Biomarkers of early SARS-CoV-2 infection before the onset of respiratory symptoms.

OBJECTIVES: Currently, limited data exist regarding the pathological changes occurring during the incubation phase of SARS-CoV-2 infection. We utilized proteomic analysis to explore changes in the circulatory host response in individuals with SARS-CoV-2 infection before the onset of symptoms. METHODS: Participants were individuals from a randomized clinical trial of prophylaxis for COVID-19 in a workers' dormitory. Proteomic signatures of blood samples collected within 7 days before symptom onset (incubation group) were compared with those collected >21 days (non-incubation group) to derive candidate biomarkers of incubation. Candidate biomarkers were assessed by comparing levels in the incubation group with both infected individuals (positive controls) and non-infected individuals (negative controls). RESULTS: The study included men (mean age 34.2 years and standard deviation 7.1) who were divided into three groups: an incubation group consisting of 44 men, and two control groups-positive (n = 56) and negative (n = 67) controls. Through proteomic analysis, we identified 49 proteins that, upon pathway analyses, indicated an upregulation of the renin-angiotensin and innate immune systems during the virus incubation period. Biomarker analyses revealed increased concentrations of plasma angiotensin II (mean 731 vs. 139 pg/mL), angiotensin (1-7) (302 vs. 9 pg/mL), CXCL10 (423 vs. 85 pg/mL), CXCL11 (82.7 vs. 32.1 pg/mL), interferon-gamma (0.49 vs. 0.20 pg/mL), legumain (914 vs. 743 pg/mL), galectin-9 (1443 vs. 836 pg/mL), and tumour necrosis factor (20.3 vs. 17.0 pg/mL) during virus incubation compared with non-infected controls (all p < 0.05). Plasma angiotensin (1-7) exhibited a significant increase before the onset of symptoms when compared with uninfected controls (area under the curve 0.99, sensitivity 0.97, and specificity 0.99). DISCUSSION: Angiotensin (1-7) could play a crucial role in the progression of symptomatic COVID-19 infection, and its assessment could help identify individuals who would benefit from enhanced monitoring and early antiviral intervention.

The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.

Background: Recent research has demonstrated that machine learning (ML) has the potential to improve several aspects of medical application for critical illness, including sepsis. This scoping review aims to evaluate the feasibility of probabilistic graphical model (PGM) methods in pediatric sepsis application and describe the use of pediatric sepsis definition in these studies. Methods: Literature searches were conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL+), and Web of Sciences from 2000-2023. Keywords included "pediatric", "neonates", "infants", "machine learning", "probabilistic graphical model", and "sepsis". Results: A total of 3,244 studies were screened, and 72 were included in this scoping review. Sepsis was defined using positive microbiology cultures in 19 studies (26.4%), followed by the 2005's international pediatric sepsis consensus definition in 11 studies (15.3%), and Sepsis-3 definition in seven studies (9.7%). Other sepsis definitions included: bacterial infection, the international classification of diseases, clinicians' assessment, and antibiotic administration time. Among the most common ML approaches used were logistic regression (n=27), random forest (n=24), and Neural Network (n=18). PGMs were used in 13 studies (18.1%), including Bayesian classifiers (n=10), and the Markov Model (n=3). When applied on the same dataset, PGMs show a relatively inferior performance to other ML models in most cases. Other aspects of explainability and transparency were not examined in these studies. Conclusions: Current studies suggest that the performance of probabilistic graphic models is relatively inferior to other ML methods. However, its explainability and transparency advantages make it a potentially viable method for several pediatric sepsis studies and applications.

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model.

Background: Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit. Methods: We conducted a retrospective study on children using the first 24-hour clinical data of the intensive care unit admission from the Pediatric Intensive Care Dataset, 2010-2019. A probabilistic graphical model method, Tree Augmented Naive Bayes, was used to build diagnosis models using combinations of four categories: vital signs, clinical symptoms, laboratory, and microbiological tests. Variables were reviewed and selected by clinicians. Sepsis cases were identified with the discharged diagnosis of sepsis or suspected infection with the systemic inflammatory response syndrome. Performance was measured by the average sensitivity, specificity, accuracy, and area under the curve of ten-fold cross-validations. Results: We extracted 3,014 admissions [median age of 1.13 (interquartile range: 0.15-4.30) years old]. There were 134 (4.4%) and 2,880 (95.6%) sepsis and non-sepsis patients, respectively. All diagnosis models had high accuracy (0.92-0.96), specificity (0.95-0.99), and area under the curve (0.77-0.87). Sensitivity varied with different combinations of variables. The model that combined all four categories yielded the best performance [accuracy: 0.93 (95% confidence interval (CI): 0.916-0.936); sensitivity: 0.46 (95% CI: 0.376-0.550), specificity: 0.95 (95% CI: 0.940-0.956), area under the curve: 0.87 (95% CI: 0.826-0.906)]. Microbiological tests had low sensitivity (<0.10) with high incidence of negative results (67.2%). Conclusions: We demonstrated that the probabilistic graphical model is a feasible diagnostic tool for pediatric sepsis. Future studies using different datasets should be conducted to assess its utility to aid clinicians in the diagnosis of sepsis.