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INTRODUCTION: Ramonda serbica and R. nathaliae are resurrection plants that have the remarkable ability to survive the complete desiccation of their vegetative organs (i.e. leaves, stem, roots) during periods of drought and rapidly revive when rewatered and rehydrated. OBJECTIVE: To investigate metabolic changes in R. serbica and R. nathaliae during their desiccation and recovery process METHODS: Proton nuclear magnetic resonance (1 H-NMR) and gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach coupled with multivariate data analysis was utilised to identify the metabolomes of the plants from 90 biological replicates. RESULTS: Sucrose and the polyphenolic glycoside myconoside were predominant in almost equal amounts in all samples studied, regardless of their water content at sampling. During the dehydration process, a decrease in the relative content of fructose, galactose, and galactinol was observed while the contents of those metabolites were preserved in the partially rehydrated plants. Raffinose and myo-inositol were accumulated in dry samples. CONCLUSION: Using 1 H-NMR and GC-MS as two complementary analytical platforms provided a more complete picture of the metabolite composition for investigation of the desiccation and recovery process in resurrection plants.
Subject(s)
Craterostigma , Craterostigma/metabolism , Desiccation , Metabolomics , Plant Leaves/metabolism , Water/metabolismABSTRACT
Plum brandy (Slivovitz (en); Sljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.
Subject(s)
Alcoholic Beverages/analysis , Food Analysis/methods , Gas Chromatography-Mass Spectrometry/methods , Metabolomics/methods , Prunus domestica , Alcoholic Beverages/microbiology , Fermentation , Food Analysis/statistics & numerical data , Gas Chromatography-Mass Spectrometry/statistics & numerical data , Humans , Metabolomics/statistics & numerical data , Multivariate Analysis , Saccharomyces cerevisiae , Taste , Volatile Organic Compounds/analysis , YeastsABSTRACT
The perception of pain is strongly influenced by various social, emotional, and cognitive factors. A psychological variable which has consistently been shown to exert its influence on pain is a cognitive process referred to as pain catastrophizing. Numerous studies have found it to be a strong predictor of pain intensity and disability across different clinical populations. It signifies a maladaptive response to pain marked by an exaggerated negative assessment, magnification of symptoms related to pain, and, in general, a tendency to experience marked pain-related worry, as well as experiencing feelings of helplessness when it comes to dealing with pain. Pain catastrophizing has been correlated to many adverse pain-related outcomes, including poor treatment response, unsatisfactory quality of life, and high disability related to both acute and chronic pain. Furthermore, there has been consistent evidence in support of a correlation between pain catastrophizing and mental health disorders, such as anxiety and depression. In this review, we aim to provide a comprehensive overview of the current state of knowledge regarding pain catastrophizing, with special emphasis on its clinical significance, and emerging treatment modalities which target it.
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There are very few treatments in psychiatry, even in medicine, that have experienced longevity and effectiveness such as electroconvulsive therapy (ECT), despite the controversies and stigma that accompany it. The experience of the COVID-19 pandemic has highlighted the need to strengthen mental health systems in most countries, given that depression is one of the leading health problems and that there is an evident shortage of psychiatrists worldwide. The Fourth Industrial Revolution, has witnessed great progress in artificial intelligence (AI) technology, which opens up the possibility of its application both in the diagnosis and in the therapy of mental disorders. It is no exaggeration to suggest that tools such as AI, neuroimaging and blood tests will bring significant change to psychiatry in the coming years, but even so, treating severe mental disorders remains a challenge. The present review summarized the development of ECT over time, its application in clinical practice, neurobiological correlates and mechanisms of action and sheds light on the important place of ECT in the era of technological development, considering that ECT is still the most effective therapy for the treatment of severe mental disorders, especially depressive disorder.
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Data processing and data extraction are the first, and most often crucial, steps in metabolomics and multivariate data analysis in general. There are several software solutions for these purposes in GC-MS metabolomics. It becomes unclear which platform offers what kind of data and how that information influences the analysis's conclusions. In this study, selected analytical platforms for GC-MS metabolomics profiling, SpectConnect and XCMS as well as MestReNova software, were used to process the results of the HS-SPME/GC-MS aroma analyses of several blackberry varieties. In addition, a detailed analysis of the identification of the individual components of the blackberry aroma club varieties was performed. In total, 72 components were detected in the XCMS platform, 119 in SpectConnect, and 87 and 167 in MestReNova, with automatic integral and manual correction, respectively, as well as 219 aroma components after manual analysis of GC-MS chromatograms. The obtained datasets were fed, for multivariate data analysis, to SIMCA software, and underwent the creation of PCA, OPLS, and OPLS-DA models. The results of the validation tests and VIP-pred. scores were analyzed in detail.
