Mining Strategy Research Articles

Chemical and metabolic profiling of Amaryllidaceae alkaloids in the bulbs of Narcissus tazetta L. var. chinensis Roem using liquid chromatography–tandem mass spectrometry with data mining strategy

RationaleThe bulbs of Narcissus tazetta L. var. chinensis Roem (BNTCR) has been used as an herbal medicine for the treatment of carbuncles in China. The previous phytochemical studies indicated that the main active components of BNTCR were Amarllidaceae alkaloids (AAs). However, the comprehensive chemical and metabolic profiling of AAs in BNTCR remains underexplored. Therefore, it is necessary to establish an accurate and rapid approach for chemical characterization and metabolic profiling of AAs in BNTCR.MethodsThe structural elucidation of natural products by ultra‐high performance liquid chromatography coupled to quadrupole time of flight tandem mass spectrometry (UHPLC‐QTOF‐MS/MS) with outstanding properties is still limited due to complex mixtures that contain hundreds of compounds. In this study, we employed UHPLC‐QTOF‐MS/MS with a data mining strategy, including diagnostic fragment ions (DFIs) and neutral loss (NL), for rapid classifying and identifying AAs in BNTCR and prototypes absorbed into the blood of mice after oral administration of BNTCR extract.ResultsAs a result, 87 AAs, including 13 narciclasine type, 13 homolycorine type, 17 lycorine type, 12 galanthamine type, 25 haemanthamine type, and 7 tazettine type, were tentatively characterized (6 verified with authentic standards). In addition, a total of 44 prototypes were identified in mice plasma, urine, and fecal samples. Among them, 8 prototypes were found in plasma, 36 in urine, and 40 in feces, respectively. Lycorine, pseudolycorine, homolycorine, 8‐O‐demetylhomolycorine, and tazettine, as major AAs in BNTCR, were absorbed into the blood.ConclusionsOur findings provided a crucial step for the elucidation of the active compounds in BNTCR for treatment of carbuncles. In the future, the developed strategy could also be applied for identifying AAs in other herbal medicines from Amarllidaceae.

Predicting Student Performance Using Educational Data Mining: A Review

Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified

Network intrusion detection system using machine learning models and data mining strategies: comprehensive study

Cybersecurity concerns have increased as a result of the development of computat ing and intelligent gadgets and the growing interconnectivity of numerous systems. This study investigates the use of data mining techniques and machine learning models in building intrusion detection systems for network security. By investigating the use of several machine learning approaches, such as naive Bayes, random forest, support vec tor machines, decision tree, k-nearest neighbours, and XGBoost, this study seeks to answer this problem. Furthermore, data mining techniques including association rule mining and clustering algorithms are investigated. The network intrusion detection dataset, which can be downloaded from Kaggle, is used to train and evaluate the sys tem. The primary aim of this study is to provide a more effective and adaptable solution to the network intrusion problem, with the ultimate goal of developing a system that can accurately and efficiently detect and respond to network intrusions. Проблемы кибербезопасности увеличились в результате развития вычислительных и интеллектуальных гаджетов и возросшей взаимосвязности многочисленных систем. В этом исследовании изучается использование методов получения данных и моделей машинного обучения при создании систем обнаружения вторжений для сетевой безопасности. Изучая использование нескольких подходов машинного обучения, таких как наивный байесовский алгоритм, случайный лес, метод опорных векторов, дерево решений, метод k-ближайших соседей и XGBoost, предлагаемое исследование пытается ответить на эту проблему. Кроме того, исследуются методы получения данных, включая алгоритмы нахождения ассоциативных правил и кластеризации. Набор данных обнаружения сетевых вторжений, который можно загрузить с веб-сайта Kaggle, используется для обучения и оценки системы. Основная цель этого исследования — предоставить более эффективное и адаптируемое решение проблемы сетевых вторжений, с конечной целью разработки системы, которая может точно и эффективно обнаруживать и реагировать на сетевые вторжения.

