Development and validation of a prediction model for frailty in breast cancer patients with extended survival.

BACKGROUND: Breast cancer (BC) patients with extended survival show a higher incidence of frailty. This study aimed to develop and validate a novel model combining sociodemographic factors (SF) and disease-related factors (DRF) to identify frailty in BC patients with extended survival. METHODS: This cross-sectional study examined data from 1167 patients admitted to a large urban academic medical centre. Three types of predictive models were constructed in the training set (817 patients): the SF model, the DRF model, and the SF + DRF model (combined model). The model performance and effectiveness were assessed using receiver operating characteristic (ROC) curves, calibration plots and decision curves analysis (DCA). Then the model was subsequently validated on the validation set. RESULTS: The incidence of frailty in BC patients with extended survival was 35.8%. We identified six independent risk factors including age, health status, chemotherapy, endocrine therapy, number of comorbidities and oral medications. Ultimately, we constructed an optimal model (combined model C) for frailty. The predictive model showed significantly high discriminative accuracy in the training set AUC: 0.754, (95% CI, 0.719-0.789; sensitivity: 76.8%, specificity: 62.2%) and validation set AUC: 0.805, (95% CI, 0.76-0.85), sensitivity: 60.8%, specificity: 87.1%) respectively. A prediction nomogram was constructed for the training and validation sets. Calibration and DCA were performed, which indicated that the clinical model presented satisfactory calibration and clinical utility. Ultimately, we implemented the prediction model into a mobile-friendly web application that provides an accurate and individualized prediction for BC. CONCLUSIONS: The present study demonstrated that the prevalence of frailty in BC patients with extended survival was 35.8%. We developed a novel model for screening frailty, which may provide evidence for frailty screening and prevention.

Study on uranium ion adsorption property of porous glass modified with amidoxime group.

In this paper, we prepared three types of porous glasses (PGs) with specific surface areas of 311.60 m2/g, 277.60 m2/g, and 231.38 m2/g, respectively, via borosilicate glass phase separation. These glasses were further modified with amidoxime groups (AO) using the hydroxylamine method, yielding adsorbents named 1.5-PG-AO, 2-PG-AO, and 3-PG-AO. The adsorption performance of these adsorbents under various conditions was investigated, including sorption kinetics and adsorption mechanisms. The results reveal that the number of micropores and specific surface area of PG are significantly reduced after AO modification. All three adsorbents exhibit similar adsorption capabilities. Particularly, pH has a pronounced effect on U (VI) adsorption of PG-AO, with a maximum value at pH = 4.5. Equilibrium adsorption is achieved within 2 h, with a maximum adsorption capacity of 129 mg/g. Notably, a uranium removal rate of 99.94% is attained. Furthermore, the adsorbents show high selectivity in uranium solutions containing Na+ or K+. Moreover, the adsorbents demonstrate exceptional regeneration ability, with the removal rate remaining above 80% even after undergoing five adsorption-desorption cycles. The adsorption reaction of uranium on PG-AO involves a combination of multiple processes, with monolayer chemisorption being the dominant mechanism. Both the complex adsorption of AO and the ion exchange and physical adsorption of PG contribute to the adsorption of uranyl ions on the PG-AO adsorbents.