[Establishment of a nomogram model for the early diagnosis of childhood sepsis]. / 基于列线图的儿童脓毒症早期诊断模型的建立.

OBJECTIVES: To establish a nomogram model for the early diagnosis of sepsis in children. METHODS: A total of 76 children with sepsis who were admitted to Sichuan Maternal and Child Health Hospital from January 2018 to June 2021 were retrospectively selected as the sepsis group. After matching for sex and age (±2 years) at a ratio of 1:1:1, 76 children with local infection who were hospitalized during the same period were enrolled as the local infection group, and 76 children with non-infectious diseases were enrolled as the control group. The three groups were compared in terms of laboratory markers and the results of quick Sequential Organ Failure Assessment (qSOFA) and Pediatric Critical Illness Score (PCIS). A multivariate logistic regression analysis was used to investigate the association between the above indicators and sepsis. R4.1.3 software was used to establish and validate the nomogram model for the early diagnosis of sepsis based on the results of the multivariate analysis. A receiver operating characteristic (ROC) curve analysis was used to evaluate the value of the nomogram model, and the Bootstrap method was used to perform the internal validation of the model. RESULTS: The multivariate logistic regression analysis showed that soluble triggering receptor expressed on myeloid cells-1, qSOFA score, PCIS score, C-reactive protein, interleukin-6, and interleukin-10 were independently associated with childhood sepsis (P<0.05). The above indicators were used to establish a nomogram for the early diagnosis of sepsis, with an area under the ROC curve of 0.837 (95%CI: 0.760-0.914), and the calibration curve results showed a mean absolute error of 0.024, suggesting that the performance of this model was basically consistent with that of the ideal model. CONCLUSIONS: The indicators soluble triggering receptor expressed on myeloid cells-1, qSOFA score, PCIS score, C-reactive protein, interleukin-6, and interleukin-10 are independently associated with childhood sepsis, and the nomogram model established based on these indicators has high discriminatory ability and accuracy in the early diagnosis of sepsis in children.

Strong, Tough, and Adhesive Polyampholyte/Natural Fiber Composite Hydrogels.

Hydrogels with high mechanical strength, good crack resistance, and good adhesion are highly desirable in various areas, such as soft electronics and wound dressing. Yet, these properties are usually mutually exclusive, so achieving such hydrogels is difficult. Herein, we fabricate a series of strong, tough, and adhesive composite hydrogels from polyampholyte (PA) gel reinforced by nonwoven cellulose-based fiber fabric (CF) via a simple composite strategy. In this strategy, CF could form a good interface with the relatively tough PA gel matrix, providing high load-bearing capability and good crack resistance for the composite gels. The relatively soft, sticky PA gel matrix could also provide a large effective contact area to achieve good adhesion. The effect of CF content on the mechanical and adhesion properties of composite gels is systematically studied. The optimized composite gel possesses 35.2 MPa of Young's modulus, 4.3 MPa of tensile strength, 8.1 kJ m-2 of tearing energy, 943 kPa of self-adhesive strength, and 1.4 kJ m-2 of self-adhesive energy, which is 22.1, 2.3, 1.8, 6.0, and 4.2 times those of the gel matrix, respectively. The samples could also form good adhesion to diverse substrates. This work opens a simple route for fabricating strong, tough, and adhesive hydrogels.