Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.

BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication. METHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types. RESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells. CONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.

The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.

Background: The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes. Methods: The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes. Results: We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes. Conclusion: We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.

The prognostic value of platelet aggregation in patients with sepsis.

BACKGROUND: This study aims to investigate the relevance of platelet aggregation markers, specifically arachidonic acid (AA) and adenosine diphosphate (ADP), in relation to the prognosis of sepsis patients. METHODS: A cohort of 40 sepsis patients was included and stratified, based on their 28-day post-treatment prognosis, into two groups: a survival group (n = 31) and a severe sepsis group (n = 9. Then, their various clinical parameters, including patient demographics, platelet counts (PLT), inflammatory markers, and platelet aggregation rates (PAR) induced by AA and ADP between the two groups, were compared. Long-term health implications of sepsis were assessed using the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, and logistic regression analysis was conducted to evaluate the prognostic significance of PAR in sepsis patients. RESULTS: Patients with severe sepsis exhibited significantly elevated levels of procalcitonin (PCT), platelet adhesion rates, and PAR induced by ADP (P < 0.05), but having lower PLT (P < 0.05), compared to those in the survival group. Logistic regression analysis demonstrated that PAR induced by ADP was a protective factor in predicting prognosis in sepsis patients (P < 0.01). CONCLUSIONS: Activation of platelets in sepsis intensifies inflammatory response. Patients with sepsis whose ADP-induced PAR was < 60% displayed significant impairment in platelet aggregation function, and had higher mortality rate. Monitoring ADP-induced PAR is crucial for management of sepsis.

Diagnostic accuracy of pancreatic stone protein in patients with sepsis: a systematic review and meta-analysis.

BACKGROUND: Sepsis is a common syndrome of multiorgan system dysfunction secondary to the dysregulated inflammatory response to infection. The role of pancreatic stone protein (PSP) in diagnosing sepsis has been investigated in previous studies. The meta-analysis aimed to comprehensively investigate the diagnostic value of PSP in identifying sepsis. METHODS: PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI), were systematically searched. Studies investigating the diagnostic performance of PSP were included. Pooled sensitivity, specificity, positive Likelihood Ratio (+ LR) and negative Likelihood Ratio (-LR), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (SROC) were calculated. RESULTS: The sensitivity of PSP was 0.88 (95% CI: 0.77-0.94), and the pooled specificity was 0.78 (95% CI: 0.65-0.87). Pooled + LR, -LR, and DOR were 4.1 (2.3, 7.3), 0.16 (0.07, 0.34), and 26 (7, 98). The AUC value for the SROC of PSP was 0.90 (0.87, 0.92). The pooled sensitivity, specificity, + LR and - LR, and DOR for PSP among neonates were 0.91 (95% CI: 0.84, 0.96), 0.66 (95% CI: 0.58, 0.74), 3.97 (95% CI: 0.53, 29.58), 0.13 (95% CI: 0.02, 1.00), and 31.27 (95% CI: 0.97, 1004.60). CONCLUSIONS: This study indicates that PSP demonstrated favorable diagnostic accuracy in detecting sepsis. Well-designed studies are warranted to ascertain the value of PSP measurement to guide early empirical antibiotic treatment, particularly in neonates.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. OBJECTIVES: This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity. RESULTS: The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models. CONCLUSION: Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.

[Tissue perfusion monitoring in children with acute circulatory dysfunction: narrative review]. / Monitorización de la perfusión tisular en el niño con disfunción circulatoria aguda: revisión narrativa.

