Assessment of pulmonary fibrosis induced by paraquat using Al18F-NODA-FAPI-04 PET/CT.

The lack of a highly sensitive method to evaluate paraquat (PQ)-induced pulmonary fibrosis and predict disease progression remains an unresolved clinic issue. Fibroblast activation protein (FAP) may play an important role in the pathogenesis of PQ-induced pulmonary fibrosis. We aimed to evaluate the role of FAP in the PQ-induced pulmonary fibrosis and the utility of fibroblast activation protein inhibitor (FAPI) for positron emission tomography (PET) imaging in PQ-induced pulmonary fibrosis. In our study, two cases of PQ poisoning were presented and FAPI PET/CT was performed as a novel imaging technique. The uptake of FAPI increased in both cases of PQ poisoning. Animal experiments were then performed to validate the findings in the patients. Physiological FAPI lung uptake was higher in mice of the PQ group than in the control group. The results of histological analysis and Western blot were consistent with the findings of PET/CT imaging. The pulmonary fibrosis animal model was developed by intragastric gavage of PQ. PET/CT imaging was performed after injection of FAPI. Lung tissues of mice were collected for fibrosis assessment after imaging. Immunohistochemistry for FAP, histology and Western blot for collagen were performed to further validate the imaging findings. In conclusion, FAPI was involved in the pathogenesis of fibrosis induced by PQ, and PET/CT with FAPI could detect lung fibrogenesis, making it a promising tool to assess early disease activity and predict disease progression.

Development and validation of a predictive model for the assessment of potassium-lowering treatment among hyperkalemia patients.

BACKGROUND: Hyperkalemia is common among patients in emergency department and is associated with mortality. While, there is a lack of good evaluation and prediction methods for the efficacy of potassium-lowering treatment, making the drug dosage adjustment quite difficult. We aimed to develop a predictive model to provide early forecasting of treating effects for hyperkalemia patients. METHODS: Around 80% of hyperkalemia patients (n=818) were randomly selected as the training dataset and the remaining 20% (n=196) as the validating dataset. According to the serum potassium (K+) levels after the first round of potassium-lowering treatment, patients were classified into the effective and ineffective groups. Multivariate logistic regression analyses were performed to develop a prediction model. The receiver operating characteristic (ROC) curve and calibration curve analysis were used for model validation. RESULTS: In the training dataset, 429 patients had favorable effects after treatment (effective group), and 389 had poor therapeutic outcomes (ineffective group). Patients in the ineffective group had a higher percentage of renal disease (P=0.007), peripheral edema (P<0.001), oliguria (P=0.001), or higher initial serum K+ level (P<0.001). The percentage of insulin usage was higher in the effective group than in the ineffective group (P=0.005). After multivariate logistic regression analysis, we found age, peripheral edema, oliguria, history of kidney transplantation, end-stage renal disease, insulin, and initial serum K+ were all independently associated with favorable treatment effects. CONCLUSION: The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment, which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.