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All available SARS-CoV-2 spike protein crystal and cryo-EM structures have shown missing electron densities for cytosolic C-terminal regions (CTR). Generally, the missing electron densities point towards the intrinsically disordered nature of the protein region (IDPR). This curiosity has led us to investigate the cytosolic CTR of the spike glycoprotein of SARS-CoV-2 in isolation. The spike CTR is supposed to be from 1235 to 1273 residues or 1242–1273 residues based on our used prediction. Therefore, we have demonstrated the structural conformation of cytosolic region and its dynamics through computer simulations up to microsecond timescale using OPLS and CHARMM forcefields. The simulations have revealed the unstructured conformation of cytosolic region. Further, we have validated our computational observations with circular dichroism (CD) spectroscopy-based experiments and found its signature spectra at 198 nm. We believe that our findings will surely help in understanding the structure-function relationship of the spike protein's cytosolic region., Graphical abstract Image 1

Yacon (Smallanthus sonchifolius (Poepp.) H. Robinson) leaves is traditionally consumed as herbal tea in many countries including Indonesia. This plant's antidiabetic properties have been extensively researched, but studies on the responsible active compound identification are scarce. Information on the active compounds is critical for the consistency of Yacon herbal tea quality. The aim of this study was to identify α-glucosidase inhibitors in Indonesian Yacon leaves grown in two different locations using FTIR- and LC-MS/MS-based metabolomics in combination with in silico technique. Yacon leaves ethanol (50 and 95%) and water extracts were tested for α-glucosidase inhibitory activity, with the 95% ethanol extract being the most active. Geographical origins were found to have no major impact on the activity. In parallel, chemical profile of Yacon leaves extract was determined using FTIR and LC-MS/MS. Orthogonal Projection to Latent Structure (OPLS) was used to analyze both sets of data. OPLS analysis of FTIR data showed that compounds associated to α-glucosidase inhibitor activity included those with functional groups -OH, stretched CH, carbonyl, and alkene. It was consistent with the result of OPLS analysis of LC-MS/MS data, which revealed that based on their VIP and Y-related coefficient value, nystose, 1-kestose, luteolin-3'-7-di-O-glucoside, and 1,3-O-dicaffeoilquinic acid isomers, strongly linked to Yacon's α-glucosidase inhibitor activity. In silico study supported these findings, revealing that the four compounds were potent α-glucosidase inhibitors with docking score in the range of - 100.216 to - 115.657 kcal/mol, which are similar to acarbose (- 115.774 kcal/mol) as a reference drug.

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

The effect of replacements of amino-acid residues that form the interface between monomers in a dimer of the HU protein from Spiroplasma Melliferum (HUSpm) on the stability and conformational dynamics of the molecule is investigated. The behavior of single and multiple HUSpm mutants is studied by molecular dynamics. Simulation is carried out using the GROMACS software package, and OPLS is used as a force field. For each mutant, a full-atom simulation is performed with a duration of 50 ns. The data obtained show that disruption of the interface between monomers in the alpha-helical domain increase the mobility of the molecule in the DNA-binding domain.

In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of

Face super-resolution (FSR) is an effective way to deal with low-resolution (LR) face images, which can infer the latent high-resolution (HR) face images from the LR inputs. In contrast with traditional FSR methods such as interpolation, learning-based methods generate more realistic HR images of LR faces by exploiting the relationship between HR and LR images . In this paper, we propose a novel FSR learning approach based on orthonormalized partial least squares referred to as OPLS-SR. It first learns a latent coherent feature space of low-dimensional HR and LR face embeddings via a recursive optimization, and then super-resolves the LR face images through global face reconstruction and facial detail compensation. Experimental results on the CAS-PEAL-R1 and FERET face databases have demonstrated the effectiveness of the proposed OPLS-SR method in terms of quantitative and qualitative evaluations.

Predicting the crystal structure of an energetic compound is an important aspect of energetic crystal engineering because an accurate crystal structure prediction (CSP) presents a base for accurate...

Introduction and Aim:Epidermal Growth Factor Receptor tyrosine kinase is a well-known and widely studied cancer therapeutic target protein. Based on the reported anticancer activity of pyrimidines, a series of 13 compounds are designed. In the present studythe EGFR kinase domain is targeted with the designed 13 compounds. Materials and Methods:With missing residue in the kinase domain of EGFR crystallized structure, the domain is modelled using homology modelling, evaluated, energy-based optimization is carried out using OPLS in Gromacs. The default bindingsite was considered from the known EGFR kinase domain – Erlotinibcomplex crystallized structure. The molecular docking is carried out using AutodockVina, Insilico toxicity profiling and enrichment analysis of pathway is studied using Swiss-ADME and Enrich R. Results:Compounds 7, 9, 10 and 12 revealed a binding energy of -8.8, -8.3, -8.3 and -8.4 Kcal/mol and makes two h-bonds with MET-769. All the 13 compounds are under the range of Lipnski drug likeness, with high GI-absorption rate. Considering the metabolic enzyme activity, the entire series of compounds are predicted to inhibit the metabolizing enzyme CYP1A2, CYP2D6 and CYP3A4. Compounds 2, 3, 7, 8 and 13 acts as a substrate to CYP2C19 and compound 1, 3,4, 5, 6, 7, 8, 9, 10, 11, 12 and 13 act as a substrate to CYP2C9. Conclusion:The inhibition of metabolizing enzyme may affect the poor metabolizing and slowing down the excretion time of molecules from the body. The current in-silico molecular docking, in-silico PKPD study of compounds suggesting that they can be developed as putative lead compounds for developing new anti-cancer drugs.

