Visible-Light-Induced Hydrodifluoromethylation of Unactivated Alkenes with Difluoroacetic Anhydride.

We herein described a practical and efficient protocol for hydrodifluoromethylation of unactivated alkenes using readily available difluoroacetic anhydride as a difluoromethyl source by merging photocatalysis and N-hydroxyphthalimide activation. This method features a wide substrate scope and excellent compatibility with various functional groups, as demonstrated by more than 50 examples, including bioactive molecules and pharmaceutical derivatives. Mechanism investigation indicated that N-hydroxyphthalimide may also serve as the hydrogen atom donor.

The RNA helicase Dhx15 mediates Wnt-induced antimicrobial protein expression in Paneth cells.

RNA helicases play roles in various essential biological processes such as RNA splicing and editing. Recent in vitro studies show that RNA helicases are involved in immune responses toward viruses, serving as viral RNA sensors or immune signaling adaptors. However, there is still a lack of in vivo data to support the tissue- or cell-specific function of RNA helicases owing to the lethality of mice with complete knockout of RNA helicases; further, there is a lack of evidence about the antibacterial role of helicases. Here, we investigated the in vivo role of Dhx15 in intestinal antibacterial responses by generating mice that were intestinal epithelial cell (IEC)-specific deficient for Dhx15 (Dhx15 f/f Villin1-cre, Dhx15ΔIEC). These mice are susceptible to infection with enteric bacteria Citrobacter rodentium (C. rod), owing to impaired α-defensin production by Paneth cells. Moreover, mice with Paneth cell-specific depletion of Dhx15 (Dhx15 f/f Defensinα6-cre, Dhx15ΔPaneth) are more susceptible to DSS (dextran sodium sulfate)-induced colitis, which phenocopy Dhx15ΔIEC mice, due to the dysbiosis of the intestinal microbiota. In humans, reduced protein levels of Dhx15 are found in ulcerative colitis (UC) patients. Taken together, our findings identify a key regulator of Wnt-induced α-defensins in Paneth cells and offer insights into its role in the antimicrobial response as well as intestinal inflammation.

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution.

Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human-computer interaction research. Compared to facial expressions, speech, or other physiological signals, using electroencephalogram (EEG) signals for the task of emotion recognition has advantages in terms of authenticity, objectivity, and high reliability; thus, it is attracting increasing attention from researchers. However, the current methods have significant room for improvement in terms of the combination of information exchange between different brain regions and time-frequency feature extraction. Therefore, this paper proposes an EEG emotion recognition network, namely, self-organized graph pesudo-3D convolution (SOGPCN), based on attention and spatiotemporal convolution. Unlike previous methods that directly construct graph structures for brain channels, the proposed SOGPCN method considers that the spatial relationships between electrodes in each frequency band differ. First, a self-organizing map is constructed for each channel in each frequency band to obtain the 10 most relevant channels to the current channel, and graph convolution is employed to capture the spatial relationships between all channels in the self-organizing map constructed for each channel in each frequency band. Then, pseudo-three-dimensional convolution combined with partial dot product attention is implemented to extract the temporal features of the EEG sequence. Finally, LSTM is employed to learn the contextual information between adjacent time-series data. Subject-dependent and subject-independent experiments are conducted on the SEED dataset to evaluate the performance of the proposed SOGPCN method, which achieves recognition accuracies of 95.26% and 94.22%, respectively, indicating that the proposed method outperforms several baseline methods.

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module.

Because of its ability to objectively reflect people's emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, ß, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51-15.43% compared with existing methods.

Hybrid Domain Consistency Constraints-Based Deep Neural Network for Facial Expression Recognition.

Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based on a feature constraint method that combines both spatial domain consistency and channel domain consistency. Specifically, first, the proposed HDCNet mines the potential attention consistency feature expression (different from manual features, e.g., HOG and SIFT) as effective supervision information by comparing the original sample image with the augmented facial expression image. Second, HDCNet extracts facial expression-related features in the spatial and channel domains, and then it constrains the consistent expression of features through the mixed domain consistency loss function. In addition, the loss function based on the attention-consistency constraints does not require additional labels. Third, the network weights are learned to optimize the classification network through the loss function of the mixed domain consistency constraints. Finally, experiments conducted on the public RAF-DB and AffectNet benchmark datasets verify that the proposed HDCNet improved classification accuracy by 0.3-3.84% compared to the existing methods.

A Cascade Attention Based Facial Expression Recognition Network by Fusing Multi-Scale Spatio-Temporal Features.

