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The present study determined total phenolic content (TPC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, ±-glucosidase, and nitric oxide (NO) inhibitory activities of black (BR), purple (PR), and red rice (RR) extracted with different polarity solvents and characterized the metabolites by using the 1H NMR-based metabolomics approach. The methanol extract of RR had the highest DPPH scavenging and ±-glucosidase inhibitory activities. The water extracts of PR and RR exhibited the highest NO inhibitory activity with IC50 values of 176.28 ± 18.78 and 160.69 ± 14.83 µg/mL, respectively. The metabolites that contribute to radical scavenging and ±-glucosidase inhibitory activities of RR methanol extract were chlorogenic acid, protocatechuic acid, and tyrosine, whereas nicotinic acid, ³-aminobutyric acid, and citric acid were the metabolites that contributed to the NO inhibitory activity of RR water extract. The RR extracts are potential sources of bioactive compounds with antioxidant and NO inhibitory properties.

The present study determined total phenolic content (TPC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, ±-glucosidase, and nitric oxide (NO) inhibitory activities of black (BR), purple (PR), and red rice (RR) extracted with different polarity solvents and characterized the metabolites by using the 1H NMR-based metabolomics approach. The methanol extract of RR had the highest DPPH scavenging and ±-glucosidase inhibitory activities. The water extracts of PR and RR exhibited the highest NO inhibitory activity with IC50 values of 176.28 ± 18.78 and 160.69 ± 14.83 µg/mL, respectively. The metabolites that contribute to radical scavenging and ±-glucosidase inhibitory activities of RR methanol extract were chlorogenic acid, protocatechuic acid, and tyrosine, whereas nicotinic acid, ³-aminobutyric acid, and citric acid were the metabolites that contributed to the NO inhibitory activity of RR water extract. The RR extracts are potential sources of bioactive compounds with antioxidant and NO inhibitory properties.

Background: The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome. Methodology: Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients’ samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models. Results: In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful. Conclusion: Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Background: The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome. Methodology: Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients’ samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models. Results: In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful. Conclusion: Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Introduction Muntingia calabura from the Muntingiaceae family has been documented for several medicinal uses. The combinations of drying treatment and extracting solvents for a plant species need to be determined and optimised to ensure that the extracts contain adequate amounts of the bioactive metabolites. Objective Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (1H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by 1H-NMR-based metabolomics. Results Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC50 value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC50 value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities. Conclusion The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.

Introduction Muntingia calabura from the Muntingiaceae family has been documented for several medicinal uses. The combinations of drying treatment and extracting solvents for a plant species need to be determined and optimised to ensure that the extracts contain adequate amounts of the bioactive metabolites. Objective Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (1H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by 1H-NMR-based metabolomics. Results Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC50 value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC50 value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities. Conclusion The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.

Clitorea ternatea has been used in Ayurvedic medicine as a brain stimulant to treat mental illnesses and mental functional disorders. In this study, the metabolite profiles of crude C. ternatea root extract (CTRE), ethyl acetate (EA), and 50% aqueous methanol (50% MeOH) fractions were investigated using ultrahigh-performance liquid chromatography–diode array detector–tandem mass spectrometry (UHPLC–DAD–MS/MS), while their effect on the stress-like behavior of zebrafish, pharmacologically induced with reserpine, was investigated. A total of 32 compounds were putatively identified, among which, a series of norneolignans, clitorienolactones, and various flavonoids (flavone, flavonol, isoflavone, and isoflavanone) was found to comprise the major constituents, particularly in the EA and 50% MeOH fractions. The clitorienolactones, presently unique to the species, were present in both the free and glycosylated forms in the roots. Both the EA and 50% MeOH fractions displayed moderate effects on the stress-induced zebrafish model, significantly decreasing freezing duration and elevating the total distance travelled and average velocity, 72 h post-treatment. The results of the present study provide further evidence that the basis for the use of C. ternatea roots in traditional medicine to alleviate brain-related conditions, such as stress and depression, is attributable to the presence of clitorienolactones and the isoflavonoidal constituents.