Objective: To screen the selected phytochemicals against diabetes by docking studies in comparison with experimental analysis. Methods: Ethanol crude extract was obtained from the leaves of C.intybus and its chemical compounds were identified using GC- MS. Docking studies were carried out for selected phytochemicals to find the binding affinity and H-bond interaction using Schrodinger suite. Dynamic simulations were carried out for protein-ligand complex up to 50ns using desmond OPLS AA forcefield and α- Amylase and α- Glucosidase assay were carried for the ethanolic extract to infer its inhibition. Results: Four compounds were chosen for induced fit docking based on the docking score and glide energy obtained from GLIDE-XP docking. The compounds were docked with the protein target human aldose reductase (PDB ID: 2FZD) for checking the anti-diabetic nature. The molecular dynamics simulations were carried out for the most favorable compounds and stability was checked during the simulations. The ethanol extract exhibits significant α-amylase and α-glucosidase inhibitory activities with an IC50 value of 38μg and 88μg dry extract, respectively, and well compared with standard acarbose drug. The antimicrobial activity was also carried out for various extracts (Chloroform, Ethyl acetate, and Ethanol) of the same (C. intybus) screened against four selected human pathogens. Compared to other solvent extracts, ethanol and chloroform extracts show better inhibition and their minimal inhibitory concentration (MIC) value has been calculated. Conclusion: In-silico studies and in-vitro studies reveals that C.intybus plant compounds have more potent for treating diabetes

The identification of timber properties is important for safe application. Near Infrared Spectroscopy (NIRS) technology is widely-used because of its simplicity, efficiency, and positive environmental attributes. However, in its application, weak signals are extracted from complex, overlapping and changing information. This study focused on the stability of NIR modeling. The Orthogonal Partial Least Squares(OPLS) and Successive Projections Algorithm (SPA) eliminates noise and extracts effective spectra, and an ensemble learning method MIX-PLS, is applied to establish the model. The elastic modulus of timber is taken as an example, and 201 wood samples of three species, Xylosma-congesta (Lour.) Merr., Acer pictum subsp. mono, and Betula pendula, samples were divided into three groups to investigate modelling performance. The results show that OPLS can preprocess the near-infrared spectroscopy information according to the target object in the face of the system error and reduce errors to minimum. SPA finally selects 13 spectral bands, simplifies the NIR spectral data and improves model accuracy.The Pearson's correlation coefficient of Calibration (Rc) and the Pearson's correlation coefficient of Prediction (Rp) of Mix Partial Least Squares (MIX-PLS) were 0.95 and 0.90, and Root Mean Square Error of Calibration (RMSEC) and Root Mean Square Error of Prediction (RMSEP) are 2.075 and 6.001, respectively, which shows the model has good generalization abilities.

Guan-Xin-Shu-Tong capsule is a widely used traditional Chinese medicine for the treatment of cardiovascular diseases. However, little knowledge about the metabolic profiling in vivo after treating with Guan-Xin-Shu-Tong capsule was reported. To acquire the changed metabolism pathways and search for the potential biomarkers, a metabonomics approach of Guan-Xin-Shu-Tong capsule against hyperlipidemia was developed based on UPLC-MS/MS. A Shimpack XR-ODS C18 column (75 × 3.0 mm, 2.2 μm) was applied for separation at the flow rate of 0.4 mL min−1. Multivariate statistical analysis (OPLS and OPLS-DA) revealed obvious differentiation between the natural group, hyperlipidemia group, positive group and Guan-Xin-Shu-Tong capsule treated group in both positive and negative ion modes. A total of 19 metabolites from urine samples and 16 metabolites from plasma samples were identified as potential biomarkers. Our results showed that such a metabonomics approach could not only provide a systematic view of hyperlipidemia-related metebolism, but also reveal the mechanism of therapeutic effect of Guan-Xin-Shu-Tong capsule in treating hyperlipidemia.

Introduction In recent times, myriads of public have been infected with a novel SARS-CoV-2, and the fatality toll has reached thousands and been mounting step by step, which is a major crisis in the world. The challenge for this burning issue pertinent to repurposed medicines which prevent novel coronavirus is of immense concern for all scientists around the globe until the arrival of the vaccine. Methods Because of the global high priority rating on the search for the repurposed drugs which outfits clinical suitability to SARS-CoV-2, a unique theoretical methodology is proposed. The approach is based on explorations of biothermodynamics computed via molecular dynamics, root-mean-square deviation (RMSD), radius of gyration (Rg) and interactions. This unique methodology is tested for umifenovir/dexamethasone drugs on (SARS-CoV-2) main protease. Results This theoretical exploration not only suggested the presence of strong interactions between (SARS-CoV-2 + umifenovir/dexamethasone) but also emphasized the clinical suitability of dexamethasone over umifenovir to treat SARS-CoV-2. This supremacy of dexamethasone is well supported by the results of global clinical trials and COVID-19 therapeutic approved management guidelines of countries. Conclusions Thus, this work will pave a way for incremental advancement towards future design and development of more specific inhibitors for the treatment of SARS-CoV-2 infection in humans.

In this study, the seasonal dynamics of the flavonoids in the cones and leaves of oriental Thuja (Platycladus orientalis L. Franco) as well as the in vitro anti-inflammatory activity of their extracts were investigated. The important chemical markers of the studied extracts were determined using untargeted HPTLC profiling, which was further utilized to assess the seasonality effect on the composition of these metabolites over three seasonal cycles. A quantitative HPTLC method was developed and validated for the identified chemical markers of oriental Thuja: hyperoside, quercetrin, isoscutellarein-7-O-β-xyloside, cupressuflavone, hinokiflavone, sotetsuflavone and isoscutellarein-8-methyl ether. The highest amounts of flavonoids were observed during the summer and winter seasons, where the leaves possessed higher contents of flavonoids compared to cones. Flavone glycosides are a major class of flavones encountered in leaves, while the cones mainly accumulated biflavones. The results showed that the effect of seasonal variation on the accumulation of flavonoids within the cones was less pronounced than in the leaves. The summer leaves showed a remarkable reduction in the levels of INF-γ, where the value decreased to 80.7 ± 1.25 pg mL−1, a significantly lower level than that obtained with piroxicam (180 ± 1.47 pg mL−1); this suggests a noteworthy anti-inflammatory potential. OPLS (orthogonal projection to latent structures) models showed that flavonoidal glycosides, quercetrin, hyperoside and isoscutellarein-7-O-β-xyloside were the most contributing biomarkers to the reduction in pro-inflammatory mediators in LPS-stimulated WBCs. The results obtained in the study can thus be exploited to establish the best organs as well as the optimal periods of the year for collecting and obtaining certain biomarkers at high concentrations to guarantee the efficacy of the obtained extracts.

