Sepsis biomarkers: a review

Association between cytokines, nitric oxide, hemodynamic and microcirculation in a porcine model of sepsis

Systemic inflammation and hemodynamic or microvascular alterations are a hallmark of sepsis and play a role in organs hypoperfusion and dysfunction. Pimobendan, an inodilator agent, could be an interesting option for inotropic support and microcirculation preservation during shock. The objectives of this study were to evaluate effect of pimobendan on cytokine and nitric oxide (NO) release and investigate whether changes of macro and microcirculation parameters are associated with the release of cytokines and NO in pigs sepsis model. After circulatory failure, induced by intravenous inoculation of live Pseudomonas aeruginosa, eight animals were treated with pimobendan and eight with placebo. Pimobendan did not affect cytokines secretion (TNF-α, IL-6 and IL-10), but decreased time-dependently NO release. Data of macro and microcirculation parameters, NO and TNF- α recorded at the time of circulatory failure (Thypotension) and the time maximum of production cytokines was used for analyses. A positive correlation was observed between TNF-α and cardiac index (r = 0.55, p = 0.03) and a negative with systemic vascular resistance (r = -0.52, p = 0.04). Positive correlations were seen both between IL-10, 30 min after resuscitation (T30min), and systolic arterial pressure (r = 0.57, p = 0.03) and cardiac index (r = 0.67, p = 0.01), and also between IL-6, taken 2 h after resuscitation and systolic arterial pressure (r = 0.53, p = 0.04). Negative correlations were found between IL-10 and lactate, measured resuscitation time (r = -0.58, p = 0.03). Regarding microcirculation parameters, we observed a positive correlation between IL-6 and IL-10 with the microvascular flow index (r = 0.52, p = 0.05; r = 0.84, p = 0.0003) and a negative correlation with the heterogeneity index with TNF-α and IL-10 (r = -0.51, p = 0.05; r = -0.74, p = 0.003) respectively. NO derivatives showed a positive correlation with temperature gradient (r = 0.54, p = 0.04). Pimobendan did not show anti-inflammatory effects in cytokines release. Our results also, suggest changes of macro- and microcirculation are associated mainly with low levels of IL-10 in sepsis.

Affinity-based 3D-printed microfluidic chip for clinical sepsis detection with CD69, CD64, and CD25

Sepsis is a life-threatening immune response to infection in the body, eventually resulting in fatal organ failure. Current methods utilize blood cultures and quick-Sequential-Organ-Failure-Assessment (qSOFA), but there is a need for more accurate and time-sensitive diagnostic methods to improve survival rates. We present a 3D-printed microfluidic chip that bioconjugates antibodies CD69, CD64, and CD25 to channel surfaces to capture sepsis cells in blood samples and validate it with clinical samples (n = 125 septic, n = 10 healthy). Other variables were taken such as healthy volunteer blood samples and patient demographics to validate and confirm our device's diagnostic ability. Statistical differences were found between healthy volunteer and sepsis patient antigen cell counts (CD69 p-value < 0.001, CD64 p-value < 0.004, CD25 p-value < 0.0009), and were confirmed using principal component analysis. Demographics such as length of stay, age, culture results, and need for surgery also factored into sepsis detection on a smaller scale than the antigen cell counts. The receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.989, 0.988, and 0.992 for CD69, CD64, and CD25, respectively, and a combined biomarker panel of 0.997. Overall, the device performed within a shorter time frame of 4 h compared to standard blood culture tests and was validated for use in detecting sepsis in patients.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death

Background

Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death.

Methods

Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank.

Results

After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant.

Conclusions

The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Features of Metabolites and Biomarkers in Inflammatory and Infectious Complications of Childhood Cancers

Background: In the treatment of oncological diseases in children, the search for opportunities for the earlier detection of complications to improve treatment results is very important. Metabolomic studies are actively conducted to stratify different groups of patients in order to identify the most promising markers. Methods: Three groups of patients participated in this study: healthy children as a control group (n = 18), children with various malignant oncological diseases (leukemia, lymphoma, nephroblastoma, ependymoma, etc.) as patients (n = 40) without complications, and patients (n = 31) with complications (inflammatory and infectious). The mitochondrial metabolites (succinic and fumaric acids); biomarkers related to inflammation such as C-reactive protein (CRP), procalcitonin (PCT), and presepsin (PSP); and sepsis-associated aromatic metabolites, such as phenyllactic (PhLA), hydroxyphenyllactic (p-HPhLA), and hydroxyphenylacetic acids (p-HPhAA), were identified. Results: It was found that children with malignant oncological diseases had profound metabolic dysfunction compared to healthy children, regardless of the presence of systemic inflammatory response syndrome (SIRS) or sepsis. The prognostic ability of procalcitonin and presepsin for detecting sepsis was high: AUROC = 0.875, cut-off value (Youden index) = 0.913 ng/mL, and AUROC = 0.774, with cut-off value (Youden index) of 526 pg/mL, respectively. Conclusions: A significant increase in aromatic microbial metabolites and biomarkers in non-survivor patients that is registered already in the first days of the development of complications indicates the appropriateness of assessing metabolic dysfunction for its timely targeted correction.

Glycolysis-associated lncRNAs in cancer energy metabolism and immune microenvironment: a magic key

The dependence of tumor cells on glycolysis provides essential energy and raw materials for their survival and growth. Recent research findings have indicated that long chain non-coding RNAs (LncRNAs) have a key regulatory function in the tumor glycolytic pathway and offer new opportunities for cancer therapy. LncRNAs are analogous to a regulatory key during glycolysis. In this paper, we review the mechanisms of LncRNA in the tumor glycolytic pathway and their potential therapeutic strategies, including current alterations in cancer-related energy metabolism with lncRNA mediating the expression of key enzymes, lactate production and transport, and the mechanism of interaction with transcription factors, miRNAs, and other molecules. Studies targeting LncRNA-regulated tumor glycolytic pathways also offer the possibility of developing new therapeutic strategies. By regulating LncRNA expression, the metabolic pathways of tumor cells can be interfered with to inhibit tumor growth and metastasis, thus affecting the immune and drug resistance mechanisms of tumor cells. In addition, lncRNAs have the capacity to function as molecular markers and target therapies, thereby contributing novel strategies and approaches to the field of personalized cancer therapy and prognosis evaluation. In conclusion, LncRNA, as key molecules regulating the tumor glycolysis pathway, reveals a new mechanism of abnormal metabolism in cancer cells. Future research will more thoroughly investigate the specific mechanisms of LncRNA glycolysis regulation and develop corresponding therapeutic strategies, thereby fostering new optimism for the realization of precision medicine.

The Impact of Extracellular Histones and Absence of Toll-like Receptors on Cardiac Functional and Electrical Disturbances in Mouse Hearts

In polymicrobial sepsis, the extracellular histones, mainly released from activated neutrophils, significantly contribute to cardiac dysfunction (septic cardiomyopathy), as demonstrated in our previous studies using Echo-Doppler measurements. This study aims to elucidate the roles of extracellular histones and their interactions with Toll-like receptors (TLRs) in cardiac dysfunction. Through ex vivo assessments of ECG, left ventricle (LV) function parameters, and in vivo Echo-Doppler studies in mice perfused with extracellular histones, we aim to provide comprehensive insights into the mechanisms underlying sepsis-induced cardiac dysfunction. Langendorff-perfused hearts from both wild-type and TLR2, TLR3, or TLR4 knockout (KO) mice were examined. Paced mouse hearts were perfused with histones to assess contractility and relaxation. Echo-Doppler studies evaluated cardiac dysfunction after intravenous histone injection. Histone perfusion caused defects in contractility and relaxation, with TLR2 and TLR3 KO mice being partially protected. Specifically, TLR2 KO mice exhibited the greatest reduction in Echo-Doppler abnormalities, while TLR4 KO exacerbated cardiac dysfunction. Among individual histones, H1 induced the most pronounced abnormalities in cardiac function, apoptosis of cardiomyocytes, and LDH release. Our data highlight significant interactions between histones and TLRs, providing insights into histones especially H1 as potential therapeutic targets for septic cardiomyopathy. Further studies are needed to explore specific histone-TLR interactions and their mechanisms.