Clitorea ternatea has been used in Ayurvedic medicine as a brain stimulant to treat mental illnesses and mental functional disorders. In this study, the metabolite profiles of crude C. ternatea root extract (CTRE), ethyl acetate (EA), and 50% aqueous methanol (50% MeOH) fractions were investigated using ultrahigh-performance liquid chromatography–diode array detector–tandem mass spectrometry (UHPLC–DAD–MS/MS), while their effect on the stress-like behavior of zebrafish, pharmacologically induced with reserpine, was investigated. A total of 32 compounds were putatively identified, among which, a series of norneolignans, clitorienolactones, and various flavonoids (flavone, flavonol, isoflavone, and isoflavanone) was found to comprise the major constituents, particularly in the EA and 50% MeOH fractions. The clitorienolactones, presently unique to the species, were present in both the free and glycosylated forms in the roots. Both the EA and 50% MeOH fractions displayed moderate effects on the stress-induced zebrafish model, significantly decreasing freezing duration and elevating the total distance travelled and average velocity, 72 h post-treatment. The results of the present study provide further evidence that the basis for the use of C. ternatea roots in traditional medicine to alleviate brain-related conditions, such as stress and depression, is attributable to the presence of clitorienolactones and the isoflavonoidal constituents.

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the –CH=CH₂ or CH=CH (990–900 cm⁻¹) and C-Cl stretch (800–700 cm⁻¹) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible te

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the –CH=CH₂ or CH=CH (990–900 cm⁻¹) and C-Cl stretch (800–700 cm⁻¹) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible te

Optimization process is an important aspect in the natural product extractions. Herein, an alternative approach is proposed for the optimization in extraction, namely, the Generalized Likelihood Uncertainty Estimation (GLUE). The approach combines the Latin hypercube sampling, the feasible range of independent variables, the Monte Carlo simulation, and the threshold criteria of response variables. The GLUE method is tested in three different techniques including the ultrasound, the microwave, and the supercritical CO2 assisted extractions utilizing the data from previously published reports. The study found that this method can: provide more information on the combined effects of the independent variables on the response variables in the dotty plots; deal with unlimited number of independent and response variables; consider combined multiple threshold criteria, which is subjective depending on the target of the investigation for response variables; and provide a range of values with their distribution for the optimization.

Optimization process is an important aspect in the natural product extractions. Herein, an alternative approach is proposed for the optimization in extraction, namely, the Generalized Likelihood Uncertainty Estimation (GLUE). The approach combines the Latin hypercube sampling, the feasible range of independent variables, the Monte Carlo simulation, and the threshold criteria of response variables. The GLUE method is tested in three different techniques including the ultrasound, the microwave, and the supercritical CO2 assisted extractions utilizing the data from previously published reports. The study found that this method can: provide more information on the combined effects of the independent variables on the response variables in the dotty plots; deal with unlimited number of independent and response variables; consider combined multiple threshold criteria, which is subjective depending on the target of the investigation for response variables; and provide a range of values with their distribution for the optimization.

The present study sought to identify the key biomarkers and pathways involved in the induction of allergic sensitization to ovalbumin and to elucidate the potential anti-anaphylaxis property of Clinacanthus nutans (Burm. f.) Lindau water leaf extract, a Southeast Asia herb in an in vivo ovalbumin-induced active systemic anaphylaxis model evaluated by 1H-NMR metabolomics. The results revealed that carbohydrate metabolism (glucose, myo-inositol, galactarate) and lipid metabolism (glycerol, choline, sn-glycero-3-phosphocholine) are the key requisites for the induction of anaphylaxis reaction. Sensitized rats treated with 2000 mg/kg bw C. nutans extract before ovalbumin challenge showed a positive correlation with the normal group and was negatively related to the induced group. Further 1H-NMR analysis in complement with Kyoto Encyclopedia of Genes and Genomes (KEGG) reveals the protective effect of C. nutans extract against ovalbumin-induced anaphylaxis through the down-regulation of lipid metabolism (choline, sn-glycero-3-phosphocholine), carbohydrate and signal transduction system (glucose, myo-inositol, galactarate) and up-regulation of citrate cycle intermediates (citrate, 2-oxoglutarate, succinate), propanoate metabolism (1,2-propanediol), amino acid metabolism (betaine, N,N-dimethylglycine, methylguanidine, valine) and nucleotide metabolism (malonate, allantoin). In summary, this study reports for the first time, C. nutans water extract is a potential anti-anaphylactic agent and 1H-NMR metabolomics is a great alternative analytical tool to explicate the mechanism of action of anaphylaxis.

