Impact of serodiagnosis on the management of Lyme borreliosis at Angers University Hospital

Introduction Lyme borreliosis (LB) is an emerging arthropod-borne disease the diagnosis of which is made on clinical and biological data. We assessed the Angers University Hospital physicians’ management of LB, in case of positive serology, and estimated their compliance to European recommendations (EUCALB). Methods We retrospectively included 75 cases with positive ELISA serologies confirmed by Western-Blot, performed at the Angers University Hospital between 2008 and 2012. Results and discussion There were 4 cases of early localized phase, 26 of early-disseminated phase (including 17 cases of neuroborreliosis), and one case of late phase. The curative management complied with EUCALB guidelines in 28 cases out of 31. Conclusion Serology remains a reference diagnostic tool for LB, as long as the practitioner is aware of the main clinical and biological criteria

Mitochondrial dysfunction increases fatty acid β-oxidation and translates into impaired neuroblast maturation

The metabolic transition from anaerobic glycolysis and fatty acid \u3b2-oxidation to glycolysis coupled to oxidative phosphorylation is a key process for the transition of quiescent neural stem cells to proliferative neural progenitor cells. However, a full characterization of the metabolic shift and the involvement of mitochondria during the last step of neurogenesis, from neuroblasts to neuron maturation, is still elusive. Here, we describe a model of neuroblasts, Neuro2a cells, with impaired differentiation capacity due to mitochondrial dysfunction. Using a detailed biochemical characterization consisting of steady-state metabolomics and metabolic flux analysis, we find increased fatty acid \u3b2-oxidation as a peculiar feature of neuroblasts with altered mitochondria. The consequent metabolic switch favors neuroblast proliferation at the expense of neuron maturation

Ketogenic Diet : a New Light Shining on Old but Gold Biochemistry

Diets low in carbohydrates and proteins and enriched in fat stimulate the hepatic synthesis of ketone bodies (KB). These molecules are used as alternative fuel for energy production in target tissues. The synthesis and utilization of KB are tightly regulated both at transcriptional and hormonal levels. The nuclear receptor peroxisome proliferator activated receptor \u3b1 (PPAR\u3b1), currently recognized as one of the master regulators of ketogenesis, integrates nutritional signals to the activation of transcriptional networks regulating fatty acid \u3b2-oxidation and ketogenesis. New factors, such as circadian rhythms and paracrine signals, are emerging as important aspects of this metabolic regulation. However, KB are currently considered not only as energy substrates but also as signaling molecules. \u3b2-hydroxybutyrate has been identified as class I histone deacetylase inhibitor, thus establishing a connection between products of hepatic lipid metabolism and epigenetics. Ketogenic diets (KD) are currently used to treat different forms of infantile epilepsy, also caused by genetic defects such as Glut1 and Pyruvate Dehydrogenase Deficiency Syndromes. However, several researchers are now focusing on the possibility to use KD in other diseases, such as cancer, neurological and metabolic disorders. Nonetheless, clear-cut evidence of the efficacy of KD in other disorders remains to be provided in order to suggest the adoption of such diets to metabolic-related pathologies

The imaging properties of the Gas Pixel Detector as a focal plane polarimeter

X-rays are particularly suited to probe the physics of extreme objects. However, despite the enormous improvements of X-ray Astronomy in imaging, spectroscopy and timing, polarimetry remains largely unexplored. We propose the photoelectric polarimeter Gas Pixel Detector (GPD) as an instrument candidate to fill the gap of more than thirty years of lack of measurements. The GPD, in the focus of a telescope, will increase the sensitivity of orders of magnitude. Moreover, since it can measure the energy, the position, the arrival time and the polarization angle of every single photon, allows to perform polarimetry of subsets of data singled out from the spectrum, the light curve or the image of source. The GPD has an intrinsic very fine imaging capability and in this work we report on the calibration campaign carried out in 2012 at the PANTER X-ray test facility of the Max-Planck-Institut f\"ur extraterrestrische Physik of Garching (Germany) in which, for the first time, we coupled it to a JET-X optics module with a focal length of 3.5 m and an angular resolution of 18 arcsec at 4.5 keV. This configuration was proposed in 2012 aboard the X-ray Imaging Polarimetry Explorer (XIPE) in response to the ESA call for a small mission. We derived the imaging and polarimetric performance for extended sources like Pulsar Wind Nebulae and Supernova Remnants as case studies for the XIPE configuration, discussing also possible improvements by coupling the detector with advanced optics, having finer angular resolution and larger effective area, to study with more details extended objects.Comment: Accepted for publication in The Astrophysical Journal Supplemen

