A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Chronic bacterial prostatitis: efficacy of short-lasting antibiotic therapy with prulifloxacin (Unidrox®) in association with saw palmetto extract, lactobacillus sporogens and arbutin (Lactorepens®)

Bacterial prostatitis (BP) is a common condition accounting responsible for about 5-10% of all prostatitis cases; chronic bacterial prostatitis (CBP) classified as type II, are less common but is a condition that significantly hampers the quality of life, (QoL) because not only is it a physical condition but also a psychological distress. Commonly patients are treated with antibiotics alone, and in particular fluoroquinolones are suggested by the European Urology guidelines. This approach, although recommended, may not be enough. Thus, a multimodal approach to the prolonged antibiotic therapy may be helpful.210 patients affected by chronic bacterial prostatitis were enrolled in the study. All patients were positive to Meares-Stamey test and symptoms duration was > 3 months. The purpose of the study was to evaluate the efficacy of a long lasting therapy with a fluoroquinolone in association with a nutraceutical supplement (prulifloxacin 600 mg for 21 days and an association of Serenoa repens 320 mg, Lactobacillus Sporogens 200 mg, Arbutin 100 mg for 30 days). Patients were randomized in two groups (A and B) receiving respectively antibiotic alone and an association of antibiotic plus supplement.Biological recurrence at 2 months in Group A was observed in 21 patients (27.6%) and in Group B in 6 patients (7.8%). Uropathogens found at the first follow-up were for the majority Gram - (E. coli and Enterobacter spp.). A statistically significant difference was found at the time of the follow-up between Group A and B in the NIH-CPSI questionnaire score, symptoms evidence and serum PSA.Broad band, short-lasting antibiotic therapy in association with a nutritional supplement (serenoa repens, lactobacillus sporogens and arbutin) show better control and recurrence rate on patients affected by chronic bacterial prostatitits in comparison with antibiotic treatment alone.NCT02130713Date of trial Registration: 30/04/2014

In Silico Testing and in Vivo Experiments with Closed-Loop Control of Blood Glucose in Diabetes

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Brca germline mutations in prostate cancer: The future is tailored

: Prostate cancer (PCa) is the second most common neoplasm in men and the fifth leading cause of death worldwide [...]

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Metachronous bladder metastases from renal cell carcinoma: a case report and review of the literature

INTRODUCTION: adrenal gland, parotid gland, pharynx, eye and bladder are rare localizations of metastases of renal cell carcinoma (RCC). We report a case of metachronous RCC metastases to the bladder in a patient with a medical history of transitional cell carcinoma (TCC) of the bladder. MATERIALS AND METHODS: a case study and review of the relevant literature are presented. RESULTS: during a follow-up cystoscopy examination following treatment of TCC, a single 5-mm lesion was detected and endoscopically resected. The histology of the resected sample was confirmed to be RCC, comparable to a primary kidney cancer and not recurrent TCC. CONCLUSION: the patient had a probability of metastases three years after nephrectomy of 62.9%. Survival rates following single metastasectomy are 60% and 38% at three and five years, respectively; metachronous diagnosis has a better prognosis than synchronous. During RCC follow-up, each lesion should be considered as a possible metastasis of RCC