Enhancing grain shape, thermotolerance, and alkaline tolerance via Gγ protein manipulation in rice.

KEY MESSAGE: Our findings highlight a valuable breeding resource, demonstrating the potential to concurrently enhance grain shape, thermotolerance, and alkaline tolerance by manipulating Gγ protein in rice. Temperate Geng/Japonica (GJ) rice yields have improved significantly, bolstering global food security. However, GJ rice breeding faces challenges, including enhancing grain quality, ensuring stable yields at warmer temperatures, and utilizing alkaline land. In this study, we employed CRISPR/Cas9 gene-editing technology to knock out the GS3 locus in seven elite GJ varieties with superior yield performance. Yield component measurements revealed that GS3 knockout mutants consistently enhanced grain length and reduced plant height in diverse genetic backgrounds. The impact of GS3 on the grain number per panicle and setting rate depended on the genetic background. GS3 knockout did not affect milling quality and minimally altered protein and amylose content but notably influenced chalkiness-related traits. GS3 knockout indiscriminately improved heat and alkali stress tolerance in the GJ varieties studied. Transcriptome analysis indicated differential gene expression between the GS3 mutants and their wild-type counterparts, enriched in biological processes related to photosynthesis, photosystem II stabilization, and pathways associated with photosynthesis and cutin, suberine, and wax biosynthesis. Our findings highlight GS3 as a breeding resource for concurrently improving grain shape, thermotolerance, and alkaline tolerance through Gγ protein manipulation in rice.

An Efficient and Rapid Medical Image Segmentation Network.

Accurate medical image segmentation is an essential part of the medical image analysis process that provides detailed quantitative metrics. In recent years, extensions of classical networks such as UNet have achieved state-of-the-art performance on medical image segmentation tasks. However, the high model complexity of these networks limits their applicability to devices with constrained computational resources. To alleviate this problem, we propose a shallow hierarchical Transformer for medical image segmentation, called SHFormer. By decreasing the number of transformer blocks utilized, the model complexity of SHFormer can be reduced to an acceptable level. To improve the learned attention while keeping the structure lightweight, we propose a spatial-channel connection module. This module separately learns attention in the spatial and channel dimensions of the feature while interconnecting them to produce more focused attention. To keep the decoder lightweight, the MLP-D module is proposed to progressively fuse multi-scale features in which channels are aligned using Multi-Layer Perceptron (MLP) and spatial information is fused by convolutional blocks. We first validated the performance of SHFormer on the ISIC-2018 dataset. Compared to the latest network, SHFormer exhibits comparable performance with 15 times fewer parameters, 30 times lower computational complexity and 5 times higher inference efficiency. To test the generalizability of SHFormer, we introduced the polyp dataset for additional testing. SHFormer achieves comparable segmentation accuracy to the latest network while having lower computational overhead.

The effects of self-other overlap and group efficacy on group-based anger and collective action tendency: An online experimental study.

Collective emotions and actions represent foundational constructs in social psychology, significantly influencing societal dynamics and responses. Within this framework, the Self-Other Overlap (SOO) - wherein individuals perceive minimal distinction between their own and others' identities - has been identified as an impactful factor at the interpersonal level. However, the extrapolation of SOO's implications at the collective, group level remains an underexplored domain in contemporary research. In addressing this lacuna, the present research endeavors to elucidate the multifaceted implications of SOO on group emotions and actions, contextualized within societal challenges such as "food hygiene problems". Utilizing validated instruments including the Self-Other Overlap Scale, Group-Based Anger Scale, Collective Action Tendency Scale, and Group Efficacy Scale for Coping Situations, this study adopts a tripartite situational experiment, engaging a collective sample of 359 participants, systematically recruited via the Credamo smart research platform to ensure representativeness. Study 1 examined the potential influence of variable SOO degrees on Group-Based Anger (GA) and Collective Action Tendency (CAT). Study 2 further refined the exploration, discerning the differential impacts of SOO targets on GA and CAT. Conclusively, Study 3 sought to ascertain the potential moderating role of Group Efficacy (GE) within the SOO-GA-CAT relationship. The empirical findings yielded several salient insights: notably, an augmentation in SOO levels corresponded with an amplification of GA and CAT. Furthermore, a delineation in SOO targets, specifically from external to ingroup entities, manifested in a pronounced augmentation of GA and CAT. Intriguingly, while elevated SOO predisposed heightened CAT, the modulatory effect of GE on CAT manifested predominantly in lower SOO contexts. In summation, the present study underscores the pivotal role of SOO magnitude and orientation as determinants of GA and CAT. The nuanced interplay between SOO degree and GE, particularly vis-à-vis CAT, provides a fresh scholarly perspective, contributing to the enriched understanding of group dynamics and collective behavioral paradigms.