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Chemotaxonomy presents various challenges that need to be overcome in order to obtain valid and reliable results. Individual genetic and environmental variations can give a false picture and lead to wrong conclusions. Applying a holistic approach, based on multivariate data analysis, these challenges can be overcome. Thus, a metabolomics approach has to be optimized depending on the subject of research. We used 1H NMR-based metabolomics as a potential chemotaxonomic tool on the selected Euphorbia species growing wild in Serbia. Principal components analysis (PCA), soft independent modeling by class analogy (SIMCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) were used to analyze obtained NMR data in order to reveal chemotaxonomic biomarkers. The standard protocol for plant metabolomics was optimized aiming to extract more specific metabolites, which are characteristic for the Euphorbia genus. The obtained models were validated, which revealed that variables unique for each species were associated with certain classes of molecules according to literature data. In E. salicifolia, acacetin-7-O-glycoside (not found before in the species) was detected, and the structure of the aglycone part was solved based on 2D NMR data. In the presented paper, we have shown that metabolomics can be successfully used in Euphorbia chemotaxonomy.
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Bipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.
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Allium ursinum and poisonous adulterants Convallaria majalis and Arum maculatum were used as a model for detection of adulterants in edible plant. A. ursinum samples were spiked with C. majalis and A. maculatum to mimic adulteration. Metabolomic fingerprinting of all samples was performed using 1H NMR spectroscopy, and the resulting data sets were subjected to multivariate data analysis. As a result of this analysis, signals of adulterants were extracted from the data, and the structures of biomarkers of adulteration from partially purified samples were elucidated using 2D NMR and LC-MS techniques. Thus, isovitexin and vicenin II, azetidine-2-carboxylic acid, and trigonelline indicated adulteration of A. ursinum samples with C. majalis. Isovitexin was also recognized to be an indicator of adulteration of A. ursinum with A. maculatum. In conclusion, the case study of A. ursinum suggested that plant metabolomics approach could be utilized for identification of low molecular weight biomarkers of adulteration in edible plants.
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Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.
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Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated years. This variation in volatiles composition could be a consequence of weather conditions, as in the case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles from literature and average values from this study, it could be said that there are several chemotypes: E-caryophyllene, ß-bisabolene, α-pinene, ß-farnesene, E-caryophyllene + caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be consequence of adaptation to grow in different environment, especially ecological conditions such as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes. In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile compounds was used to build the prediction models. The coefficients of determination were 0.956 and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction error and high r2.
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INTRODUCTION: Osteoporosis usually affects post-menopausal women. Treatment is individualized and requires an approach that will provide long-term compliance to prevent fractures. Studies conducted so far suggest inadequate compliance and persistence in weekly bisphosphonate treatment (under 43% after a year of treatment). Ibandronate, as a powerful bisphosphonate, has made it possible for the first time to treat osteoporosis with a single tablet per month. OBJECTIVE: Study of efficacy, safety and tolerance of ibandronate applied once a month in female patients with decreased bone mineral density (BMD). METHODS: The prospective study was conducted in 34 centres in Serbia covering the total of 370 women with reduced BMD with ibandronate once a month. Demographic data, risk factors for osteoporosis, mode of diagnosis establishment, previous treatment for osteoporosis and concomitant diseases were investigated. Efficacy of the treatment was evaluated by T-score value after 12 months versus the baseline values. Tolerance of the treatment, compliance and adverse effects were recorded. RESULTS: The sample included 97.5% post-menopausal women, 92.7% with osteoporosis. In 80% of the cases, the diagnosis was established by DXA measurement. In more than 90% of the sample, the level of physical activity was unsatisfactory, and 70% had an accompanying risk factor for osteoporosis in addition to menopause. After 12 months of treatment, 100% compliance was recorded in 84% of the patients and significant reduction (p < 0.0001) of the bone mineral loss, regardless of the previous aminobisphosphonate treatment. The treatment was tolerated well, with no serious adverse reactions. Some, mainly gastrointestinal complaints, registered in the first month (6%), were significantly relieved (p < 0.0001) after 12 months of treatment (1%). CONCLUSION: Ibandronate manifested significant improvement of the BMD after 12 months of treatment of patients with decreased BMD, with good tolerance and excellent treatment compliance.