Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.

BACKGROUND: Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. METHODS: Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. RESULTS: This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. CONCLUSIONS: The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis.

Background: Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. Methods: We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. Results: The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786-0.834), 0.794 (95 % CI 0.756-0.831), and 0.723 (95 % CI 0.647-0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. Conclusion: The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

Sustainable separation of bio-based cadaverine based on carbon dioxide capture by forming carbamate.

Bio-based cadaverine, manufactured by the decarboxylation of l-lysine, is an important raw material. However, the extractive-distillation separation and purification of cadaverine from bioconversion fluids require high energy consumption and leads to the loss of self-released carbon dioxide during the decarboxylation of l-lysine. This study focuses on the green and sustainable separation of bio-based cadaverine based on the capture of self-released carbon dioxide by cadaverine forming carbamate. Results showed that granular-activated carbon JK1 shows the best decolorization efficiency and achieves a higher cadaverine yield. After three times of solventing-out crystallization, refined cadaverine carbamate with 99.1% purity and total 57.48% yield was obtained. It was also found that the refined cadaverine carbamate consists of mixed crystals having numerous structural forms that can easily dissociate carbon dioxide. Furthermore, the amine carbamate strategy may be of great value for the development of a green and sustainable separation mode of bio-based amines and carbon dioxide capture.

A Simple Preparation Route for Bio-Phenol MQ Silicone Resin via the Hydrosilylation Method and its Autonomic Antibacterial Property.

MQ silicone resins represent a broad range of hydrolytic condensation products of monofunctional silane (M units) and tetrafunctional silane (Q units). In this work, a Bio-Phenol MQ silicone resin (BPMQ) was designed and synthesized by the hydrosilylation of hydrogen containing MQ silicone resin and eugenol in the presence of chloroplatinic acid. The structure, thermal property, and antibacterial property against Escherichia coli of the modified MQ silicone resin were investigated. The results showed that BPMQ has been prepared successfully, and the thermal stability of this modified polymer improved significantly because of the introduction of phenyl in eugenol. The temperature at the maximum degradation rate increased from 250 °C to 422.5 °C, and the residual yields mass left at 600 °C were increased from 2.0% to 28.3%. In addition, its antibacterial property against Escherichia coli was also enhanced markedly without adding any other antimicrobial agents. This improved performance is ascribed to special functional groups in the structure of eugenol. The BPMQ polymer is expected to be applied to pressure-sensitive adhesives and silicone rubber products for the biomedical field due to its reinforcing effect and antioxidant quality.