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PubMedDate not supplied

Proton nuclear magnetic resonance spectroscopy of plasma lipoprotein: technical problems and potential interest in cancer disease.

This paper reviews several methods presently available for analysing lipoprotein NMR spectra. Two main steps can be distinguished: NMR signal processing and data analysis. Time domain (wavelet transform) and frequency domain (curve fitting) signal processing methods are compared. Statistical methods of data analysis (Ascending Hierarchical Classification, Correspondence Analysis and Principal Component Analysis) have been tested on simulated NMR data of plasma lipoprotein with different numbers of sampling points and different noise levels. These few examples clearly attest that the NMR approach to complex "mixture" (such as body fluids) analysis is emerging from its infancy. New interest in plasma lipoprotein analysis in cancer biology is finally discussed in the light of previous clinical and experimental results and of understanding of lipid metabolism in cancer.

Humans↗

PubMed1997

Effect of acarbose on glucose intolerance in patients with non-insulin-dependent diabetes mellitus.

We evaluated the effect of acarbose, an alpha-glucosidase inhibitor, on glucose intolerance in patients with non-insulin-dependent diabetes mellitus (NIDDM). Acarbose was given orally (300 mg/day) for 24 weeks to 20 NIDDM patients. Data in an oral glucose tolerance test (OGTT) were evaluated before and after 24 weeks of treatment using principal component analysis. Acarbose administration significantly reduced the postprandial plasma glucose level over 24 weeks of treatment. Principal component analysis suggested that the patients were separated into responders and non-responders. There was a significant improvement of fasting and postprandial glucose levels after 12 and 24 weeks in the responders, but not in the non-responders. Plasma glucose level following the OGTT improved significantly after 24 weeks of treatment in the responders (Hotelling T2 value = 47.098, P = 0.022500), but not in the non-responders. The immunoreactive insulin level did not change in either group. Results thus suggest that acarbose improved insulin resistance in some patients with NIDDM (responders as classified by principal component analysis).

Acarbose↗

PubMed1990

Comparing principal components analyses of evoked potentials recorded from heterogeneous groups of subjects.

Principal components analysis of evoked potentials differing between groups presents an interpretive problem, particularly in psychiatric research. Two sets of principal components and associated factor scores may appear to differ. The issue is to determine the extent to which visually differing principal components and resultant factor scores span the same factor space. Sets of evoked potentials from controls and from schizophrenics were each subjected to principal components analysis, from which factor score coefficients were computed for all subjects. This allowed determination of the extent to which (1) the two sets of basis waves were similar and (2) the factor scores resulting from the set of basis waves derived from principal components analysis of the control subjects' evoked potential data adequately represented those of the schizophrenics and vice-versa. Canonical correlation analyses indicated substantial similarities between the principal component structures (sets of basis waves). Multiple correlational analyses confirmed that the basis waves from either group spanned the other group's factor space. Factor scores from either set of basis waves were highly correlated. These results suggest that principal component structures derived from evoked potentials of a control group may be used in computing evoked potential factor scores of psychiatrically diverse populations even though the average evoked potentials of the groups may differ in several ways.

Evoked Potentials↗

PubMed1997

Direction selectivity of synaptic potentials in simple cells of the cat visual cortex.

Direction selectivity of synaptic potentials in simple cells of the cat visual cortex. J. Neurophysiol. 78: 2772-2789, 1997. The direction selectivity of simple cells in the visual cortex is generated at least in part by nonlinear mechanisms. If a neuron were spatially linear, its responses to moving stimuli could be predicted accurately from linear combinations of its responses to stationary stimuli presented at different positions within the receptive field. In extracellular recordings, this has not been found to be the case. Although the extracellular experiments demonstrate the presence of a nonlinearity, the cellular process underlying the nonlinearity, whether an early synaptic mechanism such as a shunting inhibition or simply the spike threshold at the output, is not known. To differentiate between these possibilities, we have recorded intracellularly from simple cells of the intact cat with the whole cell patch technique. A linear model of direction selectivity was used to analyze the synaptic potentials evoked by stationary sine-wave gratings. The model predicted the responses of cells to moving gratings with considerable accuracy. The degree of direction selectivity and the time course of the responses to moving gratings were both well matched by the model. The direction selectivity of the synaptic potentials was considerably smaller than that of the intracellularly recorded action potential, indicating that a nonlinear mechanism such as threshold enhances the direction selectivity of the cell's output over that of its synaptic inputs. At the input stage, however, the cells apparently sum their synaptic inputs in a highly linear fashion. A more constrained test of linearity of synaptic summation based on principal component analysis was applied to the responses of direction-selective cells to stationary gratings. The analysis confirms that the summation in these cells is highly linear. The principal component analysis is consistent with a model in which direction selectivity in cortical simple cells is generated by only two subunits, each with a different receptive-field position and response time course. The response time course for each of the two subunits is derived for four analyzed cells. Each derived subunit is linear in spatial summation, suggesting that the neurons that comprise each subunit are either geniculate X-cells or receive their primary synaptic input from X-cells. The amplitude of the response of each subunit is linearly related to the contrast of the stimulus. The subunits are nonlinear in the time domain, however: the response to a stationary stimulus whose contrast is modulated sinusoidally in time is nonsinusoidal. The principal component analysis does not exclude models of direction selectivity based on more than two subunits, but such higher-order models would have to include the constraint that the extra subunits form a smooth continuum of interpolation between the properties derived from the two subunit solution.

