conclusion, our work indicated that serum REG3A may be a novel biomarker for sepsis.

Clinical usefulness of biomarkers for diagnosis and prediction of prognosis in sepsis and septic shock

Sepsis is a life-threatening condition and remains a major cause of mortality. The aim of this study was to evaluate the role of biomarkers in the diagnosis of sepsis and septic shock in patients admitted to the emergency department (ED). Medical records of patients who underwent measurement of serum biomarkers including lactic acid, C-reactive protein, procalcitonin (PCT), and presepsin in the ED between May 2019 and May 2020 were retrospectively reviewed. Patients were subdivided into 3 groups; non-sepsis, sepsis, and septic shock according to the new definition using the sequential organ failure assessment score. The mean age was 69.3 years, and 55.8% of the study population was female. Of 249 subjects, 98 patients confined to sepsis group, and 35.7% of them were septic shock. In the multivariable analysis, a high level of PCT was an independent predictor of sepsis (odds ratio [OR], 1.028; 95% confidence interval [CI], 1.006-1.051; P = .011) along with a simplified acute physiology score III (SAPS III) (OR, 1.082; 95% CI, 1.062-1.103, P < .001). PCT was also an independent risk factor for septic shock (OR, 1.043; 95% CI, 1.016-1.071, P = .02). In the receiver operating characteristic curve analysis, the area under the curve of PCT to predict sepsis and septic shock were 0.691 (P < .001) and 0.734 (P < .001), respectively. The overall 30-days mortality rate was 8.8%, and the mortality rate was significantly higher in the sepsis group (sepsis vs non-sepsis, 15.3% vs 4.6%; P = .004). In the multivariate Cox analysis, a higher level of lactic acid (hazard ratio [HR], 1.328; 95% CI, 1.061-1.663, P = .013), predisposing chronic pulmonary diseases (HR, 7.035; 95% CI, 1.687-29.341, P = .007), and a high SAPSIII value (HR, 1.046; 95% CI, 1.015-1.078, P = .003) were independent risk factors for mortality in sepsis patients. PCT was a useful biomarker for predicting sepsis and septic shock in the ED. A higher level of lactic acid, predisposing chronic pulmonary diseases, and a high SAPS III score were associated with a greater mortality risk in patients with sepsis.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Implementation of Procalcitonin in Antibiotic Stewardship: Derivation of a Consensus Algorithm for Procalcitonin Use in Clinical Practice

Reducing antibiotics overuse is essential to minimize antibiotics related side effects and to prevent the emergence of multidrug-resistant bacteria. Procalcitonin (PCT) guided antibiotics therapy has been reported to be safe in patients with acute respiratory infections and sepsis, improving clinical outcomes as well as reducing the duration of antibiotics use. However, there is still no universal agreement on clinical guidelines in Korea for optimal PCT applications. Through this expert consensus meeting, clinical research findings in the PCT-guided antibiotics treatment interventions and real-world clinical applications were discussed. From the perspective of antibiotic stewardship, PCT application target groups, cut-offs, and testing cycles were discussed to reach a consensus on the PCT-guided antibiotics treatment algorithm for application in Korea. Combining clinical assessment for patients with an appropriate PCT-guided antibiotics treatment algorithm could improve the diagnosis and treatment of acute respiratory infections and sepsis. In addition, continuous education and regular feedback would improve the effectiveness of antibiotic stewardship.

