Orthogonal Projections To Latent Structures Research Articles

Towards objective measurements of habitual dietary intake patterns: comparing NMR metabolomics and food frequency questionnaire data in a population-based cohort

BackgroundLow-quality, non-diverse diet is a main risk factor for premature death. Accurate measurement of habitual diet is challenging and there is a need for validated objective methods. Blood metabolite patterns reflect direct or enzymatically diet-induced metabolites. Here, we aimed to evaluate associations between blood metabolite patterns and a priori and data-driven food intake patterns.Methods1, 895 participants in the Northern Sweden Health and Disease Study, a population-based prospective cohort study, were included. Fasting plasma samples were analyzed with 1H Nuclear Magnetic Resonance. Food intake data from a 64-item validated food frequency questionnaire were summarized into a priori Healthy Diet Score (HDS), relative Mediterranean Diet Score (rMDS) and a set of plant-based diet indices (PDI) as well as data driven clusters from latent class analyses (LCA). Orthogonal projections to latent structures (OPLS) were used to explore clustering patterns of metabolites and their relation to reported dietary intake patterns.ResultsAge, sex, body mass index, education and year of study participation had significant influence on OPLS metabolite models. OPLS models for healthful PDI and LCA-clusters were not significant, whereas for HDS, rMDS, PDI and unhealthful PDI significant models were obtained (CV-ANOVA p < 0.001). Still, model statistics were weak and the ability of the models to correctly classify participants into highest and lowest quartiles of rMDS, PDI and unhealthful PDI was poor (50%/78%, 42%/75% and 59%/70%, respectively).ConclusionAssociations between blood metabolite patterns and a priori as well as data-driven food intake patterns were poor. NMR metabolomics may not be sufficiently sensitive to small metabolites that distinguish between complex dietary intake patterns, like lipids.

Differences in Supervision on Peer Learning Wards: A Pilot Survey of the Supervisor's Perspective.

Multiple students are placed on clinical wards simultaneously due to increasing student numbers. This has the potential to create stress for the supervisor and reduce quality of student learning. Peer learning as a pedagogical framework to supervise multiple students has been widely shown to have advantages for the students by developing teaching skills, team collaboration, and independence. However, whether peer learning impacts the characteristics of supervision and the experience of the supervisor is less understood. It is unknown whether wards that use peer learning as a pedagogical framework (peer learning wards) are any different compared to wards that do not (non-peer learning wards), from the supervisor's perspective. We aimed to develop and pilot test a questionnaire to compare peer-learning wards and non-peer learning wards from the supervisor's perspective. We used the AMEE 7-step guide to develop questions investigating supervision, the learning environment and satisfaction. We piloted the questionnaire with 46 nurse supervisors working on inpatient hospital wards in Stockholm, Sweden. We compared answers from peer learning with non-peer learning wards. We used Orthogonal Projections to Latent Structures (OPLS) discriminant analysis to show what differed between the wards. Peer learning wards compared to non-peer learning wards had more student-centred activities, the physical space had more adaptations for students, more support available to the supervisor, and supervisors perceived greater overall satisfaction with the quality of education and with the ward as a whole. The variables that had most influence on the discrimination between the two ward types related to peer learning activities and perceptions (p=0.0034). This pilot study shows that peer learning wards differ compared to non-peer learning wards regarding peer learning activities and perceptions among supervisors. Our questionnaire needs to be distributed on a larger scale to validate our findings and explore further the way in which the pedagogical framework and peer learning can affect supervision and satisfaction.

Development of bioreactor scale-down model using orthogonal projections to latent structures method and CO2 supplementation.

Scale-down model qualification is an important step for developing a large-scale cell culture process to enhance process understanding and support process characterization studies. Traditionally, only harvest data are used to show consistency between small-scale and large-scale bioreactor performance, allowing attributes that are dynamic over the cell culture period to be overlooked. A novel statistical method, orthogonal projections to latent structures (OPLS) analysis, can be utilized to compare time-course cell culture data across scales. Here we describe an example where OPLS is used to identify gaps between small-scale and large-scale bioreactor performances. In this case, differences in the partial pressure of carbon dioxide (pCO2 ) and lactate profiles were observed between small- and large-scale bioreactors, which were linked to differences in the product-quality attributes fragments and galactosylation. An improved small-scale model was developed, leading to improved consistency in the process performance and product qualities across scales and qualification of the scale-down model for regulatory submissions. This new statistical approach can provide valuable insights into process understanding and process scale-up.

