Synchronous and baroceptor-sensitive oscillations in skin microcirculation: evidence for central autonomic control.

To determine whether skin blood flow is local or takes part in general regulatory mechanisms, we recorded laser-Doppler flowmetry (LDF; left and right index fingers), blood pressure, muscle sympathetic nerve activity (MSNA), R-R interval, and respiration in 10 healthy volunteers and 3 subjects after sympathectomy. We evaluated 1) the synchronism of LDF fluctuations in two index fingers, 2) the relationship with autonomically mediated fluctuations in other signals, and 3) the LDF ability to respond to arterial baroreflex stimulation (by neck suction at frequencies from 0.02 to 0.20 Hz), using spectral analysis (autoregressive uni- and bivariate, time-variant algorithms). Synchronous LDF fluctuations were observed in the index fingers of healthy subjects but not in sympathectomized patients. LDF fluctuations were coherent with those obtained for blood pressure, MSNA, and R-R interval. LDF fluctuations were leading blood pressure in the low-frequency (LF; 0.1 Hz) band and lagging in the respiratory, high-frequency (HF; approximately 0.25 Hz) band, suggesting passive "downstream" transmission only for HF and "upstream" transmission for LF from the microvessels. LDF fluctuations were responsive to sinusoidal neck suction up to 0.1 Hz, indicating response to sympathetic modulation. Skin blood flow thus reflects modifications determined by autonomic activity, detectable by frequency analysis of spontaneous fluctuations.

The "reversed" treatment approach for synchronous liver metastases and colorectal cancer. Feasability and first results

Background: The « reversed treatment» approach inverts the treatment sequence of¦advanced synchronous colorectal liver metastases - i.e. the liver metastasis is¦treated first, followed by resection of the primary tumor. Chemotherapy is performed¦before and after liver surgery. We recently started to use a reversed treatment¦approach in selected patients. The aim of this study is to critically assess this new¦treatment modality.¦Methods: Nine patients (7 male, 2 female, mean age 62 years) benefited from this¦new treatment between November 2008 and May 2010. The data were collected¦retrospectively.¦Results: All patients responded to the neoadjuvant chemotherapy. The median¦number of liver metastases was 6 (range 1 - 22). The median size of the largest liver¦metastases was 4.3 cm (range 2.6 - 13 cm). Three patients had portal vein¦embolization prior to liver surgery. Two patients could not complete the treatment.¦One had to undergo emergency surgery for occluding colonic tumor. The second one¦showed liver recurrence before starting the adjuvant chemotherapy. The seven¦patients who completed the treatment are still alive after a median time of 27 months¦(range 17 - 37 months). Seven of them had recurrence (1 rectal, 6 liver). The median¦disease-free survival was 9 months (range 0 - 17 months).¦Conclusion: Based on our preliminary experiences, the reversed strategy shows¦encouraging results for the treatment of advanced synchronous colorectal liver¦metastases in well selected patients. The treatment was generally well tolerated and¦long term survival seems to be prolonged.

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Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.