A lightweight CNN for multi-source infrared ship detection from unmanned marine vehicles.

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device.

Is Azvudine Comparable to Nirmatrelvir-Ritonavir in Real-World Efficacy and Safety for Hospitalized Patients with COVID-19? A Retrospective Cohort Study.

INTRODUCTION: Azvudine and nirmatrelvir-ritonavir are more extensively used to treat COVID-19 in China due to their earlier approval by the National Medical Products Administration. However, there has been a scarcity of research directly comparing the clinical outcomes between azvudine and nirmatrelvir-ritonavir till now. We aimed to make a head-to-head comparison of the efficacy and safety of azvudine or nirmatrelvir-ritonavir in hospitalized patients with COVID-19 in China. METHODS: This retrospective cohort study was conducted using data collected from Tongde Hospital of Zhejiang Province between December 2022 and January 2023. All-cause mortality, risk of progressing to a critical condition, proportion with nucleic-acid negative conversion (PNANC), time to first nucleic-acid negative conversion (TFNANC), length of hospital stay and incidence of adverse events were systematically assessed as outcomes. Multi-model regression analysis, propensity-score-matching analysis, subgroup analysis and several sensitivity analyses were applied to compare these outcomes. RESULTS: This study included a total of 1571 hospitalized patients with COVID-19, among whom 272 received nirmatrelvir-ritonavir and 156 received azvudine. We found no significant differences in all-cause mortality (HR 1.41; 95% CI 0.56-3.56; P = 0.471), risk of progressing to critical COVID-19 (HR 1.67; 95% CI 0.78-3.60; P = 0.189), PNANC (HR 0.87; 95% CI 0.69-1.09; P = 0.220), length of stay (ß - 0.82; 95% CI - 2.78 to 1.15; P = 0.414) and adverse event rate (3.21% vs. 4.41%, P = 0.538) between the two groups, although azvudine was slightly less effective than nirmatrelvir-ritonavir. Meanwhile, the azvudine group exhibited a significantly longer TFNANC (ß 2.53; 95% CI 0.76-4.29; P = 0.005) than the nirmatrelvir-ritonavir group. Results were similar for propensity-score matching and multiple sensitivity analyses. CONCLUSION: Azvudine probably possessed comparable efficacy and safety to nirmatrelvir-ritonavir, although it was less effective than nirmatrelvir-ritonavir for some outcomes.

Impact of personality traits on learners' navigational behavior patterns in an online course: a lag sequential analysis approach.

Personality is considered as the internal factor that defines a person's behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners' personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners' behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners' navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners' personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners' personalities.

Mechanistic Insights into Harmine-Mediated Inhibition of Human DNA Methyltransferases and Prostate Cancer Cell Growth.

Mammalian DNA methyltransferases (DNMTs), including DNMT1, DNMT3A, and DNMT3B, are key DNA methylation enzymes and play important roles in gene expression regulation. Dysregulation of DNMTs is linked to various diseases and carcinogenesis, and therefore except for the two approved anticancer azanucleoside drugs, various non-nucleoside DNMT inhibitors have been identified and reported. However, the underlying mechanisms for the inhibitory activity of these non-nucleoside inhibitors still remain largely unknown. Here, we systematically tested and compared the inhibition activities of five non-nucleoside inhibitors toward the three human DNMTs. We found that harmine and nanaomycin A blocked the methyltransferase activity of DNMT3A and DNMT3B more efficiently than resveratrol, EGCG, and RG108. We further determined the crystal structure of harmine in complex with the catalytic domain of the DNMT3B-DNMT3L tetramer revealing that harmine binds at the adenine cavity of the SAM-binding pocket in DNMT3B. Our kinetics assays confirm that harmine competes with SAM to competitively inhibit DNMT3B-3L activity with a Ki of 6.6 µM. Cell-based studies further show that harmine treatment inhibits castration-resistant prostate cancer cell (CRPC) proliferation with an IC50 of ∼14 µM. The CPRC cells treated with harmine resulted in reactivating silenced hypermethylated genes compared to the untreated cells, and harmine cooperated with an androgen antagonist, bicalutamide, to effectively inhibit the proliferation of CRPC cells. Our study thus reveals, for the first time, the inhibitory mechanism of harmine on DNMTs and highlights new strategies for developing novel DNMT inhibitors for cancer treatment.

Registration method for infrared and visible image of sea surface vessels based on contour feature.