Sepsis is one of the main causes of admission to Intensive Care Units (ICU). The hemodynamic objectives usually sought during the resuscitation of the patient in septic shock correspond to macrohemodynamic parameters (heart rate, blood pressure, central venous pressure). However, persistent alterations in microcirculation, despite the restoration of macrohemodynamic parameters, can cause organ failure. This dissociation between the macrocirculation and microcirculation originates the need to evaluate organ tissue perfusion, the most commonly used being urinary output, lactatemia, central venous oxygen saturation (ScvO2), and veno-arterial pCO2 gap. Because peripheral tissues, such as the skin, are sensitive to disturbances in perfusion, noninvasive monitoring of peripheral circulation, such as skin temperature gradient, capillary refill time, mottling score, and peripheral perfusion index may be helpful as early markers of the existence of systemic hemodynamic alterations. Peripheral circulation monitoring techniques are relatively easy to interpret and can be used directly at the patient's bedside. This approach can be quickly applied in the intra- or extra-ICU setting. The objective of this narrative review is to analyze the various existing tissue perfusion markers and to update the evidence that allows guiding hemodynamic support in a more individualized therapy for each patient.

Diagnostic and Prognostic Value of C1q in Sepsis-Induced Coagulopathy.

Early identification of biomarkers that can predict the onset of sepsis-induced coagulopathy (SIC) in septic patients is clinically important. This study endeavors to examine the diagnostic and prognostic utility of serum C1q in the context of SIC. Clinical data from 279 patients diagnosed with sepsis at the Departments of Intensive Care, Respiratory Intensive Care, and Infectious Diseases at the Renmin Hospital of Wuhan University were gathered spanning from January 2022 to January 2024. These patients were categorized into two groups: the SIC group comprising 108 cases and the non-SIC group consisting of 171 cases, based on the presence of SIC. Within the SIC group, patients were further subdivided into a survival group (43 cases) and non-survival group (65 cases). The concentration of serum C1q in the SIC group was significantly lower than that in the non-SIC group. Furthermore, A significant correlation was observed between serum C1q levels and both SIC score and coagulation indices. C1q demonstrated superior diagnostic and prognostic performance for SIC patients, as indicated by a higher area under the curve (AUC). Notably, when combined with CRP, PCT, and SOFA score, C1q displayed the most robust diagnostic efficacy for SIC. Moreover, the combination of C1q with the SOFA score heightened predictive value concerning the 28-day mortality of SIC patients.

Machine learning reveals ferroptosis features and a novel ferroptosis classifier in patients with sepsis.

OBJECTIVE: Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis. Even though early diagnosis is very important, the technology and clinical approaches that are now available are inadequate. Hence, there is an immediate necessity to investigate biological markers that are sensitive, specific, and reliable for the prompt detection of sepsis to reduce mortality and improve patient prognosis. Mounting research data indicate that ferroptosis contributes to the occurrence, development, and prevention of sepsis. However, the specific regulatory mechanism of ferroptosis remains to be elucidated. This research evaluated the expression profiles of ferroptosis-related genes (FRGs) and the diagnostic significance of the ferroptosis-related classifiers in sepsis. METHODS AND RESULTS: We collected three peripheral blood data sets from septic patients, integrated the clinical examination data and mRNA expression profile of these patients, and identified 13 FRGs in sepsis through a co-expression network and differential analysis. Then, an optimal classifier tool for sepsis was constructed by integrating a variety of machine learning algorithms. Two key genes, ATG16L1 and SRC, were shown to be shared between the algorithms, and thus were identified as the FRG signature of classifier. The tool exhibited satisfactory diagnostic efficiency in the training data set (AUC = 0.711) and two external verification data sets (AUC = 0.961; AUC = 0.913). In the rat cecal ligation puncture sepsis model, in vivo experiments verified the involvement of ATG16L1 and SRC in the early sepsis process. CONCLUSION: These findings confirm that FRGs may participate in the development of sepsis, the ferroptosis related classifiers can provide a basis for the development of new strategies for the early diagnosis of sepsis and the discovery of new potential therapeutic targets for life-threatening infections.

A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1st to December 31st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.

Usefulness of the qSOFA score for predicting hospital mortality in cancer patients.

