Soluble sugar, organic acid and phenolic composition and flavor evaluation of plum fruits.

Plums (Prunus salicina and Prunus domestica) are prevalent in southwestern China, and have attracted interest owing to their delectable taste and exceptional nutritional properties. Therefore, this study aimed to investigate the nutritional and flavor properties of plum to improve its nutritional utilization. Specifically, we determined the soluble sugars, organic acids, and phenolic components in 86 accessions using high-performance liquid chromatography. Notably, glucose, fructose, malic, and quinic acids were the predominant sweetness and acidity in plums, with sucrose contributing more to the sweetness of the flesh than the peel. Moreover, The peel contains 5.5 fold more phenolics than flesh, epicatechin, gallic acid, and proanthocyanidins C1 and B2 were the primary sources of astringency. Correlation and principal component analyses showed eight core factors for plum flavor rating, and a specific rating criterion was established. Conclusively, these findings provide information on the integrated flavor evaluation criteria and for enhancing optimal breeding of plums.

Synthesis of Inherently Chiral Homo-heteracalixaromatics.

There is a resurgence of research interest in inherently chiral calixarenes and heteracalixaromatics. However, the examples of macrocyclic ring-expanded homocalixarenes and heterocalixaromatics of inherent chirality are not known. Here we report an efficient method to construct inherently chiral N2,O2-linked homo-heteracalixaromatics from reductive amination reactions between bis-aldehydes and aliphatic diamines. Examples of post-macrocyclization derivatization were also demonstrated. Enantiomers were obtained from resolution and did not undergo racemization at an elevated temperature. This study provides a new strategy to design novel and functional inherently chiral macrocycles that have potential applications in supramolecular chemistry.

Bortezomib, lenalidomide, and dexamethasone versus bortezomib, doxorubicin, and dexamethasone in newly diagnosed multiple myeloma.

Bortezomib, lenalidomide, and dexamethasone (VRD), and bortezomib, doxorubicin, and dexamethasone (PAD), are commonly used in induction regimens for patients with newly diagnosed multiple myeloma (NDMM) in China. This real-world study enrolled 390 patients, 195 receiving VRD and 195 receiving PAD induction. The primary endpoint was progression-free survival (PFS) and stringent complete remission/complete remission. Across the entire cohort, VRD demonstrated significantly improved five-year overall survival (OS) (74% vs. 59%, p = 0.0024) and five-year PFS (67% vs. 37%, p = 0.0018) compared to PAD. Notably, the median OS and PFS were not reached for VRD-treated patients, while they were 77 months (60-not reached [NR]) and 46 months (36-NR), respectively, for PAD. In patients with standard-risk cytogenetics, VRD showed superior five-year OS (83% vs. 58%, p = 0.0038) and PFS (78% vs. 48%, p = 0.0091) compared to PAD. However, these differences were not statistically significant in high-risk patients. For transplanted patients, VRD was associated with superior five-year OS (91% vs. 67%, p = 0.014) and PFS (79% vs. 47%, p = 0.015) compared to PAD. In non-transplanted patients, VRD showed a trend towards improved five-year OS (p = 0.085) and PFS (p = 0.073) compared to the PAD group. In conclusion, VRD displayed superior OS and PFS outcomes in standard-risk patients and those who underwent transplantation. These findings suggest potential advantages of VRD over PAD in real-world clinical settings for NDMM treatment. However, due to the imbalance in transplantation rates between the VRD and PAD groups, limitations in testing for high-risk cytogenetic abnormalities (HRA), and the difference between the received cycles and salvage therapies, the conclusions of this study should be interpreted with caution.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

HA-TiNet: Learning a Distinctive and General 3D Local Descriptor for Point Cloud Registration.

Extracting geometric features from 3D point clouds is widely applied in many tasks, including registration and recognition. We propose a simple yet effective method, termed height-azimuth image based transformation-invariant net (HA-TiNet), to learn a distinctive, general and rotation-invariant 3D local descriptor. HA-TiNet is composed of a height-azimuth image generator and a feature extraction net. Based on a local reference axis (LRA), the height-azimuth image generator first partitions local region along the plane-radial direction, and then implements a statistic of height and azimuth information in each divided space to generate a set of height-azimuth images. The generated height-azimuth images are invariant in the rotation around x- and y-axes and have high accuracy due to the high repeatability of an LRA. Besides, they can be easily embedded in 2D convolutional neural networks (CNNs). Our feature extraction net learns the information on the height-azimuth images using a ResNet-based backbone and a rotation-invariant layer. The ResNet-based backbone is lightweight while very effective. The rotation-invariant layer removes the rotation-variance around z-axis, making our descriptor have full rotation-invariance. Extensive experiments on indoor and outdoor datasets show that our method presents superior overall performance, and exhibits strong descriptiveness and generalization ability compared to the state-of-the-art descriptors. The source code will be made publicly available at https://github.com/ahulq/HA-TiNet.