Identification of sedative-hypnotic compounds shared by five medicinal Polyporales mushrooms using UPLC-Q-TOF-MS/MS-based untargeted metabolomics

BackgroundFive Polyporales mushrooms, namely Amauroderma rugosum, Ganoderma lucidum, G. resinaceum, G. sinense and Trametes versicolor, are commonly used in China for managing insomnia. However, their active components for this application are not fully understood, restricting their universal recognition. PurposeIn this study, we aimed to identify sedative-hypnotic compounds shared by these five Polyporales mushrooms. Study design and MethodsA UPLC-Q-TOF-MS/MS-based untargeted metabolomics, including OPLS-DA (orthogonal projection of potential structure discriminant analysis) and OPLS (orthogonal projections to latent structures) analysis together with mouse assays, were used to identify the main sedative-hypnotic compounds shared by the five Polyporales mushrooms. A pentobarbital sodium-induced sleeping model was used to investigate the sedative-hypnotic effects of the five mushrooms and their sedative-hypnotic compounds. ResultsNinety-two shared compounds in the five mushrooms were identified. Mouse assays showed that these mushrooms exerted sedative-hypnotic effects, with different potencies. Six triterpenes [four ganoderic acids (B, C1, F and H) and two ganoderenic acids (A and D)] were found to be the main sedative-hypnotic compounds shared by the five mushrooms. ConclusionWe for the first time found that these six triterpenes contribute to the sedative-hypnotic ability of the five mushrooms. Our novel findings provide pharmacological and chemical justifications for the use of the five medicinal mushrooms in managing insomnia.

Screening of bioactive components endowing hawthorn with turbidity-eliminating and lipid-lowering functions and development of quality control method of hawthorn

Hawthorn has the efficacy of eliminating turbidity and lowering the blood lipid level, and it is used for treating hyperlipidemia in clinic. However, the bioactive components of hawthorn are still unclear. In this study, the spectrum-effect relationship was employed to screen the bioactive components of hawthorn in the treatment of hyperlipidemia, and then the bioactive components screened out were verified in vivo. Furthermore, the quality control method for hawthorn was developed based on liquid chromatography-mass spectrometry(LC-MS). The hyperlipidemia model of rats was built, and different polar fractions of hawthorn extracts and their combinations were administrated by gavage. The effects of different hawthorn extract fractions on the total cholesterol(TC), triglycerides(TG), and low-density lipoprotein-cholesterol(LDL-C) in the serum of model rats were studied. The orthogonal projections to latent structures(OPLS) algorithm was used to establish the spectrum-effect relationship model between the 24 chemical components of hawthorn and the pharmacodynamic indexes, and the bioactive components were screened out and verified in vivo. Finally, 10 chemical components of hawthorn, including citric acid and quinic acid, were selected to establish the method for evaluating hawthorn quality based on LC-MS. The results showed that different polar fractions of hawthorn extracts and their combinations regulated the TG, TC, and LDL-C levels in the serum of the model rats. The bioactive components of hawthorn screened by the OPLS model were vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, rutin, citric acid, malic acid, and quinic acid. The 10 chemical components of hawthorn, i.e., citric acid, quinic acid, rutin, gallic acid, vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, malic acid, vanillic acid, neochlorogenic acid, and fumaric acid were determined, with the average content of 38, 11, 0.018, 0.009 5, 0.037, 0.017, 8.1, 0.009 5, 0.073, and 0.98 mg·g~(-1), respectively. This study provided a scientific basis for elucidating the material basis of hawthorn in treating hyperlipidemia and developed a content determination method for evaluating the quality of hawthorn.