Inside-Out Rational Design of Ornithine Cyclodeaminase RlOCD from Rhizobium leguminosarum by a Multiregion Synergy Strategy for Efficient Synthesis of l-Pipecolic Acid.

Lysine cyclodeaminase (LCD)-mediated synthesis of l-pipecolic acid (l-PA) from l-lysine (l-Lys) is a promising approach. However, only one LCD has been reported, and its inadequate activity limits industrial applications. To address this problem, a substrate analogue-guided enzyme mining strategy was employed. A novel ornithine cyclodeaminase (OCD) from Rhizobium leguminosarum (RlOCD) was identified in combination with directed macrogenomic approaches. RlOCD displayed a conversion rate of 28% at a substrate loading as high as 1000 mM. A multiregion synergy strategy consisting of pocket reshaping, dynamical cross-correlation matrix-guided coevolutionary design, and surface modification was used to design RlOCD from the inside-out. A quadruple mutant (V93C/L119C/I170T/R90L) designated Mu4 with significantly increased activity was obtained, which showed a 28.46-fold increase in the catalytic efficiency. The conversion of Mu4 was 91% within 10 h at 1000 mM (146.19 g L-1) loading. The space-time yield of 282.1 g L-1 d-1 is the highest level ever reported. Molecular dynamics simulations and interaction analyses revealed that efficient pocket expansion and unique conformational rearrangements increased the affinity for the substrate, resulting in a more catalytically active conformation. This study expands the toolbox for the production of l-PA and demonstrates the effectiveness and potential of Mu4 for its production.

Text Mining Strategies: RoBERTa Optimization for Efficient Pain Assessment in Hospice Care

The hospice unit in medical care offers comprehensive, personalized care to patients, yet the recent epidemic and associated illnesses have strained medical resources, leading to a shortage in capacity. The necessity for frequent physiological documentation and patient assessments places a considerable burden on the nursing staff, particularly in the context of limited personnel. This study addresses this challenge by leveraging natural language processing to aid in the evaluation of pain indices, aiming to enhance implementation quality and reduce associated costs. Three BERT models— BERT, MacBERT, and RoBERTa were employed for training purposes. Among these models, RoBERTa demonstrated exceptional performance, achieving an impressive accuracy rate of 99%. This research highlights the potential of natural language processing tools, specifically the RoBERTa model, in alleviating the workload of nursing staff and improving the efficiency of pain assessment in hospice care during times of heightened demand and limited resources.

Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available.Scientific contributionHere, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies.Graphical

Unveiling Extremism: Leveraging Digital Data Mining Strategies

In the theoretical exploration of "Unveiling Extremism: Leveraging Digital Data Mining Strategies," the intricate web of extremist behavior is dissected through the lens of digital data mining. By utilizing advanced computational techniques, this study delves into the depths of online platforms, analyzing patterns, sentiments, and interactions to uncover the underlying mechanisms of extremism. The focus lies not only on identifying extremist content but also on understanding the processes that lead individuals towards radicalization. Through theoretical modeling and simulation, this research seeks to map out the pathways of radicalization, shedding light on the factors that contribute to the formation and spread of extremist ideologies. Moreover, the study examines the efficacy of various digital data mining strategies in detecting and countering extremism, proposing innovative approaches to enhance the effectiveness of online monitoring and intervention. Ultimately, this theoretical exploration serves as a foundation for developing proactive measures to combat extremism in the digital age, offering insights that can inform policy-making, intervention programs, and the design of online platforms.

