MS Data Research Articles

Home practices can mitigate furan and derivatives in vegetable-based infant meals

This study assessed the impact of current home practices including reheating, standing, and stirring on mitigation of furan and its derivatives in vegetable-based infant meals. Three vegetable-based infant meals (vegetables alone, with fish, with meat) underwent different home practices including reheating, post-reheating standing (60, 120 and 240 s) and post-reheating stirring (30, 60, 120 and 240 s). Targeted quantification of furan, 2-methylfuran (2-MF) and 3-methylfuran (3-MF) and exploration of additional furan derivatives were undertaken in treated and untreated vegetable-based infant meals using SHS-GC-Q Exactive-Orbitrap-MS. For the three compounds, the quality of the measurements was first validated with suitable linearity, limits of quantification, precision and recoveries. A second step highlighted high concentrations of furan (78.5–103.9 µg/kg), 2-MF (4.8–10.1 µg/kg) and 3-MF (3.4–5.8 µg/kg) in the three vegetable-based infant meals before preparation and the assessment of the cumulative risk related to these three furan compounds confirmed the relevance of studying home mitigation strategies. The third step showed that post-reheating stirring was the most effective home practice for mitigation, with maximum observed reductions of 66.3, 59.9 and 57.7 % for furan, 2-MF and 3-MF, respectively. In a fourth step, a suspect screening approach carried out on SHS-GC-Q Exactive-Orbitrap MS data revealed the presence of 2-ethyl-, 2-ethyl-5-methyl-, 2-butyl- and 2-vinyl-furan in vegetable-based meals and showed a similar mitigation trend of home practices on the relative concentrations of these four additional furan derivatives. Finally, despite a significant mitigation reaching 69 % of the furan concentration, the combined effect of home practices on furan compounds was not sufficient to rule out the risk associated with the consumption of vegetable-based infant foods and additional options are discussed.

Comprehensive Analysis of Chemical Ingredients of Waiganfengsha Granule and Absorbed Components in Rat Plasma Based on UHPLC-Q-TOF-MS.

Waiganfengsha Granule, an over-the-counter drug, is commonly used for treating windheat cold and sore throat in clinical settings. However, its material basis of medicinal efficacy is still unclear. In this study, an efficient integrated analytical strategy was established for its chemical and metabolite profiles study. Firstly, to avoid the possible false-positive results of structural elucidation, an in-house component library that contains chemical constituents reported in the literature from the six individual medicines of Waiganfengsha Granule was established. Secondary, mass data post-processing techniques, including precursor ion list and neutral loss filtering, were applied to enhance the identification accuracy. Thirdly, for the rapid characterization of those absorbed components after oral administration in rats, the identified chemical constituents were used as candidate components for the serum analysis. By comparing the retention time and analyzing mass data, the metabolites in rat plasma were identified. As a result, 57 chemical ingredients were identified, including 21 phenolic acids, 9 alkaloids, 2 flavonoids, 5 lignins, 13 saponins, and 7 other compounds. Among these, 12 compounds were unambiguously identified by comparison with reference standards, and 45 were tentatively characterized by analyzing their accurate MS data, MS/MS fragmentation patterns, and also by comparison with those data reported in the literature. Additionally, 46 metabolites were detected and identified in rat plasma. This study is beneficial for understanding the chemical composition and metabolic profiles of Waiganfengsha Granule, and the results obtained might provide a solid basis for further studies on its functional mechanism.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Quantitative proteomics reveals serum proteome alterations during metastatic disease progression in breast cancer patients