An innovative processing driven efficient transformation of rare ginsenosides enhances anti-platelet aggregation potency of notoginseng by integrated analyses of processing-(chemical) profiling-pharmacodynamics

Ethnopharmacological relevancePanax notoginseng (Burk.) F. H. Chen, a valuable Chinese herb medicine, shows a characteristic bi-directional regulation of hemostasis and activating blood circulation with ginsenosides as the predominant bioactive compounds and is a typical representative of “processing triggered heteropotency”. Aim of the studyProcessing triggered heteropotency, one of the unique theories and practices in traditional Chinese medicine, refers to that the processing will lead to change in physical and chemical properties, and eventually disparate efficacy of the crude drugs, yet the optimum process and underlying mechanism remains unclear. In this study, using Panax notoginseng (PN) as a representative sample, a processing-(chemical) profiling-pharmacodynamics (3-P) relationship was proposed to investigate the processing mechanism of PN. Materials and methodsFirstly, a temperature programmed steaming process was designed to evaluate the steaming triggered chemical transformation of triterpene saponins and the corresponding enhancement in anti-platelet aggregation activity. The steaming process was programed from the conventional 100 °C–150 °C in a time course of 0–12 h, aiming to achieve the maximized conversion of rare ginsenosides (RGs), and dynamic profile of ginsenosides were constructed by a UPLC-Q-TOF-MS/MS analysis. Then, a processing-(chemical) profiling-pharmacodynamics (3-P) relationship was assessed by using the grey relational analysis (GRA) and orthogonal projections to latent structures (OPLS), and validated by bioactive fraction of 140 °C steamed PN. Subsequently, the P2Y12-ligand binding affinity of potential candidates was analyzed by molecular docking. Finally, the dynamic changes of ginsenosides during steaming of SPN were quantitatively detected by UPLC-QQQ-MS/MS. ResultsA total of 48 differential ginsenosides were characterized and monitored including the primary and secondarily transformed saponins. The higher temperature steaming especially at 140 °C induces not only the predominant production of the RGs, but also the stronger anti-platelet aggregation activity. The 3-P relationship showed the fraction (3) of 140 °C steamed PN rich in RGs exhibits the most predominant efficacy, in which, a series of RGs including ginsenosides Rg5, Rk1, 20(S/R)-Rg3 were proven to be potent components. Molecular docking analysis suggested that ginsenosides Rg5 and Rk1 showed more strong interaction with the platelet P2Y12 receptor. Quantitative analysis found 140 °C-2h PN possessed highest contents of Rk1 and Rg5 and total RGs. ConclusionsThe integrated 3-P strategy uncovered the promising ginsenosides with anti-platelet effect, thereby revealing the material basis of PN steaming, which could provide a new enlightenment for the investigation of processing mechanism of traditional Chinese medicines.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study.

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

Metabolic bioprofiling of different Glycyrrhiza glabra solvent fractions for the identification of anti-adenoviral compounds using LC-HRMS/MS and in-vitro cytopathic assay coupled with chemometry

Human adenovirus type-7 (HAdV-7) is a common pathogen that may cause significant morbidity as well as severe complications. Currently, there is no approved drug for the treatment of HAdV-7 infections and the contemporary strategy mainly rely on nonspecific antivirals. Glycyrrhiza glabra; (licorice), is a well-recognized edible plant used in food and beverages. The plant is renowned for its pharmacological and biological activities including antiviral activities against wide range of viruses. The following study reported for the first time the anti-adenoviral activity of licorice extract using in-vitro cytopathic inhibitory assay. Different solvent fractions showed promising activity with IC50 reaching 1.431 μg/ml. Certain fractions had selectivity index (SI) greater than 10 indicating their efficacy together with relatively low cytotoxic effect. Metabolic profiling using LC-HRMS/MS resulted in identification of 41 compounds from licorice fractions. Chemometric modeling using Orthogonal Projections to Latent Structures (OPLS) suggested the compounds; kaempferol-3-O-rutinoside, violanthin, rhamnoliquiritin, isoliquiritigenin isomer, licoagroside B and liquiritin apioside as potential markers against (HAdV-7). Finally, kaempferol-3-O-rutinoside was further confirmed via in-vitro adenovirus inhibitory assay to possess strong antiviral activity with IC50 and CC50 of 54.7 ± 1.93 μM and 655.7 ± 2.22 μM, respectively.