We examine the effect of equilibration methodology and sampling on ab initio molecular dynamics (AIMD) simulations of systems of common solvents and salts found in lithium-oxygen batteries. We compare two equilibration methods: (1) using an AIMD temperature ramp and (2) using a classical MD simulation followed by a short AIMD simulation both at the target simulation temperature of 300 K. We also compare two different classical all-atom force fields: PCFF+ and OPLS. By comparing the simulated association/dissociation behavior of lithium salts in different solvents with the experimental behavior, we find that equilibration with the classical force field that produces more physically accurate behavior in the classical MD simulations, namely, OPLS, also results in more physically accurate behavior in the AIMD runs compared to equilibration with PCFF+ or with the AIMD temperature ramp. Equilibration with OPLS outperforms even the pure AIMD equilibration because the classical MD equilibration is much longer than the AIMD equilibration (nanosecond vs picosecond timescales). These longer classical simulations allow the systems to find a more physically accurate initial configuration, and in the short simulation times available for the AIMD production runs, the initial configuration has a large impact on the system behavior. We also demonstrate the importance of averaging coordination number over multiple starting configurations and Li+ ions, as the majority of Li+ ions do not undergo a single association or dissociation event even in an ∼40 ps long simulation and thus do not sample a statistically significant portion of the phase space. These results show the importance of both equilibration method and sufficient independent sampling for extracting experimentally relevant quantities from AIMD simulations.

Infrared spectroscopy can provide significant insight into the structures and dynamics of molecules of all sizes. The information that is contained in the spectrum is, however, often not easily extracted without the aid of theoretical calculations or simulations. We present here the calculation of the infrared spectra of a database of 703 gas phase compounds with four different force fields (CGenFF, GAFF-BCC, GAFF-ESP, and OPLS) using normal-mode analysis. Modern force fields increasingly use virtual sites to describe, e.g., lone-pair electrons or the o -holes on halogen atoms. This requires some adaptation of code to perform normal-mode analysis of such compounds, the implementation of which into the GROMACS software is briefly described as well. For the quantitative comparison of the obtained spectra with experimental reference data, we discuss the application of two different statistical correlation coefficients, Pearson and Spearman. The advantages and drawbacks of the different methods of comparison are discussed, and we find that both methods of comparison give the same overall picture, showing that present force field methods cannot match the performance of quantum chemical methods for the calculation of infrared spectra.

Marine organisms have evolved to survive against predators in complex marine ecosystems via the production of chemical compounds. Soft corals (Cnidaria, Anthozoa, Octocorallia) are an important source of chemically diverse metabolites with a broad spectrum of biological activities. Herein, we perform a comparative study between high-resolution proton nuclear magnetic resonance (1H-NMR) and pure shift yielded by chirp excitation (PSYCHE) experiments to analyze the metabolic profile of 24 soft corals from the Colombian Caribbean to correlate chemical fingerprints with their cytotoxic activity against three cancer cell lines (human cervical carcinoma (SiHa), human prostatic carcinoma (PC3) and human lung adenocarcinoma (A549)). All data obtained were explored using multivariate analysis using principal components analysis (PCA) and orthogonal partial least squares (OPLS) analysis. The results did not show a significant correlation between clusters using 1H-NMR data in the PCA and OPLS-DA models and therefore did not provide conclusive evidence; on the other hand, a metabolomic analysis of PSYCHE data obtained under the same parameters revealed that when a decoupled experiment is performed, it was possible to establish a statistically valid correlation between the chemical composition of soft corals and their cytotoxic activity against the PC3 cancer cell line, where the asperdiol and plexaurolone markers were putatively identified and related to the cytotoxic activity presented by extracts of Plexaurella sp. and Plexaura kukenthali, respectively. These results increase the speed, effectiveness and reliability of analyses for the study of this type of complex matrices.

Background The histopathological diagnosis of oral premalignant lesions (OPLs) such as oral epithelial dysplasia (ED) and carcinoma in situ (CIS), as well as epithelial hyperplasia (EHP), is important for the early detection and precise treatment of oral squamous cell carcinoma. However, the standardization of detection and treatment by histological criteria alone remains challenging owing to the complicated and varied histology. We evaluated practically useful immunohistochemical (IHC) markers that might complement the histopathological diagnosis of OPLs. Methods We re-evaluated the histopathological diagnoses and IHC patterns of Ki-67, TP53, CK13, and CK17 in 200 cases of OPLs and performed multiple logistic regression analyses for their predictive accuracy. Results We identified and compared atypical IHC patterns in OPLs and in the normal epithelium. Ki-67 expression showed specific patterns in categorized OPLs as EHP, low-grade dysplasia (LED), high-grade dysplasia (HED), and CIS. Multiple logistic regression analyses in the quadrant categories revealed that EHP and CIS had high predictive accuracies of 90.1% and 96.2%, respectively, and in binary categories, combined EHP and LED versus combined HED and CIS showed predictive accuracies of 92.1% and 89.9%, respectively. Binominal logistic regression analysis between each quadrant category revealed satisfactory predictive accuracy of EHP vs. LED, LED vs. HED, and HED vs. CIS (75.2%, 78.9%, and 87.9%, respectively), and Ki-67 showed the highest adjusted odds ratio, followed by TP53. Conclusion The proposed atypical IHC patterns might serve as useful markers to supplement the morphological diagnosis of OPLs, and established IHC methods for Ki-67 and TP53 might provide stable results.

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a “normal” renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

Virgin Olive Oil (VOO) shelf life is determined by the varietal-specific chemical composition and principally by the of phenolic composition. The aim of this study was to investigate the changes in fatty acid profile, phenolic composition, and quality parameters of nine Italian monovarietal VOOs obtained under the same pedoclimatic, agronomic and technological conditions and stored for 12 months at 15 degrees C in the dark. The varieties with medium-high concentrations of secoiridoids and balanced values between the individual molecules were those with the highest stability. Orthogonal Projections to Latent Structures (OPLS) regression revealed that oleuropein derivatives and phenolic alcohols had the highest antioxidant activity. OPLS discriminant analysis separated well fresh and stored oils. PV, K270, tyrosol, hydroxytyrosol, and oxidated oleacein were the most effective indicators of VOO ageing. Oleacein and oleocanthal decreased after storage, phenolic alcohols, oleacein and ligstroside aglycon increased.

