Local production of 17ß-oestradiol in the endometrium during the implantation window: a pilot study.

Abstract: Sex steroids are converted to bioactive metabolites and vice versa by endometrial steroid-metabolising enzymes. Studies indicate that alterations in this metabolism might affect endometrial receptivity. This pilot study determined whether the endometrial formation and inactivation of 17ß-oestradiol differed between the supposedly embryo-receptive endometrium and non-receptive endometrium of women undergoing IVF/intracytoplasmic sperm injection (ICSI). Endometrial biopsies were obtained from IVF/ICSI patients 5-8 days after ovulation in a natural cycle, prior to their second IVF/ICSI cycle with fresh embryo transfer (ET). Endometrial biopsies from patients who achieved clinical pregnancy after fresh ET (n = 15) were compared with endometrial biopsies from patients that did not conceive after fresh ET (n = 15). Formation of 17ß-oestradiol (oxidative 17ß-hydroxysteroid dehydrogenases (HSDs)), oestrone (reductive HSD17Bs) and inhibition of HSD17B1 activity were determined by high-performance liquid chromatography. The endometrial transcriptome was profiled using RNA sequencing followed by principal component analysis and differentially expressed gene analysis. The false discovery rate-adjusted P < 0.05 and log fold change >0.5 were selected as the screening threshold. Formation and inactivation of 17ß-oestradiol resulted similar between groups. Inhibition of HSD17B1 activity was significantly higher in the non-pregnant group when only primary infertile women (n = 12) were considered (27.1%, n = 5 vs 16.2%, n = 7, P = 0.04). Gene expression analysis confirmed the presence of HSD17B1 (encoding HSD17B1), HSD17B2 (encoding HSD17B2) and 33 of 46 analysed steroid metabolising enzymes in the endometrium. In the primary infertile subgroup (n = 10) 12 DEGs were found including LINC02349 which has been linked to implantation. However, the exact relationship between steroid-metabolising enzyme activity, expression and implantation outcome requires further investigation in larger, well-defined patient groups. Lay summary: Sex hormones are produced and broken down by enzymes that can be found in the endometrium (the inner lining of the womb). This enzyme activity might influence the chances of becoming pregnant. We compared (i) enzyme activity in the endometrium of 15 women who did and 15 women who did not become pregnant in their second in vitro fertilisation attempt, (ii) how enzyme activity can be blocked by an inhibitor, and (iii) differences in gene expression (the process by which instructions in our DNA are converted into a product). Enzyme activity was similar between groups. We found that in women who have never been pregnant in the past, inhibition of enzyme activity was higher and found differences in a gene that has been linked to the implantation of the embryo, but future studies should be performed in larger, well-defined patient groups to confirm these findings.

Food identification by high performance liquid chromatography fingerprinting and mathematical processing.

Food fingerprinting methods comprise a wide range of analytical strategies for obtaining analytical signals such as spectra or chromatograms that can be related to the composition of foodstuffs. Mathematical processing of fingerprints in such signals allows some foodstuffs to be characterized and/or authenticated. This paper deals at length with food identification by High Performance Liquid Chromatography in combination with mathematical processing. Also, it discusses existing approaches to the integrated acquisition of chromatographic signals and chemometric processing of chromatographic data, and illustrates the uses of fingerprinting methods for different types of foodstuffs.

A critical review on the use of artificial neural networks in olive oil production, characterization and authentication.

Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.

Tackling Fraudsters with Global Strategies to Expose Fraud in the Food Chain.

Deliberate adulteration of food products is as old as food processing and production systems. Food adulteration is occurring increasingly often today. With globalization and complex distribution systems, adulteration may have a far-reaching impact and even adverse consequences on well-being. The means of the international community to confront and solve food fraud today are scattered and largely ineffective. A collective approach is needed to identify all stakeholders in the food supply chain, certify and qualify them, exclude those failing to meet applicable standards, and track food in a real time. This review provides some background into the drivers of fraudulent practices (economically motivated adulteration, food-industry perspectives, and consumers' perceptions of fraud) and discusses a wide range of the currently available technologies for detecting food adulteration followed by multivariate pattern recognition tools. Food chain integrity policies are discussed. Future directions in research, concerned not only with food adulterers but also with food safety and climate change, may be useful for researchers in developing interdisciplinary approaches to contemporary problems.

A fast and direct spectrophotometric method for the simultaneous determination of methyl paraben and hydroquinone in cosmetic products using successive projections algorithm.

OBJECTIVE: To develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of methyl paraben (MP) and hydroquinone (HQ) in cosmetic products, and specifically, to: (i) evaluate the potential use of successive projections algorithm (SPA) to derivative spectrophotometric data in order to provide sufficient accuracy and model robustness and (ii) determine MP and HQ concentration in cosmetics without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. METHODS: The absorption spectra were measured in the wavelength range of 200-350 nm. Prior to performing chemometric models, the original and first-derivative absorption spectra of binary mixtures were used as calibration matrices. Variable selected by successive projections algorithm was used to obtain multiple linear regression (MLR) models based on a small subset of wavelengths. The number of wavelengths and the starting vector were optimized, and the comparison of the root mean square error of calibration (RMSEC) and cross-validation (RMSECV) was applied to select effective wavelengths with the least collinearity and redundancy. Principal component regression (PCR) and partial least squares (PLS) were also developed for comparison. The concentrations of the calibration matrix ranged from 0.1 to 20 µg mL(-1) for MP, and from 0.1 to 25 µg mL(-1) for HQ. The constructed models were tested on an external validation data set and finally cosmetic samples. RESULTS: The results indicated that successive projections algorithm-multiple linear regression (SPA-MLR), applied on the first-derivative spectra, achieved the optimal performance for two compounds when compared with the full-spectrum PCR and PLS. The root mean square error of prediction (RMSEP) was 0.083, 0.314 for MP and HQ, respectively. To verify the accuracy of the proposed method, a recovery study on real cosmetic samples was carried out with satisfactory results (84-112%). CONCLUSION: The proposed method, which is an environmentally friendly approach, using minimum amount of solvent, is a simple, fast and low-cost analysis method that can provide high accuracy and robust models. The suggested method does not need any complex extraction procedure which is time-consuming and requires hazardous solvents.