Analyzing Resampling Techniques for Addressing the Class Imbalance in NIDS using SVM with Random Forest Feature Selection

The purpose of Network Intrusion Detection Systems (NIDS) is to ensure and protect computer networks from harmful actions. A major concern in NIDS development is the class imbalance problem, i.e., normal traffic dominates the communication data plane more than intrusion attempts. Such a state of affairs can pose certain hazards to the effectiveness of detection algorithms, including those useful for detecting less frequent but still highly dangerous intrusions. This paper aims to utilize resampling techniques to tackle this problem of class imbalance in NIDS using a Support Vector Machine (SVM) classifier alongside utilizing features selected by Random Forest to improve the feature subset selection process. The analysis highlights the combativeness of each sampling method, offering insights into their efficiency and practicality for real-world applications. Four resampling techniques are analyzed. Such techniques include Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling (RUS), Random Over-sampling (ROS) and SMOTE with two different combinations i.e., RUS SMOTE and RUS ROS. Feature selection was done using Random Forest, which was improved by Bayesian methods to create subsets of features with feature rankings determined by Cumulative Feature Importance Score (CFIS). The CIDDS-2017 dataset is used for the performance evaluation, and the metrics used include accuracy, precision, recall, F-measure and CPU time. The algorithm that performs best overall in the CFIS feature subsets is SMOTE, and the features that give the best result are selected at the 90% level with 25 features. This subset accomplishes a relative accuracy enhancement of 0.08% than the other approaches. The RUS+ROS technique is also fine but somehow slower than SMOTE. On the other hand, RUS+SMOTE shows relatively poor results although it consumes less time in terms of computational time compared to other methods, giving about 50% of the performance shown by the other methods. This paper's novelty is adapting the RUS method as a standalone test for screening new and potentially contaminated datasets. The standalone RUS method is more efficient in terms of computations; the algorithm returned the best result of 98.13% accuracy at 85% at the CFIS level of 34 features with a computation time of 137.812 s. It is also noted that SMOTE is considered to be proficient among all resampling techniques used for handling the problem of class imbalance in NIDS, vice 90% CFIS feature subset. Future research directions could include using these techniques in different data sets and other machine learning and deep learning methods together with ROC curve analysis to provide useful pointers to NIDS designers on how to select the right data mining tools and strategies for their projects.

Data Mining Technique for Diagnosing Autism Spectrum Disorder

Early detection of autistic symptoms can help lower overall medical expenses, which is beneficial given that autism is a developmental disease that is associated with high medical costs. To assess whether or not a kid may have autism spectrum disorder (ASD), screening for ASD involves asking the child's parents, caregivers, and other members of the child's immediate family a series of questions. The current methods for screening for autism, such as the autistic quotient (AQ), might require a significant number of questions in addition to careful question design, which can make an autism examination more time-consuming. The effectiveness and reliability of the test could be improved, for example, by employing data mining strategies. It could be possible to create a system that can foretell ASD at an early stage and give patients, caregivers, and medical professionals dependable and precise findings on the probable need for expert diagnostic services. This research aims to develop a reliable model for estimating the likelihood of an individual being diagnosed with autism spectrum disorder between the ages of 4 and 17. To identify varying degrees of autism, one such model was constructed by utilizing the stochastic gradient descent (SGD) algorithm. Mining data is typically understood to be a decision-making process that enables more effective utilization of available resources in terms of overall performance. The results showed that the suggested prediction model, which used the stochastic gradient descent (SGD) algorithm, could find ASD with an average error of 0.03% and an accuracy of up to 94.5%.

A high-efficiency local and global detector for diatom-based drowning diagnosis

Detection and classification of diatoms has been considered as the most effective method for the drowning diagnosis in forensic practice. However, challenges (such as limited samples, complex background interference, and detecting multiple objects) remain during detection and classification. To address these challenges, we propose a MDMS (multi-scale dynamic multi-head self-attention)-based deep learning network for the diatom-based drowning diagnosis. This framework uses TFFT (two-stage full fine-tuning training strategy) to solve the challenges of limited diatom samples and detecting multiple objects, and builds a multi-scale dynamic multi-head self-attention module to extract the local and global features of diatom images. In the meantime, we introduce an online hard example mining strategy to attenuate the complex background interference. The experimental results show that the proposed framework can effectively reduce the missing and false detection rates of diatom objects, with the mAP (mean Average Precision) reaching 92.434%, which is better than the result using the mainstream methods.

