다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Background: The assessment of asthma control is pivotal to the evaluation of treatment response in individuals and in clinical trials. Previously, asthma control, severity, and exacerbations were defined and assessed in many different ways.Purpose: The Task Force was established to provide recommendations about standardization of outcomes relating to asthma control, severity, and exacerbations in clinical trials and clinical practice, for adults and children aged 6 years or older.Methods: A narrative literature review was conducted to evaluate the measurement properties and strengths/weaknesses of outcome measures relevant to asthma control and exacerbations. The review focused on diary variables, physiologic measurements, composite scores, biomarkers, quality of life questionnaires, and indirect measures.Results: The Task Force developed new definitions for asthma control, severity, and exacerbations, based on current treatment principles and clinical and research relevance. In view of current knowledge ...

/pdf/an-official-american-thoracic-society-european-respiratory-18b801porh.pdf

An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice.

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

Applications of solid-phase microextraction in food analysis.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(08)61452-X.pdf

Endotyping asthma: new insights into key pathogenic mechanisms in a complex, heterogeneous disease

Supervised pattern recognition in food analysis.

Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. A supplier database (
 https://mcule.com/
 ) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics.

/pdf/why-is-tanimoto-index-an-appropriate-choice-for-fingerprint-ux13cytxi3.pdf

Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Quantitative structure-(chromatographic) retention relationships.

Rationale: Metabolomic analysis provides biochemical profiles of low-molecular-weight endogenous metabolites in biological fluids.Objectives: The aim of this study was to assess the feasibility of nuclear magnetic resonance (NMR)–based metabolomic analysis applied to exhaled breath condensate (“breathomics”). Information coming from NMR spectra was analyzed with a view to establish the NMR variables that best discriminate between children with asthma and healthy children.Methods: Twenty-five children with asthma (17 with persistent asthma treated with inhaled corticosteroids, 8 with intermittent asthma inhaled corticosteroid naive; age, 7–15 yr) and 11 healthy age-matched control subjects were enrolled. Every child performed exhaled nitric oxide measurement, spirometry, and condensate collection. Condensate samples were analyzed by means of NMR spectroscopy. Linear and partial least squares discriminant analyses were applied to data obtained from the NMR spectra.Measurements and Main Results: The combinat...

https://www.atsjournals.org/doi/pdf/10.1164/rccm.200606-769OC

Metabolomics Applied to Exhaled Breath Condensate in Childhood Asthma

This review covers a novel approach to comparing methods, based on the sum of ranking differences (SRD) Many method-comparison studies suffer from ambiguity or from comparisons not being quite fair This problem can be avoided if there are differences between ideal and actual rankings The absolute values of differences for the ideal and actual ranking are summed up and the procedure is repeated for each (actual) method The SRD values obtained such a way order the methods simply If the ideal ranking is not known, it can be replaced by the average (maximum or minimum of all methods or by a known sequence) SRD corresponds to the principle of parsimony and provides an easy tool to evaluate the methods: the smaller the sum the better the method Models and other items can be similarly ranked Validation can be carried out using simulated random numbers for comparison: an empirical histogram (bootstrap-like) shows whether the SRD values are far from random Two case studies (clustering of HPLC columns and prediction of retention data) illustrate and validate the applicability of this novel approach to comparing methods The technique is entirely general; it can be used in different fields (eg, for stationary-phase (column) selection in chromatography, model and descriptor selection, comparing analytical and chemometric techniques, determination of panel consistency, etc) The only prerequisite is that the data can be arranged in matrix form without empty cells

Károly Héberger

Papers

Supervised pattern recognition in food analysis.

Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Quantitative structure-(chromatographic) retention relationships.

Metabolomics Applied to Exhaled Breath Condensate in Childhood Asthma

Sum of ranking differences compares methods or models fairly