Nitrate Measurement in Seawater Based on Environmental Correction Algorithm

The accuracy of the in-situ nitrate measurement in seawater is affected by many environmental factors, such as salinity, temperature, and turbidity. Along these lines, in this work, an environmental correction (including salinity, temperature, and turbidity) algorithm was proposed and experimentally verified, demonstrating a good prediction accuracy of the nitrate concentration. The orthogonal projections to latent structures (OPLS) algorithm was used for performing one-time turbidity and temperature correction of the acquired spectral data. After the environmental correction, the support vector machine (SVM) was used to establish the nitrate calculation model. The OPLS-SVM algorithm was systematically examined in spiked samples with four different groups of seawater matrices (the seawater samples from Western Pacific, Aoshan Bay of Qingdao, China, South China Sea, and Yellow Sea with addition of various nitrate concentrations and turbidity). From the extracted results, it was proven that the SVM calculation model with the OPLS correction algorithm could significantly improve the accuracy of the nitrate measurement. More specifically, a prediction performance with root mean square error (RMSE) of 0.22 μmol/L and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.999 was demonstrated when the temperature and turbidity ranges were 5-25 °C and 0-50 NTU (nephelometric turbidity unit), respectively. The introduced method is suitable for measuring the nitrate concentration in turbidity seawater with a high degree of accuracy, which is undoubtedly of great importance for performing in-situ nitrate measurement in complex seawater environments.

Nitrate Measurement in Seawater with a Turbidity Correction Algorithm based on Ultraviolet Spectrophotometry

Turbidity usually has a great impact on the actual measurement of the value of nitrate concentrations in the seawater. In this study, a new turbidity correction approach for nitrate concentrations measurement in the seawater based on ultraviolet absorption spectroscopy is presented. Experiments were carried out to prove the improvement of the turbidity correction method with ultraviolet spectroscopy. The orthogonal projections to latent structures (O-PLS) algorithm was chosen to correct the additional information in absorbance caused by turbidity. The experimental results show that the influence of turbidity could be excellently removed. Compared with the standard normalized variate (SNV) transformation, the O-PLS algorithm could better reduce the influence of turbidity on the nitrate absorbance and improve the measurement accuracy in turbid seawater. The linear correlation coefficient (R2) of the prediction results in test samples is 0.9999 and RMSEP is 0.1886 μmol/L.

Influence of Dietary n-3 Long Chain Polyunsaturated Fatty Acid Intake on Oxylipins in Erythrocytes of Women with Rheumatoid Arthritis.

Oxylipins derived from n-3 fatty acids are suggested as the link between these fatty acids and reduced inflammation. The aim of the present study was to explore the effect of a randomized controlled cross-over intervention on oxylipin patterns in erythrocytes. Twenty-three women with rheumatoid arthritis completed 2 × 11-weeks exchanging one cooked meal per day, 5 days a week, for a meal including 75 g blue mussels (source for n-3 fatty acids) or 75 g meat. Erythrocyte oxylipins were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results were analyzed with multivariate data analysis. Orthogonal projections to latent structures (OPLS) with effect projections and with discriminant analysis were performed to compare the two diets' effects on oxylipins. Wilcoxon signed rank test was used to test pre and post values for each dietary period as well as post blue-mussel vs. post meat. The blue-mussel diet led to significant changes in a few oxylipins from the precursor fatty acids arachidonic acid and dihomo-ɣ-linolenic acid. Despite significant changes in eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) and free EPA in erythrocytes in the mussel group, no concurrent changes in their oxylipins were seen. Further research is needed to study the link between n-3 fatty-acid intake, blood oxylipins, and inflammation.

Untargeted Metabolomics Approach of Cross-Adaptation in Salmonella Enterica Induced by Major Compounds of Essential Oils.

