Chemometric models for toxicity classification based on NMR spectra of biofluids.

1H NMR spectroscopic and pattern recognition (PR)-based methods were used to investigate the biochemical variability in urine obtained from control rats and from rats treated with a hydrazine (a model hepatotoxin) or HgCl(2) (a model renal cortical toxin). The 600 MHz (1)H NMR spectra of urine samples obtained from vehicle- or toxin-treated Han-Wistar (HW) and Sprague-Dawley (SD) rats were acquired, and principal components analysis (PCA) and soft independent modeling of class analogy (SIMCA) analysis were used to investigate the (1)H NMR spectral data. Variation and strain differences in the biochemical composition of control urine samples were assessed. Control urine (1)H NMR spectra obtained from the two rat strains appeared visually similar. However, chemometric analysis of the control urine spectra indicated that HW rat urine contained relatively higher concentrations of lactate, acetate, and taurine and lower concentrations of hippurate than SD rat urine. Having established the extent of biochemical variation in the two populations of control rats, PCA was used to evaluate the metabolic effects of hydrazine and HgCl(2) toxicity. Urinary biomarkers of each class of toxicity were elucidated from the PC loadings and included organic acids, amino acids, and sugars in the case of mercury, while levels of taurine, beta-alanine, creatine, and 2-aminoadipate were elevated after hydrazine treatment. SIMCA analysis of the data was used to build predictive models (from a training set of 416 samples) for the classification of toxicity type and strain of rat, and the models were tested using an independent set of urine samples (n = 124). Using models constructed from the first three PCs, 98% of the test samples were correctly classified as originating from control, hydrazine-treated, or HgCl(2)-treated rats. Furthermore, this method was sensitive enough to predict the correct strain of the control samples for 79% of the data, based upon the class of best fit. Incorporation of these chemometric methods into automated NMR-based metabonomics analysis will enable on-line toxicological assessment of biofluids and will provide a tool for probing the mechanistic basis of organ toxicity.

A comparison of spousal aggression prevalence rates in U.S. Army and civilian representative samples.

This study compared prevalence rates from reasonably representative U.S. Army (N = 33,762) and civilian (N = 3,044) samples. Age and race were controlled by weighting each sample to 1990 U.S. Census characteristics for married, full-time employed persons. Men's reports of moderate husband-to-wife spousal aggression were not significantly higher, but reports of severe aggression were significantly higher in the standardized Army sample than in the comparable civilian sample (adjusted rates of 2.5% vs. 0.7%, respectively). Thus, controlling for age and race results in reasonably similar prevalence rates. Future studies that more carefully control for any background differences in military and civilian respondents could discern whether military service adds any increased risk for partner violence.

Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition.

Pattern recognition approaches were developed and applied to the classification of 600 MHz 1H NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in either the liver or kidney. Male rats were separated into groups (n = 5) and each treated with one of the following compounds; adriamycin, allyl alcohol, 2-bromoethanamine hydrobromide, hexachlorobutadiene, hydrazine, lead acetate, mercury II chloride, puromycin aminonucleoside, sodium chromate, thioacetamide, 1,1,2-trichloro-3,3,3-trifluoro-1-propene or dose vehicle. Urine samples were collected over a 7 day time-course and analysed using 600 MHz 1H NMR spectroscopy. Each NMR spectrum was data-reduced to provide 256 intensity-related descriptors of the spectra. Data corresponding to the periods 8-24 h, 24-32 h and 32-56 h post-dose were first analysed using principal components analysis (PCA). In addition, samples obtained 120-144 h following the administration of adriamycin and puromycin were included in the analysis in order to compensate for the late onset of glomerular toxicity. Having established that toxin-related clustering behaviour could be detected in the first three principal components (PCs), three-quarters of the data were used to construct a soft independent modelling of class analogy (SIMCA) model. The remainder of the data were used as a test set of the model. Only three out of 61 samples in the test set were misclassified. Finally as a further test of the model, data from the 1H NMR spectra of urine from rats that had been treated with uranyl nitrate were used. Successful prediction of the toxicity type of the compound was achieved based on NMR urinalysis data confirming the robust nature of the derived model.

Predicting mild and severe husband-to-wife physical aggression.

Nonordered multinomial logistic models were used to estimate the odds of mild and severe husband-to-wife physical aggression in 11,870 White men. Being younger, having a lower income, and having an alcohol problem significantly increased the odds of either mild or severe physical aggression. A drug problem uniquely increased the risk of severe physical aggression. Marital discord and depressive symptomatology, but not work stress, further increased the odds of both mild and severe physical aggression, with martial discord being the most influential psychological variable. Compared with mildly physically aggressive men, those who were severely physically aggressive earned lower income, were more likely to report an alcohol or a drug problem, and had more marital discord and depressive symptomatology.

Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples.

A general method of automatically reducing NMR spectra to provide numerical descriptors of samples has been developed and investigated. These descriptors can be used as input to pattern recognition or multivariate algorithms for sample classification. The methods have been tested using 600 MHz one-dimensional 1H NMR spectra of biofluids which are complex mixtures. The approach is, in principle, applicable to multidimensional and heteronuclear NMR spectra and to other types of liquid samples such as oils and foodstuffs as well as to situations such as 1H or 31P NMR in vivo and solid state NMR in drug formulation analysis. The method relies upon apportioning the information in the spectra to individual contiguous segments and allowing specified regions of the spectra to be omitted. Three approaches, based on the number of peaks, the summed peak heights and the summed peak areas respectively in each segment, have been tested. The effect of segment width and overlap and the effects of manipulation of the NMR spectra have been evaluated in terms of the classification of the samples using principal components analysis. A simple method of generating NMR based spectral descriptors for object classification is thus proposed.

An investigation of procedures reported to increase potency of marijuana: a chemical analysis and psychological interpretation.

Three basic procedures from the "underground press" for increasing the potency of marijuana were tested quantitatively: Black Merta, Dry Ice, and isopropanol--water extraction. In all methods there was not significant change in THC, CBN, or CBD content before and after treatment. The persistence of procedures which have no measurable effect on the chemical properties of the drug is presented as additional evidence for the importance of psychological factors in perceived drug effects.