Power processing circuits for electromagnetic, electrostatic and piezoelectric inertial energy scavengers

Accepted versio

Effect of sitagliptin, a dipeptidyl peptidase-4 inhibitor, on beta-cell function in patients with type 2 diabetes: a model-based approach

Purpose:  The purpose of this exploratory analysis was to assess the effect of sitagliptin, a dipeptidyl peptidase-4 inhibitor, on pancreatic beta-cell function using a model-based analysis. Methods:  Data for this analysis were from three large, placebo-controlled clinical studies that examined sitagliptin 100 mg q.d. as add-on to metformin therapy or as monotherapy over 18 or 24 weeks. In these studies, subsets of patients consented to undergo extensive blood sampling as part of a nine-point meal tolerance test performed at baseline and study end-point. Blood samples were collected at −10, 0, 10, 20, 30, 60, 90, 120 and 180 min relative to the start of a meal and subsequently were assayed for plasma glucose and serum C-peptide concentrations. Parameters for beta-cell function were calculated using the C-peptide minimal model, which estimates insulin secretion rate (ISR) and partitions the ISR into basal (Φb; ISR at basal glucose concentrations), static (Φs; ISR at above basal glucose concentrations following a meal) and dynamic (Φd; ISR in response to the rate of increase in above basal glucose concentrations following a meal) components. The total responsivity index (Φtotal; average ISR over the average glucose concentration) is calculated as a function of Φs, Φd and Φb. Insulin sensitivity was assessed with a validated composite index (ISI). Disposition indices (DI), which assess insulin secretion in the context of changes in insulin sensitivity, were calculated as the product of Φand ISI. Results:  When administered in combination with ongoing metformin therapy or as monotherapy, sitagliptin was associated with substantial reductions in postprandial glycaemic excursion following a meal challenge relative to placebo. Sitagliptin produced significant (p < 0.05 vs. placebo) improvements in Φs and Φtotal, regardless of treatment regimen (add-on to metformin or as monotherapy). For Φd, there was a numerical, but not statistically significant, improvement with sitagliptin relative to placebo. Treatment with sitagliptin increased Φb, but the difference relative to placebo was only significant with monotherapy. ISI was not significantly different between sitagliptin and placebo. The DIs for the static, dynamic and total measures were significantly (p < 0.05) increased with sitagliptin treatment relative to placebo. Conclusions:  In this model-based analysis, sitagliptin improved beta-cell function relative to placebo in both fasting and postprandial states in patients with type 2 diabetes

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND:With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP. METHODS AND FINDINGS:We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent [hepatic] porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed. CONCLUSIONS:The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP

Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

[This corrects the article DOI: 10.1371/journal.pone.0235574.]

Physiological adjustments and arteriolar remodelling within skeletal muscle during acclimation to chronic hypoxia in the rat

We have investigated the physiological and structural changes that occur in skeletal muscle vasculature during acclimation to chronic hypoxia in rats exposed to 12% O2 in a hypoxic chamber for 7 or 18 days (7CH and 18CH rats, respectively) and in age-matched normoxic (7N and 18N) rats.Under anaesthesia and breathing 12% O2, 7CH and 18CH rats had lower arterial blood pressure (ABP) than 7N and 18N rats breathing air, but the haematocrit of the CH rats was increased so that their arterial O2 content equalled that of N rats. Blood flow recorded from the iliac or femoral artery and used to compute muscle vascular conductance (MVC: blood flow/ABP) showed that, in 18CH rats, MVC was comparable with that of 18N rats.Maximal MVC induced by infusion of sodium nitroprusside (SNP) was used as an index of structural vascular conductance and compared with the MVC evoked by acute hypoxia (breathing 8% O2). Hypoxia induced similar increases in MVC in 7N and 7CH rats and in 18N and 18CH rats, even though N rats were switched from air to 8% O2 and CH rats were switched from 12 to 8% O2. The MVCs attained with 8% O2 and SNP were similar in 7N and 18N rats. However, the MVCs attained with 8% O2 in 7CH and 18CH rats were only ≈60% of those evoked by SNP, while the MVC attained with SNP was greater in 18CH than in 18N rats.Vascular casts of the spinotrapezius muscle analysed ex vivo showed that interbranch intervals along primary, secondary and terminal arterioles (22–50, 13–18 and 7–13 μm diameter, respectively) were 30–50% shorter in 7CH and 18CH rats than in 7N and 18N rats. Further, the proportions of branches that were of the secondary and terminal arteriolar categories were increased such that the mean diameter of the branches was lower in 7CH than in 7N rats and lower in 18CH than in 18N rats.These results indicate that arteriolar remodelling and angiogenesis occurs in skeletal muscle during acclimation to chronic hypoxia, beginning by the 7th day and progressing at least until the 18th day, so that the number of small arterioles and the functional size of the vascular bed is increased. We propose that these structural and functional changes enhance the ability of skeletal muscle to respond to acute hypoxia by facilitating the increase in vascular conductance, blood flow and thereby the O2 that can be delivered to muscle

Smart pipes-instrumented water pipes, can this be made a reality?

Several millions of kilometres of pipes and cables are buried beneath our streets in the UK. As they are not visible and easily accessible, the monitoring of their integrity as well as the quality of their contents is a challenge. Any information of these properties aids the utility owners in their planning and management of their maintenance regime. Traditionally, expensive and very localised sensors are used to provide irregular measurements of these properties. In order to have a complete picture of the utility network, cheaper sensors need to be investigated which would allow large numbers of small sensors to be incorporated into (or near to) the pipe leading to so-called smart pipes. This paper focuses on a novel trial where a short section of a prototype smart pipe was buried using mainly off-the-shelf sensors and communication elements. The challenges of such a burial are presented together with the limitations of the sensor system. Results from the sensors were obtained during and after burial indicating that off-the-shelf sensors can be used in a smart pipes system although further refinements are necessary in order to miniaturise these sensors. The key challenges identified were the powering of these sensors and the communication of the data to the operator using a range of different methods