The performance of a facial expression recognition network degrades obviously under situations of uneven illumination or partial occluded face as it is quite difficult to pinpoint the attention hotspots on the dynamically changing regions (e.g., eyes, nose, and mouth) as precisely as possible. To address the above issue, by a hybrid of the attention mechanism and pyramid feature, this paper proposes a cascade attention-based facial expression recognition network on the basis of a combination of (i) local spatial feature, (ii) multi-scale-stereoscopic spatial context feature (extracted from the 3-scale pyramid feature), and (iii) temporal feature. Experiments on the CK+, Oulu-CASIA, and RAF-DB datasets obtained recognition accuracy rates of 99.23%, 89.29%, and 86.80%, respectively. It demonstrates that the proposed method outperforms the state-of-the-art methods in both the experimental and natural environment.

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features.

Understanding learners' emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function.

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.

Effect of Helicobacter Pylori on Plasma Metabolic Phenotype in Patients With Gastric Cancer.

BACKGROUND: Although Helicobacter pylori (Hp) as high risk factor for gastric cancer have been investigated from human trial, present data is inadequate to explain the effect of Hp on the changes of metabolic phenotype of gastric cancer in different stages. PURPOSE: Herein, plasma of human superficial gastritis (Hp negative and positive), early gastric cancer and advanced gastric cancer analyzed by UPLC-HDMS metabolomics can not only reveal metabolic phenotype changes in patients with gastric cancer of different degrees (30 Hp negative, 30 Hp positive, 20 early gastric cancer patients, and 10 advanced gastric cancer patients), but also auxiliarily diagnose gastric cancer. RESULTS: Combined with multivariate statistical analysis, the results represented biomarkers different from Hp negative, Hp positive, and the alterations of metabolic phenotype of gastric cancer patients. Forty-three metabolites are involved in amino acid metabolism, and lipid and fatty acid metabolism pathways in the process of cancer occurrence, especially 2 biomarkers glycerophosphocholine and neopterin, were screened in this study. Neopterin was consistently increased with gastric cancer progression and glycerophosphocholine tended to consistently decrease from Hp negative to advanced gastric cancer. CONCLUSION: This method could be used for the development of rapid targeted methods for biomarker identification and a potential diagnosis of gastric cancer.

MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy.

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners' log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners' online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners' academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners' academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

Hybrid Attention Cascade Network for Facial Expression Recognition.

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

Impact of mechanical simulator practice on clinical ERCP performance by novice surgical trainees: a randomized controlled trial.

BACKGROUND: Lack of forward-viewing endoscopy experience impairs training in endoscopic retrograde cholangiopancreatography (ERCP). We evaluated the effect of ERCP mechanical simulator (EMS) practice on ERCP performance by surgical trainees. PATIENTS AND METHODS: 12 surgical trainees without endoscopy experience were randomly allocated to non-EMS (n = 6) programs or to EMS (n = 6) programs with coaching and 20 hours of supervised EMS practice. All trainees then received supervised hands-on clinical ERCP training. Trainers provided verbal instructions and hands-on assistance, and took over if cannulation was not achieved by 20 minutes. Blinded trainers rated clinical performance. RESULTS: Each group performed 150 clinical ERCPs. Biliary cannulation success was significantly higher in the EMS vs. the non-EMS group (P = 0.006), with shorter mean times (in minutes) for intubation, cannulation, and completion (all P < 0.001). EMS trainees showed a significantly better mean performance score (P = 0.006). In multivariate analysis, after adjusting for case sequence, CBD stone, complexity, and EMS training, the effect of EMS practice on odds for successful cannulation remained highly significant (odds ratio [OR] 2.10 [95 %CI 1.46 - 3.01]). At 6 months EMS trainees still had better cannulation success vs. non-EMS controls (P = 0.045); no difference was observed after 1 year. CONCLUSIONS: EMS practice shortens the ERCP early learning curve of inexperienced surgical trainees, improves clinical success in selective biliary cannulation, and may reduce complications.

CD4 T Helper Cell Subsets and Related Human Immunological Disorders.

The immune system plays a critical role in protecting hosts from the invasion of organisms. CD4 T cells, as a key component of the immune system, are central in orchestrating adaptive immune responses. After decades of investigation, five major CD4 T helper cell (Th) subsets have been identified: Th1, Th2, Th17, Treg (T regulatory), and Tfh (follicular T helper) cells. Th1 cells, defined by the expression of lineage cytokine interferon (IFN)-γ and the master transcription factor T-bet, participate in type 1 immune responses to intracellular pathogens such as mycobacterial species and viruses; Th2 cells, defined by the expression of lineage cytokines interleukin (IL)-4/IL-5/IL-13 and the master transcription factor GAΤA3, participate in type 2 immune responses to larger extracellular pathogens such as helminths; Th17 cells, defined by the expression of lineage cytokines IL-17/IL-22 and the master transcription factor RORγt, participate in type 3 immune responses to extracellular pathogens including some bacteria and fungi; Tfh cells, by producing IL-21 and expressing Bcl6, help B cells produce corresponding antibodies; whereas Foxp3-expressing Treg cells, unlike Th1/Th2/Th17/Tfh exerting their effector functions, regulate immune responses to maintain immune cell homeostasis and prevent immunopathology. Interestingly, innate lymphoid cells (ILCs) have been found to mimic the functions of three major effector CD4 T helper subsets (Th1, Th2, and Th17) and thus can also be divided into three major subsets: ILC1s, ILC2s, and ILC3s. In this review, we will discuss the differentiation and functions of each CD4 T helper cell subset in the context of ILCs and human diseases associated with the dysregulation of these lymphocyte subsets particularly caused by monogenic mutations.

