A unified modelling framework for type I (discrete) settling and rising of microplastics in primary sedimentation tanks.

Sewage treatment plants (STPs) are considered as a significant source of microplastic pollution into the terrestrial and aquatic environment. Existing observations suggest that primary treatment accounts for major microplastics removal in STPs, though with high variability due to the complex nature of the polymer compositions, abundance, and sizes in the incoming sewage. Here, we develop a unified modelling framework to simulate the Type I (or discrete) settling or rising behaviour of microplastics to predict their eventual fate in Primary Sedimentation Tank (PST). The model was developed as per the conventional design protocol for PST involving Stokes equation and modifications as per flow regime for settling of nylon and polystyrene microplastics. It was subsequently validated with independent column experiments for both settling (nylon and polystyrene) and rising (low-density polyethylene and polypropylene) microplastics in different size ranges. The validated model was then applied for multiple realistic scenarios of polymer compositions, relative abundance, and size distributions in the incoming sewage. The model predicts removals ranging from 12% to 94% for a mixture of microplastics in the size fraction 0-500 µm. Model simulations also suggest better microplastics removal with the integration of skimming in PST, and optimization of surface overflow velocity.

Development and Validation of a Model for Predicting Diabetic Nephropathy in Chinese People.

OBJECTIVE: To develop a risk model for predicting later development of diabetic nephropathy (DN) in Chinese people with type 2 diabetes mellitus (T2DM) and evaluate its performance with independent validation. METHODS: We used data collected from the project 'Comprehensive Research on the Prevention and Control of Diabetes', which was a community-based study conducted by the Jiangsu Center for Disease Control and Prevention in 2013. A total of 11,771 eligible participants were included in our study. The endpoint was a clear diagnosis of DN. Data was divided into two components: a training set for model development and a test set for validation. The Cox proportional hazard regression was used for survival analysis in men and women. The model's performance was evaluated by discrimination and calibration. RESULTS: The incidence (cases per 10,000 person-years) of DN was 9.95 (95% CI; 8.66-11.43) in women and 11.28 (95% CI; 9.77-13.03) in men. Factors including diagnosis age, location, body mass index, high-density-lipoprotein cholesterol, creatinine, hypertension, dyslipidemia, retinopathy, diet control, and physical activity were significant in the final model. The model showed high discrimination and good calibration. CONCLUSION: The risk model for predicting DN in people with T2DM can be used in clinical practice for improving the quality of risk management and intervention.

AML risk stratification models utilizing ELN-2017 guidelines and additional prognostic factors: a SWOG report.

BACKGROUND: The recently updated European LeukemiaNet risk stratification guidelines combine cytogenetic abnormalities and genetic mutations to provide the means to triage patients with acute myeloid leukemia for optimal therapies. Despite the identification of many prognostic factors, relatively few have made their way into clinical practice. METHODS: In order to assess and improve the performance of the European LeukemiaNet guidelines, we developed novel prognostic models using the biomarkers from the guidelines, age, performance status and select transcript biomarkers. The models were developed separately for mononuclear cells and viable leukemic blasts from previously untreated acute myeloid leukemia patients (discovery cohort, N = 185) who received intensive chemotherapy. Models were validated in an independent set of similarly treated patients (validation cohort, N = 166). RESULTS: Models using European LeukemiaNet guidelines were significantly associated with clinical outcomes and, therefore, utilized as a baseline for comparisons. Models incorporating age and expression of select transcripts with biomarkers from European LeukemiaNet guidelines demonstrated higher area under the curve and C-statistics but did not show a substantial improvement in performance in the validation cohort. Subset analyses demonstrated that models using only the European LeukemiaNet guidelines were a better fit for younger patients (age < 55) than for older patients. Models integrating age and European LeukemiaNet guidelines visually showed more separation between risk groups in older patients. Models excluding results for ASXL1, CEBPA, RUNX1 and TP53, demonstrated that these mutations provide a limited overall contribution to risk stratification across the entire population, given the low frequency of mutations and confounding risk factors. CONCLUSIONS: While European LeukemiaNet guidelines remain a critical tool for triaging patients with acute myeloid leukemia, the findings illustrate the need for additional prognostic factors, including age, to improve risk stratification.