Current Status of Antibiotic Stewardship and the Role of Biomarkers in Antibiotic Stewardship Programs

The importance of antibiotic stewardship is increasingly emphasized in accordance with the increasing incidences of multidrug-resistant organisms and accompanying increases in disease burden. This review describes the obstacles in operating an antibiotic stewardship program (ASP), and whether the use of biomarkers within currently available resources can help. Surveys conducted around the world have shown that major obstacles to ASPs are shortages of time and personnel, lack of appropriate compensation for ASP operation, and lack of guidelines or appropriate manuals. Sufficient investment, such as the provision of full-time equivalent ASP practitioners, and adoption of computerized clinical decision systems are useful measures to improve ASP within an institution. However, these methods are not easy in terms of both time commitments and cost. Some biomarkers, such as C-reactive protein, procalcitonin, and presepsin are promising tools in ASP due to their utility in diagnosis and forecasting the prognosis of sepsis. Recent studies have demonstrated the usefulness of algorithmic approaches based on procalcitonin level to determine the initiation or discontinuation of antibiotics, which would be helpful in decreasing antibiotics use, resulting in more appropriate antibiotics use.

Construction of a potentially functional lncRNA-miRNA-mRNA network in sepsis by bioinformatics analysis

Objective: Sepsis is a common disease in internal medicine, with a high incidence and dangerous condition. Due to the limited understanding of its pathogenesis, the prognosis is poor. The goal of this project is to screen potential biomarkers for the diagnosis of sepsis and to identify competitive endogenous RNA (ceRNA) networks associated with sepsis.

Methods: The expression profiles of long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) were derived from the Gene Expression Omnibus (GEO) dataset. The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened by bioinformatics analysis. DEmRNAs were analyzed by protein-protein interaction (PPI) network analysis, transcription factor enrichment analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Set Enrichment Analysis (GSEA). After the prediction of the relevant database, the competitive ceRNA network is built in Cytoscape. The gene-drug interaction was predicted by DGIgb. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm five lncRNAs from the ceRNA network.

Results: Through Venn diagram analysis, we found that 57 DElncRNAs, 6 DEmiRNAs and 317 DEmRNAs expressed abnormally in patients with sepsis. GO analysis and KEGG pathway analysis showed that 789 GO terms and 36 KEGG pathways were enriched. Through intersection analysis and data mining, 5 key KEGG pathways and related core genes were revealed by GSEA. The PPI network consists of 247 nodes and 1,163 edges, and 50 hub genes are screened by the MCODE plug-in. In addition, there are 5 DElncRNAs, 6 DEmiRNAs and 28 DEmRNAs in the ceRNA network. Drug action analysis showed that 7 genes were predicted to be molecular targets of drugs. Five lncRNAs in ceRNA network are verified by qRT-PCR, and the results showed that the relative expression of five lncRNAs was significantly different between sepsis patients and healthy control subjects.

Conclusion: A sepsis-specific ceRNA network has been effectively created, which is helpful to understand the interaction between lncRNAs, miRNAs and mRNAs. We discovered prospective sepsis peripheral blood indicators and proposed potential treatment medicines, providing new insights into the progression and development of sepsis.

A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS

ABSTRACT

Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.

Application of Biomarkers in the Diagnostic Distinction of Bacterial and Viral Infections

Infectious diseases, which pose a great threat worldwide, have a significant impact on public health and the world economy. It contributes to increased healthcare costs, unnecessary drug-related side effects, and increased antimicrobial resistance. It is not always easy to distinguish the etiological differentiation of diseases that can develop with bacteria and viruses. Therefore, one of the biggest challenges in medicine is how to correctly distinguish between the different causes of these infections and how to manage the patient. Because bacterial and viral infections often present similar symptoms. The real decision is whether the infection is caused by bacteria or viruses and whether to treat the patient with antibiotics. There are many different methodological approaches to diagnosing infections. Biomarkers have been used in the diagnosis of diseases and other conditions for many years. Biomarkers are molecules found in blood and body fluids in measurable amounts, which can evaluate biological and pathological processes. These key indicators can provide vital information in determining disease prognosis, predicting response to treatments, adverse events and drug interactions, and identifying key risks. An effective biomarker is extremely important for the early diagnosis of various diseases. The explosion of interest in biomarker research is driving the development of new predictive, diagnostic, and prognostic products in modern medical practice. The purpose of this review is to demonstrate the use and diagnostic potential of current and investigational biomarkers in the distinction between bacterial and viral infections.

Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures

Purpose

Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis.

Methods

Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148).

Results

Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703).

Conclusion

Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06769-z.

Increased progranulin as an independent predictive biomarker for poor prognosis in sepsis

BACKGROUND

Recently, many diagnostic biomarkers were reported, but each had its own limitation. However, there is a need for an effective sensitivity and specificity of biomarker in diagnosis and prognosis of sepsis. In this context, progranulin (PGRN), at elevated levels, has been associated with poor prognosis in infectious diseases. Moreover, increased PGRN levels were seen in septic mice. As the prognostic value of PGRN in humans is unclear, we aimed to identify the predictive value of serum PGRN for the prognosis of sepsis.

METHODS

A total of 128 participants with sepsis and 58 healthy controls were recruited in this study. The levels of serum PGRN were detected by enzyme-linked immunosorbent assay. According to the outcomes, patients were divided into survival and non-survival groups.

RESULTS

Serum PGRN levels had upregulated in patients with sepsis compared with those in healthy controls (P < 0.001) as well as in non‑survivors compared with those in survivors (P < 0.001). Furthermore, serum PGRN levels exhibited positive correlation with hypersensitive C-reactive protein, procalcitonin, sepsis‑related organ failure assessment (SOFA) scores, and acute physiology and chronic health evaluation II (APACHE II) scores. PGRN had a higher predictive effect, especially the 28-day in-hospital mortality (p < 0.001), when using it with SOFA or APACHE II scores. Cox proportional regression analysis showed that PGRN was an independent predictor for 28-day mortality risk in sepsis.

CONCLUSIONS

PGRN, as a biomarker of sepsis, could improve the prognostic power of traditional parameters. This study is the first to report the clinical significance of PGRN levels in terms of the severity and prognosis of sepsis.

Global health systems' data science approach for precision diagnosis of sepsis in early life

Neonates and children in low-income and middle-income countries (LMICs) contribute to the highest number of sepsis-associated deaths globally. Interventions to prevent sepsis mortality are hampered by a lack of comprehensive epidemiological data and pathophysiological understanding of biological pathways. In this review, we discuss the challenges faced by LMICs in diagnosing sepsis in these age groups. We highlight a role for multi-omics and health care data to improve diagnostic accuracy of clinical algorithms, arguing that health-care systems urgently need precision medicine to avoid the pitfalls of missed diagnoses, misdiagnoses, and overdiagnoses, and associated antimicrobial resistance. We discuss ethical, regulatory, and systemic barriers related to the collection and use of big data in LMICs. Technologies such as cloud computing, artificial intelligence, and medical tricorders might help, but they require collaboration with local communities. Co-partnering (joint equal development of technology between producer and end-users) could facilitate integration of these technologies as part of future care-delivery systems, offering a chance to transform the global management and prevention of sepsis for neonates and children.

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glycoisoform distributions of 597 abundant serum glycopeptides and nonglycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR < 0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glycoisoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding and, potentially, the clinical management of serious acute infectious conditions.

Relationship between amikacin pharmacokinetics and biological parameters associated with organ dysfunction: a case series study of critically ill patients with intra-abdominal sepsis

OBJECTIVES

This study aimed to evaluate the relationship between amikacin pharmacokinetics and the biomarkers associated with organ dysfunction in critically ill patients with intra-abdominal sepsis.

METHODS

A case series involving critically ill patients with intra-abdominal sepsis who received an amikacin loading dose of 20-25 mg/kg intravenous infusion was studied. The 1-, 2-, 4-, 6- and 24-hour amikacin serum concentrations were measured to calculate the pharmacokinetic parameters. The Sequential Organ Failure Assessment (SOFA) score, white blood cells, neutrophil to lymphocyte ratio, platelet count, serum creatinine, creatinine clearance, bilirubin, partial pressure of oxygen to fraction of inspired oxygen ratio, serum albumin, procalcitonin, lactate level, erythrocyte sedimentation rate (ESR) and C-reactive protein were recorded. A linear regression analysis was performed to examine the relationship between the amikacin pharmacokinetics and the biological parameters.

