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Siderophore is a potential biomarker for pathogen diagnosis. In this study, a siderophore impedance sensor was constructed based on the formation of a siderophore-hemin complex prepared by modification of hemin on an interdigitated array microelectrode. Siderophore reacted with hemin and formed a complex on the electrode surface. As the conductivity of hemin was higher than that of siderophore, the formation of the complex led to decrease of conductivity on electrode surface which was measured by electrochemical impedance spectroscopy. The impedance change of constructed sensor was linear with the concentration of siderophore between 1 nM and 100 µM, with a limit of detection of 0.86 nM. The detection time was less than 15 min. When the sensor was used to analyze medium, the recovery was between 86.7% and 102.8%. Compared with conventional methods for siderophore, this approach is faster, more sensitive, and has potential application for the determination of pathogens. [ABSTRACT FROM AUTHOR]

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.