BACKGROUND: The quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) is a score that has been proposed to quickly identify patients at higher risk of death. OBJECTIVE: To describe the usefulness of the qSOFA score to predict in-hospital mortality in cancer patients. MATERIAL AND METHODS: Cross-sectional study carried out between January 2021 and December 2022. Hospital mortality was the dependent variable. The area under the ROC curve (AUC) was calculated to determine the discriminative ability of qSOFA to predict in-hospital mortality. RESULTS: A total of 587 cancer patients were included. A qSOFA score higher than 1 obtained a sensitivity of 57.2%, specificity of 78.5%, a positive predictive value of 55.4% and negative predictive value of 79.7%. The AUC of qSOFA for predicting in-hospital mortality was 0.70. In-hospital mortality of patients with qSOFA scores of 2 and 3 points was 52.7 and 64.4%, respectively. In-hospital mortality was 31.9% (187/587). CONCLUSION: qSOFA showed acceptable discriminative ability for predicting in-hospital mortality in cancer patients.

ANTECEDENTES: El quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) es una puntuación propuesta para identificar de forma rápida a pacientes con mayor probabilidad de morir. OBJETIVO: Describir la utilidad de la puntuación qSOFA para predecir mortalidad hospitalaria en pacientes con cáncer. MATERIAL Y MÉTODOS: Estudio transversal realizado entre enero de 2021 y diciembre de 2022. La mortalidad hospitalaria fue la variable dependiente. Se calculó el área bajo la curva ROC (ABC) para determinar la capacidad discriminativa de qSOFA para predecir mortalidad hospitalaria. RESULTADOS: Se incluyeron 587 pacientes con cáncer. La puntuación qSOFA < 1 obtuvo una sensibilidad de 57.2 %, una especificidad de 78.5 %, un valor predictivo positivo de 55.4 % y un valor predictivo negativo de 79.7 %. El ABC de qSOFA para predecir mortalidad hospitalaria fue de 0.70. La mortalidad hospitalaria de los pacientes con qSOFA de 2 y 3 puntos fue de 52.7 y 64.4 %, respectivamente. La mortalidad hospitalaria fue de 31.9 % (187/587). CONCLUSIÓN: qSOFA mostró capacidad discriminativa aceptable para predecir mortalidad hospitalaria en pacientes con cáncer.

Sepsis best practices: Definitions, guidelines, and updates.

ABSTRACT: Sepsis remains a complex and costly disease with high morbidity and mortality. This article discusses Sepsis-2 and Sepsis-3 definitions, highlighting the 2021 Surviving Sepsis International guidelines as well as the regulatory requirements and reimbursement for the Severe Sepsis and Septic Shock Management Bundle (SEP-1) measure.

Blood calprotectin as a biomarker for infection and sepsis - the prospective CASCADE trial.

BACKGROUND: Early in the host-response to infection, neutrophils release calprotectin, triggering several immune signalling cascades. In acute infection management, identifying infected patients and stratifying these by risk of deterioration into sepsis, are crucial tasks. Recruiting a heterogenous population of patients with suspected infections from the emergency department, early in the care-path, the CASCADE trial aimed to evaluate the accuracy of blood calprotectin for detecting bacterial infections, estimating disease severity, and predicting clinical deterioration. METHODS: In a prospective, observational trial from February 2021 to August 2022, 395 patients (n = 194 clinically suspected infection; n = 201 controls) were enrolled. Blood samples were collected at enrolment. The accuracy of calprotectin to identify bacterial infections, and to predict and identify sepsis and mortality was analysed. These endpoints were determined by a panel of experts. RESULTS: The Area Under the Receiver Operating Characteristic (AUROC) of calprotectin for detecting bacterial infections was 0.90. For sepsis within 72 h, calprotectin's AUROC was 0.83. For 30-day mortality it was 0.78. In patients with diabetes, calprotectin had an AUROC of 0.94 for identifying bacterial infection. CONCLUSIONS: Calprotectin showed notable accuracy for all endpoints. Using calprotectin in the emergency department could improve diagnosis and management of severe infections, in combination with current biomarkers. CLINICAL TRIAL REGISTRATION NUMBER: DRKS00020521.