RESULTS

Twenty-one patients were studied. A significant correlation was found between the volume of distribution and ESR (p<0.05, r=0.844). Moreover, drug clearance had a significant inverse correlation with serum lactate (p<0.05, r=-0.603). No other significant correlations were found.

CONCLUSIONS

ESR and serum lactate were identified as useful predictors of amikacin pharmacokinetics in critically ill patients with intra-abdominal sepsis and may help guide the selection of appropriate empirical dosing.

Development and validation of a nomogram for predicting the prognosis in cancer patients with sepsis

BACKGROUND

To develop a multiparameter-based, easy-to-use nomogram and to predict the prognosis of cancer patients with sepsis in the intensive care unit (ICU).

METHODS

Clinical data on cancer patients with sepsis who met the definition of sepsis 3.0 admitted to the ICU from January 2016 to October 2021 were collected. All patients were randomly entered into the development cohort or validation cohort according to the ratio of 7:3. Patients in the development cohort were divided into the survivors and the nonsurvivors according to the outcome of 28 days in ICU. The independent risk factors of mortality due to sepsis were screened out from the two groups (the survivors and the nonsurvivors) in the development cohort through multivariate logistic regression analysis. A nomogram was established with these independent risk factors, and the calibration plot was subsequently evaluated. Finally, the predictive power of the nomogram was verified in the validation cohort.

RESULTS

A total of 317 cancer patients with sepsis who met the requirements were enrolled in this study, of which 229 entered into the development cohort and 88 entered into the validation cohort. The 28-day mortality rates of the two cohorts were 17.5% and 20.5%, respectively. The neutrophil-to-lymphocyte ratio (NLR) on day 3 (d3), brain natriuretic peptide (BNP) d3, fluid accumulation at 72 hours (h), and Sequential Organ Failure Assessment (SOFA) score were independent risk factors for the 28-day mortality between the survivors and the nonsurvivors in the development cohort. A nomogram was established on the above variables. The calibration plots fit well with the nomogram and had good statistical consistency in predicting the 28-day mortality of sepsis (the C value was 0.938 and 0.968 in the two cohorts, respectively). With a nomogram score of 83.8 points, the diagnostic accuracy was 90.8% vs 92.0%, the sensitivity was 72.5% vs 77.7%, the specificity was 94.7% vs 95.7%, the positive predictive value was 72.3% vs 82.4%, and the negative predictive value was 94.2% vs 94.4% for predicting the 28-day mortality in the development cohort and the validation cohort, respectively.

CONCLUSION

This easy-to-use nomogram based on NLR d3, BNP d3, and fluid accumulation at 72 h and SOFA score provides an accurate 28-day prognosis prediction for cancer patients with sepsis admitted to the ICU.

Inflammasome Activation in an In Vitro Sepsis Model Recapitulates Increased Monocyte Distribution Width Seen in Patients With Sepsis

OBJECTIVES:

Increased monocyte distribution width (MDW) has recently been shown to be a reliable indicator of early sepsis detection. This study therefore sought to determine if inflammasome activation can be linked to monocyte size changes in sepsis.

DESIGN:

An in vitro sepsis model using bacterial endotoxin (lipopolysaccharide [LPS]) to study the effect of inflammasome activation on monocyte cell size distribution by microscopy and MDW measurements using a standard clinical hematology analyzer.

SETTING:

University research laboratory.

SUBJECTS:

Healthy adult volunteers and cultured human monocyte cells in wild-type state and after clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 knockout of key inflammasome components (apoptosis-associated speck-like protein containing a caspase recruitment domain, caspase-1, gasdermin-D).

INTERVENTIONS:

In vitro treatment of specimens with bacterial LPS.