Cross-adaptation phenomena in bacterial populations, induced by sublethal doses of antibacterial solutions, are a major problem in the field of food safety. In this regard, essential oils and their major compounds appear as an effective alternative to common sanitizers in food industry environments. The present study aimed to evaluate the untargeted metabolomics perturbations of Salmonella enterica serovar Enteritidis that has been previously exposed to the sublethal doses of the major components of essential oils: cinnamaldehyde, citral, and linalool (CIN, CIT, and LIN, respectively). Cinnamaldehyde appeared to be the most efficient compound in the assays evaluating the inhibitory effects [0.06% (v/v) as MBC]. Also, preliminary tests exhibited a phenotype of adaptation in planktonic and sessile cells of S. Enteritidis when exposed to sublethal doses of linalool, resulting in tolerance to previously lethal concentrations of citral. A metabolomics approach on S. Enteritidis provided an important insight into the phenomenon of cross-adaptation induced by sublethal doses of major compounds of some essential oils. In addition, according to the results obtained, when single molecules were used, many pathways may be involved in bacterial tolerance, which could be different from the findings revealed in previous studies regarding the use of phytocomplex of essential oils. Orthogonal projection to latent structures (OPLS) proved to be an interesting predictive model to demonstrate the adaptation events in pathogenic bacteria because of the global engagement to prevent and control foodborne outbreaks.

Spectrum-Effect Relationships Between High-Performance Liquid Chromatography Fingerprints and Hepatoprotective Activities of Cuscutae Semen.

Cuscutae Semen (CS) is a commonly used hepatoprotective traditional Chinese medicine, but the chemical components responsible for its hepatoprotective activity are unclear. The purpose of this study was to evaluate the spectrum-effect relationships between HPLC fingerprints and hepatoprotective effects of CS, and to identify its bioactive components. Phytochemical isolation of CS extracts was first carried out and 14 potential bioactive compounds were obtained. Chemical fingerprinting was performed on 27 batches of CS from different sources by HPLC, and further analyzed by similarity analysis (SA) and hierarchical clustering analysis (HCA). Pharmacodynamic testing was performed in a CCl4-induced, acute liver injury cell model to assess the hepatoprotective activity of CS by measuring the cell viability and levels of alanine transaminase (ALT) and aspartate aminotransferase (AST). Bivariate correlations analysis (BCA) and orthogonal projections to latent structures (OPLS) were used to analyze the spectrum-effect relationships of CS. The results showed that the chemical fingerprints of CS were closely correlated with its hepatoprotective activity. Peaks 1, 10, 18, 19, 21, 22, and 24 might be potential hepatoprotective compounds in CS, and the validation experiments of isolated compounds indicated that chlorogenic acid (P10), hyperoside (P21), isoquercitrin (P22), and astragalin (P24) were the main hepatoprotective components. By combining chemical fingerprints with hepatoprotective evaluation, the present study provides important guidance for QC and clinical use of CS. (1) Ten potential bioactive compounds were isolated from CS; (2) The spectrum-effect relationship of CS was molded by HPLC and analysed by OPLS and BCA. (3) Four compounds including chlorogenic acid were the main hepatoprotective components.

The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants.

The growing market of herbal medicines, the increase in international trade in Latvia, and the lack of adequate analytical methods have raised the question of the potential use of herbal fingerprinting methods. In this study, high-performance liquid chromatography (HPLC) and thin layer chromatography (TLC) methods were developed for obtaining chromatographic fingerprints of four taxonomically and evolutionary different medicinal plants (Hibiscus sabdariffa L., Calendula officinalis L., Matricaria recutita L., Achillea millefolium L.). Retention time shifting, principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal projections to latent structures (OPLS) analysis were used to improve and analyze the obtained fingerprints. HPLC data detection at 270 nm was determined superior to 360 nm for the distinction of medicinal plants and used data alignment method significantly increased similarity between samples. Analyzed medicinal plant extracts formed separate, compact clusters in PCA, and the results of HCA correlated with the evolutionary relationships of the analyzed medicinal plants. Herbal fingerprinting using chromatographic analysis coupled with multivariate analysis has a great potential for the identification of medicinal plants as well as for the distinction of Latvian native medicinal plants.