V-211.3: Natural killer cells involvement in kidney allograft antibody-mediated rejection through biopsy transcriptome assay by data mining strategy.

V-211.3: Natural killer cells involvement in kidney allograft antibody-mediated rejection through biopsy transcriptome assay by data mining strategy.

Expanding the repertoire of Antibody Drug Conjugate (ADC) targets with improved tumor selectivity and range of potent payloads through in-silico analysis.

Antibody-Drug Conjugates (ADCs) have emerged as a promising class of targeted cancer therapeutics. Further refinements are essential to unlock their full potential, which is currently limited by a lack of validated targets and payloads. Essential aspects of developing effective ADCs involve the identification of surface antigens, ideally distinguishing target tumor cells from healthy types, uniformly expressed, accompanied by a high potency payload capable of selective targeting. In this study, we integrated transcriptomics, proteomics, immunohistochemistry and cell surface membrane datasets from Human Protein Atlas, Xenabrowser and Gene Expression Omnibus utilizing Lantern Pharma's proprietary AI platform Response Algorithm for Drug positioning and Rescue (RADR®). We used this in combination with evidence based filtering to identify ADC targets with improved tumor selectivity. Our analysis identified a set of 82 targets and a total of 290 target indication combinations for effective tumor targeting. We evaluated the impact of tumor mutations on target expression levels by querying 416 genes in the TCGA mutation database against 22 tumor subtypes. Additionally, we assembled a catalog of compounds to identify potential payloads using the NCI-Developmental Therapeutics Program. Our payload mining strategy classified 729 compounds into three subclasses based on GI50 values spanning from pM to 10 nM range, in combination with sensitivity patterns across 9 different cancer indications. Our results identified a diverse range of both targets and payloads, that can serve to facilitate multiple choices for precise ADC targeting. We propose an initial approach to identify suitable target-indication-payload combinations, serving as a valuable starting point for development of future ADC candidates.

An algorithm-driven intelligent mining and identification strategy for natural product mass spectrometry

Efficiently mining and identification of new compounds from the extensive MS/MS datasets of plant extracts poses a significant challenge due to the structural diversity and compositional complexity inherent in natural products (NPs). Various data post-processing techniques have been developed to simplify the interpretation of MS/MS data; however, they often suffer from limited specificity and precision. Meanwhile, structure annotation following data post-processing is particularly time-consuming. In this study, we introduced an innovative strategy named MS-SMART, which integrates three intelligent algorithms: automatic mining of diagnostic ions, rapid filtration of alkaloids from untargeted MS/MS data, and structural recommendations for filtered components. The feasibility of this approach for rapidly discovering novel compounds was demonstrated using berberine-type alkaloids as an example. Firstly, diagnostic ions were automatically extracted and validated using available reference data. Subsequently, berberine-type compounds were filtered from raw MS/MS data. Finally, the structures of the target components were recommended using building blocks derived from berberines reported in various plants. A total of 103, 198, 60, 80 and 51 berberines were efficiently identified in diverse families and genera, including Stephaniae Epigaeae Radix, Coptidis Rhizoma, Phellodendri Chinensis Cortex, Phellodendri Amurensis Cortex and Corydalis Decumbentis Rhizoma, with 99, 169, 50, 64 and 40 new compounds identified, respectively. Among these, 8, 14, 8, 7 and 12 berberines were confirmed by reference compounds. This strategy provides a new research paradigm for the rapid discovery and identification of different types of new compounds in complex samples.

A Deep Mining Strategy for Peptide Rapid Identification in Lactobacillus reuteri Based on LC-MS/MS Integrated with FBMN and De Novo Sequencing.

