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Biomedical subjects

S Wold

Publications and source records attributed to S Wold.

At least 55 records · Page 3Linked to original sources
PubMed1984

Use of a microcomputer for the definition of multivariate confidence regions in medical diagnosis based on clinical laboratory profiles.

The use of multivariate confidence regions is proposed for the classification of clinical laboratory profiles into diagnostic classes. For this purpose, a multivariate confidence region is developed for each diagnostic class. Three methods (UNEQ, EQ, and SIMCA) are evaluated and compared with classical linear discriminant analysis. As an example, a small data set concerning the differentiation of the thyroid functional states on the basis of five laboratory tests is used. It is shown that related procedures can produce results of very different quality and that the multivariate region approach is attractive for the clinician's daily practice since the methods are easily implemented on a microcomputer.

Computers↗
PubMed1984

Ulcer recurrence after parietal cell vagotomy for duodenal ulcer. A multivariate pattern recognition study.

The object of the study was to identify individual high-risk patients with regard to ulcer recurrence after parietal cell vagotomy for duodenal ulcer. The study comprises a multivariate analysis of 14 variables (age, sex, duration of symptoms, site of ulcer, pre- and post-operative acid secretion) for 37 patients with and 111 patients without recurrence 5-11 years after parietal cell vagotomy for duodenal ulcer. The data were analyzed using a supervised pattern recognition technique, SIMCA (Soft Independent Modeling of Class Analogy), which analyzes complex data as geometrical elements in a multidimensional space. We found no statistical difference between the patients with and without ulcer recurrence. Thus, no predictive value for the selection of patients liable to develop recurrent ulcer after parietal cell vagotomy was contained in the variables generally registered in current surgery.

Adult↗
PubMed1983

Computer methods for the assessment of toxicity.

The prediction of the biological activity of chemical compounds by means of mathematical models is discussed. Biological activity of chemicals, including their toxicity on man and other biological organisms and systems, involves too complex phenomena to presently be predictable by fundamental models such as ab initio quantum mechanical models or statistical mechanical models. Hence one takes recourse to semi-empirical and empirical models which relate the variation in chemical structure of chemical compounds to the variation in their measured biological activity, e.g. toxicity in one or several test systems. These models are "calibrated" on series of similar compounds with "known" toxicity, the training set. Thereafter the models can--in fortunate cases--be used to predict the toxicity of compounds which are structurally similar to the training set compounds. The formulation and applicability of semi-empirical and empirical models relating chemical structure to biological activity is discussed. Causes and remedies for commonly encountered fallacies are presented.

Animals↗
PubMed1981

Classification of human cancer cells by means of capillary gas chromatography and pattern recognition analysis.

The metabolic profiles of brain biopsies obtained at surgery were recorded using capillary gas chromatography (GC). About 160 peaks were seen, of which 105 were used for data analysis. Three classes of brain tissue were examined: normal cerebral cortex, pituitary tumours and " brain" tumours. Pattern recognition analyses of the GC profiles using the SIMCA multivariate programme clearly resolved normal brain tissue from the tumours. Subclassification of the different tumours was more difficult, probable because the number of samples in each tumour class was too small. High-resolution two-dimensional electrophoresis separated the brain biopsies into several hundred different proteins. The combined use of the latter technique and capillary GC-mass spectrometry and pattern recognition analyses gives the possibility of the classification of diseased cells based solely on differences in their biochemical compositions.

Brain Neoplasms↗
PubMed1981

Distribution of arsenic, manganese, and selenium in the human brain in chronic renal insufficiency, Parkinson's disease, and amyotrophic lateral sclerosis.

The concentrations of arsenic, manganese and selenium/g wet tissue weight were determined in samples from 24 areas of the human brain from 3 patients with chronic renal insufficiency, 2 with Parkinson's disease and 1 with amyotrophic lateral sclerosis. The concentrations of the 3 elements were determined for each sample by neutron activation analysis with radiochemical separation. Overall arsenic concentrations were about 2.5 times higher in patients with chronic renal failure than in controls, and lower than normal in the patients with Parkinson's disease and amyotrophic lateral sclerosis. There were no obvious differences in the overall concentrations of manganese and selenium from one group to another. Even multivariate data analysis by the SIMCA method failed to reveal any significant difference in the distribution pattern of manganese and selenium in Parkinson's disease compared to normal controls.

Adult↗
PubMed1981

An assessment of carcinogenicity of N-nitroso compounds by the SIMCA method of pattern recognition.