Transcriptome analysis reveals the promoting effects of exogenous melatonin on the selenium uptake in grape under selenium stress.

Introduction: Exogenous melatonin (MT) can promote horticultural crops growth under stress conditions. Methods: In this study, the effects of exogenous MT on the accumulation of selenium (Se) in grape were studied under Se stress. Results and discussion: Under Se stress, exogenous MT increased the biomass, content of photosynthetic pigments and antioxidant enzyme activity of grapevines. Compared with Se treatment, MT increased the root biomass, shoot biomass, chlorophyll a content, chlorophyll b content, carotenoids, superoxide dismutase activity, and peroxidase activity by 18.11%, 7.71%, 25.70%, 25.00%, 25.93%, 5.73%, and 9.41%, respectively. Additionally, MT increased the contents of gibberellin, auxin, and MT in grapevines under Se stress, while it decreased the content of abscisic acid. MT increased the contents of total Se, organic Se and inorganic Se in grapevines. Compared with Se treatment, MT increased the contents of total Se in the roots and shoots by 48.82% and 135.66%, respectively. A transcriptome sequencing analysis revealed that MT primarily regulated the cellular, metabolic, and bioregulatory processes of grapevine under Se stress, and the differentially expressed genes (DEGs) were primarily enriched in pathways, such as aminoacyl-tRNA biosynthesis, spliceosome, and flavonoid biosynthesis. These involved nine DEGs and nine metabolic pathways in total. Moreover, a field experiment showed that MT increased the content of Se in grapes and improved their quality. Therefore, MT can alleviate the stress of Se in grapevines and promote their growth and the accumulation of Se.

An additional proofreader contributes to DNA replication fidelity in mycobacteria.

The removal of mis-incorporated nucleotides by proofreading activity ensures DNA replication fidelity. Whereas the ε-exonuclease DnaQ is a well-established proofreader in the model organism Escherichia coli, it has been shown that proofreading in a majority of bacteria relies on the polymerase and histidinol phosphatase (PHP) domain of replicative polymerase, despite the presence of a DnaQ homolog that is structurally and functionally distinct from E. coli DnaQ. However, the biological functions of this type of noncanonical DnaQ remain unclear. Here, we provide independent evidence that noncanonical DnaQ functions as an additional proofreader for mycobacteria. Using the mutation accumulation assay in combination with whole-genome sequencing, we showed that depletion of DnaQ in Mycolicibacterium smegmatis leads to an increased mutation rate, resulting in AT-biased mutagenesis and increased insertions/deletions in the homopolymer tract. Our results showed that mycobacterial DnaQ binds to the ß clamp and functions synergistically with the PHP domain proofreader to correct replication errors. Furthermore, the loss of dnaQ results in replication fork dysfunction, leading to attenuated growth and increased mutagenesis on subinhibitory fluoroquinolones potentially due to increased vulnerability to fork collapse. By analyzing the sequence polymorphism of dnaQ in clinical isolates of Mycobacterium tuberculosis (Mtb), we demonstrated that a naturally evolved DnaQ variant prevalent in Mtb lineage 4.3 may enable hypermutability and is associated with drug resistance. These results establish a coproofreading model and suggest a division of labor between DnaQ and PHP domain proofreader. This study also provides real-world evidence that a mutator-driven evolutionary pathway may exist during the adaptation of Mtb.

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer with18F-FDG PET/CT images.