Serial Trend of Neutrophil CD64, C-reactive Protein, and Procalcitonin as a Prognostic Marker in Critically Ill Patients with Sepsis/Septic Shock: A Prospective Observational Study from a Tertiary Care ICU

Aim and background

Neutrophil CD64 (nCD64) is evolving as a prognostic biomarker in sepsis. The primary objective of this study was to evaluate whether serial trend of nCD64, procalcitonin (PCT), and C-reactive protein (CRP) predict 28-day mortality in patients with sepsis/septic shock, as per Sepsis-3 criteria.

Materials and methods

This prospective, observational single-center cohort study included 60 adult patients (age ≥18 years) with sepsis. Serial biomarker levels with SOFA score were measured at admission (day 0), on day 4, and on day 8.

Results

Of the 60 patients, 42 (70%) had septic shock. Biomarker levels at admission did not differ between patients with sepsis and septic shock. Thirty-seven patients survived and 23 were non-survivors by day 28. There was a significant fall in serial trend of all three biomarkers from admission till day 8 (Friedman p < 0.001) in survivors compared to a non-significant change in non-survivors. On multivariate analysis, SOFA score at admission (OR 1.731), more days with vasopressor support (OR 1.077), rise in CD64 from day 0 to day 8 (OR 1.074), and rise in CRP from day 0 to 8 (OR 1.245) were the significant predictors of 28-day mortality (p < 0.05). The highest area under the ROC curve was obtained for more days of vasopressor therapy (0.857), followed by a rise in CD64 from day 0 to day 8 (0.798).

Conclusion

Serial trend of biomarkers has prognostic utility. The rise in CD64 from day 0 to day 8 was a good predictor of mortality compared to the trend of other biomarkers.

How to cite this article

Patnaik R, Azim A, Singh K, Agarwal V, Mishra P, Poddar B, et al. Serial Trend of Neutrophil CD64, C-reactive Protein, and Procalcitonin as a Prognostic Marker in Critically Ill Patients with Sepsis/Septic Shock: A Prospective Observational Study from a Tertiary Care ICU. Indian J Crit Care Med 2024;28(8):777-784.

The protagonist of contemporary and emerging nanotechnology-based theranostics and therapeutic approaches in reshaping intensive care unit

To maintain a clean and hygienic environment in the intensive care unit (ICU) is crucial for ensuring patient safety, preventing infections, and reducing healthcare-associated complications. With the increasing prevalence of infections and the emergence of viral and bacterial resistance to standard antiseptics, there is a pressing need for innovative antiseptic solutions. Nanotechnology is increasingly being employed in medicine, particularly focusing on mitigating the activities of various pathogens, including those associated with hospital-acquired infections. This paper explores the current impact of nanotechnology, with a particular focus on bacterial infections and SARS-CoV-2, which significantly strain healthcare systems, and then discusses how nanotechnology can enhance existing treatment methodologies. We highlight the effectiveness of the nanotechnology-based bactericide Bio-Kil in reducing bacterial counts in an ICU. The aim is to educate healthcare professionals on the existing role and prospects of nanotechnology in addressing prevalent infectious diseases.

The Global Burden of Sepsis and Septic Shock

A dysregulated host response to infection causes organ dysfunction in sepsis and septic shock, two potentially fatal diseases. They continue to be major worldwide health burdens with high rates of morbidity and mortality despite advancements in medical care. The goal of this thorough review was to present a thorough summary of the current body of knowledge about the prevalence of sepsis and septic shock worldwide. Using widely used computerized databases, a comprehensive search of the literature was carried out, and relevant studies were chosen in accordance with predetermined inclusion and exclusion criteria. A narrative technique was used to synthesize the data that were retrieved. The review's conclusions show how widely different locations and nations differ in terms of sepsis and septic shock's incidence, prevalence, and fatality rates. Compared to high-income countries (HICs), low- and middle-income countries (LMICs) are disproportionately burdened more heavily. We talk about risk factors, comorbidities, and difficulties in clinical management and diagnosis in a range of healthcare settings. The review highlights the need for more research, enhanced awareness, and context-specific interventions in order to successfully address the global burden of sepsis and septic shock.

Exploring biomarkers for diagnosing and predicting organ dysfunction in patients with perioperative sepsis: a preliminary investigation

OBJECTIVE

Early diagnosis and prediction of organ dysfunction are critical for intervening and improving the outcomes of septic patients. The study aimed to find novel diagnostic and predictive biomarkers of organ dysfunction for perioperative septic patients.

METHOD

This is a prospective, controlled, preliminary, and single-center study of emergency surgery patients. Mass spectrometry, Gene Ontology (GO) functional analysis, and the protein-protein interaction (PPI) network were performed to identify the differentially expressed proteins (DEPs) from sepsis patients, which were selected for further verification via enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was used to estimate the relative correlation of selected serum protein levels and clinical outcomes of septic patients. Calibration curves were plotted to assess the calibration of the models.

RESULTS

Five randomized serum samples per group were analyzed via mass spectrometry, and 146 DEPs were identified. GO functional analysis and the PPI network were performed to evaluate the molecular mechanisms of the DEPs. Six DEPs were selected for further verification via ELISA. Cathepsin B (CatB), vascular cell adhesion protein 1 (VCAM-1), neutrophil gelatinase-associated lipocalin (NGAL), protein S100-A9, prosaposin, and thrombospondin-1 levels were significantly increased in the patients with sepsis compared with those of the controls (p < 0.001). Logistic regression analysis showed that CatB, S100-A9, VCAM-1, prosaposin, and NGAL could be used for preoperative diagnosis and postoperative prediction of organ dysfunction. CatB and S100-A9 were possible predictive factors for preoperative diagnosis of renal failure in septic patients. Internal validation was assessed using the bootstrapping validation. The preoperative diagnosis of renal failure model displayed good discrimination with a C-index of 0.898 (95% confidence interval 0.843-0.954) and good calibration.

CONCLUSION

Serum CatB, S100-A9, VCAM-1, prosaposin, and NGAL may be novel markers for preoperative diagnosis and postoperative prediction of organ dysfunction. Specifically, S100-A9 and CatB were indicators of preoperative renal dysfunction in septic patients. Combining these two biomarkers may improve the accuracy of predicting preoperative septic renal dysfunction.

TRIAL REGISTRATION

The study was registered at the Chinese Clinical Trials Registry (ChiCTR2200060418) on June 1, 2022.

Risk factors of bloodstream infection in erythroderma from atopic dermatitis, psoriasis, and drug reactions: a retrospective observational cohort study

Background

Atopic dermatitis (AD), psoriasis, and drug reactions associated with erythroderma are frequently complicated by infections. However, bloodstream infection (BSI) have received less research attention.

Objectives

This study aimed to investigate the clinical characteristics and risk factors associated with BSI in patients with erythroderma.

Methods

A retrospective analysis was conducted on 141 erythroderma cases. Eleven cases were identified as having BSI. Clinical records of both BSI and non-BSI groups were reviewed and compared.

Results

BSI was diagnosed in 7.80% (11/141) of erythroderma cases, with a breakdown of 7.14% in AD, 2.00% in psoriasis, and 17.14% in drug reactions. Notably, all positive skin cultures (7/7) showed bacterial isolates concordant with blood cultures. Univariate logistic regression analysis revealed several significant associations with BSI, including temperature (≤36.0 or ≥38.5 °C; odds ratio (OR) = 28.06; p < 0.001), chilling (OR = 22.10; p < 0.001), kidney disease (OR = 14.64; p < 0.001), etiology of drug reactions (OR = 4.18; p = 0.03), albumin (ALB) (OR = 0.86; p < 0.01), C-reaction protein (CRP) (OR = 1.01; p = 0.02), interleukin 6 (IL-6) (OR = 1.02; p = 0.02), and procalcitonin (PCT) (OR = 1.07; p = 0.03). Receiver operating characteristic (ROC) curves demonstrated significant associations with ALB (p < 0.001; the area under curve (AUC) = 0.80), PCT (p = 0.009; AUC = 0.74), and CRP (p = 0.02; AUC = 0.71).

