Uterine histopathology and steroid metabolism in a polycystic ovary syndrome rat model.

The aim of this study was to investigate uterine lesions, uterine endocrine status and expression of genes involved in uterine differentiation in a rat model of polycystic ovary syndrome (PCOS). The possible involvement of the androgen receptor (AR) was also investigated. PCOS rats showed an increased incidence of uterine epithelial and glandular lesions and elevated serum testosterone level, which was not detected in uterine tissue. Uterine 17ß-estradiol, estrone and progesterone were detected in 100%, 75% and 50% of the animals, respectively. This was associated with a decrease in Star and an increase in Hsd17b2, Srd5a1 and Cyp19a1, suggesting that uterine steroids are not synthesized de novo in PCOS and that alterations in these enzymes may explain the absence of testosterone and low progesterone. In addition, ESR2 decreased and AR increased, suggesting possible steroid receptor crosstalk. Genes associated with uterine differentiation, PTEN and WNT5a, also showed reduced expression. PCOS rats treated with flutamide, an AR antagonist, were similar to PCOS rats in terms of uterine lesions, serum steroid levels, ESR2, PTEN and WNT5a expression. However, testosterone, AR and aromatase levels were similar to control rats, with decreased expression of ESR1 and HOXA10, suggesting that these expressions are AR dependent. Our results suggest that the primary cause of the observed uterine lesions in the PCOS rat model is the altered endocrine status and consequently changes in genes related to uterine differentiation.

External cavity-quantum cascade laser (EC-QCL) spectroscopy for protein analysis in bovine milk.

The analytical determination of bovine milk proteins is important in food and non-food industrial applications and yet, rather labour-intensive wet-chemical, low-throughput methods have been employed since decades. This work proposes the use of external cavity-quantum cascade laser (EC-QCL) spectroscopy for the simultaneous quantification of the most abundant bovine milk proteins and the total protein content based on the chemical information contained in mid-infrared (IR) spectral features of the amide I band. Mid-IR spectra of protein standard mixtures were used for building partial least squares (PLS) regression models. Protein concentrations in commercial bovine milk samples were calculated after chemometric compensation of the matrix contribution employing science-based calibration (SBC) without sample pre-processing. The use of EC-QCL spectroscopy together with advanced multivariate data analysis allowed the determination of casein, α-lactalbumin, ß-lactoglobulin and total protein content within several minutes.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.

Modeling four and three-way fast high-performance liquid chromatography with fluorescence detection data for quantitation of fluoroquinolones in water samples.

This paper presents a study regarding the acquisition and analytical utilization of four and three-way data, acquired by following the excitation-emission fluorescence matrices at different elution times, in a fast liquid chromatographic HPLC procedure. This kind of data were implemented for first time for quantitative purposes, and applied to the determination of two fluoroquinolones in tap water samples, as a model to show the potentiality of the proposed strategy of four-way data generation. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated interferences. PARAFAC and MCR-ALS were the algorithms that better exploited the second-order advantage when no peak time shifts occurred among samples. On the other hand, when the quadrilinearity was lost due to the occurrence of temporal shifts, MCR-ALS furnished the better results. Relative error of prediction (REP%) obtained were 9.9% for ofloxacin and 14.0% for ciprofloxacin. In addition, a significant enhancement in the analytical figures of merit was observed when going from second- to third-order data (reduction of ca. 70% in LODs).