The plant sesquiterpene lactone parthenolide inhibits Wnt/ß-catenin signaling by blocking synthesis of the transcriptional regulators TCF4/LEF1.

The Wnt/ß-catenin pathway is essential for embryonic development and homeostasis, but excessive activation of this pathway is frequently observed in various human diseases, including cancer. Current therapeutic drugs targeting the Wnt pathway often lack sufficient efficacy, and new compounds targeting this pathway are therefore greatly needed. Here we report that the plant-derived natural product parthenolide (PTL), a sesquiterpene lactone, inhibits Wnt signaling. We found that PTL dose-dependently inhibits Wnt3a- and CHIR99021-induced transcriptional activity assessed with the T-cell factor (TCF)/lymphoid enhancer factor (LEF) firefly luciferase (TOPFlash) assay in HEK293 cells. Further investigations revealed that PTL decreases the levels of the transcription factors TCF4/LEF1 without affecting ß-catenin stability or subcellular distribution. Moreover, this effect of PTL on TCF4/LEF1 was related to protein synthesis rather than to proteasome-mediated degradation. Of note, siRNA-mediated knockdown of RPL10, a ribosome protein PTL binds, substantially decreased TCF4/LEF1 protein levels and also Wnt3a-induced TOPFlash activities, suggesting a potential mechanism by which PTL may repress Wnt/ß-catenin signaling. In summary, PTL binds RPL10 and thereby potently inhibits the Wnt/ß-catenin pathway.

A passive microfluidic device for continuous microparticle enrichment.

A passive microfluidic device is reported for continuous microparticle enrichment. The microparticle is enriched based on the inertial effect in a microchannel with contracting-expanding structures on one side where microparticles/cells are subjected to the inertial lift force and the momentum-change-induced inertial force induced by highly curved streamlines. Under the combined effect of the two forces, yeast cells and microparticles of different sizes were continuously focused in the present device over a range of Reynolds numbers from 16.7 to 125. ∼68% of the particle-free liquid was separated from the sample at Re = 66.7, and ∼18 µL particle-free liquid was fast obtained within 10 s. Results also showed that the geometry of the contracting-expanding structure significantly influenced the lateral migration of the particle. Structures with a large angle induced strong inertial effect and weak disturbance effect of vortex on the particle, both of which enhanced the microparticle enrichment in microchannel. With simple structure, small footprint (18 × 0.35 mm), easy operation and cell-friendly property, the present device has great potential in biomedical applications, such as the enrichment of cells and the fast extraction of plasma from blood for disease diagnose and therapy.

Quantum dot SOA/silicon external cavity multi-wavelength laser.

We report a hybrid integrated external cavity, multi-wavelength laser for high-capacity data transmission operating near 1310 nm. This is the first demonstration of a single cavity multi-wavelength laser in silicon to our knowledge. The device consists of a quantum dot reflective semiconductor optical amplifier and a silicon-on-insulator chip with a Sagnac loop mirror and microring wavelength filter. We show four major lasing peaks from a single cavity with less than 3 dB power non-uniformity and demonstrate error-free 4 × 10 Gb/s data transmission.

Copper Sulfate Combined with Photodynamic Therapy Enhances Antifungal Effect by Downregulating AIF1.

Candida albicans is a clinically significant opportunistic fungus that is generally treated with antifungal drugs such as itraconazole and fluconazole. However, the recent emergence of fungal resistance has made treatment increasingly difficult. Therefore, novel antifungal treatment methods are urgently required. Hexanol ethosome photodynamic therapy (HE-PDT) is a method that uses photosensitizers (PS), such as hexanol ethosome, to exert antifungal effects, and can be used to treat resistant fungal strains. However, due to the high dose of PS required for antifungal treatment, excess photosensitizers may remain. Furthermore, once exposed to light, normal tissues or cells are damaged after photodynamic therapy, which limits the clinical application of HE-PDT. Therefore, improving the efficacy without increasing the dose is the key to this treatment. In this study, the antifungal effect of copper sulfate combined with HE-PDT was investigated, and its mechanism was explored. The results suggested that exogenous copper sulfate significantly increased the antifungal effect of HE-PDT by enhancing the rate of C. albicans inhibition, increasing reactive oxygen species (ROS) accumulation, increasing the rate of apoptosis, and altering the mitochondrial membrane potential (MMP) and ATP concentration, which is related to the downregulation of apoptosis-inducing factor (AIF1) expression. In conclusion, copper sulfate combined with photodynamic therapy significantly inhibited the activity of C. albicans by inducing apoptosis. The combined approach reported herein provides new insights for future antifungal therapy.