Conclusions

Increased awareness of BSI risk is essential in erythroderma management. Patients with specific risk factors, such as abnormal body temperature (≤36.0 or ≥38.5 °C), chilling sensations, kidney disease, a history of drug reactions, elevated CRP (≥32 mg/L), elevated PCT (≥1.00 ng/ml), and low albumin (≤31.0 g/L), require close monitoring for BSI development.

Prevalence, early predictors, and outcomes of sepsis in neurocritical illnesses: A prospective cohort study

BACKGROUND

Patients with neurocritically illness are an under-recognized population at high risk of sepsis. We aimed to investigate the prevalence, early predictors, and outcomes of sepsis in neuro-ICU.

METHODS

Daily and accumulative incidences of sepsis in neuro-ICU were explored. Demographics, medical history, baseline disease severity scores, and baseline biomarkers regarding inflammation, immunology, organ function, and nutritional status were collected and analyzed as potential predictors of sepsis. Logistic regression analyses were used to determine the independent predictors, and a nomogram was used to estimate the individual probability of sepsis in neuro-ICU.

RESULTS

153 patients were included in this study. Fifty-nine (38.6%) patients developed sepsis, and 21 (14%) patients developed septic shock. More than 86% of the septic cases occurred within the first week. Sequential organ failure assessment score ((relative risk) RR 1.334, P = .026), history of diabetes (RR 2.346, P = .049), and transferrin (RR 0.128, P = .042) on admission are independent predictors of sepsis. Septic patients had significantly higher mortality (P = .011), higher medical cost (P = .028), and a lower rate of functional independence (P = .010), compared to patients without sepsis.

CONCLUSIONS

Sepsis afflicted more than one-third of neurocritically-ill patients and occurred mostly in the first week of admission. History of diabetes, serum transferrin, and sequential organ failure assessment score on admission were early predictors. Sepsis led to significantly worse outcomes and higher medical costs.

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites

This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, particularly distinguishing between Escherichia coli (E. coli) and non-E. coli infections. Utilizing machine learning, we conducted a retrospective analysis of 119 elderly sepsis patients, employing a random forest model to evaluate clinical biomarkers and infection sites. The model demonstrated high diagnostic accuracy, with an overall accuracy of 87.5%, and impressive precision and recall rates of 93.3% and 87.5%, respectively. It identified infection sites, platelet distribution width, reduced platelet count, and procalcitonin levels as key predictors. The model achieved an F1 Score of 90.3% and an area under the receiver operating characteristic curve of 88.0%, effectively differentiating between sepsis subtypes. Similarly, logistic regression and least absolute shrinkage and selection operator analysis underscored the significance of infectious sites. This methodology shows promise for enhancing elderly sepsis diagnosis and contributing to the advancement of precision medicine in the field of infectious diseases.

Biomarkers Improve Diagnostics of Sepsis in Adult Patients With Suspected Organ Dysfunction Based on the Quick Sepsis-Related Organ Failure Assessment (qSOFA) Score in the Emergency Department

OBJECTIVES

Consensus regarding biomarkers for detection of infection-related organ dysfunction in the emergency department is lacking. We aimed to identify and validate biomarkers that could improve risk prediction for overt or incipient organ dysfunction when added to quick Sepsis-related Organ Failure Assessment (qSOFA) as a screening tool.

DESIGN

In a large prospective multicenter cohort of adult patients presenting to the emergency department with a qSOFA score greater than or equal to 1, admission plasma levels of C-reactive protein, procalcitonin, adrenomedullin (either bioavailable adrenomedullin or midregional fragment of proadrenomedullin), proenkephalin, and dipeptidyl peptidase 3 were assessed. Least absolute shrinkage and selection operator regression was applied to assess the impact of these biomarkers alone or in combination to detect the primary endpoint of prediction of sepsis within 96 hours of admission.

SETTING

Three tertiary emergency departments at German University Hospitals (Jena University Hospital and two sites of the Charité University Hospital, Berlin).

PATIENTS

One thousand four hundred seventy-seven adult patients presenting with suspected organ dysfunction based on qSOFA score greater than or equal to 1.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The cohort was of moderate severity with 81% presenting with qSOFA = 1; 29.2% of these patients developed sepsis. Procalcitonin outperformed all other biomarkers regarding the primary endpoint (area under the curve for receiver operating characteristic [AUC-ROC], 0.86 [0.79-0.93]). Adding other biomarkers failed to further improve the AUC-ROC for the primary endpoint; however, they improved the model regarding several secondary endpoints, such as mortality, need for vasopressors, or dialysis. Addition of procalcitonin with a cutoff level of 0.25 ng/mL improved net (re)classification by 35.2% compared with qSOFA alone, with positive and negative predictive values of 60.7% and 88.7%, respectively.

CONCLUSIONS

Biomarkers of infection and organ dysfunction, most notably procalcitonin, substantially improve early prediction of sepsis with added value to qSOFA alone as a simple screening tool on emergency department admission.

MicroRNA Expression Profile in Patients Admitted to ICU as Novel and Reliable Approach for Diagnostic and Therapeutic Purposes

The ability to detect early metabolic changes in patients who have an increased mortality risk in the intensive care units (ICUs) could increase the likelihood of predicting recovery patterns and assist in disease management. Markers that can predict the disease progression of patients in the ICU might also be beneficial for improving their medical profile. Although biomarkers have been used in the ICU more frequently in recent years, the clinical use of most of them is limited. A wide range of biological processes are influenced by microRNAs (miRNAs) that modulate the translation and stability of specific mRNAs. Studies suggest that miRNAs may serve as a diagnostic and therapeutic biomarker in ICUs by profiling miRNA dysregulation in patient samples. To improve the predictive value of biomarkers for ICU patients, researchers have proposed both investigating miRNAs as novel biomarkers and combining them with other clinical biomarkers. Herein, we discuss recent approaches to the diagnosis and prognosis of patients admitted to an ICU, highlighting the use of miRNAs as novel and robust biomarkers for this purpose. In addition, we discuss emerging approaches to biomarker development and ways to improve the quality of biomarkers so that patients in ICU get the best outcomes possible.

A Focus on the Pathophysiology of Adrenomedullin Expression: Endothelitis and Organ Damage in Severe Viral and Bacterial Infections

Adrenomedullin (ADM) is a peptide hormone produced primarily in the adrenal glands, playing a crucial role in various physiological processes. As well as improving vascular integrity and decreasing vascular permeability, ADM acts as a vasodilator, positive inotrope, diuretic, natriuretic and bronchodilator, antagonizing angiotensin II by inhibiting aldosterone secretion. ADM also has antihypertrophic, anti-apoptotic, antifibrotic, antioxidant, angiogenic and immunoregulatory effects and antimicrobial properties. ADM expression is upregulated by hypoxia, inflammation-inducing cytokines, viral or bacterial substances, strength of shear stress, and leakage of blood vessels. These pathological conditions are established during systemic inflammation that can result from infections, surgery, trauma/accidents or burns. The ability to rapidly identify infections and the prognostic, predictive power makes it a valuable tool in severe viral and bacterial infections burdened by high incidence and mortality. This review sheds light on the pathophysiological processes that in severe viral or bacterial infections cause endothelitis up to the development of organ damage, the resulting increase in ADM levels dosed through its more stable peptide mid-regional proadrenomedullin (MR-proADM), the most significant studies that attest to its diagnostic and prognostic accuracy in highlighting the severity of viral or bacterial infections and appropriate therapeutic insights.