BackgroundTumor recurrence and metastatic progression remains the leading cause for breast cancer related mortalities. However, the proteomes of patient- matched primary breast cancer (BC) and metastatic lesions have not yet been identified, due to the lack of clinically annotated longitudinal samples. In this study, we evaluated the global-proteomic landscape of BC patients with and without distant metastasis as well as compared the proteome of distant metastatic disease with its corresponding primary BC, within the same patient.MethodsWe performed mass spectrometry-based proteome profiling of 73 serum samples from 51 BC patients. Among the 51 patients with BC, 29 remained metastasis-free (henceforth called non-progressors), and 22 developed metastases (henceforth called progressors). For the 22 progressors, we obtained two samples: one collected within a year of diagnosis, and the other collected within a year before the diagnosis of metastatic disease. MS data were analyzed using intensity-based absolute quantification and normalized before differential expression analysis. Significantly differentially expressed proteins (DEPs; absolute fold-change ≥ 1.5, P-value < 0.05 and 30% abundance per clinical group) were subjected to pathway analyses.ResultsWe identified 967 proteins among 73 serum samples from patients with BC. Among these, 39 proteins were altered in serum samples at diagnosis, between progressors and non-progressors. Among these, 4 proteins were further altered when the progressors developed distant metastasis. In addition, within progressors, 20 proteins were altered in serum collected at diagnosis versus at the onset of metastasis. Pathway analysis showed that these proteins encoded pathways that describe metastasis, including epithelial–mesenchymal transition and focal adhesion that are hallmarks of metastatic cascade.ConclusionsOur results highlight the importance of examining matched samples from distant metastasis with primary BC samples collected at diagnosis to unravel subset of proteins that could be involved in BC progression in serum. This study sets the foundation for additional future investigations that could position these proteins as non-invasive markers for clinically monitoring breast cancer progression in patients.

Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC-QTOF-MS analysis.

The identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time-consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough. This study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR. UHPLC-QTOF-MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS-DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs. The results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model. ANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC-QTOF-MS and multivariate algorithms.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Prediction of martensitic transformation start temperature of steel using thermodynamic model, empirical formulas, and machine learning models

Three methods are used to predict the martensitic transformation start temperature (Ms ) of steel. Based on the database containing 832 compositions and corresponding Ms data, prediction models are built, modified, and trained. Firstly, Ms was re-calculated by establishing a thermodynamic model to link the martensitic transformation driving force (Gibbs free energy difference of martensite and austenite) with resistance (elastic strain energy, plastic strain energy, interface energy, and shearing energy). Secondly, the existing Ms data is cleaned and re-predicted using traditional empirical formulas within different composition application ranges. Thirdly, four different algorithms in machine learning including random forest, k nearest neighbor, linear regression, and decision tree are trained to predict 832 new Ms values. By comparing the Ms results re-predicted by the mentioned three methods with the original Ms values, the accuracy is evaluated to identify the optimal prediction model.

Chemical constituents from the roots of Cynanchum wilfordii with PCSK9 secretion inhibitory activities

From the Cynanchum wilfordii roots, 32 compounds, including 5 previously undescribed (1, 4–6, 12) and 27 known (2, 3, 7–11, 13–32) compounds, were isolated, and their structures were elucidated using NMR spectroscopic data and MS data aided by ECD calculations or the modified Mosher's reaction. All isolates were tested for their inhibitory effects on proprotein convertase subtilisin/kexin type 9 (PCSK9) secretion. Among the isolates, compound 4, a methyl cholesterol analog, exhibited the most potent effect in reducing PCSK9 secretion, along with PCSK9 downregulation at the mRNA and protein levels via FOXO1/3 upregulation. Moreover, compound 4 attenuated statin-induced PCSK9 expression and enhanced the uptake of DiI-LDL low-density lipoprotein. Thus, compound 4 is suggested to be a potential candidate for controlling cholesterol levels.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

A new canthinone glycoside isolated from the root barks of Ailanthus altissima with NO inhibitory activity

One new canthinone glycoside (1), together with six known compounds (2–7) including three lignans (2–4), two coumarins (5–6) and one phenol (7) was isolated from the root barks of Ailanthus altissima. The structure of new compound 1 was established by the interpretation of UV, IR, MS and NMR data, while its absolute configuration was determined by acid hydrolysis and GIAO NMR calculations with DP4+ probability analysis. The inhibitory effects of all compounds on Nitric oxide (NO) production were investigated in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Results showed that compounds 2 and 5 displayed NO production inhibitory activity with IC50 values of 30.1 and 15.3 μM, respectively.

Isotope dilution with isotopically labeled biomass: An effective alternative for quantitative metabolomics

BackgroundState-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully 13C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics. ResultsIndependent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 13C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The 13C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites. SignificanceThe proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of U13C-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches.

