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The research on bio-based flocculants for waste resource utilization and environmental protection has garnered significant attention. Bio-based flocculants encompass plant-based, animal-based, and microbial variants that are prepared and modified through biological, chemical, and physical methods. These flocculants possess abundant functional groups, unique structures, and distinctive characteristics. This review comprehensively discussed the removal rates of conventional pollutants and emerging pollutants by bio-based flocculants, the interaction between these flocculants and pollutants, their impact on flocculation performance in wastewater treatment, as well as their application cost. Furthermore, it described the common challenges faced by bio-based flocculants in practical applications along with various improvement strategies to address them. With their safety profile, environmental friendliness, efficiency, renewability, and wide availability from diverse sources, bio-based flocculants hold great potential for widespread use in wastewater treatment.
RESUMO
BACKGROUND: To establish and validate a nomogram model, which can incorporate clinical data, and imaging features of ultrasound (US) and contrast-enhanced ultrasound (CEUS), for improving the diagnostic efficiency of solid breast lesions. PATIENTS AND METHODS: A total of 493 patients with solid breast lesions were randomly divided into training (n = 345) and validation (n = 148) cohorts with a ratio of 7:3 and, clinical data and image features of US and CEUS were reviewed and retrospectively analyzed. The breast lesions in both the training and validation cohorts were analyzed using the BI-RADS and nomogram models. RESULTS: Five variables, including the shape and calcification features of conventional US, enhancement type and size after enhancement features of CEUS, and BI-RADS, were selected to construct the nomogram model. As compared to the BI-RADS model, the nomogram model demonstrated satisfactory discriminative function (area under the receiver operating characteristic [ROC] curves [AUC], 0.940; 95% confidence interval [CI], 0.909 to 0.971; sensitivity, 0.905; and specificity, 0.902 in the training cohort and AUC, 0.968; 95% CI, 0.941 to 0.995; sensitivity, 0.971; and specificity, 0.867 in the validation cohort). In addition, the nomogram model showed good consistency and clinical potential according to the calibration curve and DCA. CONCLUSION: The nomogram model could identify benign from malignant breast lesions with good performance.
