Paediatric bi-ventricular external assist device based on artificial muscles

Objective: Existing VADs are single-ventricle pumps needing anticoagulation. We developed a bi ventricular external assist device that reproduces the physiological heart muscle movement completely avoiding anticoagulants. Methods: The device has a carbon fibre skeleton fitting a 30-40 kg patient's heart, to which a Nitinol based artificial muscle is connected. The artificial muscle wraps both ventricles. The strength of the Nitinol fibres is amplified by a pivot articulation in contact with the ventricle wall. The fibres are electrically driven and a dedicated control unit has been developed. We assessed hemodynamic performances of this device using a previously described dedicated bench test. Volume ejected and pressure gradient has been measured with afterload ranging from 25 to 50mmHg. Results: With anafterload of 50mmHg the system has an ejection fraction (EF) of 10% on the right side and 8% on the left side. The system is able to generate a systolic ejection of 5,5 ml on the right side and 4,4 ml on the left side. With anafterload of 25mmHg the results are reduced of about 20%. The activation frequency is 80/minute resulting in a total volume displacement of 440 ml/minute on the right side and 352 ml/minute on the left side. Conclusions: The artificial muscle follows Starling's law as the ejected volume increases when afterload increases. These preliminary studies confirmed the possibility of improving the EF of a failing heart using artificial muscle for external cardiac compression. This device could be helpful in weaning CPB and/or for short-term cardio-circulatory support in paediatric population with cardiac failure.

Intravoxel incoherent motion diffusion-weighted imaging in the liver: comparison of mono-, bi- and tri-exponential modelling at 3.0-T.

PURPOSE: To determine whether a mono-, bi- or tri-exponential model best fits the intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) signal of normal livers. MATERIALS AND METHODS: The pilot and validation studies were conducted in 38 and 36 patients with normal livers, respectively. The DWI sequence was performed using single-shot echoplanar imaging with 11 (pilot study) and 16 (validation study) b values. In each study, data from all patients were used to model the IVIM signal of normal liver. Diffusion coefficients (Di ± standard deviations) and their fractions (fi ± standard deviations) were determined from each model. The models were compared using the extra sum-of-squares test and information criteria. RESULTS: The tri-exponential model provided a better fit than both the bi- and mono-exponential models. The tri-exponential IVIM model determined three diffusion compartments: a slow (D1 = 1.35 ± 0.03 × 10(-3) mm(2)/s; f1 = 72.7 ± 0.9 %), a fast (D2 = 26.50 ± 2.49 × 10(-3) mm(2)/s; f2 = 13.7 ± 0.6 %) and a very fast (D3 = 404.00 ± 43.7 × 10(-3) mm(2)/s; f3 = 13.5 ± 0.8 %) diffusion compartment [results from the validation study]. The very fast compartment contributed to the IVIM signal only for b values ≤15 s/mm(2) CONCLUSION: The tri-exponential model provided the best fit for IVIM signal decay in the liver over the 0-800 s/mm(2) range. In IVIM analysis of normal liver, a third very fast (pseudo)diffusion component might be relevant. KEY POINTS: ? For normal liver, tri-exponential IVIM model might be superior to bi-exponential ? A very fast compartment (D = 404.00 ± 43.7 × 10 (-3)  mm (2) /s; f = 13.5 ± 0.8 %) is determined from the tri-exponential model ? The compartment contributes to the IVIM signal only for b ≤ 15 s/mm (2.)

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.