We have investigated the use of spectral imaging for multi-color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier-transform spectroscopy and digital imaging. A pixel-by-pixel spectrum-based color classification is presented of single-, double-, and triple-color in situ hybridization for centromeric probes in T24 bladder cancer cells, and immunocytochemical staining of nuclear antigens Ki-67 and TP53 in paraffin-embedded cervical brush material (AgarCyto). The results demonstrate that spectral imaging unambiguously identifies three chromogenic dyes in a single bright-field microscopic specimen. Serial microscopic fields from the same specimen can be analyzed using a spectral reference library. We conclude that spectral imaging of multi-color chromogenic dyes is a reliable and robust method for pixel color recognition and classification. Our data further indicate that the use of spectral imaging (a) may increase the number of parameters studied simultaneously in pathological diagnosis, (b) may provide quantitative data (such as positive labeling indices) more accurately, and (c) may solve segmentation problems currently faced in automated screening of cell- and tissue specimens.
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http://dx.doi.org/10.1155/2001/740909 | DOI Listing |
Environ Sci Pollut Res Int
January 2025
Department of Chemistry, Banasthali Vidhyapith, Banasthali, Rajasthan, 304022, India.
Plant extracts and bacterial biofilm are acknowledged to offer impressive corrosion-inhibitory activities. However, anticorrosive properties of their combination are still less reported. Thus, in the present study, we aimed to evaluate the corrosion inhibition efficiency of Saccharum officinarum bagasse (SOB) plant extract, Pseudomonas chlororaphis (P.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409-41061, United States.
Glow discharge optical emission spectrometry (GDOES) allows fast and simultaneous multielemental analysis directly from solids and depth profiling down to the nanometer scale, which is critical for thin-film (TF) characterization. Nevertheless, operating conditions for the best limits of detection (LODs) are compromised in lieu of the best sputtering crater shapes for depth resolution. In addition, the fast transient signals from ultra-TFs do not permit the optimal sampling statistics of bulk analysis such that LODs are further compromised.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Department of Radiology, Peking Union Medical College Hospital [PUMCH], Chinese Academy of Medical Sciences & Peking Union Medical College [CAMS & PUMC], China.
Aims To evaluate the utility of unenhanced spectral imaging, electron density (ED) and overlay electron density (OED) images for assessing pulmonary embolisms in patients with suspected or confirmed acute pulmonary embolism (APE). Background Multiple spectral images can be extrapolated from spectral detector CT (SDCT), ED and OED images. ED and OED images are highly sensitive to moisture-rich tissues.
View Article and Find Full Text PDFActa Radiol
January 2025
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China.
Background: Different parotid tumors differ in terms of treatment strategies due to their distinct biological behaviors. Time-dependent diffusion magnetic resonance imaging (t-dMRI) can characterize and quantify the cytological indexes, and then aid the differential diagnosis of various tumors. However, the value of t-dMRI in the analysis of parotid gland tumors remains unclear.
View Article and Find Full Text PDFAnal Methods
January 2025
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin.
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