Clinical usefulness of NT-proBNP as a prognostic factor for septic shock patients presenting to the emergency department

Plasma N-terminal prohormone of brain natriuretic peptide (NT-proBNP) level is primarily used as a biomarker for left ventricular (LV) dysfunction. It is influenced by various conditions, such as myocardial strain and situations affecting the clearance of NT-proBNP, including sepsis and shock. In this study, we investigated the appropriateness of NT-proBNP as a prognostic factor for septic shock. Patients with septic shock who visited the emergency department of the Ewha Womans' University Mokdong Hospital between January 1, 2018, and December 31, 2020, were classified into the survival group (those who survived in the hospital and were discharged) and the death group (those who died in the hospital). The effectiveness of NT-proBNP, lactate, and blood urea nitrogen as predictive factors of in-hospital mortality was evaluated using the area under the receiver operating characteristic (AUROC) curve. The AUROC curve was 0.678 and 0.648 for lactate and NT-proBNP, respectively, with lactate showing the highest value. However, there was no significant difference between lactate and NT-proBNP levels in the comparison of their AUROC curve (p = 0.6278). NT-proBNP could be a useful predictor of in-hospital mortality in patients with septic shock who present to the emergency department.

Breath of Health: spectroscopy-based breath test for the detection of SARS-CoV-2

BACKGROUND

Nucleic acid amplification tests (NAAT) are considered the gold standard for COVID-19 diagnosis. These tests require professional manpower and equipment, long processing and swab sampling which is unpleasant to the patients. Several volatile organic compounds (VOCs) have been identified in the breath of COVID-19 patients. Detection of these VOCs using a breath test could help rapidly identify COVID-19 patients.

OBJECTIVE

Assess the accuracy of 'Breath of Health' (BOH) COVID-19 Fourier-transform infra-red (FTIR) Spectroscopy-based breath test.

METHODS

Breath samples from patients with or without symptoms suggestive for COVID-19 who had NAAT results were collected using Tedlar bags and were blindly analysed using BOH FTIR spectroscopy. BOH Measures several VOCs simultaneously and differentiating positive and negative results. BOH results were compared to NAAT results as gold standard.

RESULTS

Breath samples from 531 patients were analysed. The sensitivity of BOH breath test was found to be 79.5% and specificity was 87.2%. Positive predictive value (PPV) was 74.7% and negative predictive value (NPV) 90.0%. Calculated accuracy rate was 84.8% and area under the curve 0.834. Subgroup analysis revealed that the NPV of patients without respiratory symptoms was superior over the NPV of symptomatic patients (94.7% vs 80.7%, P-value < 0.0001) and PPV of patients with respiratory symptoms outranks the PPV of individuals without symptoms (85.3% vs 69.2%, P-value 0.0196).

CONCLUSION

We found BOH COVID-19 breath test to be a patient-friendly, rapid, non-invasive diagnostic test with high accuracy rate and NPV that could efficiently rule out COVID-19 especially among individuals with low pre-test probability.

Cell-Free Nuclear and Mitochondrial DNA as Potential Biomarkers for Assessing Sepsis Severity

Sepsis continues to be a significant public health challenge despite advances in understanding its pathophysiology and management strategies. Therefore, this study evaluated the value of cell-free nuclear DNA (cf-nDNA) and cell-free mitochondrial DNA (cf-mtDNA) for assessing the severity and prognosis of sepsis. Ninety-four patients were divided into three groups: infection (n = 32), sepsis (n = 30), and septic shock (n = 32). Plasma samples were collected at the time of diagnosis, and cfDNA concentrations were determined by qPCR assay. The results showed that plasma cfDNA levels increased with the severity of the disease. To distinguish between patients with infection and those with sepsis, the biomarker L1PA290 achieved the highest AUC of 0.817 (95% CI: 0.725-0.909), demonstrating a sensitivity of 77.0% and a specificity of 79.3%. When cf-nDNA was combined with the SOFA score, there was a significant improvement in the AUC (0.916 (0.853-0.979)), sensitivity (88.1%), and specificity (80.0%). Moreover, patients admitted to the ICU after being diagnosed with sepsis had significantly higher cf-nDNA concentrations. In patients admitted to the ICU, combining cf-nDNA with the SOFA score yielded an AUC of 0.753 (0.622-0.857), with a sensitivity of 95.2% and a specificity of 50.0%. cfDNA can differentiate between patients with infection and those with sepsis. It can also identify patients who are likely to be admitted to the ICU by predicting those with indications for intensive care, suggesting its potential as a biomarker for sepsis.

Cathepsin S Is More Abundant in Serum of Mycobacterium avium subsp. paratuberculosis-Infected Dairy Cows

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of bovine paratuberculosis, a chronic granulomatous enteritis leading to economic losses and posing a risk to human health due to its zoonotic potential. The pathogen cannot reliably be detected by standard methods, and immunological procedures during the infection are not well understood. Therefore, the aim of our study was to explore host-pathogen interactions in MAP-infected dairy cows and to improve diagnostic tests. Serum proteomics analysis using quantitative label-free LC-MS/MS revealed 60 differentially abundant proteins in MAP-infected dairy cows compared to healthy controls from the same infected herd and 90 differentially abundant proteins in comparison to another control group from an uninfected herd. Pathway enrichment analysis provided new insights into the immune response to MAP and susceptibility to the infection. Furthermore, we found a higher abundance of Cathepsin S (CTSS) in the serum of MAP-infected dairy cows, which is involved in multiple enriched pathways associated with the immune system. Confirmed with Western blotting, we identified CTSS as a potential biomarker for bovine paratuberculosis. This study enabled a better understanding of procedures in the host-pathogen response to MAP and improved detection of paratuberculosis-diseased cattle.

Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study

PURPOSE

To prospectively validate, in an ICU setting, the prognostic accuracy of the sepsis prediction algorithm NAVOY® Sepsis which uses 4 h of input for routinely collected vital parameters, blood gas values, and lab values.

MATERIALS AND METHODS

Patients 18 years or older admitted to the ICU at Skåne University Hospital Malmö from December 2020 to September 2021 were recruited in the study. A total of 304 patients were randomized into one of two groups: Algorithm group with active sepsis alerts, or Standard of care. NAVOY® Sepsis made silent predictions in the Standard of care group, in order to evaluate its performance without disturbing the outcome. The study was blinded, i.e., study personnel did not know to which group patients were randomized. The healthcare provider followed standard practices in assessing possible development of sepsis and intervening accordingly. The patients were followed-up in the study until ICU discharge.

RESULTS

NAVOY® Sepsis could predict the development of sepsis, according to the Sepsis-3 criteria, three hours before sepsis onset with high performance: accuracy 0.79; sensitivity 0.80; and specificity 0.78.

CONCLUSIONS

The accuracy, sensitivity, and specificity were all high, validating the prognostic accuracy of NAVOY® Sepsis in an ICU setting, including Covid-19 patients.

A novel group decision making method based on CoCoSo and interval-valued Q-rung orthopair fuzzy sets

Interval-valued q-rung orthopair fuzzy set (IVq-ROFS) is a powerful tool for dealing with uncertainty. In this paper, we first propose a new method for aggregating multiple IVq-ROFSs, which is easier to understand and implement in the multi-attribute group decision making process compared to current aggregation operators. Secondly, this paper introduces a new fuzzy entropy with parameters based on IVq-ROFS, which is highly flexible due to its adjustable parameters. Based on this, the IVq-ROFS-based attribute weight calculation method is proposed to obtain the objective weights of the attributes, which is more reasonable and objective than the existing methods. Then, for the dimensional differences between the three compromise scores in the original Combined Compromise Solution (CoCoSo) method, the enhanced compromise scores are proposed. These scores are obtained by normalizing the three dependent compromise scores, ensuring that they fall within the same range. Finally, a novel CoCoSo mothed on IVq-ROFS using the proposed fuzzy entropy and enhanced compromise scores is presented. The proposed method is highly adaptable and scalable, not limited to IVq-ROFS. The excellent performance and robustness of the proposed method are verified in sepsis diagnosis applications.

