Fuzzy partitioning of clinical data for DMT2 patients.

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.

Evaluation of a hydrophobic gel adhering to the gingiva in comparison with a standard water-soluble 1% chlorhexidine gel after scaling and root planing in patients with moderate chronic periodontitis. A randomized clinical trial.

OBJECTIVES: To evaluate the clinical, microbiological and enzymatic activity of a hydrophobic chlorhexidine-based gingiva-adhering gel containing herbal ingredients, compared with a commercially available 1% chlorhexidine water-soluble gel, during non-surgical therapy of moderate chronic periodontitis. METHODS: A total of 34 subjects participated in this 6-month blinded randomized parallel controlled trial (ISRCTN35210084). After scaling and root planing (SRP), test group received the gel, by rubbing on the gingiva, once every second day, for 14 days. The control group received the control gel twice daily. Clinical parameters considered were the approximal plaque index, simplified oral hygiene index, modified gingival index, bleeding on probing, probing depth and clinical attachment level (primary outcome), assessed at baseline, 3 and 6 months, together with the frequency of detection of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis (P.g.), Prevotella intermedia, Treponema denticola (T.d.), Tannerella forsythia (T.f.), and activity of neutrophil elastase and myeloperoxidase (secondary outcomes). RESULTS: At 3 and 6 months, all clinical parameters improved significantly, without significant intergroup differences, except OHI-S, which improved at 3 months (P < 0.05). Microbiological data resulted in no significant intergroup differences at baseline and 6 months. At 3 months, significant differences for P.g., T.f. and T.d. were noted. A significant reduction of neutrophil elastase after 3 and 6 months was observed (P < 0.005), without significant intergroup differences. For myeloperoxidase, significant reductions were noted in both groups (P < 0.005 and P < 0.05), but no significant intergroup differences. The tested product seemed to have an increased efficacy, due to longer persistence on the gingiva, with reduced application frequency. CONCLUSIONS: Both products had a relatively similar influence on the clinical, microbiological and enzymatic outcomes at 3 and 6 months after SRP.

Epidemiological Aspects of Cognitive and Socio-Professional Integration in Schizophrenia.

Schizophrenia is one of the major psychiatric disorders with global impairment of cognition, emotional status and personality destruction having a major medical, social and economic impact worldwide. In this paper, we evaluated the correlation of disease progression with cognitive deficit, socio-professional and family disjunction using the MMSE (Mini Mental State Examination) and GAFS (Global Assessment of Functioning Scale) scales on a group of 191 schizophrenic patients admitted to the Psychiatric Clinic II Craiova during 2017. The fact that both scales show reduced values in patients with schizophrenia reveals a significant alteration in the quality of life of patients with this pathology having major implications on the individuals and on the community.

Therapeutic Particularities with Depot Medications in Schizophrenia.

Schizophrenia is a neurodegenerative disease with personality degradation, changes in behavior, cognition, affective disorder and reduced socio-professional insertion. Early diagnosis is necessary and maintenance is ensured by antipsychotic treatment and for non-compliant patients there is an alternative to depot medication that greatly improves therapeutic adherence. The study presents the case of a 43-year-old patient diagnosed with paranoid schizophrenia using the Diagnostic and Statistical Manual of Mental Disorders V-TR criteria that showed a favorable evolution under depot treatment.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA.

Determination of lipophilicity of some non-steroidal anti-inflammatory agents and their relationships by using principal component analysis based on thin-layer chromatographic retention data.

The relative lipophilicity of ten non-steroidal anti-inflammatory agents have been determined by reversed-phase thin layer chromatography using different reversed-phase high-performance thin-layer chromatography plates and water-methanol mixtures as eluents. The compounds studied showed regular retention behavior, their RM values decreasing linearly with increasing concentration of methanol in the eluent. Principal component analysis allowed a more rational and objective estimation and comparison of lipophilicity determined by reversed-phase thin-layer chromatography. It also affords a useful graphical tool, since scatterplots of the scores onto the plane described by the first two components will have the effect of separating compounds from each other most effectively, thus obtaining "congeneric lipophilicity chart".

