Music Interventions and Delirium in Adults: A Systematic Literature Review and Meta-Analysis.

Delirium is a neuropsychiatric syndrome represented by an acute disturbance in attention, awareness and cognition, highly prevalent in older, and critically ill patients, and associated with poor outcomes. This review synthesized existing evidence on the effectiveness of music interventions on delirium in adults, and music interventions (MIs), psychometric assessments and outcome measures used. We searched MEDLINE, PsychINFO, SCOPUS, Clinical Trials and CENTRAL for quantitative designs comparing any MIs to standard care or another intervention. From 1150 studies 12 met the inclusion criteria, and 6 were included in the meta-analysis. Narrative synthesis showed that most studies focused on prevention, few assessed delirium severity, with the majority of studies reporting beneficial effects. The summary relative risk for incident delirium comparing music vs. no music in postsurgical and critically ill older patients was 0.52 (95% confidential interval (CI): 0.20−1.35, I2 = 79.1%, heterogeneity <0.0001) for the random effects model and 0.47 (95% CI: 0.34−0.66) using the fixed effects model. Music listening interventions were more commonly applied than music therapy delivered by credentialed music therapists, and delirium assessments methods were heterogeneous, including both standardized tools and systematic observations. Better designed studies are needed addressing effectiveness of MIs in specific patient subgroups, exploring the correlations between intervention-types/dosages and delirium symptoms.

Differences in the Levels of the Selected Phytoestrogens and Stable Isotopes in Organic vs. Conventional Hops and Beer.

Xanthohumol (XN), isoxanthohumol (IX) and 8-prenylnaringenin (8-PN) are important prenylflavonoids present in hops with potential beneficial properties. In this study, we examined differences in the content of XN, IX and 8-PN in hops and beer produced under organic and conventional production regimes. A An ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) method for analysing XN, IX and 8-PN in hops and beer was developed and validated, with LOQ ranging from 0.5 to 10 ng/mL. Finally, we examined 15N/14N and 12C/13C isotope ratios in the hops and beer using isotope ratio mass spectrometry (IRMS). The results show no statistically significant difference in the content of the selected prenylflavonoids between organic and conventionally produced hops and beer-in the whole sample group, as well as between the matched pairs. Stable isotope analysis indicated that only δ15N values are statistically higher in organically produced hops and beer. However, the differentiation according to the type of production could not be made solely based on the δ15N signature, but it could be used to provide supporting evidence.

Characterization of Biomolecules with Antibiotic Activity from Endophytic Fungi Phomopsis Species.

Recently, growing interest is devoted to investigation of bioactive secondary metabolites of endophytic fungi. Thus, as an extension to our previous achievements related to antimicrobial potential of endophytic fungi, Phomopsis species isolated from conifer needles was selected as appropriately promising natural source for drug discovery. Its dichloromethane and ethanol extracts considerably inhibited growth of Escherichia coli and Staphylococcus aureus. Moreover, the individual compounds of dichloromethane extract have been separated, collected and purified using semi preparative liquid chromatographic analysis and comprehensively characterized using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). Based on their antimicrobial activity and unique structural characteristics in comparison with well-established drugs from the same therapeutic category, two dominant compounds (Z)-(Z)-2-acetoxyprop-1-en-1-yl-3-(3-((E)-3,4-dihydroxypent-1-en-1-yl)oxiran-2-yl)acrylate (denoted as 325-3) and (Z)-(Z)-2-acetoxyprop-1-en-1-yl 3-(3-((E)-4-hydroxy-3-oxopent-1-en-1-yl)oxiran-2-yl)acrylate (denoted as 325-5) were recognized as valuable leading structures for future discovery of novel antibiotics.

Validation challenges in liquid chromatography-tandem mass spectrometry methods for the analysis of naturally occurring compounds in foodstuffs.

LC-MS/MS is the mainstream technique for the analysis of naturally occurring compounds in food. Because of the complex chemical composition of food, the most challenging validation parameters are those related to the matrix - linearity (calibration) and trueness. The influence of the interfering compounds on the analytical results is reflected in the extraction efficiency and matrix effect. These effects must be compensated for, if they cannot be removed or reduced by optimizing the extraction and the LC-MS/MS method. The calibration strategy is selected on the basis of the analytical conditions - complexity of matrix, and chemical structure and number of analytes. It is advisable to estimate trueness - both extraction efficacy and matrix effect during the preliminary experiments in order to select the right type of calibration. Finally, it is essential to describe the validation procedure in detail and refer to the used guidelines in order to provide a reproducible method.

Isolation and Determination of Fomentariol: Novel Potential Antidiabetic Drug from Fungal Material.

Diabetes mellitus is one of the leading world's public health problems. Therefore, it is of a huge interest to develop new antidiabetic drugs. Apart from traditional therapy of diabetes, nowadays, importance is given to natural substances with antidiabetic potential. Fomes fomentarius is a mushroom widely used for different purposes, due to its range of already confirmed activities. Fomentariol is a constituent of Fomes fomentarius, responsible for its antidiabetic potential. In that respect, it is important to develop a method for isolation and quantification of fomentariol from fungal material, which will be simple and efficient. Multistep, complex extraction applied in the previously reported studies was avoided with ethanol, providing rapid single-step extraction. The presence of fomentariol in ethanolic extract was confirmed by high-resolution mass spectrometry. Semipreparative HPLC method was developed and applied for isolation from ethanol extract and purification of the active compound fomentariol. It was a gradient reversed-phase method with a mobile phase consisting of acetonitrile and 0.1% formic acid in water and total run time of 15 minutes. The amount of 6.5 mg of high-purity fomentariol was determined by quantitative NMR with toluene as internal standard. The isolated and determined amount of substance can be further used for the quantitative estimation of activity of fomentariol.

Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases.

Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound-CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and ß-CD complexes and predict which part of the molecule structure will most likely incorporate into the ß-CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR model was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R2) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated. Graphical Abstract Complex association constants as inputs of QSRR model in ß-cyclodextrin modified HPLC system and development of green chromatography methods.

Multicriteria Optimization Methodology in Stability-Indicating Method Development of Cilazapril and Hydrochlorothiazide.

Multicriteria optimization methodology was applied in development of UHPLC-UV-MS method for separation of cilazapril, hydrochlorothiazide and their degradation products. This method is also applicable for analysis of cilazapril, hydrochlorothiazide and their degradation products in combined tablet formulation. Prior to method optimization forced degradation studies were conducted. Cilazapril and hydrochlorothiazide were subjected to acidic (0.1, 0.5 and 1.0 M HCl), basic (0.1, 0.5 and 1.0 M NaOH), thermal (70°C), oxidative (3-30% H2O2) degradation and photodegradation (day light). Cilazapril appeared to be unstable toward acid and base and resulted in formation of cilazaprilat. Hydrochlorothiazide significantly degraded after acid, base and thermal hydrolysis and formed degradation product was 4-amino-6-chlorobenzene-1.3-disulfonamide. For both substances, after oxidative degradation unknown products have arisen. Initial percentage of acetonitrile in mobile phase, final percentage of acetonitrile in mobile phase, time of gradient elution and column temperature were defined as variables to be optimized toward two chromatographic responses by means of central composite design and Derringer's desirability function. The satisfactory chromatographic analysis was achieved on Kinetex C18 (2.6 µm, 50 × 2.1 mm) column with temperature set at 25°C. The final mobile phase consisted of acetonitrile and 20 mM ammonium formate buffer (pH adjusted to 8.5). The flow rate of the mobile phase was 400 µL min-1 and it was pumped in a gradient elution mode.

Structure-response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks.

Quantitative structure-property relationship (QSPR) methods are based on the hypothesis that changes in the molecular structure are reflected in changes in the observed property of the molecule. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. For the first time a quantitative structure-response relationship in electrospray ionization-mass spectrometry (ESI-MS) by means of artificial neural networks (ANN) on the group of angiotensin II receptor antagonists--sartans has been established. The investigated descriptors correspond to different properties of the analytes: polarity (logP), ionizability (pKa), surface area (solvent excluded volume) and number of proton acceptors. The influence of the instrumental parameters: methanol content in mobile phase, mobile phase pH and flow rate was also examined. Best performance showed a multilayer perceptron network with the architecture 6-3-3-1, trained with backpropagation algorithm. It showed high prediction ability on the previously unseen (test) data set with a coefficient of determination of 0.994. High prediction ability of the model would enable prediction of ESI-MS responsiveness under different conditions. This is particularly important in the method development phase. Also, prediction of responsiveness can be important in case of gradient-elution LC-MS and LC-MS/MS methods in which instrumental conditions are varied during time. Polarity, chargeability and surface area all appeared to be crucial for electrospray ionization whereby signal intensity appeared to be the result of a simultaneous influence of the molecular descriptors and their interactions. Percentage of organic phase in the mobile phase showed a positive, while flow rate showed a negative impact on signal intensity.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans.

Liquid chromatography/tandem mass spectrometry for simultaneous determination of undeclared corticosteroids in cosmetic creams.

RATIONALE: Undeclared corticosteroids in creams intended for frequent use might cause serious side-effects, especially in children. In order to prevent this or find the cause, it was essential to develop a method for quick detection and quantification of low levels of corticosteroids. METHODS: Eleven corticosteroids were used in this study: prednisolone, methylprednisolone, prednisolone-21-acetate, fluocinolone acetonide, fluocinolone acetonide-21-acetate, hydrocortisone-21-acetate, dexamethasone, betamethasone, betamethasone dipropionate, clobetasol propionate and triamcinolone. Separation was achieved via liquid chromatography (LC), and mass spectrometric analysis was conducted by electrospray ionization triple-quadrupole mass spectrometry (MS/MS) in the multiple reaction monitoring mode using corticosterone as internal standard. RESULTS: Good separation by using a gradient-elution LC/MS/MS method with run time of 25 min enabled the use of a segmented detection method and consecutive decrease in detection limits. The proposed method has been validated in the linearity range of 10-1000 ng/mL with coefficients of determination higher than 0.990. The method has shown to have very low limits of quantification (0.75-3 ng/mL) with satisfactory precision and accuracy for each of the corticosteroids. CONCLUSIONS: An LC/MS/MS method for the rapid and simultaneous determination of low levels of eleven topical corticosteroids in creams was developed, optimized and validated. The proposed method can be used for testing of different products indicated for the treatment of atopic dermatitis, including "natural products", and "herbal creams" with "miraculous effects".

Quantitative structure-retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography.

Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure-retention relationships (QSRRs) model of antifungal agents-imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.