Defining sepsis in small animals

OBJECTIVE

To discuss the definitions of sepsis in human and veterinary medicine.

DESIGN

International, multicenter position statement on the need for consensus definitions of sepsis in veterinary medicine.

SETTING

Veterinary private practice and university teaching hospitals.

ANIMALS

Dogs and cats.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Sepsis is a life-threatening condition associated with the body's response to an infection. In human medicine, sepsis has been defined by consensus on 3 occasions, most recently in 2016. In veterinary medicine, there is little uniformity in how sepsis is defined and no consensus on how to identify it clinically. Most publications rely on modified criteria derived from the 1991 and 2001 human consensus definitions. There is a divergence between the human and veterinary descriptions of sepsis and no consensus on how to diagnose the syndrome. This impedes research, hampers the translation of pathophysiology insights to the clinic, and limits our abilities to optimize patient care. It may be time to formally define sepsis in veterinary medicine to help the field move forward. In this narrative review, we present a synopsis of prior attempts to define sepsis in human and veterinary medicine, discuss developments in our understanding, and highlight some criticisms and shortcomings of existing schemes.

CONCLUSIONS

This review is intended to serve as the foundation of current efforts to establish a consensus definition for sepsis in small animals and ultimately generate evidence-based criteria for its recognition in veterinary clinical practice.

Ultrasensitive and Rapid Detection of Procalcitonin via Waveguide-Enhanced Nanogold-Linked Immunosorbent Assay for Early Sepsis Diagnosis

Sepsis, a life-threatening inflammatory response, demands economical, accurate, and rapid detection of biomarkers during the critical "golden hour" to reduce the patient mortality rate. Here, we demonstrate a cost-effective waveguide-enhanced nanogold-linked immunosorbent assay (WENLISA) based on nanoplasmonic waveguide biosensors for the rapid and sensitive detection of procalcitonin (PCT), a sepsis-related inflammatory biomarker. To enhance the limit of detection (LOD), we employed sandwich assays using immobilized capture antibodies and detection antibodies conjugated to gold nanoparticles to bind the target analyte, leading to a significant evanescent wave redistribution and strong nanoplasmonic absorption near the waveguide surface. Experimentally, we detected PCT for a wide linear response range of 0.1 pg/mL to 1 ng/mL with a record-low LOD of 48.7 fg/mL (3.74 fM) in 8 min. Furthermore, WENLISA has successfully identified PCT levels in the blood plasma of patients with sepsis and healthy individuals, offering a promising technology for early sepsis diagnosis.

Microfluidic systems for infectious disease diagnostics

Microorganisms, encompassing both uni- and multicellular entities, exhibit remarkable diversity as omnipresent life forms in nature. They play a pivotal role by supplying essential components for sustaining biological processes across diverse ecosystems, including higher host organisms. The complex interactions within the human gut microbiota are crucial for metabolic functions, immune responses, and biochemical signalling, particularly through the gut-brain axis. Viruses also play important roles in biological processes, for example by increasing genetic diversity through horizontal gene transfer when replicating inside living cells. On the other hand, infection of the human body by microbiological agents may lead to severe physiological disorders and diseases. Infectious diseases pose a significant burden on global healthcare systems, characterized by substantial variations in the epidemiological landscape. Fast spreading antibiotic resistance or uncontrolled outbreaks of communicable diseases are major challenges at present. Furthermore, delivering field-proven point-of-care diagnostic tools to the most severely affected populations in low-resource settings is particularly important and challenging. New paradigms and technological approaches enabling rapid and informed disease management need to be implemented. In this respect, infectious disease diagnostics taking advantage of microfluidic systems combined with integrated biosensor-based pathogen detection offers a host of innovative and promising solutions. In this review, we aim to outline recent activities and progress in the development of microfluidic diagnostic tools. Our literature research mainly covers the last 5 years. We will follow a classification scheme based on the human body systems primarily involved at the clinical level or on specific pathogen transmission modes. Important diseases, such as tuberculosis and malaria, will be addressed more extensively.

Presepsin is a more useful predictor of septic AKI and ARDS for very-old sepsis patients than for young sepsis patients in ICUs: a pilot study

OBJECTIVE

Sepsis is a syndrome of life-threatening organ dysfunction. This study aimed to determine whether presepsin is a useful predictor of septic acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), and shock in very-old sepsis patients aged 75 years in intensive care units (ICUs).

RESULTS

A total of 83 adult patients diagnosed with sepsis were prospectively examined and divided into two groups: those aged 75 years and older (over 75 group) and those aged younger than 75 years (under 75 group). Presepsin values were measured after ICU admission. Inflammation-based prognostic scores were also examined. For category classification, total scores ("inflammation-presepsin scores [iPS]") were calculated. Presepsin values, inflammation-based prognostic scores, and iPS were compared between patients with septic AKI, ARDS, DIC, or shock and those without these disorders in the over 75 and under 75 groups. Areas under the curve of presepsin for predicting septic AKI and ARDS in the over 75 group were both > 0.7, which were significantly higher than those in the under 75 group. In conclusion, presepsin is a more useful predictor of septic AKI and ARDS for very-old sepsis patients (over 75 years) than for younger sepsis patients (under 75 years).

Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis

Background

Multiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear.

Method

We screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC).

Results

In sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793-0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues.

Conclusions

Cuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

AZU1: a new promising marker for infection in orthopedic and trauma patients?

Early and reliable detection of infection is vital for successful treatment. Serum markers such as C-reactive protein (CRP) and procalcitonin (PCT) are known to increase with a time lag. Azurocidin 1 (AZU1) has emerged as a promising marker for septic patients, but its diagnostic value in orthopedic and trauma patients remains unexplored. Between July 2020 and August 2023, all patients necessitating inpatient treatment for periprosthetic joint infection (PJI), peri-implant infection (II), soft tissue infection, chronic osteomyelitis, septic arthrodesis, bone non-union with and without infection were enrolled. Patients undergoing elective total joint arthroplasty (TJA) served as the control group. Blood samples were collected and analyzed for CRP, white blood cell count (WBC), PCT, and AZU1. Based on the inclusion and exclusion criteria 222 patients were included in the study (trauma = 38, soft tissue infection = 75, TJA = 33, PJI/II = 39, others = 37). While sensitivity and specificity were comparably high for AZU1 (0.734/0.833), CRP and PCT had higher specificity (0.542/1 and 0.431/1, respectively), and WBC a slightly higher sensitivity (0.814/0.455) for septic conditions. Taken together, the area under the curve (AUC) showed the highest accuracy for AZU1 (0.790), followed by CRP (0.776), WBC (0.641), and PCT (0.656). The Youden-Index was 0.57 for AZU1, 0.54 for CRP, 0.27 for WBC, and 0.43 for PCT. Elevated AZU1 levels effectively distinguished patients with a healthy condition from those suffering from infection. However, there is evidence suggesting that trauma may influence the release of AZU1. Additional research is needed to validate the diagnostic value of this new biomarker and further explore its potential clinical applications.

BIOINFORMATICS APPLICATIONS UNDER CONDITION CONTROL: HIGH DIAGNOSTIC VALUE OF DDX47 IN REAL MEDICAL SETTINGS

ABSTRACT

Sepsis is an organ dysfunction caused by a dysregulated host response to infection and remains an ongoing threat to human health worldwide. Septic shock is the most severe subset of sepsis as characterized by abnormalities in cells, circulation, and metabolism. As a time-dependent condition, early recognition allowing appropriate therapeutic measures to be started in a timely manner becomes the most effective way to improve prognosis. However, because of the lack of a criterion standard, most diagnoses merely rely on medical history, empirical diagnosis, and blood culture results. Gene expression profiles have specific diagnostic value, as they reflect a subjective host response to pathogens. We propose a method, Condition Control based on Real-life Medical Scenarios, to control for factors in realistic medical scenarios. Restricted variables are used as much as possible to identify unique differential genes and progressively test their diagnostic value by relaxing restrictions. In total, three data sets were included in the study; the first two data sets were from the Gene Expression Omnibus database, and the third involved patients who were diagnosed with sepsis or septic shock within 7 days of admission to the intensive care unit at West China Hospital of Sichuan University from 2020 to 2021. DDX47 showed preferable diagnostic value in various scenarios, especially in patients with common infections or sepsis and septic shock. Here we also show that hub genes may regulate immune function and immune cell counts through the interaction of different apoptotic pathways and immune checkpoints based on the high correlation. DDX47 is closely associated with B cells according to single-cell sequencing results.