Objective. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly important for improving patient prognosis. This study aimed to investigate the feasibility of a multimodal radiomics-based method using 3D neural networks trained on18F-FDG PET/CT images to predict BMs in NSCLC patients.Approach. We included 226 NSCLC patients who underwent18F-FDG PET/CT scans of areas, including the lung and brain, prior to EGFR-TKI therapy. Moreover, clinical data (age, sex, stage, etc) were collected and analyzed. Shallow lung features and deep lung-brain features were extracted using PyRadiomics and 3D neural networks, respectively. A support vector machine (SVM) was used to predict BMs. The receiver operating characteristic (ROC) curve and F1 score were used to assess BM prediction performance.Main result. The combination of shallow lung and shallow-deep lung-brain features demonstrated superior predictive performance (AUC = 0.96 ± 0.01). Shallow-deep lung-brain features exhibited strong significance (P < 0.001) and potential predictive performance (coefficient > 0.8). Moreover, BM prediction by age was significant (P < 0.05).Significance. Our approach enables the quantitative assessment of medical images and a deeper understanding of both superficial and deep tumor characteristics. This noninvasive method has the potential to identify BM-related features with statistical significance, thereby aiding in the development of targeted treatment plans for NSCLC patients.

ß-resorcylic acid released by Limosilactobacillus reuteri protects against cisplatin-induced ovarian toxicity and infertility.

Chemotherapy-induced premature ovarian insufficiency (CIPOI) triggers gonadotoxicity in women undergoing cancer treatment, leading to loss of ovarian reserves and subfertility, with no effective therapies available. In our study, fecal microbiota transplantation in a cisplatin-induced POI mouse model reveals that a dysbiotic gut microbiome negatively impacts ovarian health in CIPOI. Multi-omics analyses show a significant decrease in Limosilactobacillus reuteri and its catabolite, ß-resorcylic acid , in the CIPOI group in comparison to healthy controls. Supplementation with L. reuteri or ß-RA mitigates cisplatin-induced hormonal disruptions, morphological damages, and reductions in follicular reserve. Most importantly, ß-RA pre-treatment effectively preserves oocyte function, embryonic development, and fetus health, thereby protecting against chemotherapy-induced subfertility. Our results provide evidence that ß-RA suppresses the nuclear accumulation of sex-determining region Y-box 7, which in turn reduces Bcl-2-associated X activation and inhibits granulosa cell apoptosis. These findings highlight the therapeutic potential of targeting the gut-ovary axis for fertility preservation in CIPOI.

Analysis of structural composition and antioxidant activity of traditional fermented sour meat peptides.

Chinese traditional fermented sour meat has a unique flavor and nutritional value. The antioxidant activity of sour meat peptides is related to their molecular weights, amino acid compositions, and structural characteristics. Therefore, this study explores the relationships between them. The results indicate that sour meat peptides with molecular weights <1 kDa exhibit significant antioxidant properties both in vitro and in vivo. The smaller the molecular weights, the higher the content of typical amino acids with antioxidant activity (p <0.05), and the characteristic peaks of ultraviolet absorption decrease. The absorption peak at 284.5 nm blue-shifted, and the polarity of the microenvironment increased. The peak intensity and peak area of the Raman characteristic peaks of tyrosine residues and aliphatic amino acids were enhanced. In the secondary structure, there is a high content of ß-turns and a low content of α-helix, which are closely related to the enhancement of antioxidant activity.

The unfolded protein response sensor PERK mediates mechanical stress-induced maturation of focal adhesion complexes in glioblastoma cells.

Stiffening of the brain extracellular matrix (ECM) in glioblastoma promotes tumor progression. Previously, we discovered that protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) plays a role in glioblastoma stem cell (GSC) adaptation to matrix stiffness through PERK/FLNA-dependent F-actin remodeling. Here, we examined the involvement of PERK in detecting stiffness changes via focal adhesion complex (FAC) formation. Compared to control GSCs, PERK-deficient GSCs show decreased vinculin and tensin expression, while talin and integrin-ß1 remain constant. Furthermore, vimentin was also reduced while tubulin increased, and a stiffness-dependent increase of the differentiation marker GFAP expression was absent in PERK-deficient GSCs. In conclusion, our study reveals a novel role for PERK in FAC formation during matrix stiffening, which is likely linked to its regulation of F-actin remodeling.

Magnetic resonance imaging combined with serum endolipin and galactagoglobin-3 to diagnose cerebral infarction in the elderly with diabetes mellitus.