Zinc‐mediated efficient and environmentally benign synthesis of salicylate esters

AbstractConsiderable synthetic effort has been devoted to acid‐ or base‐catalysed transesterification of alkyl salicylate with alcohol in order to synthesize salicylate esters efficiently. In this study, the transesterification of methyl salicylate with 12 different alcohols was carried out by the use of Zn dust as a catalyst. Depending on the alcohol used in the transesterification reaction, the completion of the reaction was within a range of 8–15 h at a temperature between 100 and 170°C. The use of Zn dust provided reusability and almost complete recovery with moderate to excellent yields (42%–97%). The structural elucidation of the products was performed using IR, 1H NMR, 13C NMR and GC–MS data obtained from the Wiley GC/MS Library and the in‐house “Parkim Fragrance House GC/MS Library.”

Polyhydroxylated Spirostanol Saponins from the Rhizomes of Paris dulongensis.

Three new polyhydroxylated spirostanol steroidal saponins, dulongenosides B-D (2-4), along with 14 known compounds, dulongenoside A (1), padelaoside B (5), parisyunnanoside G (6), polyphyllin D (7), ophiopogonin C' (8), formosanin C (9), dioscin (10), paris saponin VII (11), paris H (12), parisyunnanoside I (13), protodioscin (14), proprotogracillin (15), crustecdysone (16), and stigmasterol-3-O-β-d-glucopyranoside (17), were isolated from the rhizomes of Paris dulongensis (Melanthiaceae). Their chemical structures were elucidated based on extensive analyses of NMR and MS data and acidic hydrolyses. The isolates were evaluated for their cytotoxicity to five human cancer cell lines (HL-60, SW480, MDA-MB-231, A549, and A549/Taxol) and the normal human bronchial epithelial cell line BEAS-2B by the MTS test. Compounds 7-12 and 14 showed cytotoxic activity, with IC50 values ranging from 0.20 to 4.35 μM. Proprotogracillin selectively inhibited A549 (IC50=0.58 μM) and A549/Taxol (IC50=0.74 μM) cells, with no significant cytotoxic activity against HL-60, SW480, MDA-MB-231, or BEAS-2B cells, with IC50 values greater than 40 μM.

A new coumarin and a new flavonoid from Ochrocarpus longifolius

A new coumarin (1) and a new flavonoid (2) were isolated from the air-dried flower buds of Ochrocarpus longifolius, together with ten known compounds (3–12). The structures of two new compounds were established by 1D and 2D NMR and MS data. In addition, the new compound 2 showed significant proliferation inhibitory activity on Eca-109 and MGC-803 cells. The results of this study may enrich the diversity of compounds from O. longifolius and provide a basis for further research on its natural products and pharmacological activities.

Exploring the molecular characteristics of organic matter in low-rank coals using GC×GC/TOF-MS plus data mining

In-depth understanding the form of organic matter in low-rank coals contributes to the clean and efficient utilization of coals. Two low-rank coals were successively dissolved with cyclohexane, acetone and methanol at 300 °C, respectively. Six thermal dissolution (TD) extracts were comparatively analyzed using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC×GC/TOF-MS). Cyclohexane tends to extract low-polar compounds inherent in coal, such as aliphatic hydrocarbons, arenes and phenols. Acetone can extract monocyclic aromatic hydrocarbons containing alkyl side chains. Meanwhile, acetone can form N-H⋯O hydrogen bonds with nitrogen-containing organic compounds (NCOCs), facilitating the extraction of a greater quantity of NCOCs. Methanol breaks the ArCH2OArR structural unit within coal molecules and forms the dominant alkyl-substituted phenol. It was revealed that sequentially TD at 300 °C using different solvents only breaks intermolecular forces and weak covalent bonds. Additionally, a series of isomers of tricyclic aromatic hydrocarbons and alkylphenols were authenticated by GC×GC/TOF-MS. Principal component analysis and hierarchical clustering analysis were applied to mine effective molecular information from the extensive MS data. The statistical results indicate that solvent had a significant impact on the structure and composition of the TD extracts, more so than the variations in coal species. Thus, the conjunction of GC×GC/TOF-MS and chemometrics provided methodological guidance for an enhanced comprehension of the molecular structure of coals.