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

OBJECTIVES

To identify cytokine signature clusters in patients with septic shock.

DESIGN

Prospective observational cohort study.

SETTING

Single academic center in the United States.

PATIENTS

Adult (≥ 18 yr old) patients admitted to the medical ICU with septic shock requiring vasoactive medication support.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred fourteen patients with septic shock completed cytokine measurement at time of enrollment (t 1 ) and 24 hours later (t 2 ). Unsupervised random forest analysis of the change in cytokines over time, defined as delta (t 2 -t 1 ), identified three clusters with distinct cytokine profiles. Patients in cluster 1 had the lowest initial levels of circulating cytokines that decreased over time. Patients in cluster 2 and cluster 3 had higher initial levels that decreased over time in cluster 2 and increased in cluster 3. Patients in clusters 2 and 3 had higher mortality compared with cluster 1 (clusters 1-3: 11% vs 31%; odds ratio [OR], 3.56 [1.10-14.23] vs 54% OR, 9.23 [2.89-37.22]). Cluster 3 was independently associated with in-hospital mortality (hazard ratio, 5.24; p = 0.005) in multivariable analysis. There were no significant differences in initial clinical severity scoring or steroid use between the clusters. Analysis of either t 1 or t 2 cytokine measurements alone or in combination did not reveal clusters with clear clinical significance.

CONCLUSIONS

Longitudinal measurement of cytokine profiles at initiation of vasoactive medications and 24 hours later revealed three distinct cytokine signature clusters that correlated with clinical outcomes.

Patterns of Circulating Cytokines and Vascular Markers' Response in the Presence of COVID-19 in Kidney Transplant Recipients Compared with Non-Transplanted Patients

COVID-19's severity has been associated with a possible imbalance in the cross-regulation of cytokines and vascular mediators. Since the beginning of the pandemic, kidney transplant recipients (KTRs) have been identified as patients of high vulnerability to more severe diseases. Thus, aiming to describe the patterns of cytokines and vascular mediators and to trace patients' differences according to their KTR status, this prospective study enrolled 67 COVID-19 patients (20 KTRs) and 29 non-COVID-19 controls before vaccination. A panel comprising 17 circulating cytokines and vascular mediators was run on samples collected at different time points. The cytokine and mediator patterns were investigated via principal component analysis (PCA) and correlation-based network (CBN). In both groups, compared to their respective controls, COVID-19 was associated with higher levels of cytokines and vascular mediators. Differentiating between the KTRs and non-KTRs, the number of correlations was much higher in the non-KTRs (44 vs. 14), and the node analysis showed the highest interactions of NGAL and sVCAM-1 in the non-KTRs and KTRs (9 vs. 4), respectively. In the PCA, while the non-KTRs with COVID-19 were differentiated from their controls in their IL-10, IFN-α, and TNF-α, this pattern was marked in the NGAL, sVCAM-1, and IL-8 of the KTRs.

Substantial heterogeneity of inflammatory cytokine production and its inhibition by a triple cocktail of toll-like receptor blockers in early sepsis

Introduction

Early sepsis is a life-threatening immune dysregulation believed to feature a "cytokine storm" due to activation of pattern recognition receptors by pathogen and danger associated molecular patterns. However, treatments with single toll-like receptor (TLR) blockers have shown no clinical benefit. We speculated that sepsis patients at the time of diagnosis are heterogeneous in relation to their cytokine production and its potential inhibition by a triple cocktail of TLR blockers. Accordingly, we analyzed inflammatory cytokine production in whole blood assays from early sepsis patients and determined the effects of triple TLR-blockade.

Methods

Whole blood of 51 intensive care patients sampled within 24h of meeting Sepsis-3 criteria was incubated for 6h without or with specific TLR2, 4, and 7/8 stimuli or suspensions of heat-killed S. aureus or E. coli bacteria as pan-TLR challenges, and also with a combination of monoclonal antibodies against TLR2 and 4 and chloroquine (endosomal TLR inhibition), subsequent to dose optimization. Concentrations of tumor necrosis factor (TNF), Interleukin(IL)-6, IL-8, IL-10, IL-1α and IL-1β were measured (multiplex ELISA) before and after incubation. Samples from 11 sex and age-matched healthy volunteers served as controls and for dose-finding studies.

Results

Only a fraction of sepsis patient samples revealed ongoing cytokine production ex vivo despite sampling within 24 h of first meeting Sepsis-3 criteria. In dose finding studies, inhibition of TLR2, 4 and endosomal TLRs reliably suppressed cytokine production to specific TLR agonists and added bacteria. However, inflammatory cytokine production ex vivo was only suppressed in the high cytokine producing samples but not in the majority. The suppressive response to TLR-blockade correlated both with intraassay inflammatory cytokine production (r=0.29-0.68; p<0.0001-0.04) and cytokine baseline concentrations (r=0.55; p<0.0001).

Discussion

Upon meeting Sepsis-3 criteria for less than 24 h, a mere quarter of patient samples exhibits a strong inflammatory phenotype, as characterized by increased baseline inflammatory cytokine concentrations and a stark TLR-dependent increase upon further ex vivo incubation. Thus, early sepsis patient cohorts as defined by Sepsis-3 criteria are very heterogeneous in regard to inflammation. Accordingly, proper ex vivo assays may be useful in septic individuals before embarking on immunomodulatory treatments.

Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis

Purpose

Sepsis is one of the most common causes of death after surgery. Several conventional scoring systems have been developed to predict the outcome of sepsis; however, their predictive power is insufficient. The present study applies explainable machine-learning algorithms to improve the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.

Methods

We performed a retrospective analysis of data from demographic, clinical, and laboratory analyses, including the delta neutrophil index (DNI), WBC and neutrophil counts, and CRP level. Laboratory data were measured before surgery, 12-36 hours after surgery, and 60-84 hours after surgery. The primary study output was the probability of mortality. The areas under the receiver operating characteristic curves (AUCs) of several machine-learning algorithms using the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS) 3 models were compared. 'SHapley Additive exPlanations' values were used to indicate the direction of the relationship between a variable and mortality.

Results

The CatBoost model yielded the highest AUC (0.933) for mortality compared to SAPS3 and SOFA (0.860 and 0.867, respectively). Increased DNI on day 3, septic shock, use of norepinephrine therapy, and increased international normalized ratio on day 3 had the greatest impact on the model's prediction of mortality.

Conclusion

Machine-learning algorithms increase the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.

Evaluation of blood serum iron concentration as an alternative biomarker for inflammation in dairy cows

This study aimed to clarify the relationship between acute phase protein (APP) concentrations and serum Fe concentrations to determine whether serum iron (Fe) can be clinically applied as a substitute for APPs in cows. One hundred five Holstein-Friesian breed lactating dairy cows were enrolled in this study. Cows with inflammatory diseases were 16 subclinical, and 15 severe mastitis cows, in addition to 15 mild and 16 severe sole ulcer cows. The plasma haptoglobin (HPT), alpha-1 acid glycoprotein (AGP), SAA, serum Fe levels, and other biochemical parameters in the cows were measured. The two-sample t-tests and multiple logistic regression analysis were used to compare the control and inflammatory disease groups. ROC analysis was used to evaluate the ability to diagnose inflammation disease. From the results, the proposed diagnostic cutoff value for plasma SAA and serum Fe concentrations to identify dairy cows with inflammatory diseases based on analyses of ROC curves were set at > 3.65 mg/l and < 120.50 µg/dl, respectively. Therefore, instead of using expensive inflammatory markers to evaluate the inflammatory state at the first treatment day for inflammatory diseases in cow, it shows the useful for screening with serum Fe concentration that can be measured easily and inexpensively as alternative inflammatory biomarkers.