Lactobacillus reuteri (L. reuteri) is widely recognized as a probiotic that produces prebiotics. However, studies on bioactive peptides or amino acid (AA) derivatives produced by L. reuteri are still lacking, whereas many bioactive peptides and AA derivatives have been found in other Lactobacillus species. In addition, rapid identification of peptides is challenged by the large amount of data and is limited by the coverage of protein databases. In this study, we performed a rapid and thorough profile of peptides in L. reuteri incorporating Global Natural Products Social Molecular Networking (GNPS) platform database searching, de novo sequencing, and deep mining, based on feature-based molecular networking (FBMN). According to FBMN, it was found that peptides containing identical or similar AA compositions were grouped into the same clusters, especially cyclic dipeptides (CDPs). Therefore, the grouping characteristics of clusters, differences in precursor ions, and characteristic fragment ions were utilized for the mining of deeply unknown compounds. Through this strategy, a total of 192 compounds, including 184 peptides, were rapidly identified. Among them, 53 CDPs, including four novel ones, were found for the first time in L. reuteri. Then, one of the novel CDPs, cyclo(5-OMe-Glu-4-OH-Pro), was isolated and characterized, which was consistent with the identification results. Moreover, some of the identified peptides exhibited considerable interactions with seven anti-inflammatory-related target proteins through molecular docking. According to the binding energies of peptides with different AA consistencies, it was considered that the existence of unnatural AAs in CDPs might contribute to their anti-inflammatory activity. These results provide a valuable strategy for the rapid identification of peptides, including CDPs. This study also reveals the substance basis for the potential anti-inflammatory effects exerted by L. reuteri.

Effect of decrease in pore water salinity induced by hydrate decomposition in clayey silt sediment on the destabilization-migration-clogging characteristics of illite

The clayey silt sediments are characterized by low permeability and high clay content, which makes fines migration not negligible. This work investigated the fines migration characteristics of illite under the condition of pore water salinity decreased, analyzed the effect on reservoir seepage characteristics, and evaluated the fines migration capability. The results show that the rapid decrease in pore water salinity caused by the decomposition of hydrate with high saturation produces higher particle concentration and results in more serious damage. At lower production pressures(0.1 MPa), the stability of illite was directly destroyed and flocculated to form aggregates that directly clogged the pores. The illite still tends to adsorb to the wall at higher production pressure(2.0 MPa), and then destabilizes and segregates under the effect of fluid dynamic. At higher production pressures, suspended particles are less likely to flocculate, which is more likely to block pores through bridging. The evaluation results of fines migration capability further clarified that salinity has a significant effect on fines migration characteristics and reservoir seepage capacity damage under different production pressures. This work demonstrates it is necessary to pay attention to the effect of fines migration on reservoir seepage characteristics when developing hydrate mining strategy.

Global industrial cultural heritage protection and mining strategies based on big data

Global industrial cultural heritage protection and mining strategies based on big data

Robust face recognition model based sample mining and loss functions

Traditional face recognition algorithms rely on margin-based softmax loss functions merely. However, these algorithms tend to perform poorly with low-quality images owing to the varying hardness of the datasets. To address this issue, we introduce a face recognition algorithm based on sample mining (FRABSM), an innovative face recognition algorithm that improves performance by integrating sample mining with conventional margin-based methods. Sample mining selectively focuses on specific samples during model training. FRABSM prioritises information-dense samples with more distinctive features. In this study, we present a probability-driven mining strategy that enhances the ability of the model to handle hard samples, thereby significantly increasing its robustness and adaptability. Mathematical evaluations demonstrate the effectiveness of FRABSM. An accuracy of 94.70% is achieved on the CPLFW. Additionally, the experimental results show that our approach achieves improvements over state-of-the-art methods on three renowned datasets (CPLFW, IJB-B, and TinyFace), highlighting its potential and efficiency. The source code is available at https://github.com/Xkf0/FRABSM.

Discovery of a Hybrid Molecule with Phytotoxic Activity by Genome Mining, Heterologous Expression, and OSMAC Strategy.