BACKGROUND: Magnetic resonance imaging (MRI) combined with serum endothelin and galactagoglobin-3 (Gal-3) can improve the clinical diagnosis of diabetes mellitus complicated with cerebral infarction. AIM: To analyze the clinical value of MRI combined with serum endolipin and Gal-3 for the diagnosis of cerebral infarction in the elderly with diabetes mellitus. METHODS: One hundred and fifty patients with acute cerebral infarction hospitalized between January 2021 and December 2023 were divided into two groups according to comorbid diabetes mellitus, including 62 and 88 cases in the diabetic and nondiabetic cerebral infarction groups. Serum samples were collected to detect the expression of serum endolipoxins, and Gal-3, and cranial MRI was performed at admission. Differences between the two groups were compared to analyze the diagnostic value of these parameters. RESULTS: Serum endolipin and Gal-3 expression were higher in the diabetic cerebral infarction group (P < 0.05). The arterial wall area, vessel area, normalized wall index, and lumen stenosis rate were higher in the diabetic cerebral infarction group, while the rate of arterial lumen moderate and severe stenosis was 48.39% higher (36.36%, P < 0.05). The percentage of large (29.03%) and multiple infarcts (33.87%) in the diabetic cerebral infarction group was higher (13.64% and 20.45%), and the incidence rate of lacunar infarcts was lower (37.10% vs 65.91%) (P < 0.05). The total incidence of arterial plaque in patients in the diabetic cerebral infarction group was 96.77% higher (69.32%), while the incidence of necrotic lipid core plaque was 58.06% higher (26.14%) (P < 0.05). Receiver operating characteristic curve analysis was performed to assess the diagnosis utility of these techniques. MRI in combination with serum endoglin and Gal-3 had the highest area under the curve, the Yoden index, sensitivity and specificity (P < 0.05). CONCLUSION: The expression of serum endolipin and Gal-3 in elderly patients with diabetes mellitus with cerebral infarction showed an elevated trend, and the degree of luminal stenosis was severe. MRI predominantly revealed large and multiple infarct foci. This combined index examination can improve the clinical diagnosis of diabetes mellitus combined with cerebral infarction.

Stability and passivity analysis of delayed neural networks via an improved matrix-valued polynomial inequality.

The stability and passivity of delayed neural networks are addressed in this paper. A novel Lyapunov-Krasovskii functional (LKF) without multiple integrals is constructed. By using an improved matrix-valued polynomial inequality (MVPI), the previous constraint involving skew-symmetric matrices within the MVPI is removed. Then, the stability and passivity criteria for delayed neural networks that are less conservative than the existing ones are proposed. Finally, three examples are employed to demonstrate the meliority and feasibility of the obtained results.

Tamarixetin ameliorates cerebral ischemia-reperfusion injury via suppressing nicotinamide adenine dinucleotide phosphate oxidase 2/nucleotide-binding oligomerization domain like receptor family pyrin domain-containing 3 inflammasome activation.

Tamarixetin, a natural dietary flavone, exhibits remarkable potential for the treatment of ischemic stroke. The present article aimed to explore the impact of tamarixetin on ischemic stroke and elucidate the underlying mechanisms. Effects of tamarixetin on ischemic stroke were evaluated in rats using the middle cerebral artery occlusion and reperfusion (MCAO/R) model, by assessing the neurological deficit scores, brain water content, brain infraction, and neuronal damage. The levels of proinflammatory cytokines, NLRP3 inflammasome activation, reactive oxygen species (ROS) production, and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase expression were measured in MCAO/R rats and lipopolysaccharide-stimulated cells. Tamarixetin administration improved the neurological dysfunction and neuronal loss in MCAO/R rats. In addition, tamarixetin reduced microglial hyperactivation and proinflammatory cytokines expression in vivo and in vitro. Tamarixetin attenuated NF-κB p65 phosphorylation and promoter activity, reduced NLRP3 expression and caspase-1 cleavage, and downregulated IL-1ß and IL-18 secretions to suppress NLRP3 inflammasome activation. The levels of superoxide anion, hydrogen peroxide, and ROS were also suppressed by tamarixetin. The downregulation of NADP+ and NADPH levels, and gp91phox expression indicated the ameliorative effects of tamarixetin on NADPH oxidase activation. In the gp91phox knockdown cells treated with lipopolysaccharide, the effects of tamarixetin on NADPH oxidase activation, ROS generation, and NLRP3 inflammasome activation were diminished. Moreover, tamarixetin protects neurons against microglial hyperactivation in vitro. Our findings support the potential of tamarixetin as a therapeutic agent for ischemic stroke, and its mechanism of action involves the inhibition of NADPH oxidase-NLRP3 inflammasome signaling.

Association between enlarged perivascular spaces in basal ganglia and cerebral perfusion in elderly people.