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

1. The goals of this study were to analyse the changes in microbiota composition of chilled chicken during storage and identify microbial biomarkers related to meat freshness.2. The study used 16S rDNA sequencing to track the microbiota shift in chilled chicken during storage. Associations between microbiota composition and storage time were analysed and microbial biomarkers were identified.3. The results showed that microbial diversity of chilled chicken decreased with the storage time. A total of 27 and 24 microbial biomarkers were identified by using orthogonal partial least squares (OPLS) and the random forest regression approach, respectively. The receiver operating characteristic (ROC) curve analysis indicated that the OPLS regression approach had better performance in identifying freshness-related biomarkers. The multiple stepwise regression analysis identified four key microbial biomarkers, including Streptococcus, Carnobacterium, Serratia and Photobacterium genera and constructed a predictive model.4. The study provided microbial biomarkers and a model related to the freshness of chilled chicken. These findings provide a basis for developing detection methods of the freshness of chilled chicken.

Metabolomics and in vitro α-glucosidase inhibitory (AGI) activities of pepper leaves were used to identify bioactive compounds and select genotypes for the management of type 2 diabetes mellitus (T2DM). Targeted metabolite analysis using UPLC-DAD-QToF-MS was employed and identified compounds that belong to flavone and hydroxycinnamic acid derivatives from extracts of pepper leaves. A total of 21 metabolites were detected from 155 samples and identified based on MS fragmentations, retention time, UV absorbance, and previous reports. Apigenin-O-(malonyl) hexoside, luteolin-O-(malonyl) hexoside, and chrysoeriol-O-(malonyl) hexoside were identified for the first time from pepper leaves. Pepper genotypes showed a huge variation in their inhibitory activity against α-glucosidase enzyme(AGE) ranging from 17% to 79%. Genotype GP38 with inhibitory activity of 79% was found to be more potent than the positive control acarbose (70.8%.). Orthogonal partial least square (OPLS) analyses were conducted for the prediction of the AGI activities of pepper leaves based on their metabolite composition. Compounds that contributed the most to the bioactivity prediction model (VIP >1.5), showed a strong inhibitory potency. Caffeoyl-putrescine was found to show a stronger inhibitory potency (IC50 = 145 µM) compared to acarbose (IC50 = 197 µM). The chemometric procedure combined with high-throughput AGI screening was effective in selecting polyphenols of pepper leaf for T2DM management.

Sakanaka A., Kuboniwa M., Katakami N., et al. Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis. Frontiers in Molecular Biosciences, 8, , 742002. https://doi.org/https://doi.org/10.3389/fmolb.2021.742002., Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease.

Background Noising is an undesirable phenomenon accompanying the development of widely used chemometric models such as partial least square regression (PLSR) and support vector regression (SVR). Objective Optimizations of these chemometric models by applying orthogonal projection to latent structures (OPLS) as a preprocessing step which is characterized by canceling noise is the purpose of this research study. Additionally, a comprehensive comparative study between the developed methods was undertaken highlighting pros and cons. Methods OPLS was conducted with PLSR and SVR for quantitative determination of pyridoxine HCl, cyclizine HCl, and meclizine HCl in the presence of their related impurities. The training set was formed from 25 mixtures as there were five mixtures for each compound at each concentration level. Additionally, to check the validity and predictive ability of the developed chemometric models, independent test set mixtures were prepared by repeating the preparation of four mixtures of the training set plus preparation of another four independent mixtures. Results Upon application of the OPLS processing method, an upswing of the predictive abilities of PLSR and SVR was found. The root-mean-square error of prediction of the test set was the basic benchmark for comparison. Conclusion The major finding from the conducted research is that processing with OPLS reinforces the ability of models to anticipate the future samples. Highlights Novel optimizations of the widely used chemometric models; application of a comparative study between the suggested methods; application of OPLS preprocessing methods; quantitative determination of pyridoxine HCl, cyclizine HCl and meclizine HCl; checking the predictive power of developed chemometric models; analysis of active ingredients in their pharmaceutical dosage forms.

Coffee is a popular beverage because of its pleasant aroma and distinctive flavor. The flavor of coffee results from chemical transformations influenced by various intrinsic and extrinsic factors, including altitude, geographical origin, and postharvest processing. Despite is the importance of grading coffee quality, there is no report on the dominant factor that influences the metabolomic profile of green coffee beans and the correlated metabolites for each factor. This study investigated the total metabolite profile of coffees from different altitudes and coffees subjected to different postharvest processing. Arabica green coffee beans obtained from different geographical origins and different altitudes (400 and 800 m) and produced by different postharvest processes (dry, honey, and washed process) were used in this study. Coffee samples obtained from altitudes of 400–1600 m above sea level from various origins that were produced by the washed method were used for further study with regard to altitudes. Samples were subjected to gas chromatography/mass spectrometry (GC/MS) analysis and visualized using principal component analysis (PCA) and orthogonal partial least squares (OPLS) regression analysis. The PCA results showed sample separation based on postharvest processing in PC1 and sample separation based on altitude in PC2. A clear separation between samples from different altitudes was observed if the samples were subjected to the same postharvest processing method, and the samples were of the same origin. Based on this result, OPLS analysis was conducted using coffee samples obtained from various altitudes with the same postharvest processing. An OPLS model using altitude as a response variable and 79 metabolites annotated from the GC/MS analysis as an explanatory variable was constructed with good R2 and Q2 values. Postharvest processing was found to be the dominant factor affecting coffee metabolite composition; this was followed by geographical origin and altitude. The metabolites glutamic acid and galactinol were associated with the washed and honey process, while glycine, lysine, sorbose, fructose, glyceric acid, and glycolic acid were associated with the dry process. Two metabolites with high variable influence on projection scores in the OPLS model for altitude were inositol and serotonin, which showed positive and negative correlations, respectively. This is the first study to report characteristic coffee metabolites obtained from different altitudes.

