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Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD. Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice. In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.<br />The primary aim was to develop and validate a novel mammography positioning measure, specifically incorporating parameters which might relate to mammography pain. We then explored relationships between the new adverse positioning score and (1) pain; (2) patient and technique factors.<br />A 15-item instrument incorporating positioning features with potential to relate to mammography pain was developed. Participants' mammograms (n = 310) were reviewed for presence of these features. Validity was investigated using the Rasch model. [https://www.selleckchem.com/products/srt2104-gsk2245840.html GSK2245840 Sirtuin activator] Scores produced by the resultant measure were investigated for associations with patients' pain scores and relevant patient and technique factors, using Pearson correlation, analysis of variance, and multiple linear regression.<br />Statistical indices within the Rasch measurement framework provided good evidence that the measure reflected a coherent construct of adverse positioning. Thus, the scores produced with the measurement instrument were valid for use in further statistical analyaphy pain. The measure warrants expansion, further refinement, and testing in larger studies.Consumers are daily exposed to a range of mineral oil hydrocarbons via food consumption. Major sources of MOH in food are packaging and additives, processing aids, and lubricants. In 2019, an EU guidance was released covering specific directions for sampling and analysis of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) in food and food contact materials within the frame of Recommendation (EU) 2017/84 for the monitoring of mineral oils. The parameters required by the guide are increasingly stringent, and coping with this type of analysis is now very challenging. It is within such a context that the present research is confined, inasmuch that it is focused on the construction of a low-cost, lab-made Y-interface for liquid-gas chromatography coupling used for the determination of MOSH and MOAH in foodstuffs. The response ratios of alkanes comprised between C10 and C50 were measured and were comprised between 0.9 and 1.1, with a maximum coefficient of variation of 4% (n = 5). Intermediate precision was evaluated for the fat/oil category during a period of 48 days obtaining a value of 10%. Qualitative and quantitative analysis of both MOSH and MOAH were performed in a single run and in a fully-automated manner. Seventeen different foods were analyzed in order to cover the categories reported in the EU guide. Saturated hydrocarbon contamination was detected only in a few samples (in the range 1-153 ppm); MOAH contamination was found only in one sample (sunflower oil 15 ppm).Two commercial stationary phases allowing both reversed phase mechanism and anion-exchange with different selectivity, i.e. CSH C18 and Atlantis PREMIER BEH C18 AX, were tested for the separation of a complex mixture of 21 fatty acids (FAs) encompassing saturated medium-, long- and very long chain FAs, unsaturated long and very long chain FAs, cis/trans isomers, and isomers of odd- and branched-chain FAs. For this purpose, the role of surface area of stationary phase and the effect of pH of the mobile phase on the retention of the analytes were investigated. Separation was performed by ultra-high-performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS). BEH C18 AX was shown to be more versatile and to offer superior retention of these analytes to CSH C18 owing to a higher surface area and anion-exchange capacity up to pH 8.5. The UHPLC system allows shortening analysis time, the chromatographic analysis being accomplished in about 5 min, affording a high throughput of samples without the need for derivatization or ion-pairing reagents compared to techniques based upon gas chromatography approaches or LC. Finally, the application of the BEH C18 AX column using UHPLC-HRMS was demonstrated for the separation and unambiguous identification of FAs of nutritional interest in a dietary supplement sample.Public exposure to pesticides through tobacco has attracted serious attention. Here we report a simultaneous screening and quantitation method for the non-target multiresidue analysis of pesticides in different tobacco types. The method involved extraction of a homogenate (20 g, containing 2 g tobacco) in ethyl acetate (10 mL), cleanup of 2 mL extract by dispersive solid phase extraction with PSA (50 mg)+C18 (50 mg)+GCB (25 mg)+MgSO4 (100 mg), followed by reconstitution in 1 mL acetonitrilewater (37) and analysis using HPLC with Quadrupole-Orbitrap mass spectrometry. The high resolution accurate mass analysis was performed through sequential full-scan (resolution=35000) and variable data independent acquisition (resolution=17500) events. When the method was evaluated in a mixture of 181 pesticides, it effectively minimised matrix interferences and false negatives. The target compounds included 5 pairs of isomers and 27 pairs of isobars, which were distinguished based on chromatographic separation, mass resolving power and/or unique product ions.