Background and objective: Enlarged perivascular spaces in basal ganglia (BG-EPVS) are considered an imaging marker of cerebral small vessel disease (CSVD), but its pathogenesis and pathophysiological process remain unclear. While decreased cerebral perfusion is linked to other CSVD markers, the relationship between BG-EPVS and cerebral perfusion remains ambiguous. This study aimed to explore this association. Methods: Elderly individuals with severe BG-EPVS (n = 77) and age/sex-matched controls (n = 89) underwent head CT perfusion imaging. The cerebral perfusion parameters including mean transit time (MTT), time to maximum (TMAX), cerebral blood flow (CBF), and cerebral blood volume (CBV) were quantitatively measured by symmetric regions of interest plotted in the basal ganglia region. Point-biserial correlation and logistics regression analysis were performed to investigate the association between BG-EPVS and cerebral perfusion. Results: There were no significant differences in MTT, TMAX, or CBF between BG-EPVS group and control group. CBV was significantly lower in the BG-EPVS group (p = 0.035). Point-biserial correlation analysis showed a negative correlation between BG-EPVS and CBV (r = -0.198, p = 0.011). BG-EPVS group and control group as the dependent variable, binary logistics regression analysis showed that CBV was not an independent risk factor for severe BG-EPVS (p = 0.448). All enrolled patients were divided into four groups according to the interquartile interval of CBV. The ordered logistic regression analysis showed severe BG-EPVS was an independent risk factor for decreased CBV after adjusting for confounding factors (OR = 2.142, 95%CI: 1.211-3.788, p = 0.009). Conclusion: Severe BG-EPVS is an independent risk factor for decreased CBV in the elderly, however, the formation of BG-EPVS is not solely dependent on changes in CBV in this region. This finding provides information about the pathophysiological consequence caused by severe BG-EPVS.

Deep learning for intracranial aneurysm segmentation using CT angiography.

Objective.This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.Approach.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.MainResults.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Significance.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction.

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

Volatile metabolomics and transcriptomics analyses provide insights into the mechanism of volatile changes during fruit development of 'Ehime 38' (Citrus reticulata) and its bud mutant.

Volatile compounds are important determinants affecting fruit flavor. Previous study has identified a bud mutant of 'Ehime 38' (Citrus reticulata) with different volatile profile. However, the volatile changes between WT and MT during fruit development and underlying mechanism remain elusive. In this study, a total of 35 volatile compounds were identified in the pulps of WT and MT at five developmental stages. Both varieties accumulated similar and the highest levels of volatiles at stage S1, and showed a downward trend as the fruit develops. However, the total volatile contents in the pulps of MT were 1.4-2.5 folds higher than those in WT at stages S2-S5, which was mainly due to the increase in the content of d-limonene. Transcriptomic and RT-qPCR analysis revealed that most genes in MEP pathway were positively correlated with the volatile contents, of which DXS1 might mainly contribute to the elevated volatiles accumulation in MT by increasing the flux into the MEP pathway. Moreover, temporal expression analysis indicated that these MEP pathway genes functioned at different developmental stages. This study provided comprehensive volatile metabolomics and transcriptomics characterizations of a citrus mutant during fruit development, which is valuable for fruit flavor improvement in citrus.

Fragmentation Patterns of Phenolic C-Glycosides in Mass Spectrometry Analysis.

BACKGROUND: Many phenolic C-glycosides possess nutritional benefits and pharmacological efficacies. However, the MS/MS fragmentation pattern of phenolic C-glycosides analysis is understudied. This paper aims to determine the MS/MS fragmentation patterns of phenolic C-glycosides. METHOD: Ten compounds with different sugar moieties, aglycones, and substitutes were analyzed to determine the impact of these structural features on MS/MS fragmentation using UPLC-QTOF-MS analysis. RESULTS: The results showed that water loss followed by RDA reaction and alpha cleavage in the C-C bonded sugar moieties are the major fragmentation pathways. Additionally, the sugar cleavage was not affected by the skeleton and the substitute of the aglycones. These results suggested that the fragmentation patterns of phenolic C-glycosides differ from those in the O-glycosides, where the O-C glycosidic bond is the most cleavage-liable bond in MS/MS analysis. CONCLUSIONS: These MS/MS fragmentation patterns can be used for the identification of C-glycosides from dietary components and herbal medicine as well as developing robust methods using MRM methods to quantify C-glycosides.