Tetracera scandens is a medicinal shrub that belongs to Dilleniaceae. The leaves of the plant have been traditionally used in the treatment of diabetes mellitus in Malaysia. The conventional quality control analysis of medicinal plants that relies on the quantification of few major metabolites is considered time-consuming since it requires extensive sample preparation and neglects the possible impacts that the minor metabolites could have on the activity. This study was aimed to investigate the α-glucosidase inhibitory (AGI) potential of different hydromethanolic extracts of T. scandens leaves and to establish a predictive multivariate model that could be used for the quality evaluation of T. scandens leaf based on the Fourier transform infrared (FT-IR) spectra of its extracts. Different solvent ratios (0%, 20%, 40%, 60%, 80% and 100% methanol in water) were used to prepare a total of 36 extracts. The AGI potential and the FT-IR fingerprint spectrum were acquired for each extract. A four components orthogonal partial least squares (OPLS) model (1 + 3 + 0) with R2Y of 0.951 and Q2Y of 0.916 was established to describe the correlation between the fingerprint FT-IR spectra of different T. scandens extracts and their corresponding AGI activities. The carbon-halide, carbon–oxygen single bonds, as well as the hydroxyl and carbonyl groups were identified to be positively correlated with the AGI activity. To sum up, an OPLS model was successfully developed as a rapid quality evaluation method to predict the AGI activity of T. scandens.

To prevent the spread of anthracnose in strawberry plants and characterize the metabolic changes occurring during plant–pathogen interactions, we developed a method for the early diagnosis of disease based on an analysis of the metabolome by gas chromatography-mass spectrometry. An examination of the metabolic profile revealed 189 and 202 total ion chromatogram peaks for the control and inoculated plants, respectively. A partial least squares discriminant analysis (PLS-DA) model was conducted for the reliable and accurate discrimination between healthy and diseased strawberry plants, even in the absence of disease symptoms (e.g., early stages of infection). ANOVA (analysis of variance) and orthogonal partial least squares analysis (OPLS) identified 20 metabolites as tentative biomarkers of Colletotrichum theobromicola infection (e.g., citric acid, d-xylose, erythrose, galactose, gallic acid, malic acid, methyl α-galactopyranoside, phosphate, and shikimic acid). At least some of these potential biomarkers may be applicable for the early diagnosis of anthracnose in strawberry plants. Moreover, these metabolites may be useful for characterizing pathogen infections and plant defense responses. This study confirms the utility of metabolomics research for developing diagnostic tools and clarifying the mechanism underlying plant–pathogen interactions. Furthermore, the data presented herein may be relevant for developing new methods for preventing anthracnose in strawberry seedlings cultivated under field conditions.

This paper proposes a methodology to characterize the following petroleum properties: UOP characterization factor (K), nitrogen content, solubility parameter, sulfur content, maximum and minimum pour point, and kinematic viscosity (at 20 °C, 30 °C, 40 °C and 50 °C), in petroleum within a single 1H NMR (proton nuclear magnetic resonance) spectra. Multivariate calibration tools, such as partial least squares (PLS), orthogonal projections to latent structures (OPLS) (with variable selection), interval PLS (iPLS), synergy interval PLS (siPLS) and model population analysis (MPA), were applied to 138 samples. The models were evaluated by coefficient of determination (R2), root mean square error of prediction (RMSEP), and residuals. In general, model population analysis had better results in the determination of petroleum's physicochemical properties than PLS and OPS. Thus, MPA provided models with better accuracy in the determination of the following properties: UOP characterization factor (0.06), nitrogen content (0.048 wt%), kinematic viscosity at 40 °C (relative error of 21.9%) and pour point (14.58 °C and 19.2 °C, respectively). The sulfur content was estimated with a 0.09 wt% mean error of prediction, using the PLS model, and the solubility parameter with a 1.06 (MPa)1/2 mean error of prediction, using the OPLS model.

Gelsemium elegans is a traditional Chinese medicine that has been used to treat eczema, bruises, rheumatoid arthritis and skin ulcers for many years, and alkaloids are its major active and toxic constituents. This study aimed to comprehensively assess the quality of G. elegans samples including different plant parts and origins using ultra high-performance liquid chromatography coupled with photo-diode array and quadrupole time-of-flight mass spectrometry (UHPLC-PDA-QTOF/MS) and high-performance liquid chromatography coupled with UV detector (HPLC-UV). Firstly, the UHPLC-PDA-QTOF/MS approach was developed for the characterization of alkaloids in G. elegans and understanding the differences between multiple groups of samples. Based on the exact mass information, the fragmentation characteristics and the retention time of compounds, 38 alkaloids were identified or tentatively identified. 24 potential chemical markers for differentiating different plant parts of G. elegans were selected through PCA and OPLS/PLS-DA analysis. Secondly, a heatmap visualization was employed for clarifying the distribution of 24 selected alkaloids with high response in the UV. The roots, stems and leaves from Yunnan Province possess relatively consistent alkaloids composition, respectively. Most compounds in the root have a higher content than stems and leaves. Thirdly, a HPLC-UV approach was developed for quantitative analysis of three major alkaloids (gelsemine, koumine and gelsenicine) of G. elegans, and the results showed remarkable variation in the contents of these constituents. While, the contents of three alkaloids fluctuate relatively less in the stem. These results indicated that integrated chemical profiling and quantitative analysis of alkaloids in G. elegans from different plant parts and origins could be assessed by this method, which would establish the foundation for the application of G. elegans.

We test the OPLS/AA force field for a single PNIPAM 40-mer in aqueous solution using replica exchange molecular dynamics simulations and find that the force field fails to reproduce the experimental temperature behavior. To resolve this issue, we apply a modification on the partial charges previously suggested to reproduce the liquid-liquid phase separation of NIPAM aqueous solutions. The modified force field features stronger amide-water electrostatic interactions than the original OPLS model, predicts a weaker water-mediated monomer-monomer attraction, and reproduces the experimental coil-globule collapse enthalpy of PNIPAM in water. We revisit the cononsolvency problem of PNIPAM in methanol/water mixtures with the modified model and show that the dependence of the coil-globule collapse enthalpy on methanol concentration follows the experimental trend of the lower critical solution temperature. The calculations with the modified force field confirm that polymer dehydration is the determining factor for chain collapse in the cononsolvency regime.

Significant improvements have been made to the OPLS-AA force field for modeling RNA. New torsional potentials were optimized based on density functional theory (DFT) scans at the ωB97X-D/6-311++G(d,p) level for potential energy surfaces of the backbone α and γ dihedral angles. In combination with previously reported improvements for the sugar puckering and glycosidic torsion terms, the new force field was validated through diverse molecular dynamics simulations for RNAs in aqueous solution. Results for dinucleotides and tetranucleotides revealed both accurate reproduction of 3 J couplings from NMR and the avoidance of several unphysical states observed with other force fields. Simulations of larger systems with noncanonical motifs showed significant structural improvements over the previous OPLS-AA parameters. The new force field, OPLS-AA/M, is expected to perform competitively with other recent RNA force fields and to be compatible with OPLS-AA models for proteins and small molecules.

