Colon cancer is the third leading class of cancer causing increased mortality in developed countries. A polyp is one type of lesion observed in a majority of colon cancer patients. Here, we report a microscopic Fourier transform infrared (FTIR) study of normal, adenomatous polyp and malignant cells from biopsies of 24 patients. The goal of our study was to differentiate an adenomatous polyp from a malignant cell using FTIR microspectroscopy and artificial neural network (ANN) analysis. FTIR spectra and biological markers such as phosphate, RNA/DNA derived from spectra, were useful in identifying normal cells from abnormal ones that consisted of adenomatous polyp and malignant cells. However, the biological markers failed to differentiate between adenomatous polyp and malignant cases. By employing a combination of wavelet features and an ANN based classifier, we were able to classify the different cells as normal, adenomatous polyp and cancerous in a given tissue sample. The percentage of success of classification was 89%, 81%, and 83% for normal, adenomatous polyp, and malignant cells, respectively. A comparison of the method proposed with the pathological method is also discussed.

Elucidation of the evolution of inflammatory bowel disease (IBD) to cancer by clinical symptoms and histopathology of biopsies is important. Fourier transform infrared microspectroscopy (FTIR‐MSP) has shown promise as a diagnostic tool for distinction of normal and cancer cells and tissues. In the present work, FTIR‐MSP is used to evaluate IBD cases and to study the IR spectral characteristic with respect to cancer and normal tissues from formalin‐fixed colonic biopsies from patients. Specific regions of the spectra were analyzed by statistical tools to study variations in metabolites that signified changes between the two pathological conditions: IBD and cancer. IBD tissues can be grouped with cancer or normal tissue using certain parameters such as phosphate content and RNA/DNA ratio as calculated from the spectra and show intermediate levels with regard to these metabolites. Further classification of the spectra by cluster analysis indicated which cases of Crohn's disease (3 of 10 cases) or ulcerative colitis (7 of 10 cases) were more likely to progress to cancer. The study exhibits that FTIR‐MSP can detect gross biochemical changes in morphologically identical IBD and cancer tissues and suggest which cases of IBD may require further evaluation for carcinogenesis. © 2004 Wiley Periodicals, Inc. Biopolymers, 2004

Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involved in many diseases, and antibiotics have been the most effective treatment for them. It is essential to treat an infection with an antibiotic to which the infecting bacteria is sensitive; otherwise, the treatment is not effective and may lead to life-threatening progression of disease. Classical microbiology methods that are used for determination of bacterial susceptibility to antibiotics are time consuming, accounting for problematic delays in the administration of appropriate drugs. Infrared-absorption microscopy is a sensitive and rapid method, enabling the acquisition of biochemical information from cells at the molecular level. The combination of Fourier transform infrared (FTIR) microscopy with new statistical classification methods for spectral analysis has become a powerful technique, with the ability to detect structural molecular changes associated with resistivity of bacteria to antibiotics. It was possible to differentiate between isolates of Escherichia (E.) coli that were sensitive or resistant to different antibiotics with good accuracy. The objective computational classifier, based on infrared absorption spectra, is highly sensitive to the subtle infrared spectral changes that correlate with molecular changes associated with resistivity. These changes enable differentiating between the resistant and sensitive E. coli isolates within a few minutes, following the initial culture. This study provides proof-of-concept evidence for the translational potential of this spectroscopic technique in the clinical management of bacterial infections, by characterizing and classifying antibiotic resistance in a much shorter time than possible with current standard laboratory methods.

A simple blood test for the diagnosis of Alzheimer's disease using FTIR microscopy.