Biomedical subjects

M Praisler

Publications and source records attributed to M Praisler.

3 recordsLinked to original sources

PubMed2006

Class identity assignment for amphetamines using neural networks and GC-FTIR data.

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.

Amphetamines↗

PubMed2002

Chemometric detection of thermally degraded samples in the analysis of drugs of abuse with gas chromatography-Fourier-transform infrared spectroscopy.

We present a chemometric procedure for the identification of the reference standard chromatographic peak in cases where the GC-FTIR analysis of commercial standards results in the appearance of more than one peak in the GC chromatogram. The procedure has been designed for phenethylamines, which represent the class with the largest number of individual molecules on the illicit drug market, and which are abused for their stimulant and/or hallucinogenic effects. The similarity between their vapor-phase FTIR spectra was modeled using principal component analysis (PCA), and class identity was assigned on the basis of soft independent modeling of class analogy (SIMCA). Additional peaks could be assigned to impurities in the standards, but most often they were artifacts formed during the GC-FTIR analysis of thermolabile or chemically unstable compounds. The latter case is illustrated by the identification of the reference standard chromatographic peak and FTIR spectrum of the potent psychotropic amphetamine derivative N-methyl-1-(3,4-methylenedioxyphenyl)-2-butanamine (MBDB), and by the elucidation of the chemical changes that occur in the molecule of MBDB due to thermal degradation.

Chromatography, Gas↗

PubMedDate not supplied

Computer-aided screening for hallucinogenic and stimulant amphetamines with gas chromatography-Fourier transform infrared spectroscopy (GC-FTIR).

An expert system applied as a screening test for amphetamine analogues found in recreational-drug exhibits (tablets or powders) is described. The knowledge base defining the reference Fourier transform infrared spectroscopic (FTIR) spectral patterns has been built according to criteria encompassing toxicological, pharmacological, and neurochemical aspects. The class identity of a compound is determined within seconds using soft independent modeling of class analogy (SIMCA). The predictive value of the system, as assessed at a testing accuracy of 95%, is expressed by a total correct classification rate of 93.93% and by a 96.30% rate of true-positive amphetamines. The specificity and the selectivity of the screening test, evaluated by testing 159 toxicologically relevant compounds, are discussed, emphasizing the chemical and physical factors affecting these parameters. Medicinal amphetamines giving cross-reactions with traditional screening techniques produce a negative result. The specificity of the system characterizes the expert system as a highly sensitive, selective, fast, and user-friendly screening test that screens for amphetamines with prediction accuracy adequate for investigations in analytical toxicology.

Amphetamines↗