Objectives Cardiac magnetic resonance (CMR) provides a unique approach to the characterization of hypertensive heart disease (HHD), enabling the measurement of left ventricular mass and expansion of extracellular volume (ECV). Combining plasma biomarkers with CMR could provide potential insights into the pathophysiological mechanisms in ventricular remodelling. Methods In this study, we estimated correlations between plasma biomarkers and CMR parameters of HHD. Patients with a history of hypertension with or without left ventricular hypertrophy (LVH) and healthy volunteers (17 hypertensive non-LVH, 13 hypertensive LVH and 11 controls) underwent CMR on a Siemens 1.5T Avanto. T1 mapping was performed before (native T1) and serially after injection of 0.15 mmol/kg gadolinium-DTPA. Mean ECV and left ventricular mass index (LVMI) were determined. Blood samples were obtained and analysed using the Olink CVD 92-plex biomarker panel. Results Individual groups were compared on the basis of 91 plasma biomarkers using partial least squares discriminant analysis (PLS-DA). ECV and LVMI were correlated with the 91 distinct plasma biomarkers via orthogonal projection to latent structures by partial least square (OPLS) analysis. A two-dimensional PLS-DA explained 49% of the differences between the three groups. OPLS analysis showed that four plasma biomarkers were significantly correlated to both ECV and LVMI, eight were significantly correlated with LVMI only and 11 were significantly correlated to ECV only. Conclusion ECV and LVMI correlate differentially in plasma biomarker patterns. Top predictors of ECV consisted of well established biomarkers of systemic inflammation and metabolic function.

Rapeseed oil, the third most commonly consumed vegetable oil in the world, can easily deteriorate under photooxidative conditions. However, there is no data describing its lipid profile, which provides good information on the stability and quality of the oil. This paper aims to study the lipid composition of rapeseed oil during storage for 12 days under light by ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). 112 kinds of triacylglycerol (TAG), 13 kinds of diacylglycerol (DAG) and 32 kinds of (phospholipid) PL were identified quantitatively. The total lipid content decreased from 4.14 × 108 to 3.21 × 108 ng mL−1. Unsupervised principal component analysis (PCA) and supervised orthogonal partial least square (OPLS) analysis were employed for the characterization of statistically significant differences in identified lipid species, providing better visualization of lipidomic differences between control and experimental samples. The distribution of lipid classes was modified with a decreased proportion of TAG accompanied by the increase of DAG and PLs. Some unique lipid species, such as TG (18:1/18:2/18:2), TG (18:1/18:1/18:1), TG (18:2/22:1/22:1), DG (18:2/18:2), and DG (18:1/18:1) showed great changes and some PA species, including PA (18:2/18:2), PA (18:1/18:2), PA (18:1/18:1) and PA (16:0/18:2), emerged during the last three days of storage. The data can serve as a theoretical basis for the quality assessment of rapeseed oil during storage.

Objectives Early diagnosis of breast cancer is extremely important because it is the most common female cancer and a leading cause of cancer death in adult women. In this study, it is aimed to create Raman mapping with developed chemometrics‑assisted Raman and FT-IR spectroscopy methods for the diagnosis of invasive ductal carcinoma (IDC) in breast tissue samples. Methods Samples were deparaffinized and 20‑micron layers of each tissue were located on a coverslip. Mapping of both healthy and cancerous tissues were performed by exposing them to Raman laser at 532 and 758 nm while excitation was recorded at wavenumbers in range of 100-4,000 cm-1. Orthogonal partial least square (OPLS) algorithm was applied to evaluate obtained Raman spectra. Latent variable was selected to explain the whole model. Results Healthy and IDC tissues were accurately and precisely clustered with Raman mapping and obtained results were compared to those obtained by means of histopathology and FT-IR methods. It is claimed that the proposed method has a great potential in clustering and separating IDC tissues from the healthy ones. Conclusion This novel, rapid, precise, easy and objective diagnosis method may be an alternative to conventional diagnostic methods for IDC in breast tissue (Fig. 5, Ref. 22).

A β -amyloid self-assembly is related to the changing the structure of the β -amyloid from the helix to the sheet. This structural change is one of the main reasons for developing Alzheimer’s disease. Usually, the addition of non-polar solvents to water is used to study the role of hydrophobic forces in the self-assembly behavior. However, adding non-polar solvents also causes unwanted structural changes. Here, by changing the Lennard-Jones potential repulsion expression, structural changes in Amyloid β -peptide (1-40) (A β 40) have been studied using molecular dynamics simulation. For this purpose, in the Lennard-Jones potential n = 6 and m = 8 , 9, 10, 11, 12 were placed in attractive and repulsion terms, respectively. Then this change in the potential was applied to the GROMOS96 and OPLS-AA/L force fields. Molecular dynamics simulations of A β 40 were performed based on these 10 potentials. The results show that the change in the Lennard-Jones repulsion term in both of the applied force fields does not have a regular impact on the structure and dynamics of the A β 40. For example, with the change of m = 12 to m = 11 in the GROMOS96 force field, the diffusion coefficient of A β 40 decreases, while with the change of m = 11 to m = 10, the diffusion coefficient increases in this force field. The change zones in the secondary and tertiary structures are also different. However, the results indicate that the OPLS AA/L force field is more sensitive to the change in the Lennard-Jones potential repulsion term.

Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together. Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

An important demand exists in the field of forensic analysis to objectively determine the post-mortem interval (PMI) when human skeletal remains are discovered. It is widely known that bones undergo different chemical and physical processes after death, mainly due to their interaction with the environment in which they are found, although it is not known exactly what these processes consist of. Multiple techniques have been used so far to follow up these and other post-mortem changes and thus establish the time elapsed since the individual’s death, but they present important drawbacks in terms of reliability and accuracy. The aim of this research was to propose an analytical methodology capable of determining the PMI by using non-destructive Raman spectroscopy measurements of human skeletal remains. The recorded Raman spectra provided valuable and potentially useful information from which a multivariate study was performed by means of orthogonal partial least squares regression (OPLSR) in order to correlate the PMI with the detected spectral modifications. A collection of 53 real human skeletal remains with known PMI (15 years ≤ PMI ≤ 87 years) was analysed and used for building and validating the OPLS model. The PMI of 10 out of 14 validation samples could be determined with an accuracy error of less than 30%, demonstrating the adequate predictive performance of the OPLS model even in spite of the large inter-individual variability it handled. This opens up the possibility of applying the OPLS model in combination with non-destructive techniques to the determination of the PMI of human skeletal remains that have been buried in conditions similar or equal to those of cemetery niches and in a geographic location with a Mediterranean climate, which is an important achievement for forensic medicine and anthropology.

Experimental solvation free energies are nowadays commonly included as target properties in the validation of condensed-phase force fields, sometimes even in their calibration. In a previous article [Kashefolghetaet al.,J. Chem. Theory. Comput., 2020,16, 7556-7580], we showed how the involved comparison between experimental and simulation results could be made more systematic by considering a full matrix of cross-solvation free energies. For a set ofNmolecules that are all in the liquid state under ambient conditions, such a matrix encompassesN×Nentries for considering each of theNmolecules either as solute (A) or as solvent (B). In the quoted study, a cross-solvation matrix of 25 × 25 experimental value was introduced, considering 25 small molecules representative for alkanes, chloroalkanes, ethers, ketones, esters, alcohols, amines, and amides. This experimental data was used to compare the relative accuracies of four popular condensed-phase force fields, namely GROMOS-2016H66, OPLS-AA, AMBER-GAFF, and CHARMM-CGenFF. In the present work, the comparison is extended to five additional force fields, namely GROMOS-54A7, GROMOS-ATB, OPLS-LBCC, AMBER-GAFF2, and OpenFF. Considering these nine force fields, the correlation coefficients between experimental values and simulation results range from 0.76 to 0.88, the root-mean-square errors (RMSEs) from 2.9 to 4.8 kJ mol−1, and average errors (AVEEs) from −1.5 to +1.0 kJ mol−1. In terms of RMSEs, GROMOS-2016H66 and OPLS-AA present the best accuracy (2.9 kJ mol−1), followed by OPLS-LBCC, AMBER-GAFF2, AMBER-GAFF, and OpenFF (3.3 to 3.6 kJ mol−1), and then by GROMOS-54A7, CHARM-CGenFF, and GROMOS-ATB (4.0 to 4.8 kJ mol−1). These differences are statistically significant but not very pronounced, and are distributed rather heterogeneously over the set of compounds within the different force fields., Physical Chemistry Chemical Physics, 23 (23), ISSN:1463-9084, ISSN:1463-9076

Oregano is one of the most used culinary herb and it is often adult­erated with cheaper plants. In this study, GC–MS was used for identification and quan­tification of metabolites from 104 samples of oregano (Origanum vul­gare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggy­gria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the reg­ression models on the basis of the oregano-to-adulterants mixing ratio. Ortho­gonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled dis­crimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite. Оригано је једна од најчешће коришћених кулинарских биљака и често се криво- твори јефтинијим биљкама. У овој студији, гаснa хроматографијa–масенa спектро- метријa коришћена је за идентификацију и квантификацију метаболита из 104 узорка оригана (Origanum vulgare и O. onites) кривотвореног маслином (Olea europea), венеци- јанским сумаком (Cotinus coggygria) и миртом (Myrtus communis), у пет различитих концентрација. Метаболомички профили добијени након двостепене дериватизације, која укључује метоксиаминовање и силанизацију, подвргнути су мултиваријантној ана- лизи података како би се открили маркери кривотворења и направили регресиони модели на основу односа мешања оригана и биљака за кривотворење. Ортогонална дели- мична анализа најмањих квадрата је омогућила детекцију кривотворења оригана лишћем маслине, венецијанског сумака и мирте. Садржај сорбитола разликовао је узорке оригана кривотворених лишћем маслине, док су шикиминска и кининска кисе- лина препознате као фактор разликовања за кривотворење оригана венецијанским сумаком. Садржај фруктозе и кининске киселине у корелацији су са кривотворењем оригана миртом. Ортогонална делимична анализа најмањих квадрата – дискриминантна анализа је омогућила разликовање узорака O. vulgare и O. onites, при чему је одређено да је катехоллактат метаболит који разликује ове две биљне врсте. Part of the theme issue honoring Professor Emeritus Slobodan Milosavljevićs 80th birthday.

BACKGROUND The aberrant activation of the PI3K/mTOR signaling circuitry is one of the most frequently dysregulated signaling events in head and neck squamous cell carcinoma (HNSCC). Here, we conducted a single-arm, open-label phase IIa clinical trial in individuals with oral premalignant lesions (OPLs) to explore the potential of metformin to target PI3K/mTOR signaling for HNSCC prevention. METHODS Individuals with OPLs, but who were otherwise healthy and without diabetes, underwent pretreatment and posttreatment clinical exam and biopsy. Participants received metformin for 12 weeks (week 1, 500 mg; week 2, 1000 mg; weeks 3–12, 2000 mg daily). Pretreatment and posttreatment biopsies, saliva, and blood were obtained for biomarker analysis, including IHC assessment of mTOR signaling and exome sequencing. RESULTS Twenty-three participants were evaluable for response. The clinical response rate (defined as a ≥50% reduction in lesion size) was 17%. Although lower than the proposed threshold for favorable clinical response, the histological response rate (improvement in histological grade) was 60%, including 17% complete responses and 43% partial responses. Logistic regression analysis revealed that when compared with never smokers, current and former smokers had statistically significantly increased histological responses (P = 0.016). Remarkably, a significant correlation existed between decreased mTOR activity (pS6 IHC staining) in the basal epithelial layers of OPLs and the histological (P = 0.04) and clinical (P = 0.01) responses. CONCLUSION To our knowledge this is the first phase II trial of metformin in individuals with OPLs, providing evidence that metformin administration results in encouraging histological responses and mTOR pathway modulation, thus supporting its further investigation as a chemopreventive agent. TRIAL REGISTRATION NCT02581137 FUNDING NIH contract HHSN261201200031I, grants R01DE026644 and R01DE026870