Nine varieties of Algerian date palm fruits (Phoenix dactylifera L.) including Lahmira, Timjouhert, Adham Talmine, Deglet Talmine, Adam Boullah, Tinasser, Deglet, Deglet Nour, and Takerbouch were distinguished via Proton Nuclear Magnetic Resonance (1H‐NMR) metabolomics approach. The quality was evaluated based on the metabolite composition, proximate analysis, physicochemical characteristics, and biological activities which include nitric oxide (NO) inhibition via the cell‐based approach and NO scavenging abilities. A total of 49 metabolites were tentatively characterized including amino acids, organic acids, and phenolic compounds. The Principal Component Analysis and Hierarchical Clustering Analysis revealed a consistent grouping of Deglet, Takerbouch, and Deglet Noor from the rest of six varieties. Among the metabolites that contributed toward the observed segregation were serine, glycine, glucose, fructose, and sucrose. With regard to metabolite composition, NO scavenging activities, total soluble solids, and proximate analysis, Deglet dates displayed a better quality as compared to the other Algerian dates.

The present study sought to identify the key biomarkers and pathways involved in the induction of allergic sensitization to ovalbumin and to elucidate the potential anti-anaphylaxis property of Clinacanthus nutans (Burm. f.) Lindau water leaf extract, a Southeast Asia herb in an in vivo ovalbumin-induced active systemic anaphylaxis model evaluated by 1H-NMR metabolomics. The results revealed that carbohydrate metabolism (glucose, myo-inositol, galactarate) and lipid metabolism (glycerol, choline, sn-glycero-3-phosphocholine) are the key requisites for the induction of anaphylaxis reaction. Sensitized rats treated with 2000 mg/kg bw C. nutans extract before ovalbumin challenge showed a positive correlation with the normal group and was negatively related to the induced group. Further 1H-NMR analysis in complement with Kyoto Encyclopedia of Genes and Genomes (KEGG) reveals the protective effect of C. nutans extract against ovalbumin-induced anaphylaxis through the down-regulation of lipid metabolism (choline, sn-glycero-3-phosphocholine), carbohydrate and signal transduction system (glucose, myo-inositol, galactarate) and up-regulation of citrate cycle intermediates (citrate, 2-oxoglutarate, succinate), propanoate metabolism (1,2-propanediol), amino acid metabolism (betaine, N,N-dimethylglycine, methylguanidine, valine) and nucleotide metabolism (malonate, allantoin). In summary, this study reports for the first time, C. nutans water extract is a potential anti-anaphylactic agent and 1H-NMR metabolomics is a great alternative analytical tool to explicate the mechanism of action of anaphylaxis.

Nine varieties of Algerian date palm fruits (Phoenix dactylifera L.) including Lahmira, Timjouhert, Adham Talmine, Deglet Talmine, Adam Boullah, Tinasser, Deglet, Deglet Nour, and Takerbouch were distinguished via Proton Nuclear Magnetic Resonance (1H‐NMR) metabolomics approach. The quality was evaluated based on the metabolite composition, proximate analysis, physicochemical characteristics, and biological activities which include nitric oxide (NO) inhibition via the cell‐based approach and NO scavenging abilities. A total of 49 metabolites were tentatively characterized including amino acids, organic acids, and phenolic compounds. The Principal Component Analysis and Hierarchical Clustering Analysis revealed a consistent grouping of Deglet, Takerbouch, and Deglet Noor from the rest of six varieties. Among the metabolites that contributed toward the observed segregation were serine, glycine, glucose, fructose, and sucrose. With regard to metabolite composition, NO scavenging activities, total soluble solids, and proximate analysis, Deglet dates displayed a better quality as compared to the other Algerian dates.