HGG-24. DIFFERENTIAL CELL SURFACE PROTEOMICS OF FLOTILLIN-1, HMGB1 AND AMINOPEPTIDASE N IN H3 MUTATED PAEDIATRIC HIGH-GRADE GLIOMAS

Abstract High-grade gliomas (HGG) are the deadliest central nervous system tumour in children. Despite this, treatment options remain elusive, leaving children with a dismal prognosis. Histone H3.3 mutations, K27M and G34V/R have been identified as the two most common recurrent somatic mutations in paediatric HGG (pHGG). These mutations are present in 50% of pHGG compared to only 1% in adult gliomas. Our previous proteomic research on cell membrane protein through protein MS data analyses have identified different signatures of differentially expressed cell membrane proteins in H3-G34R/V mutated pHGG cells, in comparison to H3 wild type tumour cells in culture. Among these proteins we have identified flotillin-1, HMGB1 (High mobility group box 1) and aminopeptidase N. Flotillin-1 is associated with numerous malignancies and HMGB1 also functions as an oncogene and aminopeptidase N is a cell surface metalloprotease involved in tumour progression. We have now verified the expression and localisation of these cell surface proteins in H3 mutated and wild type tumour cells, in order to validate as targets for developing novel therapeutics. Expression and localisation of flotillin-1, HMGB1 and aminopeptidase N in H3.3 mutated pHGG cell lines in comparison to H3 wildtype glioma and control cells was analysed, using different protein expression assays including immunofluorescence microscopy and western blotting. Experimental analyses showed statistically significant differential protein expression and localisation of these proteins. Flotillin-1 had increased cell membrane localization in H3 mutated cells.HMGB1 expression was increased, while aminopeptidase N expression was decreased in the H3 mutated cells in comparison to wildtype pHGG. This new knowledge of the cell membrane proteomics of H3 mutated pHGG may help us utilise Flotillin-1, HMGB1 and aminopeptidase N, as potential therapeutic targets for these unique H3 mutated children’s brain tumours.

Development and Evaluation of an Arduino-Based Data Logging System Integrated with Microsoft Excel for Monitoring On-Grid Photovoltaic Systems

This paper presents the development and evaluation of an Arduino-based data logging system integrated with Microsoft Excel for monitoring on-grid photovoltaic (PV) systems. The system combines open-source hardware and software to enable real-time data acquisition, logging, and analysis of key performance metrics such as solar irradiance, temperature, voltage, and current levels. Leveraging the versatility of Arduino microcontrollers and the accessibility ofMicrosoft Excel, the proposed system offers a cost-effective and user-friendly solution for PV system monitoring. The integration of the MS Data Streamer add-in for Excel facilitates seamless data logging and visualization, empowering PV system owners, researchers, and practitioners with actionable insights for optimizing system performance and contributing to a sustainable energy future. Experimental validation of the system demonstrates its effectiveness in accurately measuring and logging sensor data, highlighting its potential for widespread adoption in renewable energy monitoring applications.

LC-MS metabolomics and molecular docking approaches to identify antihyperglycemic and antioxidant compounds from Melastoma malabathricum L. Leaf

The dried leaves of Melastoma malabathricum L., locally named Karamunting or Senduduk, is traditionally consumed in many regions in Indonesia as herbal tea to cure different illnesses, including diabetes. To date, information on the compounds responsible for their antidiabetic activity is still very rare. The study aimed to identify bioactive compounds of M. malabathricum L. leaves using LC-MS based metabolomics and molecular docking approaches. The leaves brewed with different methods were subjected to LC−MS measurements and several bioactivity tests (in vivo and in vitro antihyperglycemic, and in vitro antioxidant). LC−MS data were linked to the activity data using multivariate data analysis. Molecular docking using alpha-glucosidase, alpha-amylase, and insulin receptor as protein targets was used to verify the results and study the interaction between the identified compound and protein targets. As results, isoquercetin and myricitrin were identified as compounds strongly associated with alpha-amylase inhibitors, while rutin and epicatechin were identified as alpha-glucosidase inhibitors. Quercitrin, citric acid, quercetin, epicatechin, isoquercitrin, and 7-hydroxycoumarine were strongly correlated with both antihyperglycemic and antioxidant activities. The results of metabolomics were confirmed with molecular docking studies, which showed that some of these compounds acted as competitive inhibitors, while others acted as non-competitive ones. Possible synergism between epicatechin and citric acid in their interaction with IR was detected. Metabolomics combined with molecular docking efficiently identified and confirmed several antihyperglycemic and antioxidant compounds from M. malabathricum L., leaf. This study provides scientific evidence for the traditional use of M. malabathricum L. as an antidiabetic herbal.