Animals↗

PubMed2005

Classification of chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride and glucosamine 6 sulfate using chemometric techniques.

Chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride and glucosamine sulfate are natural products that are becoming increasingly popular in the treatment of arthritis. They belong to a class of compounds known as glycosaminoglycans (GAGs). They are available over the counter as nutritional supplements. However, increasing use has led to increasing scrutiny of the quality of products on the market. There is also interest in the pharmacological properties of these compounds. To facilitate this, there is a need for better qualitative and quantitative methods of analysis. This paper describes methods for achieving the qualitative identification of chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride or glucosamine sulfate. Fourier transform infrared spectroscopy coupled with a variety of chemometric methods successfully classified these compounds. Using soft independent modeling of class analogies (SIMCA), hierarchical cluster analysis (HCA) and principal components analysis (PCA) samples were classified as either chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride or glucosamine sulfate. This work also examined the discriminating ability of different sections of the spectrum. It was found that for the classification of these compounds that using the finger print region of the spectrum (below 2000 cm(-1)) gave the best discrimination.

Chondroitin Sulfates↗

PubMed2000

Using social indicators to inform community drug and alcohol prevention policy.

In recent years, the federal government has begun to require state agencies to allocate drug prevention resources according to the needs of local communities. The methods by which this is to be accomplished have not been described, and most published social indicator studies in the field of drug abuse research have used county-level data which are too insensitive to local needs to be of use in resource allocation decisions. The present study describes a needs assessment in drug abuse prevention in the state of New Jersey using municipal-level social indicator data. In addition, it examines the extent to which the resource allocation of one state prevention agency can be predicted by the municipal-level social indicators. Thirty-six social indicators pertaining to 508 municipalities were used in the study, and data were analyzed using principal component analysis and hierarchical regression analysis. Five factors were extracted from the principal component analysis, two of which clearly describe "high risk" municipalities and one of which clearly describes "low risk" municipalities. The regression analysis showed that these factors explained very little of the variance in the state agency's drug prevention spending. The study shows that social indicators can be used to distinguish between different levels of need for drug prevention services at a municipal level, and that these data can be used to inform decisions concerning resource allocation.

Alcoholism↗

PubMed2005

Multivariate analysis of selected metals in tannery effluents and related soil.

Effluent and relevant soil samples from 38 tanning units housed in Kasur, Pakistan, were obtained for metal analysis by flame atomic absorption spectrophotometric method. The levels of 12 metals, Na, Ca, K, Mg, Fe, Mn, Cr, Co, Cd, Ni, Pb and Zn were determined in the two media. The data were evaluated towards metal distribution and metal-to-metal correlations. The study evidenced enhanced levels of Cr (391, 16.7 mg/L) and Na (25,519, 9369 mg/L) in tannery effluents and relevant soil samples, respectively. The effluent versus soil trace metal content relationship confirmed that the effluent Cr was strongly correlated with soil Cr. For metal source identification the techniques of principal component analysis, and cluster analysis were applied. The principal component analysis yielded two factors for effluents: factor 1 (49.6% variance) showed significant loading for Ca, Fe, Mn, Cr, Cd, Ni, Pb and Zn, referring to a tanning related source for these metals, and factor 2 (12.6% variance) with higher loadings of Na, K, Mg and Co, was associated with the processes during the skin/hide treatment. Similarly, two factors with a cumulative variance of 34.8% were obtained for soil samples: factor 1 manifested the contribution from Mg, Mn, Co, Cd, Ni and Pb, which though soil-based is basically effluent-derived, while factor 2 was found associated with Na, K, Ca, Cr and Zn which referred to a tannery-based source. The dendograms obtained from cluster analysis, also support the observed results. The study exhibits a gross pollution of soils with Cr at levels far exceeding the stipulated safe limit laid down for tannery effluents.