MEASUREMENTS AND MAIN RESULTS:

Wild-type THP1 cells demonstrated a significant increase in cell area (207 μm² [159–400 μm²] vs 160 μm² [134–198 μm²]; p < 0.001) and distribution width (198 vs 55 μm²; p < 0.0001) by microscopy following treatment with LPS. Increased MDW correlated with inflammasome activation as demonstrated by release of interleukin (IL)-1β and with the presence of large distended pyroptotic cells by microscopy. All of these effects were blocked in the inflammasome knockout cells. Whole blood samples treated similarly also demonstrated IL-1β release and increased MDW (median 24.7 U [22.2–27.2 U] vs 16.3 U [15.1–17.6 U]; p = 0.008) as measured using the Beckman-Coulter Unicel DxH900 analyzer. When peripheral blood mononuclear cells were isolated prior to treatment with LPS, microscopy confirmed the presence of large pyroptotic cells correlating to IL-1β release in the human subject samples as well.

CONCLUSIONS:

The increased MDW seen in patients with sepsis can be reproduced in an in vitro sepsis model and blocked using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 technology to inactivate the inflammasome. These findings suggest that pyroptotic cellular swelling underlies changes in MDW in septic patients and connect MDW to early events in the inflammatory cascade of sepsis.

Characterizing the kinetics of presepsin and associated inflammatory biomarkers in human endotoxemia

In this study, we describe the kinetics of a new potential inflammatory biomarker, presepsin, together with a panel of well-established biomarkers in a human endotoxemia study. We evaluated biomarker correlations and identified combinations that could hold valuable insights regarding the state of infection.

Circulating miR-147b as a diagnostic marker for patients with bacterial sepsis and septic shock

BACKGROUND

Early diagnosis, precise antimicrobial treatment and subsequent patient stratification can improve sepsis outcomes. Circulating biomarkers such as plasma microRNAs (miRNAs) have proven to be surrogates for diagnosis, severity and case management of infections. The expression of four selected miRNAs (miR-146-3p, miR-147b, miR-155 and miR-223) was validated for their prognostic and diagnostic potential in a clinically defined cohort of patients with sepsis and septic shock.

METHODS

The expression of plasma miRNAs was quantified by quantitative PCR (qPCR) in patients with bacterial sepsis (n = 78), in patients with septic shock (n = 52) and in patients with dengue haemorrhagic fever (DHF; n = 69) and in healthy controls (n = 82).

RESULTS

The expression of studied miRNA was significantly increased in patients with bacterial sepsis and septic shock. The plasma miR-147b was able to differentiate bacterial sepsis from non-sepsis and septic shock (AUC = 0.77 and 0.8, respectively, p≤ 0.05), while the combination of plasma miR-147b and procalcitonin (PCT) predicted septic shock (AUC = 0.86, p≤ 0.05).

CONCLUSIONS

The plasma miR-147b may be an useful biomarker independently or in combination with PCT to support clinical diagnosis of sepsis and equally prognosis of patients with septic shock.

Immunomonitoring of Monocyte and Neutrophil Function in Critically Ill Patients: From Sepsis and/or Trauma to COVID-19

Immune cells and mediators play a crucial role in the critical care setting but are understudied. This review explores the concept of sepsis and/or injury-induced immunosuppression and immuno-inflammatory response in COVID-19 and reiterates the need for more accurate functional immunomonitoring of monocyte and neutrophil function in these critically ill patients. in addition, the feasibility of circulating and cell-surface immune biomarkers as predictors of infection and/or outcome in critically ill patients is explored. It is clear that, for critically ill, one size does not fit all and that immune phenotyping of critically ill patients may allow the development of a more personalized approach with tailored immunotherapy for the specific patient. In addition, at this point in time, caution is advised regarding the quality of evidence of some COVID-19 studies in the literature.