Genome mining in association with the OSMAC (one strain, many compounds) approach provides a feasible strategy to extend the chemical diversity and novelty of natural products. In this study, we identified the biosynthetic gene cluster (BGC) of restricticin, a promising antifungal agent featuring a reactive primary amine, from the fungus Aspergillus sclerotiorum LZDX-33-4 by genome mining. Combining heterologous expression and the OSMAC strategy resulted in the production of a new hybrid product (1), along with N-acetyl-restricticin (2) and restricticinol (3). The structure of 1 was determined by spectroscopic data, including optical rotation and electronic circular dichroism (ECD) calculations, for configurational assignment. Compound 1 represents a fusion of restricticin and phytotoxic cichorin. The biosynthetic pathway of 1 was proposed, in which the condensation of a primary amine of restricticin with a precursor of cichorine was postulated. Compound 1 at 5 mM concentration inhibited the growth of the shoots and roots of Lolium perenne, Festuca arundinacea, and Lactuca sativa with inhibitory rates of 71.3 and 88.7% for L. perenne, 79.4 and 73.0% for F. arundinacea, and 58.2 and 52.9% for L. sativa. In addition, compound 1 at 25 μg/mL showed moderate antifungal activity against Fusarium fujikuroi and Trichoderma harzianum with inhibition rates of 22.6 and 31.6%, respectively. These results suggest that heterologous expression in conjunction with the OSMAC approach provides a promising strategy to extend the metabolite novelty due to the incorporation of endogenous metabolites from the host strain with exogenous compounds, leading to the production of more complex compounds and the acquisition of new physiological functions.

Engineering Single‐Atom Catalysts on Conjugated Porphyrin Polymer Photocatalysts via E‐Waste for Sustainable Photocatalysis

AbstractThis study presents a surface engineering strategy utilizing electronic waste (e‐waste) to incorporate single‐atom catalysts on conjugated polymers. Employing a conjugated porphyrin polymeric photocatalyst, gold single‐atom‐site catalysts are successfully introduced using the acidic metal leachates from e‐waste, where metal speciation and composition are regulated during the metal loading processes. The resulting photocatalyst with gold single atoms demonstrates a remarkable hydrogen peroxide (H2O2) selectivity of up to 97.56%, yielding a pure H2O2 solution at 73.3 µm h−1 under white LED illumination. The produced H2O2 is activated to •OH radicals on the same polymer with mixed gold and iron atoms, enabling a photo‐Fenton reaction and the complete degradation of toxic microcystin‐LR within 10 min under visible light. This study highlights the universal applicability of the metal mining strategy in various photoreactions. It is believed that this discovery pioneers sustainable photocatalysis, allowing the tuning of reactivity and selectivity on photocatalytic surfaces using metal waste.

Identification and characterization of GSK-9089 metabolites through high resolution-mass spectrometry based in vitro and in vivo rat biological sample analysis

Estrogen related receptors (ERRs) agonist GSK-9089 (DY-131) reported to pose a potential in increasing exercise endurance. High resolution mass spectrometry (HRMS) based analysis has utmost importance in the detection, identification, or characterization of a molecule including its metabolites in human body. In this study, in vitro metabolism profile of GSK-9089 was investigated after incubation with liver microsomes and S9 fractions. Additionally, in vivo metabolites of the molecule were identified in plasma, urine, and faeces samples of rats. Structures of all the potential metabolites were revealed by employing an in silico tool and HRMS based analysis through data-dependent and data-independent mining strategies. Nine unknown metabolites of GSK-9089 have been identified which were found to be present in a trace amount in in vivo matrices. Most of the in vitro and in vivo phase I metabolites of the molecule were formed after imine bond hydrolysis followed by deamidation, oxidation, and N-oxidation. The molecule underwent phase II metabolism to generate more polar metabolites mainly through glucuronide, sulfate conjugation biotransformation reactions. The in vitro and in vivo metabolites of GSK-9089 could be useful to identify the abuse of this ERRs agonist in the future.