Current knowledge of thrombocytopenia in sepsis and COVID-19

Thrombocytopenia, characterized by a decrease in platelet count, is commonly observed in sepsis and COVID-19. In sepsis, thrombocytopenia can result from various mechanisms, including impaired platelet production in the bone marrow, accelerated platelet destruction due to increased inflammation, sequestration of platelets in the spleen, immune-mediated platelet destruction, or dysregulated host responses. Similarly, thrombocytopenia has been reported in COVID-19 patients, but the immune-related mechanisms underlying this association remain unclear. Notably, interventions targeting thrombocytopenia have shown potential for improving outcomes in both sepsis and COVID-19 patients. Understanding these mechanisms is crucial for developing effective treatments.

Persistence is key: unresolved immune dysfunction is lethal in both COVID-19 and non-COVID-19 sepsis

Introduction

Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features, suggesting that severe COVID-19 is a form of viral sepsis. Our objective was to identify shared gene expression trajectories strongly associated with eventual mortality between severe COVID-19 patients and contemporaneous non-COVID-19 sepsis patients in the intensive care unit (ICU) for potential therapeutic implications.

Methods

Whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Using systems biology methods, drug candidates targeting key genes in the pathophysiology of COVID-19 and sepsis were identified.

Results

When compared to survivors, non-survivors (irrespective of COVID-19 status) had 3.6-fold more "persistent" genes (genes that stayed up/downregulated at both timepoints) (4,289 vs. 1,186 genes); these included persistently downregulated genes in T-cell signaling and persistently upregulated genes in select innate immune and metabolic pathways, indicating unresolved immune dysfunction in non-survivors, while resolution of these processes occurred in survivors. These findings of persistence were further confirmed using two publicly available datasets of COVID-19 and sepsis patients. Systems biology methods identified multiple immunomodulatory drug candidates that could target this persistent immune dysfunction, which could be repurposed for possible therapeutic use in both COVID-19 and sepsis.

Discussion

Transcriptional evidence of persistent immune dysfunction was associated with 28-day mortality in both COVID-19 and non-COVID-19 septic patients. These findings highlight the opportunity for mitigating common mechanisms of immune dysfunction with immunomodulatory therapies for both diseases.

Predicting sepsis using a combination of clinical information and molecular immune markers sampled in the ambulance

Sepsis is a time dependent condition. Screening tools based on clinical parameters have been shown to increase the identification of sepsis. The aim of current study was to evaluate the additional predictive value of immunological molecular markers to our previously developed prehospital screening tools. This is a prospective cohort study of 551 adult patients with suspected infection in the ambulance setting of Stockholm, Sweden between 2017 and 2018. Initially, 74 molecules and 15 genes related to inflammation were evaluated in a screening cohort of 46 patients with outcome sepsis and 50 patients with outcome infection no sepsis. Next, 12 selected molecules, as potentially synergistic predictors, were evaluated in combination with our previously developed screening tools based on clinical parameters in a prediction cohort (n = 455). Seven different algorithms with nested cross-validation were used in the machine learning of the prediction models. Model performances were compared using posterior distributions of average area under the receiver operating characteristic (ROC) curve (AUC) and difference in AUCs. Model variable importance was assessed by permutation of variable values, scoring loss of classification as metric and with model-specific weights when applicable. When comparing the screening tools with and without added molecular variables, and their interactions, the molecules per se did not increase the predictive values. Prediction models based on the molecular variables alone showed a performance in terms of AUCs between 0.65 and 0.70. Among the molecular variables, IL-1Ra, IL-17A, CCL19, CX3CL1 and TNF were significantly higher in septic patients compared to the infection non-sepsis group. Combing immunological molecular markers with clinical parameters did not increase the predictive values of the screening tools, most likely due to the high multicollinearity of temperature and some of the markers. A group of sepsis patients was consistently miss-classified in our prediction models, due to milder symptoms as well as lower expression levels of the investigated immune mediators. This indicates a need of stratifying septic patients with a priori knowledge of certain clinical and molecular parameters in order to improve prediction for early sepsis diagnosis.Trial registration: NCT03249597. Registered 15 August 2017.

Biomarkers in the intensive care setting: A focus on using procalcitonin and C-reactive protein to optimize antimicrobial duration of therapy

Managing the critically ill patient with infection is complex, requiring clinicians to synthesize considerable information relating to antimicrobial efficacy and treatment duration. The use of biomarkers may play an important role in identifying variation in treatment response and providing information about treatment efficacy. Though a vast number of biomarkers for clinical application have been described, procalcitonin and C-reactive protein (CRP) are the most thoroughly investigated in the critically ill. However, the presence of heterogeneous populations, variable end points, and incongruent methodology in the literature complicates the use of such biomarkers to guide antimicrobial therapy. This review focuses on an appraisal of evidence for use of procalcitonin and CRP to optimize antimicrobial duration of therapy (DOT) in critically ill patients. Procalcitonin-guided antimicrobial therapy in mixed critically ill populations with varying degrees of sepsis appears to be safe and might assist in reducing antimicrobial DOT. Compared to procalcitonin, fewer studies exist examining the impact of CRP on antimicrobial DOT and clinical outcomes in the critically ill. Procalcitonin and CRP have been insufficiently studied in many key intensive care unit populations, including surgical patients with concomitant trauma, renally insufficient populations, the immunocompromised, and patients with septic shock. We believe the available evidence is not strong enough to warrant routine use of procalcitonin or CRP to guide antimicrobial DOT in critically ill patients with infection. So long as its limitations are recognized, procalcitonin could be considered to tailor antimicrobial DOT on a case-by-case basis in the critically ill patient.

Point-of-care pancreatic stone protein measurement in critically ill COVID-19 patients

INTRODUCTION

Pancreatic stone protein (PSP) is a novel biomarker that is reported to be increased in pneumonia and acute conditions. The primary aim of this study was to prospectively study plasma levels of PSP in a COVID-19 intensive care unit (ICU) population to determine how well PSP performed as a marker of mortality in comparison to other plasma biomarkers, such as C reactive protein (CRP) and procalcitonin (PCT).

METHODS

We collected clinical data and blood samples from COVID-19 ICU patients at the time of admission (T0), 72 h later (T1), five days later (T2), and finally, seven days later. The PSP plasma level was measured with a point-of-care system; PCT and CRP levels were measured simultaneously with laboratory tests. The inclusion criteria were being a critical COVID-19 ICU patient requiring ventilatory mechanical assistance.

RESULTS

We enrolled 21 patients and evaluated 80 blood samples; we found an increase in PSP plasma levels according to mixed model analysis over time (p < 0.001), with higher levels found in the nonsurvivor population (p < 0.001). Plasma PSP levels achieved a statistically significant result in terms of the AUROC, with a value higher than 0.7 at T0, T1, T2, and T3. The overall AUROC of PSP was 0.8271 (CI (0.73-0.93), p < 0.001). These results were not observed for CRP and PCT.

CONCLUSION

These first results suggest the potential advantages of monitoring PSP plasma levels through point-of-care technology, which could be useful in the absence of a specific COVID-19 biomarker. Additional data are needed to confirm these results.

Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis

: Although early recognition of sepsis is essential for timely treatment and can improve sepsis outcomes, no marker has demonstrated sufficient discriminatory power to diagnose sepsis. This study aimed to compare gene expression profiles between patients with sepsis and healthy volunteers to determine the accuracy of these profiles in diagnosing sepsis and to predict sepsis outcomes by combining bioinformatics data with molecular experiments and clinical information. We identified 422 differentially expressed genes (DEGs) between the sepsis and control groups, of which 93 immune-related DEGs were considered for further studies due to immune-related pathways being the most highly enriched. Key genes upregulated during sepsis, including S100A8, S100A9, and CR1, are responsible for cell cycle regulation and immune responses. Key downregulated genes, including CD79A, HLA-DQB2, PLD4, and CCR7, are responsible for immune responses. Furthermore, the key upregulated genes showed excellent to fair accuracy in diagnosing sepsis (area under the curve 0.747-0.931) and predicting in-hospital mortality (0.863-0.966) of patients with sepsis. In contrast, the key downregulated genes showed excellent accuracy in predicting mortality of patients with sepsis (0.918-0.961) but failed to effectively diagnosis sepsis.In conclusion, bioinformatics analysis identified key genes that may serve as biomarkers for diagnosing sepsis and predicting outcomes among patients with sepsis.

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets

Background

Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq.

Objective

Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis.

Methods

PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.).

Results

After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca).

Conclusions

SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

Low-cost, facile droplet modification of screen-printed arrays for internally validated electrochemical detection of serum procalcitonin

This manuscript presents the design and facile production of screen-printed arrays (SPAs) for the internally validated determination of raised levels of serum procalcitonin (PCT). The screen-printing methodology produced SPAs with six individual working electrodes that exhibit an inter-array reproducibility of 3.64% and 5.51% for the electrochemically active surface area and heterogenous electrochemical rate constant respectively. The SPAs were modified with antibodies specific for the detection of PCT through a facile methodology, where each stage simply uses droplets incubated on the surface, allowing for their mass-production. This platform was used for the detection of PCT, achieving a linear dynamic range between 1 and 10 ng mL^-1 with a sensor sensitivity of 1.35 × 10^-10 NIC%/ng mL^-1. The SPA produced an intra- and inter-day %RSD of 4.00 and 5.05%, with a material cost of £1.14. Internally validated human serum results (3 sample measurements, 3 control) for raised levels of PCT (>2 ng mL^-1) were obtained, with no interference effects seen from CRP and IL-6. This SPA platform has the potential to offer clinicians vital information to rapidly begin treatment for "query sepsis" patients while awaiting results from more lengthy remote laboratory testing methods. Analytical ranges tested make this an ideal approach for rapid testing in specific patient populations (such as neonates or critically ill patients) in which PCT ranges are inherently wider. Due to the facile modification methods, we predict this could be used for various analytes on a single array, or the array increased further to maintain the internal validation of the system.

Point-of-care diagnostics for sepsis using clinical biomarkers and microfluidic technology

Sepsis is a life-threatening immune response which is caused by a wide variety of sources and is a leading cause of mortality globally. Rapid diagnosis and appropriate antibiotic treatment are critical for successful patient outcomes; however, current molecular diagnostic techniques are time-consuming, costly and require trained personnel. Additionally, there is a lack of rapid point-of-care (POC) devices available for sepsis detection despite the urgent requirements in emergency departments and low-resource areas. Recent advances have been made toward developing a POC test for early sepsis detection that will be more rapid and accurate compared to conventional techniques. Within this context, this review discusses the use of current and novel biomarkers for early sepsis diagnosis using microfluidics devices for POC testing.

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.

DECREASE IN THE PLATELET-TO-LYMPHOCYTE RATIO IN DAYS AFTER ADMISSION FOR SEPSIS CORRELATES WITH IN-HOSPITAL MORTALITY

ABSTRACT

Background: A previous study has linked an increase in platelet-to-lymphocyte ratio (PLR) to a poor prognosis; however, the relationship between early change in PLR and outcomes in sepsis patients is unclear. Methods : The Medical Information Mart for Intensive Care IV database was for this retrospective cohort analysis on patients meeting the Sepsis-3 criteria. All the patients meet the Sepsis-3 criteria. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte count. We collected all PLR measurements that were available within 3 days of admission for analysis of longitudinal changes over time. Multivariable logistic regression analysis was used to determine the relationship between the baseline PLR and in-hospital mortality. After correcting for possible confounders, the generalized additive mixed model was used to examine the trends in PLR over time among survivors and nonsurvivors. Results: Finally, 3,303 patients were enrolled, and both low and high PLR levels were significantly associated with higher in-hospital mortality in the multiple logistic regression analysis (tertile 1: odds ratio, 1.240; 95% confidence interval, 0.981-1.568 and tertile 3: odds ratio, 1.410; 95% confidence interval, 1.120-1.776, respectively). The generalized additive mixed model results revealed that the PLR of the nonsurvival group declined faster than that of the survival group within 3 days after intensive care unit admission. After controlling for confounders, the difference between the two groups steadily decreased and increased by an average of 37.38 daily. Conclusions : There was a U-shaped relationship between the baseline PLR and in-hospital mortality of sepsis patients, and there was a significant difference between the nonsurvival and survival groups in the change in PLR over time. The early decrease in PLR was related to an increase in in-hospital mortality.

Abundance of ACVR1B transcript is elevated during septic conditions: Perspectives obtained from a hands-on reductionist investigation

Sepsis is a complex heterogeneous condition, and the current lack of effective risk and outcome predictors hinders the improvement of its management. Using a reductionist approach leveraging publicly available transcriptomic data, we describe a knowledge gap for the role of ACVR1B (activin A receptor type 1B) in sepsis. ACVR1B, a member of the transforming growth factor-beta (TGF-beta) superfamily, was selected based on the following: 1) induction upon in vitro exposure of neutrophils from healthy subjects with the serum of septic patients (GSE49755), and 2) absence or minimal overlap between ACVR1B, sepsis, inflammation, or neutrophil in published literature. Moreover, ACVR1B expression is upregulated in septic melioidosis, a widespread cause of fatal sepsis in the tropics. Key biological concepts extracted from a series of PubMed queries established indirect links between ACVR1B and "cancer", "TGF-beta superfamily", "cell proliferation", "inhibitors of activin", and "apoptosis". We confirmed our observations by measuring ACVR1B transcript abundance in buffy coat samples obtained from healthy individuals (n=3) exposed to septic plasma (n = 26 melioidosis sepsis cases)ex vivo. Based on our re-investigation of publicly available transcriptomic data and newly generated ex vivo data, we provide perspective on the role of ACVR1B during sepsis. Additional experiments for addressing this knowledge gap are discussed.

SLAMF7 regulates the inflammatory response in macrophages during polymicrobial sepsis

Uncontrolled inflammation occurred in sepsis results in multiple organ injuries and shock, which contributes to the death of patients with sepsis. However, the regulatory mechanisms that restrict excessive inflammation are still elusive. Here, we identified an Ig-like receptor called signaling lymphocyte activation molecular family 7 (SLAMF7) as a key suppressor of inflammation during sepsis. We found that the expression of SLAMF7 on monocytes/macrophages was significantly elevated in patients with sepsis and in septic mice. SLAMF7 attenuated TLR-dependent MAPK and NF-κB signaling activation in macrophages by cooperating with Src homology 2-containing inositol-5'‑phosphatase 1 (SHIP1). Furthermore, SLAMF7 interacted with SHIP1 and TNF receptor-associated factor 6 (TRAF6) to inhibit K63 ubiquitination of TRAF6. In addition, we found that tyrosine phosphorylation sites within the intracellular domain of SLAMF7 and the phosphatase domain of SHIP1 were indispensable for the interaction between SLAMF7, SHIP1, and TRAF6 and SLAMF7-mediated modulation of cytokine production. Finally, we demonstrated that SLAMF7 protected against lethal sepsis and endotoxemia by downregulating macrophage proinflammatory cytokines and suppressing inflammation-induced organ damage. Taken together, our findings reveal a negative regulatory role of SLAMF7 in polymicrobial sepsis, thus providing sights into the treatment of sepsis.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.