The Effects of Chinese Medicine QRD, Antibiotics, and Probiotics on Therapy and Gut Microbiota in Septic Rats

Sepsis is a common and often treacherous medical emergency with a high mortality and long-term complications in survivors. Though antibiotic therapy can reduce death rate of sepsis significantly, it impairs gut microbiota (GM), which play imperative roles in human health. In this study, we compared the therapeutic effects of antibiotics, probiotics, and Chinese medicine QRD on the survival rates of septic model and observed the GM characteristics of experimental rats via 16S rRNA gene amplicon sequencing. The 72 h survival rates of septic rat demonstrated the significant therapeutic effects in the three groups treated with antibiotics (AT), Chinses medicine QRD (QT), and probiotics (PT), which were elevated from the survival rate of 26.67% for the sepsis control group (ST) to 100.0% for AT, 88.24% for QT, and 58.33% for PT. The original characteristics of GM identified in the sham operation controls (SC) were relatively similar to those in PT and QT; nevertheless, the AT rats were shown dramatically decreased in the GM diversity. In addition, the septic rats in AT were revealed the higher abundances of Escherichia Shigella, Proteus, Morganella, Enterococcus, and Lysinibacillus, but the lower those of Parabacteroides, Alistipes, Desulfovibrio, Bacteroides, Helicobacter, Mucispirillum, Oscillibacter, Lachnospiraceae, and Ruminiclostridium 9, when compared to the PT and QT rats. By contrast, the GM of PT and QT rats shared similar diversity and structure. Our findings indicated that QRD increased the survival rates without impairment of the GM characteristics, which provides novel insights into the role of Chinese medicine in therapy and long-term recovery of sepsis.

N-terminal pro-brain natriuretic peptide is an independent predictor of mortality in patients with sepsis

This study aims to evaluate the role of cardiac enzymes N-terminal pro-brain natriuretic peptide (NT-proBNP) and cardiac troponin-I (CTnI) as predictors of outcomes in patients with sepsis.78 cases with a diagnosis of sepsis were enrolled over a 2-year period. Baseline demographic, Acute Physiology and Chronic Health Evaluation-II (APACHE-II), Simplified Acute Physiology Score-II (SAPS-II), hematologic and biochemical parameters were noted. Serum NT-proBNP and CTnI were evaluated at 24 and 72 hours of admission along with echocardiography. Patients were prospectively followed up until death or discharge.Mean APACHE-II score was 19.8±9.6 and SAPS-II was 44.8±17.2. Survival rate in the study was 47.5% (36 of 78 patients). NT-proBNP was significantly higher in non-survivors with values over 4300 pg/mL at 24 hours and 5229 pg/mL at 72 hours associated with poor outcomes (p<0.05). CTnI was higher among non-survivors than in survivors, but the difference was not significant. APACHE-II score combined with NT-proBNP predicted a poor outcome in 51.2% cases compared with 14.6% cases with APACHE-II alone (p<0.05), while SAPS-II combined with NT-proBNP predicted a poor outcome in 53.6% cases as compared with 9.6% cases with SAPS-II alone (p<0.05). SAPS-II greater than 45 and NT-proBNP values at 72 hours were independent predictors of mortality in patients with sepsis.NT-proBNP is an independent predictor of mortality in patients with sepsis and its combination with APACHE-II and SAPS-II improves the predictive values of the scoring systems.

The Effects of a Meldonium Pre-Treatment on the Course of the Faecal-Induced Sepsis in Rats

