Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

Predicting the Risk of Diabetic Foot Ulcers From Diabetics With Dysmetabolism: A Retrospective Clinical Trial

BACKGROUND

Diabetic foot ulcer (DFU) in patients with type 2 diabetes mellitus (T2D) often leads to amputation. Early intervention to prevent DFU is urgently necessary. So far, there have been no studies on predictive models associated with DFU risk factors. Our study aimed to quantify the predictive risk value of DFU, promote health education, and further develop behavioral interventions to reduce the incidence of DFU.

METHODS

Data from 973 consecutive patients with T2D was collected from two hospitals. Patients from the Guangxi Medical University First Affiliated Hospital formed the training cohort (n = 853), and those from the Wuming Hospital of Guangxi Medical University formed the validation cohort (n = 120). Independent variable grouping analysis and multivariate logistic regression analysis were used to determine the risk factors of DFUs. The prediction model was established according to the related risk factors. In addition, the accuracy of the model was evaluated by specificity, sensitivity, predictive value, and predictive likelihood ratio.

RESULTS

In total, 369 of the 853 patients (43.3%) and 60 of the 120 (50.0%) were diagnosed with DFUs in the two hospitals. The factors associated with DFU were old age, male gender, lower body mass index (BMI), longer duration of diabetes, history of foot disease, cardiac insufficiency, no use of oral hypoglycemic agent (OHA), high white blood cell count, high platelet count, low hemoglobin level, low lymphocyte absolute value, and high postprandial blood glucose. After incorporating these 12 factors, the nomogram drawn achieved good concordance indexes of 0.89 [95% confidence interval (CI): 0.87 to 0.91] in the training cohort and 0.84 (95% CI: 0.77 to 0.91) in the validation cohort in predicting DFUs and had well-fitted calibration curves. Patients who had a nomogram score of ≥180 were considered to have a low risk of DFU, whereas those having ≥180 were at high risk.

CONCLUSIONS

A nomogram was constructed by combining 12 identified risk factors of DFU. These 12 risk factors are easily available in hospitalized patients, so the prediction of DFU in hospitalized patients with T2D has potential clinical significance. The model provides a reliable prediction of the risk of DFU in patients with T2D.

Statistical modeling of methylene blue degradation by yeast-bacteria consortium; optimization via agro-industrial waste, immobilization and application in real effluents

The progress in industrialization everyday life has led to the continuous entry of several anthropogenic compounds, including dyes, into surrounding ecosystem causing arduous concerns for human health and biosphere. Therefore, microbial degradation of dyes is considered an eco-efficient and cost-competitive alternative to physicochemical approaches. These degradative biosystems mainly depend on the utilization of nutritive co-substrates such as yeast extract peptone in conjunction with glucose. Herein, a synergestic interaction between strains of mixed-culture consortium consisting of Rhodotorula sp., Raoultella planticola; and Staphylococcus xylosus was recruited in methylene blue (MB) degradation using agro-industrial waste as an economic and nutritive co-substrate. Via statistical means such as Plackett-Burman design and central composite design, the impact of significant nutritional parameters on MB degradation was screened and optimized. Predictive modeling denoted that complete degradation of MB was achieved within 72 h at MB (200 mg/L), NaNO3 (0.525 gm/L), molasses (385 μL/L), pH (7.5) and inoculum size (18%). Assessment of degradative enzymes revealed that intracellular NADH-reductase and DCIP-reductase were key enzymes controlling degradation process by 104.52 ± 1.75 and 274.04 ± 3.37 IU/min/mg protein after 72 h of incubation. In addition, azoreductase, tyrosinase, laccase, nitrate reductase, MnP and LiP also contributed significantly to MB degradation process. Physicochemical monitoring analysis, namely UV-Visible spectrophotometry and FTIR of MB before treatment and degradation byproducts indicated deterioration of azo bond and demethylation. Moreover, the non-toxic nature of degradation byproducts was confirmed by phytotoxicity and cytotoxicity assays. Chlorella vulgaris retained its photosynthetic capability (˃ 85%) as estimated from Chlorophyll-a/b contents compared to ˃ 30% of MB-solution. However, the viability of Wi-38 and Vero cells was estimated to be 90.67% and 99.67%, respectively, upon exposure to MB-metabolites. Furthermore, an eminent employment of consortium either freely-suspended or immobilized in plain distilled water and optimized slurry in a bioaugmentation process was implemented to treat MB in artificially-contaminated municipal wastewater and industrial effluent. The results showed a corporative interaction between the consortium examined and co-existing microbiota; reflecting its compatibility and adaptability with different microbial niches in different effluents with various physicochemical contents.