Experimental data based machine learning classification models with predictive ability to select in vitro active antiviral and non-toxic essential oils

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combination with known drugs. Minor studies involved essential oil inspection as potential anticancer and antiviral natural remedies. In line with the authors previous reports the investigation of an in-house library of extracted essential oils as a potential blocker of HSV-1 infection is reported herein. A subset of essential oils was experimentally tested in an in vitro model of HSV-1 infection and the determined IC50s and CC50s values were used in conjunction with the results obtained by gas-chromatography/mass spectrometry chemical analysis to derive machine learning based classification models trained with the partial least square discriminant analysis algorithm. The internally validated models were thus applied on untested essential oils to assess their effective predictive ability in selecting both active and low toxic samples. Five essential oils were selected among a list of 52 and readily assayed for IC50 and CC50 determination. Interestingly, four out of the five selected samples, compared with the potencies of the training set, returned to be highly active and endowed with low toxicity. In particular, sample CJM1 from Calaminta nepeta was the most potent tested essential oil with the highest selectivity index (IC50 = 0.063 mg/mL, SI > 47.5). In conclusion, it was herein demonstrated how multidisciplinary applications involving machine learning could represent a valuable tool in predicting the bioactivity of complex mixtures and in the near future to enable the design of blended essential oil possibly endowed with higher potency and lower toxicity

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters

Cardiac resynchronization therapy after coronary sinus lead extraction: feasibility and mid-term outcome of transvenous reimplantation in a tertiary referral centre

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Incremental value of 3D echocardiography and two-dimensional speckle tracking in the early detection of cardiotoxicity linked to chemotherapy and trastuzumab in patients with HER-2 positive breast cancer

Abstract not availabl

Age-related changes in bile acid synthesis and hepatic nuclear receptor expression

BACKGROUND:Recent data highlighted the role of nuclear receptors in the transcriptional regulation of the limiting enzyme of bile acid synthesis, cholesterol 7alpha-hydroxylase, in cellular and animal models. This study was designed to analyze the effects of age on cholesterol 7alpha-hydroxylase and related nuclear receptor expression in human livers.DESIGN:Surgical liver biopsies were obtained in 23 patients requiring operation on the gastrointestinal tract. mRNA levels of cholesterol 7alpha-hydroxylase and related nuclear receptors and co-activators were assayed by quantitative real-time RT-PCR. Serum levels of 7alpha-hydroxy-4-cholesten-3-one, a marker of bile acid synthesis, were assayed by gas-liquid chromatography:mass spectrometry.RESULTS:Ageing was inversely correlated with serum 7alpha-hydroxy-4-cholesten-3-one and with cholesterol 7alpha-hydroxylase mRNA levels (r = -0.44 and r = -0.45 on a semi-log scale, respectively, P < 0.05). Among different nuclear factors, cholesterol 7alpha-hydroxylase mRNA best correlated with hepatocyte nuclear factor-4 (r = 0.55 on a log scale, P < 0.05); hepatocyte nuclear factor-4 levels were also inversely correlated with age (r = -0.64 on a semi-log scale, P < 0.05). Age was inversely correlated with serum insulin-like growth factor-I levels, which were directly correlated with hepatocyte nuclear factor-4 and cholesterol 7alpha-hydroxylase expression. No suppressive effect of short heterodimer partner expression on cholesterol 7alpha-hydroxylase was observed.CONCLUSIONS:Ageing associates with reduced bile acid synthesis, possibly related to decreased hepatic expression of hepatocyte nuclear factor-4 and consequently of cholesterol 7alpha-hydroxylase. Age-related modifications of the growth hormone/insulin-like growth factor axis might play a role. These findings may help to elucidate the pathophysiology of age-related modifications of cholesterol metabolism