Evaluation of the lipophilicity of bile acids and their derivatives by thin-layer chromatography and principal component analysis.

The lipophilic character of bile acids and their glyco- and tauro-conjugates was studied. The classical R(Mo) values were measured by means of reversed thin-layer chromatography using a mixture of methanol-water as the solvent system and compared with the factors scores obtained by principal component analysis based also onto the TLC-retention data. The reliability of the factor scores values as lipophilic indices are shown by their high correlation with the classical R(Mo) values. In addition, a better correlation was observed between scores corresponding to the first principal components and the partition coefficients (log P) of bile acids. Finally, the "lipophilicity chart" described by the first two components has the effect of separating compounds from each other most effectively from the congeneric aspect point of view.

Direct fluorescent detection of chromatographically separated amino-acids by means of 9-isothiocyanatoacridines.

A new method is presented for fluorescent detection of amino-acids separated on silica gel R plates, by spraying with a 0.1% solution of 9-isothiocyanatoacridine derivatives in dichloromethane or benzene, heating and examining in ultraviolet light at 254 and 336 nm. This method is useful for routine analysis owing to its selectivity and sensitivity.

Quantitative structure-retention and retention-activity relationships of some 1,3-oxazolidine systems by RP-HPTLC and PCA.

A quantitative structure-retention and retention-activity relationships investigations were performed on the lipophilicities of some 1,3-oxazolidine systems as estimated by RP-HPTLC retention parameters. The classical R(Mo) values were compared with the factors scores obtained by principal component analysis based also onto the TLC retention data. The lipophilicities (R(Mo) and factor scores) were correlated with the theoretical molecular descriptors of 1,3-oxazolidine derivatives providing by the ALCHEMY 2000 software package. The reliability of the factor scores values as lipophilic indices are shown by their significant correlation with the classical R(Mo) values and other molecular descriptors. In addition, the "lipophilicity chart" described by the first two components, and/or the "lipophilicity space" described by the first three components have the effect of separating compounds from each other most effectively from the congeneric (similarity) aspect point of view. Finally, these findings support the idea that the chromatographic process of the investigated compounds in this paper and consequently their partitioning over a bio-membrane are controlled mainly by lipophilicity.

Fuzzy clustering analysis of the first 10 MEIC chemicals.

In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests.

Use of fuzzy regression for calibration in thin-layer chromatography/densitometry.

A new fuzzy regression algorithm was successfully applied to calibration in thin-layer chromatography (TLC)/densitometry. Different regression techniques were also considered, and objective quality coefficients were used for evaluation of the results. Depending on the structure of the data, the fuzzy procedure can improve the results dramatically. From the results reported in this paper and other unpublished results, it appears clear that the calibration procedure based on fuzzy regression is the most suitable regression method in quantitative TLC.

Assessment of heart disease using fuzzy classification techniques.

In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients.

[Spatial misperceptions in amblyopic vision: abnormal activation of the primary visual cortex?]. / Basiert der verzerrte Seheindruck bei Amblyopie auf einer erhöhten Aktivierung in der primären Sehrinde?

BACKGROUND: The aim of this study was to provide a description of the spatial distortions and temporal instability in amblyopic vision, and to attempt to define a cortical substrate of the spatial distortions in strabismic amblyopia. MATERIAL AND METHODS: The perceptual distortions and instabilities occurring in amblyopic vision were investigated psychophysically, by asking 17 subjects to describe and sketch their percepts. This was then visualised with an animated computer programme and validated by the subjects. In a second experiment, the cortical responses of normal observers to patterns corresponding to the spatial distortions reported by amblyopic subjects were investigated using functional magnetic resonance imaging. RESULTS: Spatial distortions were more marked in strabismic than in anisometropic amblyopes or in strabismic subjects with alternating fixation. Temporal instability occurred mainly in strabismic amblyopes and affected mainly patterns with higher spatial frequencies. Experiments with functional magnetic resonance imaging showed that the patterns with the highest spatial distortions yield increased activation in the primary visual cortex of normally-sighted observers. CONCLUSIONS: The results of the imaging experiment suggest that the occurrence of spatial distortions might explain the higher activation in the primary visual cortex of some amblyopic subjects. The occurrence of temporal instability in strabismic amblyopia suggests an involvement of higher-order, extrastriate visual areas of the dorsal, "where" visual pathway in amblyopia, in addition to the known deficits in the ventral, "what" visual pathway.