The ability to predict the toxic responses of potential environmental pollutants on the basis of their physiochemical properties has many advantages. Pattern recognition methods can be used to predict such pharmacological properties. In this report the SIMCA method of pattern recognition is used to predict the carcinogenicity of N-nitroso compounds, and the advantages of this method of pattern recognition in such applications are discussed.

Carcinogens↗
PubMed1980

Structure-activity analyzed by pattern recognition: the asymmetric case.

In classification studies in which pattern-recognition methods are used to distinguish active compounds from inactive ones, a type of data structure which we call "asymmetric" can be observed. This type of data structure can be quite common and its occurrence can have a profound effect on the classification analysis outcome. The origin of asymmetric data structure and a strategy or obtaining meaningful classification results when it is observed are discussed and illustrated with an example of active and inactive antimalarial quinones.

Antimalarials↗
PubMed1979

Classification of fungi by means of pyrolysis-gas chromatography-pattern recognition.

Repetitive samples of three strains of the mould Penicillium were subjected to pyrolysis-gas chromatography (Py-GC). From the chromatograms, 26 peak heights were used in a subsequent SIMCA pattern recognition analysis. This data analysis gives a marked improvement in the classification of the samples (100% correct, 85% unique) in comparison with the traditional analysis based on the average chromatogram of each class (92% correct, 45% unique). The data analytical method is described in detail using the Py-GC data as an illustration.

Analysis of Variance↗
PubMed1978

Structure-activity study of beta-adrenergic agents using the SIMCA method of pattern recognition.

The SIMCA method of pattern recognition (PaRC) was used to analyze structure-activity data for a series of phenethylamine agonists and antagonists of the beta-adrenergic receptor. On the basis of physicochemical substituent parameters the SIMCA method classified correctly 100% of the agonists and 88% of the antagonists. In addition, parameters derived from the class models were correlated with the biological activities of the agonists and antagonists, respectively. Test compounds not included in the initial data analysis were classified and their activities estimated. The applicability of pattern recognition in structure-activity studies in general is discussed.

Adenylyl Cyclase Inhibitors↗
PubMed1978

A structure-carcinogenicity study of 4-nitroquinoline 1-oxides using the SIMCA method of pattern recognition.

Structure-carcinogenicity data for a series of 4-nitro- and 4-hydroxyaminoquinoline 1-oxides were analyzed using the SIMCA method of pattern recognition. Using physicochemically based substituent constants to describe each compound, a principal components model was derived for the carcinogens. This model was 82% successful in predicting the carcinogenic potential of the compounds. For the 6-substituted compounds, a significant relationship between those structural parameters associated with carcinogenic potential and ability to stimulate unscheduled DNA synthesis was observed. In addition, other problems unique to the classification of carcinogens were discussed.

4-Hydroxyaminoquinoline-1-oxide↗
PubMed1977

Trace-element concentrations in blood samples from welders of stainless steel or aluminium and a reference group.

The concentrations of 17 trace elements (e.g., copper, cobalt, iron, manganese, chromium, silicon and magnesium) were determined in whole blood samples of 81 persons working with different welding methods on stainless steel or aluminium and 68 nonwelders. Inorganic spark source mass spectrometry was used for the chemical analyses. The data were analyzed by the SIMCA method for pattern recognition (discriminant analysis). No differences were found between the five groups, either in the average levels of the trace elements or in the correlation structures between the trace elements. Thus no blood concentration data on the analyzed elements and collected from a single person contained any information with respect to exposure to the welding fumes investigated.

Aluminum↗
PubMed1976

Comparison of graphical and computerized methods for calculating binding parameters for two strongly bound drugs to human serum albumin.

The determination of drug-protein binding parameters (n's and K's) can lead to important information on the required therapeutic dosage regimen and possible clinical complications associated with competitive displacement of one drug by a concurrently administered agent. Graphical and computer estimates of the data are often incorrectly formulated, and and seldom are adequate data obtained at low binding ratios. Commonly used graphical procedures, inadequately formulated computer methods, and a statistically correct computer method were used to compare results obtained from a circular dichroic examination of dicumarol-human serum albumin and fenoprofen-human serum albumin interactions. Literature binding constants for dicumarol-albumin range from 1 X 10(5) to 30 times that figure, and it is shown here that a wide range in parameter estimates may be obtained depending on the method of data analysis. The parameter estimates in the case of fenoprofen-albumin are even more variable.

Binding Sites↗