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A simple method for the quantitative determination of amino-acids has been described. The method depends upon the paper-chromatographic separation of the amino-acids, followed by staining to form a coloured complex with cadmium acetate and ninhydrin. The coloured spots are eluted from the paper with methanol and determined colorimetrically. Amounts within the range 0·1 to 40 μ g (0·1 to 25 μ moles –2 ) can be determined by this method to within an over-all accuracy of ±10 per cent.

Developing a mass spectrometry-based assay for the ovarian cancer biomarker CA125 (MUC16) is a desirable goal, because it may enable detection of molecular regions that are not recognized by antibodies and are therefore analytically silent in the current immunoassay. Additionally, the ability to characterize the CA125 proteoforms expressed by individuals may offer clinical insight. Enrichment of CA125 from malignant ascites may provide a high-quality source of this important ovarian cancer biomarker, but a reliable strategy for such enrichment is currently lacking. Beginning with crude ascites isolated from three individual patients with high grade serous ovarian cancer, we enriched for MUC16 using filtration, ion exchange, and size exclusion chromatography and then performed bottom-up proteomics on the isolated proteins. This approach of enrichment and analysis reveals that the peptides detected via mass spectrometry map to the SEA domain and C-loop regions within the tandem repeat domains of CA125 and that peptide abundance correlates with clinical CA125 counts.

Fourier Transform Infrared (FT-IR) spectroscopic imaging is emerging as an automated alternative to human examination in studying development and disease in tissue. The technology's speed and accuracy, however, are limited by the trade-off with signal-to-noise ratio (SNR). Signal processing approaches to reduce noise have been suggested but often involve manual decisions, compromising the automation benefits of using spectroscopic imaging for tissue analysis. In this manuscript, we describe an approach that utilizes the spatial information in the data set to select parameters for noise reduction without human input. Specifically, we expand on the Minimum Noise Fraction (MNF) approach in which data are forward transformed, eigenimages that correspond mostly to signal selected and used in inverse transformation. Our unsupervised eigenimage selection method consists of matching spatial features in eigenimages with a low-noise gold standard derived from the data. An order of magnitude reduction in noise is demonstrated using this approach. We apply the approach to automating breast tissue histology, in which accuracy in classification of tissue into different cell types is shown to strongly depend on the SNR of data. A high classification accuracy was recovered with acquired data that was ～10-fold lower SNR. The results imply that a reduction of almost two orders of magnitude in acquisition time is routinely possible for automated tissue classifications by using post-acquisition noise reduction .

The first page of this article is displayed as the abstract.

A method has been developed for the analysis of samples containing capsaicin in amounts above and below 10 mg per 100 g. The same thin-layer procedure is used for all samples, but preliminary extraction varies according to the level of capsaicin in the sample. For levels above 10 mg per 100 g, the sample is extracted with diethyl ether; for lower levels, the ether is removed by distillation, the residue dissolved in ethanol and the solution shaken with light petroleum to remove colouring matter. The ethanolic solution containing the capsaicin is evaporated to dryness and the residue extracted with ether. The extract is transferred on to a Kieselgel G layer and developed with a chloroform-ethanol mixture. The capsaicin is made visible with iron(III) chloride-potassium ferricyanide reagent.

The first page of this article is displayed as the abstract.

The first page of this article is displayed as the abstract.

The first page of this article is displayed as the abstract.

The first page of this article is displayed as the abstract.

The first page of this article is displayed as the abstract.