Environmental Monitoring↗

PubMed2004

Application of a new statistical method to derive dietary patterns in nutritional epidemiology.

Because foods are consumed in combination, it is difficult in observational studies to separate the effects of single foods on the development of diseases. A possible way to examine the combined effect of food intakes is to derive dietary patterns by using appropriate statistical methods. The objective of this study was to apply a new statistical method, reduced rank regression (RRR), that is more flexible and powerful than the classic principal component analysis. RRR can be used efficiently in nutritional epidemiology by choosing disease-specific response variables and determining combinations of food intake that explain as much response variation as possible. The authors applied RRR to extract dietary patterns from 49 food groups, specifying four diabetes-related nutrients and nutrient ratios as responses. Data were derived from a nested German case-control study within the European Prospective Investigation into Cancer and Nutrition-Potsdam study consisting of 193 cases with incident type 2 diabetes identified until 2001 and 385 controls. The four factors extracted by RRR explained 93.1% of response variation, whereas the first four factors obtained by principal component analysis accounted for only 41.9%. In contrast to principal component analysis and other methods, the new RRR method extracted a significant risk factor for diabetes.

Alcohol Drinking↗

PubMed2003

Gaussian distributions on Lie groups and their application to statistical shape analysis.

The Gaussian distribution is the basis for many methods used in the statistical analysis of shape. One such method is principal component analysis, which has proven to be a powerful technique for describing the geometric variability of a population of objects. The Gaussian framework is well understood when the data being studied are elements of a Euclidean vector space. This is the case for geometric objects that are described by landmarks or dense collections of boundary points. We have been using medial representations, or m-reps, for modelling the geometry of anatomical objects. The medial parameters are not elements of a Euclidean space, and thus standard PCA is not applicable. In our previous work we have shown that the m-rep model parameters are instead elements of a Lie group. In this paper we develop the notion of a Gaussian distribution on this Lie group. We then derive the maximum likelihood estimates of the mean and the covariance of this distribution. Analogous to principal component analysis of covariance in Euclidean spaces, we define principal geodesic analysis on Lie groups for the study of anatomical variability in medially-defined objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.

Algorithms↗

PubMed2003

Identification of oil contaminants on polymer coated beverage cans using fluorescence spectroscopy.

In a recent publication we introduced a novel method for detection of impurities on beverage can surfaces using Raman spectroscopy. While investigating the technique, limitations emerged due to the low sensitivity of Raman scatter. This is a particular problem with the largest contributor of impurities in beverage cans: lubricants employed in the manufacturing process. This paper presents an alternative approach, using the more sensitive technique of fluorescence spectroscopy to tackle the same problem. Measurements using fluorescence spectroscopy were conducted indirectly in a cuvette as well as directly on a can surface with the aid of fiber optics. The chemometrics methods of linear discriminant analysis (LDA) and principal components analysis (PCA) were used to classify acquired spectra as belonging to one of the 16 investigated lubricants. Fiberoptic scans revealed that a successful detection and recognition of lubricant is possible down to a volume of 0.5 microL deposited on the can surface. Contaminant detection was possible at even lower levels down to 0.01 microL, but reliable identification proved difficult at such low volumes. Indirect measurements of lubricants dissolved in cyclohexane yielded the lowest detection limits between 1-0.1 ppb.

Beverages↗

PubMed2004

Dietary patterns and the adenomacarcinoma sequence of colorectal cancer.

