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Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.  相似文献   
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Background

Resistin is an immunometabolic mediator that is elevated in several inflammatory disorders. A ligand for Toll-like receptor 4, resistin modulates the recruitment and activation of myeloid cells, notably neutrophils. Neutrophils are major drivers of cystic fibrosis (CF) lung disease, in part due to the release of human neutrophil elastase- and myeloperoxidase-rich primary granules, leading to tissue damage. Here we assessed the relationship of resistin to CF lung disease.

Methods

Resistin levels were measured in plasma and sputum from three retrospective CF cohorts spanning a wide range of disease. We also assessed the ability of neutrophils to secrete resistin upon activation in vitro. Finally, we constructed a multivariate model assessing the relationship between resistin levels and lung function.

Results

Plasma resistin levels were only marginally higher in CF than in healthy control subjects. By contrast, sputum resistin levels were very high in CF, reaching 50–100 fold higher levels than in plasma. Among CF patients, higher plasma resistin levels were associated with allergic bronchopulmonary aspergillosis, and higher sputum resistin levels were associated with CF-related diabetes. Mechanistically, in vitro release of neutrophil primary granules was concomitant with resistin secretion. Overall, sputum resistin levels were negatively correlated with CF lung function, independently of other variables (age, sex, and genotype).

Conclusions

Our data establish relationships between resistin levels in the plasma and sputum of CF patients that correlate with disease status, and identify resistin as a novel mechanistic link between neutrophilic inflammation and lung disease in CF.  相似文献   
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