Sepsis is a life-threatening condition caused by the dysregulated and overwhelming response to infection, accompanied by an exaggerated pro-inflammatory state and lipid metabolism disturbance leading to sequential organ failure. Meldonium is an anti-ischemic and anti-inflammatory agent which negatively interferes with lipid metabolism by shifting energy production from fatty acid oxidation to glycolysis, as a less oxygen-demanding pathway. Thus, we investigated the effects of a four-week meldonium pre-treatment on faecal-induced sepsis in Sprague-Dawley male rats. Surprisingly, under septic conditions, meldonium increased animal mortality rate compared with the meldonium non-treated group. However, analysis of the tissue oxidative status did not provide support for the detrimental effects of meldonium, nor did the analysis of the tissue inflammatory status showing anti-inflammatory, anti-apoptotic, and anti-necrotic effects of meldonium. After performing tissue lipidomic analysis, we concluded that the potential cause of the meldonium harmful effect is to be found in the overall decreased lipid metabolism. The present study underlines the importance of uninterrupted energy production in sepsis, closely drawing attention to the possible harmful effects of lipid-mobilization impairment caused by certain therapeutics. This could lead to the much-needed revision of the existing guidelines in the clinical treatment of sepsis while paving the way for discovering new therapeutic approaches.

Proadrenomedullin in Sepsis and Septic Shock: A Role in the Emergency Department

Sepsis and septic shock represent a leading cause of mortality in the Emergency Department (ED) and in the Intensive Care Unit (ICU). For these life-threating conditions, different diagnostic and prognostic biomarkers have been studied. Proadrenomedullin (MR-proADM) is a biomarker that can predict organ damage and the risk of imminent death in patients with septic shock, as shown by a large amount of data in the literature. The aim of our narrative review is to evaluate the role of MR-proADM in the context of Emergency Medicine and to summarize the current knowledge of MR-proADM as a serum indicator that is useful in the Emergency Department (ED) to determine an early diagnosis and to predict the long-term mortality of patients with sepsis and septic shock. We performed an electronic literature review to investigate the role of MR-proADM in sepsis and septic shock in the context of ED. We searched papers on PubMed^®, Cochrane^®, UptoDate^®, and Web of Science^® that had been published in the last 10 years. Data extracted from this literature review are not conclusive, but they show that MR-proADM may be helpful as a prognostic biomarker to stratify the mortality risk in cases of sepsis and septic shock with different degrees of organ damage, guiding emergency physicians in the diagnosis and the succeeding therapeutic workup. Sepsis and septic shock are conditions of high complexity and have a high risk of mortality. In the ED, early diagnosis is crucial in order to provide an early treatment and to improve patient survival. Diagnosis and prognosis are often the result of a combination of several tests. In our opinion, testing for MR-proADM directly in the ED could contribute to improving the prognostic assessment of patients, facilitating the subsequent clinical management and intensive treatment by the emergency physicians, but more studies are needed to confirm these results.

Biomarker-Guided Individualization of Antibiotic Therapy

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

Tumor necrosis factor-α small interfering RNA alveolar epithelial cell-targeting nanoparticles reduce lung injury in C57BL/6J mice with sepsis

Background

The role of tumor necrosis factor (TNF)-α small interfering (si)RNA alveolar epithelial cell (AEC)-targeting nanoparticles in lung injury is unclear.

Methods

Sixty C57BL/6J mice with sepsis were divided into normal, control, sham, 25 mg/kg, 50 mg/kg, and 100 mg/kg siRNA AEC-targeting nanoparticles groups (n = 10 per group). The wet:dry lung weight ratio, and hematoxylin and eosin staining, western blotting, and enzyme-linked immunosorbent assays for inflammatory factors were conducted to compare differences among groups.

Results

The wet:dry ratio was significantly lower in control and sham groups than other groups. TNF-α siRNA AEC-targeting nanoparticles significantly reduced the number of eosinophils, with significantly lower numbers in the 50 mg/kg group than in 25 mg/kg and 100 mg/kg groups. The nanoparticles also significantly reduced the expression of TNF-α, B-cell lymphoma-2, caspase 3, interleukin (IL)-1β, and IL-6, with TNF-α expression being significantly lower in the 50 mg/kg group than in 25 mg/kg and 100 mg/kg groups.

Conclusion

TNF-α siRNA AEC-targeting nanoparticles appear to be effective at improving lung injury-related sepsis, and 50 mg/kg may be a preferred dose option for administration.