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Bitterness is a common aversive flavor attribute of foods associated with low consumer acceptance. Untargeted LC–MS flavoromic profiling was utilized to identify endogenous compounds that influence the generation of the bitter compound 9,12,13-trihydroxy-trans-10-octadecenoic acid (pinellic acid) during bread making. A diverse sample set of wheat germplasm was chemically profiled. The corresponding pinellic acid concentrations after dough formation were modeled by orthogonal partial least squares (OPLS) with good fit (R2Y = 0.8) and predictive ability (Q2 = 0.6). The most predictive feature (negatively correlated), postulated to interfere with the biosynthetic pathway, was identified as schaftoside, an apigenin di-C-glycoside. Recombination experiments involving the addition of schaftoside to flour prior to breadmaking resulted in a 26% decrease in pinellic acid formation and significantly lower perceived bitterness intensity in whole wheat bread. This work provides novel understanding of bitter generation pathways in wheat products and new strategies to improve flavor profiles and consumer acceptability.

This research provides an accurate description of the origin for fruit spirits. In total, 63 samples of various kinds of fruit spirits (especially from apples, pears, plums, apricots and mirabelle) were analysed using headspace-solid phase microextraction and gas chromatography with flame-ionization detector. Obtained volatile profiles were treated and analysed by multivariate regression with a reduction of dimensionality-orthogonal projections to latent structure for the classification of fruit spirits according to their fruit of origin. Basic result of statistical analysis was the differentiation of spirits to groups with respect to fruit kind. Tested kinds of fruit spirits were strictly separated from each other. The selection was achieved with a specificity of 1.000 and a sensitivity of 1.000 for each kind of spirit. The statistical model was verified by an external validation. Hierarchical cluster analysis (calculation of distances by Ward’s method) showed a similarity of volatile profiles of pome fruit spirits (apple and pear brandies) and stone fruit spirits (especially mirabelle and plum brandies). Tento výzkum poskytuje přesný popis původu ovocných destilátů. Celkem bylo analyzováno 63 vzorků různých druhů ovocných destilátů (zejména z jablek, hrušek, švestek, meruněk a mirabel) pomocí metod mikroextrakce tuhou fází z parního prostoru a plynové chromatografie s plamenovým ionizačním detektorem. Získané profily těkavých látek byly zpracovány a analyzovány metodou ortogonální projekce do latentních struktur s cílem klasifikace ovocných destilátů, přičemž základním výsledkem statistické analýzy bylo rozlišení destilátů na skupiny s ohledem na ovocné druhy. Testované druhy ovocných destilátů byly od sebe odděleny, klasifikace byla dosažena se stoprocentní specificitou a senzitivitou pro každý druh destilátu. Statistický model byl ověřen externí validací. Hierarchická shluková analýza (výpočet vzdáleností Wardovou metodou) ukázala podobnost těkavých profilů destilátů z jádřincových plodů (pálenky z jablek a hrušek) a destilátů z peckovin (zejména pálenky ze švestek a mirabelek).

International audience; Orthogonal Projection to Latent Structures (OPLS) is a preprocessing method that was presented as an improvement of the PLS algorithm it was issued from. Nevertheless, according to the bibliography its added value is questionnable both for prediction and interpretation. To contribute to a better understanding, we investigated the relationship between OPLS and the Net Analyte Signal (NAS). For four numerical applications, the matrix obtained after the OPLS deflation tended towards a matrix of rank 1 when the number of removed dimensions increased. Therefore, the row-vectors of this matrix are collinear to the NAS, and so the usual one-latent-variable PLS1 regression following the OPLS preprocessing can be replaced by almost any regression method. Moreover, the interpretation relies on a vector of rank one issued from the deflated matrix, which does not bring more than the regular PLS regression vector.

We synthesized Schiff base and its complexes derivatives of chitosan (CS) in order to develop antibiotic compounds based on functionalized-chitosan against gram-positive and gram-negative bacteria. IR, UV-Vis, AFM, SEM, Melting point, X-ray diffraction (XRD), elemental analysis, and 1H NMR techniques were employed to characterize the chemical structures and properties of these compounds. XRD, UV-Vis, and 1H NMR techniques confirmed the formation of Schiff base and its functionalized-chitosan to metals. Subsequently, our antibacterial studies revealed that antibacterial activities of [Zn(Schiff base)(CS)] against S. aureus bacteria increased compared to those of their compounds. In addition, hemolysis test of CS-Schiff base-Cu(II) demonstrated better hemolytic activity than vitamin C, CS-Schiff base, CS-Schiff base-Zn(II), and CS-Schiff base-Ni(II). In a computational strategy, we carried out the optimization of compounds with molecular mechanics (MM+), Semi-emprical (AM1), Abinitio (STO-3G), AMBER, BIO+(CHARMM), and OPLS. Frontier orbital density distributions (HOMO and LUMO), and the optimized computational UV of the compounds were assessed. The optimized computational UV-Vis was similar to the experimental UV-Vis. We applied the docking methods to predict the DNA binding affinity, Staphylococcus aureus enoyl-acyl carrier protein reductase (ENRs), and Staphylococcus aureus enoyl-acyl carrier protein reductase (saFabI). Ultimately, the obtained data herein suggested that Schiff base is more selective toward ENRs and saFabI compared to chitosan, its complexes, and metronidazole.

The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.

Two methodologies were developed to monitor the biodiesel content of mafurra in mixtures with diesel using hydrogen nuclear magnetic resonance (1H NMR) Spectroscopy combined with the multivariate regression by orthogonal projections to latent structure (OPLS) and partial least squares (PLS). The efficiency of these methodologies was analyzed based on the figures of merit and the fit of the models through the correlation of the measured and predicted values of the calibration and prediction sets. The results of the figures of merit in the OPLS model were better than in the PLS model. A high correlation between the measured and predicted values was evident in the OPLS model, with a correlation coefficient (R2) greater than 0.99, demonstrating a better fit of the OPLS model in relation to the PLS model which presented a correlation coefficient (R2) less than 0.98. The OPLS model is more robust and has good predictive capacity than the PLS model because it obtained a higher Q 2 value. The excellent results of the application of 1H NMR spectroscopy combined with multivariate regression by OPLS suggest that this analytical methodology is ideal, feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.