Instantaneous analysis of aldehydes in biological fluids using a spray interface coupled to a mass spectrometer.

A new interface coupled to a mass spectrometer was developed for the direct analysis of volatile organic compounds from small volumes of aqueous samples, including blood or tissue homogenates (St-Germain et al. 1995, Anal. Chem. 67:4536-4541). The greatest advantages of our system are minimal sample treatment, an instantaneous response time coupled with detection limits in the range of < 1 ppb for most compounds. For the analysis of low-molecular weight aldehydes, such as formaldehyde, acetaldehyde, propanal, and hexanal, lower detection limits were obtained when samples were converted to methoxime derivatives prior to injection. The detection limit for hexanal in water or Krebs-Ringer solution was 0.01 microM (10 pmol injected). The reproducibility of replicate injections was 4.4%. The usefulness of our system was illustrated by measuring aldehyde accumulation in peroxidized solutions of polyunsaturated fatty acids and rat tissue homogenates. Data confirmed that peroxidation of omega-3 fatty acids produces propanal, whereas omega-6 fatty acids form hexanal. Peroxidation of heart and brain homogenates formed predominantly propanal. However, the recovery of hexanal after sample treatment with methoxylamine depended on the derivatization time and temperature, suggesting that this aldehyde may form Schiff base linkages. These results show that spray extraction coupled to mass spectrometry provides a quick (< 1 min), clean and reproducible way to detect aldehydes produced from lipid peroxidation in aqueous samples.

[Application of correlation factors to the study of visual evoked potentials in multiple sclerosis (author's transl)]. / Applications des fonctions de corrélation a l'étude des potentiels évoqués visuels dans la sclérose en plaques.

A comparison is made between cerebral evoked potentials through the left and right eye by studying the correlation functions between the two visual responses obtained. Two values were retained: the correlation coefficient CO which shows the degree of similarity between the two curves, and the phase displacement D which indicates the duration for which a curve has to be shifted in relation to the other in order to resemble it as closely as possible. An original representation, in the form of a correlation/phase displacement diagram, is drawn for each subject, and enables easy separation of normal subjects from those with pathological conditions. This technique was applied to 20 normal subjects acting as controls and 40 patients with suspected or confirmed multiple sclerosis. In the latter group, anomalies were found in 67 p. cent whereas retrobulbar optic neuritis had been diagnosed in only 35 p. cent of cases. By reducing the sample to include only known or probable cases of MS, anomalies were found in 88 p. cent with retrobulbar optic neuritis known in only 51 p. cent. These results are comparable with those obtained by using previously described methods, but have the advantage of automatic readings which are completely objective.

The recruitment times and firing patterns of the medullary respiratory neurones of the cat.

The brainstem of anaesthetized cats was explored for respiratory neurones with microelectrodes in the regions of the nucleus retroambigualis (ventrolateral region) and the nucleus tractus solitarius (dorsomedial region). These neurones were analysed with respect to their firing patterns and recruitment times, as referenced to the respiratory cycle. All of the respiratory neurones showed a stable and ordered pattern of firing. Four groups of neurones with similar characteristics within each group, but differing from each other, were statistically examined; the inspiratory neurones of the dorsomedial region, the 'late' inspiratory neurones of the ventrolateral region, the 'early' inspiratory neurones of the ventrolateral region and the expiratory neurones of the ventrolateral region.

[An automated study of EEG: a method of detection of non-stationary points]. / Etude automatique de l'EEG: une méthode de detection des non stationnarités.

The purpose of this paper is to present an automatic method of signal analysis. To help physicians in their diagnostics, this method is implemented on a minicomputer in order to detect non-stationary points in electroencephalograms. The signal is modelled with an autoregressive filter. The parameters of this filter are adapted at each step. Identification gives the best model in the sense of a cost function representing the mean square error of noise, which is estimated during the optimisation time-window. The cost function is expressed by a quadratic formula. This allows the use of a fast algorithm, the 'conjugate gradient method'. An original statistical test is developed to detect non-stationary points in the signal. The performance of this method is tested with artificial data to determine the sensitivity of method parameters. Detection using real data is presented.