Antimicrobial activities of virgin olive oils in vitro and on lettuce from pathogen-inoculated commercial quick salad bags

Virgin olive oil (VOO) provides an important source of healthy compounds. Its high concentrations of monounsaturated fatty acids and phenolic compounds contribute toward its nutritional, antioxidant, and antimicrobial properties. VOO is used in the preparation of many dishes worldwide and forms a staple of the Mediterranean diet; however, the antimicrobial activity of VOO in food matrices has been investigated very little to date. Considering this and the growing demand to reduce the use of synthetic antimicrobials and chemical food preservatives, especially in minimally processed foods, the aim of this work was to evaluate the antibacterial activity of VOOs obtained from 13 different Italian olive varieties. All olive varieties were harvested and processed in the same area (Oristano, Sardinia, Italy). The in vitro antimicrobial activities of the resulting VOOs were tested against: Listeria monocytogenes DSM 20600, Staphylococcus aureus DSM 20231, Escherichia coli DSM 30083, Salmonella bongori DSM13772, Lacticaseibacillus casei Shirota, Limosilactobacillus reuteri DSM 17938, Lacticaseibacillus casei RI4, and the yeast Candida albicans 3393. Orthogonal Projections to Latent Structures (OPLS) analysis showed a strong correlation between the content of certain phenolic compounds, the contact time of VOOs with the microbial cells, and the antimicrobial activity of the VOOs against the microorganisms tested. The VOOs showing the strongest antimicrobial activities (the Bosana and Sivigliana varieties) were then re-tested against L. monocytogenes DSM 20600 and S. bongori DSM 13772 in artificially contaminated commercial quick salad bags (QSB) of baby green leaf lettuces. The results show that antimicrobial activities of VOO depend on the target microbial species and the concentration of the inoculum. This study provides evidence supporting that VOOs with determinate polyphenols profiles might be used as antimicrobial ingredients in minimally processed foods to significantly reduce the presence of pathogenic bacteria.

Higher levels of neurofilament light chain and total tau in CSF are associated with negative outcome after shunt surgery in patients with normal pressure hydrocephalus

BackgroundLumbar punctures are a common examination in the work-up of patients with idiopathic normal pressure hydrocephalus (iNPH) and cerebrospinal fluid (CSF) biomarkers should therefore be available for use in selection of shunt candidates. The aim of this study was to investigate if CSF biomarkers are associated with outcome after shunt surgery alone or in combination with comorbidity and imaging markers, and investigate associations between CSF biomarkers and symptoms.MethodsPreoperative CSF biomarkers were analyzed in 455 patients operated with shunt surgery for iNPH at a single center during 2011–2018. Symptoms before and 12 months after shunt surgery were graded with the Swedish iNPH scale. Neurofilament light chain protein (NfL), total tau (T-tau), phosphorylated tau (P-tau) and amyloid beta1-42 (Aβ1-42) CSF levels were measured. Evans’ index and disproportionately enlarged subarachnoid space hydrocephalus were measured on preoperative CT-scans. Preoperative evaluation and follow-up 12 months after shunt surgery were available in 376 patients.ResultsHigher levels of NfL and T-tau were associated with less improvement after shunt surgery (β = − 3.10, p = 0.016 and β = − 2.45, p = 0.012, respectively). Patients whose symptoms deteriorated after shunt surgery had higher preoperative levels of NfL (1250 ng/L [IQR:1020–2220] vs. 1020 [770–1649], p < 0.001) and T-tau (221 ng/L [IQR: 159–346] vs. 190 [135–261], p = 0.0039) than patients with postoperative improvement on the iNPH scale. Among the patients who improved ≥ 5 levels on the iNPH scale (55%), NfL was abnormal in 22%, T-tau in 14%, P-tau in 6% and Aβ1-42 in 45%, compared with normal reference limits. The inclusion of CSF biomarkers, imaging markers and comorbidity in multivariate predictive Orthogonal Projections to Latent Structures (OPLS) models to did not improve predictability in outcome after shunt surgery.ConclusionsHigher levels of T-tau and NfL were associated with a less favorable response to shunt surgery, suggesting a more active neurodegeneration in this group of patients. However, CSF levels of these biomarkers can be elevated also in patients who respond to shunt surgery. Thus, none of these CSF biomarkers, alone or used in combination, are suitable for excluding patients from surgery.

