Development of an automated asbestos counting software based on fluorescence microscopy.

An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.

Identification of a predictive biomarker for the beneficial effect of a Kampo (Japanese traditional) medicine keishibukuryogan in rheumatoid arthritis patients.

OBJECTIVES: Kampo (Japanese traditional herbal) medicines are now ethically used in Japan as pharmaceutical grade prescription drugs. However, there are distinct groups of responders and non-responders to Kampo medicines. We searched for biomarker candidates to discriminate responders from non-responders to keishibukuryogan (KBG); one of the most frequently used Kampo medicines. DESIGN AND METHODS: A combination of SELDI technology and a decision tree analysis with proprietary developed bioinformatics tools was applied to 41 (32 for tree construction and 9 for validation test) plasma samples obtained from rheumatoid arthritis (RA) patients. A candidate biomarker protein was identified using LC-MS/MS. RESULTS: The constructed tree with measurable reliability contained only a single peak which was identified as haptoglobin alpha 1 chain (Hpalpha1). CONCLUSION: Hpalpha1 is a biomarker candidate for discriminating responders from non-responders to KBG treatment for RA. The present results may open the way to the establishment of "evidence-based" complementary and alternative medicine.

A Proteomic Approach for the Diagnosis of 'Oketsu' (blood stasis), a Pathophysiologic Concept of Japanese Traditional (Kampo) Medicine.

'Oketsu' is a pathophysiologic concept in Japanese traditional (Kampo) medicine, primarily denoting blood stasis/stagnant syndrome. Here we have explored plasma protein biomarkers and/or diagnostic algorithms for 'Oketsu'. Sixteen rheumatoid arthritis (RA) patients were treated with keishibukuryogan (KBG), a representative Kampo medicine for improving 'Oketsu'. Plasma samples were diagnosed as either having an 'Oketsu' (n = 19) or 'non-Oketsu' (n = 29) state according to Terasawa's 'Oketsu' scoring system. Protein profiles were obtained by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and hierarchical clustering and decision tree analyses were performed. KBG treatment for 4 or 12 weeks decreased the 'Oketsu' scores significantly. SELDI protein profiles gave 266 protein peaks, whose expression was significantly different between the 'Oketsu' and 'non-Oketsu' states. Hierarchical clustering gave three major clusters (I, II, III). The majority (68.4%) of 'Oketsu' samples were clustered into one cluster as the principal component of cluster I. The remaining 'Oketsu' profiles constituted a minor component of cluster II and were all derived from patients cured of the 'Oketsu' state at 12 weeks. Construction of the decision tree addressed the possibility of developing a diagnostic algorithm for 'Oketsu'. A reduction in measurement/pre-processing conditions (from 55 to 16) gave a similar outcome in the clustering and decision tree analyses. The present study suggests that the pathophysiologic concept of Kampo medicine 'Oketsu' has a physical basis in terms of the profile of blood proteins. It may be possible to establish a set of objective criteria for diagnosing 'Oketsu' using a combination of proteomic and bioinformatics-based classification methods.