BACKGROUND: Food components of a diet are highly related, so that building up dietary patterns may help understand the relationship between chronic diseases and diet, and identify high risk groups that need preventive advice. AIM: The aim of this study was to determine dietary patterns associated with the colorectal adenoma-carcinoma pathway. METHODS: We performed a two-step analysis using first principal component analysis to select the most appropriate food groups, then a hierarchical agglomerative clustering method, in order to determine dietary patterns in 1372 subjects included in a case-control study. Patients with hyperplastic polyps (n = 103), adenomas < 10 mm, (n = 154) or larger adenomas (n = 208) were then compared with polyp-free controls (n = 426), and colorectal cancer cases (n = 171) compared with population controls (n = 309) using unconditional logistic regression adjusted on age and gender. RESULTS: Cluster analysis determined five food patterns. Cluster 1 identified a low-energy diet; cluster 2 a high-starch, highfat, and low-fruit diet; cluster 3 a high-processed meat, -energy, -alcohol, and -starchy foods diet; cluster 4 a high-fish, -cereals, -honey, -olive oil, -fruit and -vegetables diet; and cluster 5 a high-flour, -sugar, -chocolate, -animal fats, and -eggs diet. Logistic regression identified cluster 2 [corrected] as significantly associated with risk of small adenomas (OR = 1.7; 95% confidence interval 1.0-2.7), large adenomas (OR = 1.9; 1.2-3.0) and cancers (OR = 1.7; 1.1-2.8) compared with cluster 1 [corrected] Cluster 4 diet was inversely associated with risk of small adenomas (OR = 0.4; 0.2-1.0). There was no relationship between patterns and risk of hyperplastic polyps. Multiple adjustment decreased the strength of the relationships with cluster 2 [corrected] which remained significantly associated with adenomas, but not cancer. CONCLUSION: A lowenergy diet appeared as protective all along the adenoma-carcinoma sequence, contrary to a high-energy, high-processed meat and -animal fat diet.

Adenocarcinoma↗

PubMed2005

Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses.

The merits of complementary multivariate techniques to identify QTL associated with multiple traits were evaluated. Records from 806 F2 pigs pertaining to a Berkshire x Duroc three-generation population were available. Six multitrait groups on SSC 2, 6, 13, and 18 with information on 30 markers were studied. Multivariate techniques studied included multivariate models and principal components analysis of each multitrait group. All models included, in addition to systematic effects, additive, dominance, and imprinting coefficients corresponding to a one-QTL model and a random family effect. Multivariate analysis identified QTL associated with genomewise significant variation in four of the multitrait groups. The majority of the multivariate analysis provided greater precision of parameter estimates and higher statistical significance in some cases than univariate approaches, because of the greater parameterization of the multivariate models and moderate information content of the data. Principal component analysis results were consistent with univariate and multivariate analyses and recovered the levels of statistical significance observed in univariate analyses on the original data. In addition, principal component analysis was able to provide a location associated with LM area not detected by other analyses. The relative advantage of multivariate over the univariate approaches varied with the level of genetic covariance between traits because of the modeled QTL effect and information contained in the data; however, multivariate approaches have the unique capability to identify pleiotropic effects or multiple linked QTL.

Animals↗

PubMed2006

Development of a COPD severity score.

OBJECTIVE: Treatment of chronic obstructive pulmonary disease (COPD) is based on symptom control. This suggests that COPD severity can be determined by analyzing treatment intensity. The objective of this analysis was to develop and validate a severity score for adult COPD based on treatments. RESEARCH DESIGN: Using principal components analysis, a COPD severity score was developed using data based on treatments extracted from an employer claims database (development group). Variables included were identified from literature review and clinical expert opinion. External validity was tested in a separate group of adult chronic bronchitis patients in whom principal components analysis was re-conducted and factor loadings were compared to the development group. Construct validity was tested by comparing the incidence of acute exacerbations of chronic bronchitis (AECB) in patients with high and lower severity scores. To illustrate the use of the COPD severity score, effectiveness of alternative AECB antibiotic treatments was compared in a separate patient sample categorized by severe versus mild/moderate COPD. RESULTS: In the development group (n = 2068), principal components analysis produced a single main factor for severity scoring. Of the 12 variables contributing to this factor, the 6 with the highest factor loadings were treatment related. The factor performed similarly in the external validity group (n = 9127) as it did in the development group. In construct validity testing, severe COPD patients were 4 times more likely to have AECB episodes than mild/moderate patients. Patients with severe COPD and an AECB were more likely to fail treatment with antibiotics than those with mild/moderate COPD. Based on the COPD severity score developed, we found that treatment of patients with severe COPD and an AECB with fluoroquinolones was more likely to result in treatment failure than treatment with macrolides (OR = 2.01; p = 0.03). CONCLUSIONS: The analysis was successful in developing and validating a method to score COPD severity based on treatments. This method may prove useful in providing insights about the benefits of COPD treatments.

Anti-Bacterial Agents↗

PubMed2004

Source contributions to fine particulate matter in an urban atmosphere.

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.

Air Pollutants↗

PubMed1989

Is major depressive disorder in adolescence a distinct diagnostic entity?