Chemical profiling and unraveling of anti-COVID-19 biomarkers of red sage (Lantana camara L.) cultivars using UPLC-MS/MS coupled to chemometric analysis, in vitro study and molecular docking

Ethnopharmacological relevanceRed sage (Lantana camara L.) (Verbenaceae) is a widely spread plant that was traditionally used in Brazil, India, Kenya, Thailand, Mexico, Nigeria, Australia and Southeast Asia for treating several ailments including rheumatism and leprosy. Despite its historical role in relieving respiratory diseases, limited studies progressed to the plant’s probable inhibition to respiratory viruses especially after the striking spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Aim of the studyThis study aimed to investigate the inhibitory activity of different L. camara cultivars to SARS-CoV-2, that was not previously inspected, and clarify their mechanisms of action in the metabolomics viewpoint, and to determine the biomarkers that are related to such activity using UPLC-MS/MS coupled to in vitro-studies and chemometric analysis. Materials and methodsChemical profiling of different cultivars was accomplished via UPLC-MS/MS. Principle component analysis (PCA) and orthogonal projection to latent structures (OPLS) models were built using SIMCA® (multivariate data analysis software). Cytotoxicity and COVID-19 inhibitory activity testing were done followed by TaqMan Real-time RT-PCR (Reverse transcription polymerase chain reaction) assay that aimed to study extracts' effects on RNA-dependent RNA polymerase (RdRp) and E-genes expression levels. Detected biomarkers from OPLS analysis were docked into potential targets pockets to investigate their possible interaction patterns using Schrodinger® suite. ResultsUPLC-MS/MS analysis of different cultivars yielded 47 metabolites, most of them are triterpenoids and flavonoids. PCA plots revealed that inter-cultivar factor has no pronounced effect on the chemical profiles of extracts except for L. camara, cultivar Drap d'or flowers and leaves extracts as well as for L. camara cv Chelsea gem leaves extract. Among the tested extracts, flowers and leaves extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or showed the highest selectivity indices scoring 12.3, 10.1, 8.6 and 7.8, respectively, indicating their relative high safety and efficacy. Leaves and flowers extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or were the most promising inhibitors to viral plaques exhibiting IC50 values of 3.18, 3.67, 4.18 and 5.01 μg/mL, respectively. This was incremented by OPLS analysis that related their promising COVID-19 inhibitory activities to the presence of twelve biomarkers. Inhibiting the expression of RdRp gene is the major mechanism behind the antiviral activity of most extracts at almost all concentration levels. Molecular docking of the active biomarkers against RdRp revealed that isoverbascoside, luteolin-7,4'-O-diglucoside, camarolic acid and lantoic acid exhibited higher docking scores of −11.378, −10.64, −6.72 and −6.07 kcal/mol, respectively, when compared to remdesivir (−5.75 kcal/mol), thus these four compounds can serve as promising anti-COVID-19 candidates. ConclusionFlowers and leaves extracts of four L. camara cultivars were recognized as rich sources of phytoconstituents possessing anti-COVID-19 activity. Combination of UPLC-MS/MS and chemometrics is a promising approach to detect chemical composition differences among the cultivars and correlate them to COVID-19 inhibitory activities allowing to pinpoint possible biomarkers. Further in-vitro and in-vivo studies are required to verify their activity.

Longitudinal metabolomics profiling of serum amino acids in rotenone-induced Parkinson's mouse model.

