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Background and aimProtein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model.Materials and methodsA total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model.ResultsBinary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799–0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models.ConclusionIn this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria.

Influenza A virus infection is usually associated with acute lung injury, which is typically characterized by tracheal mucosal barrier damage and an interleukin 17A (IL-17A)-mediated inflammatory response in lung tissues. Although targeting IL-17A has been proven to be beneficial for attenuating inflammation around lung cells, it still has a limited effect on pulmonary tissue recovery after influenza A virus infection. In this research, interleukin 22 (IL-22), a cytokine involved in the repair of the pulmonary mucosal barrier, was fused to the C-terminus of the anti-IL-17A antibody vunakizumab to endow the antibody with a tissue recovery function. The vunakizumab-IL22 (vmab-IL-22) fusion protein exhibits favorable stability and retains the biological activities of both the anti-IL-17A antibody and IL-22 in vitro. Mice infected with lethal H1N1 influenza A virus and treated with vmab-mIL22 showed attenuation of lung index scores and edema when compared to those of mice treated with saline or vmab or mIL22 alone. Our results also illustrate that vmab-mIL22 triggers the upregulation of MUC2 and ZO1, as well as the modulation of cytokines such as IL-1β, HMGB1 and IL-10, indicating the recovery of pulmonary goblet cells and the suppression of excessive inflammation in mice after influenza A virus infection. Moreover, transcriptome profiling analysis suggest the downregulation of fibrosis-related genes and signaling pathways, including genes related to focal adhesion, the inflammatory response pathway, the TGF-β signaling pathway and lung fibrosis upon vmab-mIL22 treatment, which indicates that the probable mechanism of vmab-mIL22 in ameliorating H1N1 influenza A-induced lung injury. Our results reveal that the bifunctional fusion protein vmab-mIL22 can trigger potent therapeutic effects in H1N1-infected mice by enhancing lung tissue recovery and inhibiting pulmonary inflammation, which highlights a potential approach for treating influenza A virus infection by targeting IL-17A and IL-22 simultaneously.