Oesophageal afferent pathway sensitivity in non-erosive reflux disease

Patients with non-erosive reflux disease (NERD) report symptoms which commonly fail to improve on conventional antireflux therapies. Oesophageal visceral hyperalgaesia may contribute to symptom generation in NERD and we explore this hypothesis using oesophageal evoked potentials. Fifteen endoscopically confirmed NERD patients (four female, 29–56 years) plus 15 matched healthy volunteers (four female, 23–56 years) were studied. All patients had oesophageal manometry/24-h pH monitoring and all subjects underwent evoked potential and sensory testing, using electrical stimulation of the distal oesophagus. Cumulatively, NERD patients had higher sensory thresholds and increased evoked potential latencies when compared to controls (P = 0.01). In NERD patients, there was a correlation between pain threshold and acid exposure as determined by DeMeester score (r = 0.63, P = 0.02), with increased oesophageal sensitivity being associated with lower DeMeester score. Reflux negative patients had lower pain thresholds when compared to both reflux positive patients and controls. Evoked potentials were normal in reflux negative patients but significantly delayed in the reflux positive group (P = 0.01). We demonstrate that NERD patients form a continuum of oesophageal afferent sensitivity with a correlation between the degree of acid exposure and oesophageal pain thresholds. We provide objective evidence that increased oesophageal pain sensitivity in reflux negative NERD is associated with heightened afferent sensitivity as normal latency evoked potential responses could be elicited with reduced afferent input. Increased oesophageal afferent pain sensitivity may play an important role in a subset of NERD and could offer an alternate therapeutic target.

Neurophysiologic assessment of esophageal sensory processing in noncardiac chest pain

Background & Aims: Esophageal hypersensitivity is thought to be important in the generation and maintenance of symptoms in noncardiac chest pain (NCCP). In this study, we explored the neurophysiologic basis of esophageal hypersensitivity in a cohort of NCCP patients. Methods: We studied 12 healthy controls (9 women; mean age, 37.1 ± 8.7 y) and 32 NCCP patients (23 women; mean age, 47.2 ± 10 y). All had esophageal manometry, esophageal evoked potentials to electrical stimulation, and NCCP patients had 24-hour ambulatory pH testing. Results: The NCCP patients had reduced pain thresholds (PT) (72.1 ± 19.4 vs 54.2 ± 23.6, P = .02) and increased P1 latencies (P1 = 105.5 ± 11.1 vs 118.1 ± 23.4, P = .02). Subanalysis showed that the NCCP group could be divided into 3 distinct phenotypic classifications. Group 1 had reduced pain thresholds in conjunction with normal/reduced latency P1 latencies (n = 9). Group 2 had reduced pain thresholds in conjunction with increased (>2.5 SD) P1 latencies (n = 7), and group 3 had normal pain thresholds in conjunction with either normal (n = 10) or increased (>2.5 SD, n = 3) P1 latencies. Conclusions: Normal esophageal evoked potential latencies with reduced PT, as seen in group 1 patients, is indicative of enhanced afferent transmission and therefore increased esophageal afferent pathway sensitivity. Increased esophageal evoked potential latencies with reduced PT in group 2 patients implies normal afferent transmission to the cortex but heightened secondary cortical processing of this information, most likely owing to psychologic factors such as hypervigilance. This study shows that NCCP patients with esophageal hypersensitivity may be subclassified into distinct phenotypic subclasses based on sensory responsiveness and objective neurophysiologic profiles. © 2006 by the American Gastroenterological Association.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Specification and quantitative analysis of probabilistic cloud deployment patterns

Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments. © 2012 Springer-Verlag Berlin Heidelberg.

The functional neuroanatomy of auditory sensory gating and its behavioural implications

Auditory sensory gating (ASG) is the ability in individuals to suppress incoming irrelevant sensory input, indexed by evoked response to paired auditory stimuli. ASG is impaired in psychopathology such as schizophrenia, in which it has been proposed as putative endophenotype. This study aims to characterise electrophysiological properties of the phenomenon using MEG in time and frequency domains as well as to localise putative networks involved in the process at both sensor and source level. We also investigated the relationship between ASG measures and personality profiles in healthy participants in the light of its candidate endophenotype role in psychiatric disorders. Auditory evoked magnetic fields were recorded in twenty seven healthy participants by P50 ‘paired-click’ paradigm presented in pairs (conditioning stimulus S1- testing stimulus S2) at 80dB, separated by 250msec with inter trial interval of 7-10 seconds. Gating ratio in healthy adults ranged from 0.5 to 0.8 suggesting dimensional nature of P50 ASG. The brain regions active during this process were bilateral superior temporal gyrus (STG) and bilateral inferior frontal gyrus (IFG); activation was significantly stronger in IFG during S2 as compared to S1 (at p<0.05). Measures of effective connectivity between these regions using DCM modelling revealed the role of frontal cortex in modulating ASG as suggested by intracranial studies, indicating major role of inhibitory interneuron connections. Findings from this study identified a unique event-related oscillatory pattern for P50 ASG with alpha (STG)-beta (IFG) desynchronization and increase in cortical oscillatory gamma power (IFG) during S2 condition as compared to S1. These findings show that the main generator for P50 response is within temporal lobe and that inhibitory interneurons and gamma oscillations in the frontal cortex contributes substantially towards sensory gating. Our findings also show that ASG is a predictor of personality profiles (introvert vs extrovert dimension).