Spectrofluorimetric determination of amlodipine in human plasma without derivatization

A rapid and sensitive spectrofluorimetric method was developed for the determination of amlodipine (AD), a calcium channel blocker, in the plasma. The type of solvent, the wavelength range, and the range of AD concentration were selected to optimize the experimental conditions. The calibration curves were linear (r² >0.997) in the concentration range of 0.1-12.5 ppm of AD. The limit of quantitation and limit of detection values for the method for plasma samples were 0.1 ppm and 0.07 ppm, respectively. The precision calculated as the relative standard deviation was less than 3.5%, and the accuracy (relative error) was better than 5.5% (n=6). The method developed in this study can be directly and easily applied for the determination of AD in the plasma without derivatization in plasma.

Método espectrofluorometrico rápido e sensível é descrito para a determinação de anlodipina (AD), um bloqueador de canais de cálcio, em amostras de plasma. O tipo de solvente, a faixa de comprimento de onda e a faixa de concentração foram escolhidas a fim de otimizar as condições experimentais. As curvas de calibração foram lineares (r > 0,997) na faixa de concentração de 0,1-12,5 ppm de AD. Os valores LoQ e LoD do método para amostras de plasma foram 0,1 ppm e 0,07 ppm, respectivamente. A precisão calculada como desvio padrão relativo (RSD) foi menor do que 3,5% e a precisão (erro relativo) foi melhor do que 5,5% (n=6). O método desenvolvido neste estudo pode ser fácil e diretamente aplicado para a determinação de AD sem derivatização no plasma.

Autofluorescence detection of tumors in the human lung--spectroscopical measurements in situ, in an in vivo model and in vitro.

To detect bronchial carcinoma by autofluorescence, we measured the spectra of tumor and normal tissue in situ, in an in vivo model and in vitro by fiber optic spectrometer and two-dimensional resolved microspectroscopy. The in situ measurements were performed in bronchi of nine patients with squamous cell carcinoma during regular bronchoscopy with autofluorescence assistance. The fluorescence was monitored with a fiber optical spectrometer under blue light excitation (lambda=405nm). In an in vivo model, the resected lobe of a lung was perfused under physiological conditions. Tumorous and normal tissues were examined spectroscopically during perfusion and after blood removal and substitution with formol. In another setup the wavelength dependency of autofluorescence was examined on resected parts of physiological bronchi and central bronchial carcinomas. Under the variation of the excitation from 385 to 465nm the autofluorescence response was monitored with a fiber optic spectrometer. For investigation of the origin of autofluorescence, two-dimensional resolved spectroscopy was performed with the SpectraCube system on several sections of tumor and normal tissues All measurements, performed in vivo, in the in vivo model and in vitro agreed, that the main difference of the autofluorescence between tumor and normal bronchus tissue is the intensity of the fluorescences' main peak at 505nm. The signal on tumor tissue is in all cases significantly lower than that of normal tissue. The shape of the autofluorescence peaks is in healthy and carcinoma tissue approximately the same with two characteristic minima at 540 and 580nm. After the preparation with formaldehyde those minima disappeared from the spectra. A comparison with the absorption spectra of hemoglobin showed, that the variation of the spectra may be due to the blood content in the tissue. Two-dimensional spatially resolved spectroscopy showed, that the lower intensity of fluorescence in tumor tissue is due to the irregular and low-concentrated formation of fluorescent structures, which seen to be the elastic structures of bronchial tissue.

Performance estimation of diagnostic tests for cervical precancer based on fluorescence spectroscopy: effects of tissue type, sample size, population, and signal-to-noise ratio.

Fluorescence spectroscopy may provide a cost-effective tool to improve precancer detection. We describe a method to estimate the diagnostic performance of classifiers based on optical spectra, and to explore the sensitivity of these estimations to factors affecting spectrometer cost. Fluorescence spectra were obtained at three excitation wavelengths in 92 patients with an abnormal Papanicolaou smear and 51 patients with no history of an abnormal smear. Bayesian classification rules were developed and evaluated at multiple misclassification costs. We explored the sensitivity of classifier performance to variations in tissue type, sample size, tested population, signal to noise ratio (SNR), and number of excitation and emission wavelengths. Sensitivity and specificity could be evaluated within +/- 7%. Minimal decrease in diagnostic performance is observed as SNR is reduced to 15, the number of excitation-emission wavelength combinations is reduced to 15 or the number of excitation wavelengths is reduced to one. Diagnostic performance is compromised when ultraviolet excitation is not included. Significant spectrometer cost reduction is possible without compromising diagnostic ability. Decision-analytic methods can be used to rate designs based on incremental cost-effectiveness.

Spectroscopic diagnosis of esophageal cancer: new classification model, improved measurement system.

Laser-induced fluorescence spectroscopy was used to measure fluorescence emission of normal and malignant tissue during endoscopy in patients with esophageal cancer and volunteers with normal esophagus. The spectroscopy system consisted of a nitrogen-pumped dye-laser tuned at 410 nm for excitation source, an optical multichannel analyzer for spectrum analysis, and a fiberoptic probe designed for both the delivery of excitation light and the collection of fluorescence emission from tissue. The fluorescence lineshape of each spectrum was determined and sampled at 15-nm intervals from 430 to 716 nm. A calibration set of spectra from normal and malignant spectra was selected. Using stepwise discriminate analysis, significant wavelengths that separated normal from malignant spectra were selected. The intensities at these wavelengths were used to formulate a classification model using linear discriminate analysis. The model was then used to classify additional tissue spectra from 26 malignant and 108 normal sites into either normal or malignant spectra. A sensitivity of 100% and specificity of 98% were obtained.