Principal component analysis versus fuzzy principal component analysis A case study: the quality of danube water (1985-1996).

Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approach to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper we discuss and apply a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the water quality of the Danube River for a period of 11 consecutive years. Considering, for example, a two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%. Much more, PCA showed only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two principal components, whereas a much sharper differentiation of the variables and scores is observed when FPCA is applied.

A new fuzzy regression algorithm.

A new fuzzy regression algorithm is described and compared with conventional ordinary and weighted least-squares and robust regression methods. The application of these different methods to relevant data sets proves that the performance of the procedure described in this paper exceeds that of the ordinary least-squares method and equals and often exceeds that of weighted or robust methods, including the two fuzzy methods proposed previously (Otto, M.; Bandemer, H., Chemom. Intell. Lab. Syst. 1986, 1, 71. Hu, Y.; Smeyers-Verbeke, J.; Massart, D. L. Chemom. Intell. Lab. Syst. 1990, 8, 143). Moreover, we emphasize the effectiveness and the generality of the two new criteria proposed in this paper for diagnosing the linearity of calibration lines in analytical chemistry.

Validation of analytical methods using a regression procedure.

The evaluation and validation of analytical methods and instruments require comparison studies using sample material for testing accuracy and precision. In analytical chemistry, the commonly accepted means of analyzing data from method comparison studies is least-squares regression analysis, a model which has limitations. In this paper, the results from ordinary least-squares and many other regression approaches recommended in the literature were compared with a new regression procedure that takes into account the errors in both variables (methods). After a discussion of the properties of the regression procedure, recommendations are given for carrying out a method comparison study using informational analysis of variance. The efficiency of the regression procedure proposed is demonstrated by applying it to different data sets from published literature.

Fuzzy robust estimation of central location.

The problem of a new robust algorithm for estimation of central location has been described in a mathematically simpler way using the fuzzy sets theory. It was compared with ordinary mean estimator and other robust estimators - median, 5% trimmed mean and Huber-, Tukey-, Hampel-, and Andrews-type M-estimators. The performance of Fuzzy 1-means algorithm (FM) proposed is demonstrated by applying it to different data sets from published literature and has been shown to exceed that of conventional ordinary mean estimator and equals or often exceeds that of the most robust estimators.

Structural analysis of transition metal beta-X substituent interactions. Toward the use of soft computing methods for catalyst modeling

Fuzzy logic and neural network techniques are used to classify intramolecular interactions between transition metals (M) and beta-X substituents in the following structural motif (LnMC(alpha)(A1)(A2)-C(beta)(B1)(B2)X). These interactions are relevant to the direct polymerization of functionalized olefins by Ziegler-Natta (ZN) catalysis. The efficiency and effectiveness of different soft computing techniques are compared. These methods give not only encouraging results with respect to general data mining issues but also insight into the factors that effect interactions between transition metals and beta-X substituents.

Use of weighted least-squares splines for calibration in analytical chemistry

Weighted least-squares spline functions are discussed and applied for calibration processes in analytical chemistry. Different weighting techniques are also considered, and for the evaluation of the results some quality coefficients are proposed. Depending upon the structure of the data, some weighting procedures may improve the results dramatically. Considering the results obtained in the case of TLC densitometry, it seems that nonlinear weighting procedures based upon the distance to the function are the best ones, with a plus for the y-distance type. It is difficult to give general rules regarding the optimal parameters of the weighted calibration splines-function order (m), number (N) and distribution of knots, and weighting technique. These depend upon the structure of the data. However, higher order splines are not recommended since the result might become extremely unstable. The example used to illustrate the performances of the procedures discussed here involved only a single independent variable. The method is general and extends practically to any number of variables, thus resulting in a multivariate approach.