The concept of major depressive disorder in childhood and adolescence is reviewed and it is suggested that contemporary enthusiasm for this diagnosis may have outrun the evidence that it is a distinct categorical entity. To test the hypothesis that major depression is not a qualitatively distinct disorder in adolescence, but rather a continuously distributed, noncategorical syndrome, the behavioral rating scales (CBCL-P) of 216 hospitalized adolescent patients were analyzed first by principal components analysis and then by cluster analysis. Three behavioral syndromes were isolated by principal components analysis. Of three groups of patients identified by a subsequent cluster analysis, one was consistent with the concept of a categorically distinct "nuclear" depression. However, a noncategorical continuously distributed depressive syndrome appears to affect a larger number of patients in this age group, and the "nuclear" disorder may be less prevalent than is currently assumed. One explanation of these findings would combine a categorical model of nuclear depression with a dimensional model of dysthymia.

Adjustment Disorders↗

PubMed1996

[The evaluation of therapeutic effect on partial splenic embolization (PSE) for liver cirrhosis patients].

Partial splenic embolization (PSE) was performed on fifty cases with liver cirrhosis underwent no therapy. We evaluated changes of platelet count, ICGR15, GPT and Child-Pugh score which were significantly recovered by PSE. About liver cirrhosis before PSE, K.ICG, GPT, Alb, platelet count and splenic volume were selected as total characteristic factors by principal component analysis. We showed predicting formulas after PSE by multiple regression analysis between five factors selected by principal component analysis and platelet count, HPT, PT, Alb, ICGR15, K.ICG, GOT, GPT and Child-Pugh score after PSE. In conclusion, it is suggested that PSE is useful for recovering of platelet count and liver function. We made it possible to estimate therapeutic effect by predicting formulas before PSE.

Aged↗

PubMed2004

Comparison of the usefulness of the MTT, ATP, and calcein assays to predict the potency of cytotoxic agents in various human cancer cell lines.

Cell viability assays are important tools in oncological research and clinical practice to assess the tumor cell sensitivity of individual patients. The purpose of this study was to demonstrate the comparability of 3 widely used assays (MTT, ATP, calcein assays) by principal component analysis. The study included 4 different cytostatics (cisplatin, docetaxel, doxorubicin, vinblastine) and 3 different human cancer cell lines (MCF-7, A2780, doxorubicin resistant A2780adr). Ninety-three percent of the total variance of all variables included in the principal component analysis (resulting from 3 cell lines and 3 assays) could be explained by 1 principal component. Factor loadings were > 0.937 except for the variable MTT-A2780adr, which was 0.872. These results indicate the similarity of the 3 assays. A 2nd principal component analysis included literature data and showed accordance of data from this study and the literature. The MTT assay was further improved as a high-throughput screening-capable assay. The ATP assay is able to detect effects of cytostatics already after 1 h incubation. The determination of resistance factors allowed to differentiate cytostatics into P-gp or non-P-gp substrates. In conclusion, this study provides improved microplate reader-based cell viability assays and sets a statistically solid basis for a future comparison of data obtained in different laboratories by any of the 3 assays.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2002

Suitability of molecular descriptors for database mining. A comparative analysis.

Database mining methods rely on the molecular descriptors used to characterize a structural database. In the present investigation, five different types of descriptors (log P, UNITY fingerprints, ISIS keys, VolSurf, and GRIND) are applied to characterize various databases (n = 1007, 100, and 229) comprising drugs almost exclusively. The validity of the descriptors is comparatively analyzed via principal component analysis and its hierarchical variant, consensus principal component analysis. Both pharmacodynamic and pharmacokinetic aspects of database mining are treated. For pharmacodynamic aspects, clustering behavior achieved with the different descriptors is tested on the chemically homogeneous beta-blockers, benzodiazepines, and penicillins and on the chemically more diverse class I antiarrhythmics. The following ranking is observed: UNITY fingerprints > ISIS keys and GRIND > VolSurf > log P. Regarding information content, the CPCA superweight plot indicates similarity between fingerprints and ISIS keys as well as between VolSurf and log P, while GRIND differs from all the remaining descriptors. Solubility data and blood/brain barrier penetrating behavior serve as test cases for pharmacokinetic aspects. Comparison of the descriptors applied to these data reveals that VolSurf has the most realistic and consistent behavior, GRIND shows intermediate behavior, while UNITY fingerprints and ISIS keys are not well suited for pharmacokinetic profiling. From this comparative analysis, we conclude that VolSurf descriptors exhibit particular advantages in treating pharmacokinetic aspects; UNITY fingerprints, ISIS keys, and GRIND descriptors are of special value for tackling pharmacodynamic aspects of database mining. The parameter log P is of limited applicability in database mining because of rather poor reliability and lack of completeness of data.

Computing Methodologies↗