Recently, the detailed etiology and pathogenesis of Parkinson's disease (PD) have not been fully clarified yet. Increasing evidences suggested that the disturbance of peripheral branched-chain amino acids (BCAAs) metabolism can promote the occurrence and progression of neurodegenerative diseases through neuroinflammatory signaling. Although there are several studies on the metabolomics of PD, longitudinal study of metabolic pathways is still lacking. Therefore, the purpose of the present study was to determine the longitudinal alterations in serum amino acid profiles in PD mouse model. Gas chromatography-mass spectrometry (GC-MS) was applied to detect serum amino acid concentrations in C57BL/6 mice after 0, 3 and 4weeks of oral administration with rotenone. Then the data were analysed by principal component analysis (PCA) and orthogonal projection to latent structures (OPLS) analysis. Finally, the correlations between different kinds of serum amino acids and behaviors in rotenone-treated mice were also explored. Compared with 0-week mice, the levels of L-isoleucine and L-leucine were down-regulated in 3-week and 4-week mice, especially in 4-week mice. Moreover, the comprehensive analysis showed that L-isoleucine and L-leucine were negatively correlated with pole-climbing time and positively correlated with fecal weight and water content of PD mice. These results not only suggested that L-isoleucine and L-leucine may be potential biomarkers, but also pointed out the possibility of treating PD by intervening in the circulating amino acids metabolism.

Comparative metabolomics applied for valorization of Mesquite (Prosopis juliflora (Sw. DC.) by-products as potential source of anti-inflammatory functional constituents

Mesquite (Prosopis juliflora (Sw.) DC.) is widely used in food, biotechnology, pharmaceutical, and cosmetic industries. Every part of mesquite is used for medicinal purposes. In this work, stems, leaves, flowers and pods of P. juliflora were chemically profiled via UPLC/MS/MS analysis for their valorization as forest by-products with tentative identification of 44 metabolites. Multivariate statistical analysis was implemented to investigate in-between and within class discrimination of the different parts of mesquite where stems and leaves extracts were separately segregated indicating great variation in their chemical profile, while pods and flowers were clustered together. Ex-vivo anti-inflammatory activity of the tested extracts on four pro-inflammatory markers (TNF-α, IL-1β, IL-6 and INF-γ) were performed where stems extracts showed significant inhibition to the tested cytokines. Orthogonal projection to latent structures (OPLS) models and coefficient plots of each pro-inflammatory marker unraveled the important functional constituents that positively correlated to inhibitory activity of each cytokine. 3′-oxojuliprosine, isojuliprosine, and palmitic acid have high coefficient correlation values regarding inhibiting TNF-α and IL-1β pro-inflammatory markers, where they showed spatial correlation to flowers extracts. Meanwhile, stems and leaves were spatially correlated to downregulation of IL-6 and INF-γ levels with retusin-7-neohesperidoside, tryptamine and schaftoside, being the most significant coefficients. Results indicate the possible use of different parts of mesquite a potential source of functional components as high-value-added byproducts.

Determination of gross calorific value in crude oil by variable selection methods applied to 13C NMR spectroscopy

Gross Calorific Value (GCV) is one of the properties to assess the quality and value of fuel in the oil industry, but the standard method is laborious. Regression models built with nuclear magnetic resonance (13C NMR) data make it possible to estimate different physicochemical properties of oils. However, its adversity is the enormous amount of chemical information produced in a single spectrum. And not all variables contribute in the model. With variable selection methods (VS), we can find the information that has the highest correlation with the property of interest. In our study, we used different methods applied to 13C NMR data of 145 Brazilian crude oil samples with GCV ranging from 41.5 to 47 MJ∙kg−1. For variable selection we used genetic algorithm (GA), variable importance in projection (VIP), uninformative variable elimination (UVE), angular search algorithm with variance inflation factor (ASA-VIF), competitive adaptive reweighted sampling (CARS), synergy interval partial least squares (siPLS), interval partial least squares (iPLS), subwindow permutation analysis (SPA), ordered predictors selection (OPS) and particle swarm optimization (PSO). Also, we used orthogonal projection to latent structures (OPLS) and partial least squares (PLS) with full spectra (PLS-full). All models using variable selection obtained lower root mean squared error of prediction (RMSEP) compared to the model based on full range (PLS-full). PSO was the most accurate model with RMSEP of 0.152 MJ∙kg−1. PSO selected the paraffinic region (0 to 4.43 ppm), totalizing 701 variables. Statistical analysis showed no trends in residues above 5% of significance for the best models.