Medical Image Fusion Based on Local Saliency Energy and Multi-scale Fractal Dimension.

BACKGROUND: At present, there are some problems in multimodal medical image fusion, such as texture detail loss, leading to edge contour blurring and image energy loss, leading to contrast reduction. OBJECTIVE: To solve these problems and obtain higher-quality fusion images, this study proposes an image fusion method based on local saliency energy and multi-scale fractal dimension. METHODS: First, by using a non-subsampled contourlet transform, the medical image was divided into 4 layers of high-pass subbands and 1 layer of low-pass subband. Second, in order to fuse the high-pass subbands of layers 2 to 4, the fusion rules based on a multi-scale morphological gradient and an activity measure were used as external stimuli in pulse coupled neural network. Third, a fusion rule based on the improved multi-scale fractal dimension and new local saliency energy was proposed, respectively, for the low-pass subband and the 1st closest to the low-pass subband. Layerhigh pass sub-bands were fused. Lastly, the fused image was created by performing the inverse non-subsampled contourlet transform on the fused sub-bands. RESULTS: On three multimodal medical image datasets, the proposed method was compared with 7 other fusion methods using 5 common objective evaluation metrics. CONCLUSION: Experiments showed that this method can protect the contrast and edge of fusion image well and has strong competitiveness in both subjective and objective evaluation.

Qualitative and quantitative analysis of chemical components in Qianggan capsule by UHPLC-Q-TOF-MS/MS and LC-sMRM.

Qianggan capsule (QGC) is a complex preparation composed of 16 traditional Chinese medicines (TCM) that can clear heat and dampness, fortify the spleen and blood, typify qi and relieve depression. However, the chemical composition of QGC remains incompletely understood, despite its clinical use in treating chronic hepatitis and liver injury. The objective of this study was to explore the quality markers of QGC through qualitative and quantitative analysis of its chemical components. First, the chemical composition of QGC was qualitatively analyzed using UHPLC-Q-TOF-MS/MS. Subsequently, the LC-sMRM method was developed and optimized to accurately quantify various chemical components of 10 batches of QGC. Finally, the variations in chemical components between batches were analyzed via multivariate statistical analysis. UHPLC-Q-TOF-MS/MS analysis revealed 167 chemical constituents in QGC, comprised of 48 flavonoids, 32 terpenoids, 18 phenolic acids, 9 coumarins, 9 phenylpropanoids, and 51 nucleosides, sugars, amino acids, anthraquinones, and other compounds. The LC-sMRM method was established for the quantitative analysis of 42 chemical components in 10 batches of QGC. The ultrasonic-assisted extraction parameters were optimized using RSM. Compared with conventional MRM, sMRM demonstrated superior sensitivity and precision. PCA and OPLS-DA identified eight chemical components with content differences among batches. This study established the chemical composition of QGC, offering useful guidance for assessing its quality.

Metabolic activation and cytochrome P450 inhibition of piperlonguminine mediated by CYP3A4.

Piperlonguminine (PLG) is a major alkaloid found in Piper longum fruits. It has been shown to possess a variety of biological activities, including anti-tumor, anti-hyperlipidemic, anti-renal fibrosis and anti-inflammatory properties. Previous studies have reported that PLG inhibits various CYP450 enzymes. The main objective of this study was to identify reactive metabolites of PLG in vitro and assess its ability to inhibit CYP450. In rat and human liver microsomal incubation systems exposed to PLG, two oxidized metabolites (M1 and M2) were detected. Additionally, in microsomes where N-acetylcysteine was used as a trapping agent, N-acetylcysteine conjugates (M3, M4, M5 and M6) of four isomeric O-quinone-derived reactive metabolites were found. The formation of metabolites was dependent on NADPH. Inhibition and recombinant CYP450 enzyme incubation experiments showed that CYP3A4 was the primary enzyme responsible for the metabolic activation of PLG. This study characterized the O-dealkylated metabolite (M1) through chemical synthesis. The IC50 shift assay showed time-dependent inhibition of CYP3A4, 2C9, 2E1, 2C8 and 2D6 by PLG. This research contributes to the understanding of PLG-induced enzyme inhibition and bioactivation.