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns.

OBJECTIVE: To compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns. METHODS: For this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Of the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns. CONCLUSIONS: The XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. METHODS: Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms. RESULTS: Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). CONCLUSION: The SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database. / 基于MIMIC-IVæž„å»ºåŠè¯„ä¼°è„“æ¯’ç—‡æ‚£è€ è¿‘æœŸå’Œè¿œæœŸæ­»äº¡é£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. METHODS: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. RESULTS: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. CONCLUSIONS: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

Prussian Blue Analogues-Derived Molecularly Imprinted Nanozyme Array for Septicemia Detection.

Septicemia, a severe bacterial infection, poses significant risks to human health. Early detection of septicemia by tracking specific biomarkers is crucial for a timely intervention. Herein, we developed a molecularly imprinted (MI) TiO2-Fe-CeO2 nanozyme array derived from Ce[Fe(CN)6] Prussian blue analogues (PBA), specifically targeting valine, leucine, and isoleucine, as potential indicators of septicemia. The synthesized nanozyme arrays were thoroughly characterized using various analytical techniques, including Fourier transform infrared spectroscopy, X-ray diffraction, field-emission scanning electron microscope, and energy-dispersive X-ray. The results confirmed their desirable physical and chemical properties, indicating their suitability for the oxidation of 3,3',5,5'-tetramethylbenzidine serving as a colorimetric probe in the presence of a persulfate oxidizing agent, further highlighting the potential of these arrays for sensitive and accurate detection applications. The MITiO2 shell selectively captures valine, leucine, and isoleucine, partially blocking the cavities for substrate access and thereby hindering the catalyzed TMB chromogenic reaction. The nanozyme array demonstrated excellent performance with linear detection ranges of 5 µM to 1 mM, 10-450 µM, and 10-450 µM for valine, leucine, and isoleucine, respectively. Notably, the corresponding limit of detection values were 0.69, 1.46, and 2.76 µM, respectively. The colorimetric assay exhibited outstanding selectivity, reproducibility, and performance in the detection of analytes in blood samples, including C-reactive protein at a concentration of 61 mg/L, procalcitonin at 870 ng/dL, and the presence of Pseudomonas aeruginosa bacteria. The utilization of Ce[Fe(CN)6]-derived MITiO2-Fe-CeO2 nanozyme arrays holds considerable potential in the field of septicemia detection. This approach offers a sensitive and specific method for early diagnosis and intervention, thereby contributing to improved patient outcomes.

A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing.

OBJECTIVE: To integrate an enhanced molecular diagnostic technique to develop and validate a machine-learning model for diagnosing sepsis. METHODS: We prospectively enrolled patients suspected of sepsis from August 2021 to August 2023. Various feature selection algorithms and machine learning models were used to develop the model. The best classifier was selected using 5-fold cross validation set and then was applied to assess the performance of the model in the testing set. Additionally, we employed the Shapley Additive exPlanations (SHAP) method to illustrate the effects of the features. RESULTS: We established an optimized mNGS assay and proposed using the copies of microbe-specific cell-free DNA per milliliter of plasma (CPM) as the detection signal to evaluate the real burden, with strong precision and high accuracy. In total, 237 patients were eligible for participation, which were randomly assigned to either the training set (70 %, n = 165) or the testing set (30 %, n = 72). The random forest classifier achieved accuracy, AUC and F1 scores of 0.830, 0.918 and 0.856, respectively, outperforming other machine learning models in the training set. Our model demonstrated clinical interpretability and achieved good prediction performance in differentiating between bacterial sepsis and non-sepsis, with an AUC value of 0.85 and an average precision of 0.91 in the testing set. Based on the SHAP value, the top nine features of the model were PCT, CPM, CRP, ALB, SBPmin, RRmax, CREA, PLT and HRmax. CONCLUSION: We demonstrated the potential of machine-learning approaches for predicting bacterial sepsis based on optimized mcfDNA sequencing assay accurately.