In this paper, a modified infrared and visible image registration method based on contour feature is proposed. Our method firstly extracts the feature contour and eliminates sparkling waves contour of the sea surface, determines the main direction of the contour based on the contour image, then uses the improved Scale Invariant Feature Transform (SIFT) method as the feature point to construct the descriptor, completes the registration of the two images. 30 sets of infrared and visible-band vessels images were selected for registration experiments. Compared with previous reports, the experimental results showed that the proportion of effective feature points detected by this method can reach 70%, and the average number of effective feature points detected by proposed method can reach 196 in visible band image and 279 in infrared image. The running time was 5.3599s, shortened by 25% compared with previous reports, and the average Root Mean Square Error (RMSE) value was 2.3566, smaller by 75% compared with previous reports. An effective registration method is provided, which can be used for infrared and visible image processing and comprehensive utilization of information in marine scenes.

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective.

Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs work in real and natural educational contexts and the associated challenges of ITS application and evaluation. Through a systematic literature review method, this study analyzed 40 qualified studies that applied social experiment methods to examine the effectiveness of ITS during 2011-2022. The obtained results highlighted a complicated landscape regarding the effectiveness of ITS in real educational contexts. Specifically, there was an "intelligent" regional gap regarding the distribution of countries where ITS studies using social experiment methods were conducted. Compared to learning performance, relatively less attention was paid to investigating the impact of ITS on non-cognitive factors, process-oriented factors, and social outcomes, calling for more research in this regard. Considering the complexities and challenges existing in real educational fields, there was a lack of scientific rigor in terms of experimental design and data analysis in some of the studies. Based on these findings, suggestions for future study and implications were proposed.

The Light and Dark Sides of Student Engagement: Profiles and Their Association with Perceived Autonomy Support.

School engagement has assumed an important place in current developmental psychology and educational research due to its potential to address students' low achievement, high dropout rates, and misbehavior. Although much has been written about the antecedents and outcomes of student engagement, literature on how students' level of engagement differs in response to different teaching styles was missing on a large scale. Understanding the patterns and risks linked with student engagement provides opportunities for targeted intervention. This study explored primary school students' engagement and burnout profiles and how different profiles interacted with perceived classroom teaching styles (i.e., autonomy-supportive & autonomy suppressive). Latent profile analysis resulted in four student engagement subgroups: moderately engaged, engaged, moderately burned out, and burned out. Students clustered into engagement groups were likely to report higher autonomy support from teachers. In contrast, burned-out groups were more likely to rate teachers' teaching styles as suppressive (i.e., autonomy suppressive). Collectively, the study indicated that autonomy-supportive teaching behaviors are pivotal in understanding student engagement and school burnout. Thus, tailored teacher-focused intervention programs that enhance teachers' awareness of autonomy-supportive teaching is important. The significance of the findings with the demand-resource model (in the education context) was discussed.

Metaverse as a possible tool for reshaping schema modes in treating personality disorders.

Personality disorders (PD) are usually treated with face-to-face sessions and/or digital mental health services. Among many schools of therapies, schema therapy stands out because rather than simply targeting the symptoms of PD, it cordially targets the cause of PD and heals the early maladaptive schema, thus is exceptionally good at soothing emotional disturbances before enacting cognitive restructuring, resulting in long-term efficacy. However, according to Piaget's genetic epistemology, the unmet needs lie in the fact that the schemata that determine the adaptive behavior can only be formed in the interaction with the real world that the patient is living in and reconsolidated by the feedback from the object world upon the patient's newly-formed behavior. Therefore, in order to reshape the patient's schema modes to support adaptive behavior and regain emotional regulation capabilities of the healthy adult, one may have to reconstruct the object world surrounding the patient. Metaverse, the bestowed successor to the Internet with the cardinal feature of "the sense of full presence," can become a powerful tool to reconstruct a new object world for the patient with the prescription of a psychotherapist, so as to transform the treatment techniques in schema therapy into the natural autobiographical experiences of patients in the new object world, thus gradually reshape the patient's schema modes that can ultimately result in an adaptive, and more inclusive, interaction with the real world. This work describes the underlying theory, the mechanism, the process, and ethical considerations of such promising technology for the not-too-far future.

Revealing the novel autophagy-related genes for ligamentum flavum hypertrophy in patients and mice model.

Background: Fibrosis is a core pathological factor of ligamentum flavum hypertrophy (LFH) resulting in degenerative lumbar spinal stenosis. Autophagy plays a vital role in multi-organ fibrosis. However, autophagy has not been reported to be involved in the pathogenesis of LFH. Methods: The LFH microarray data set GSE113212, derived from Gene Expression Omnibus, was analyzed to obtain differentially expressed genes (DEGs). Potential autophagy-related genes (ARGs) were obtained with the human autophagy regulator database. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to elucidate the underlying biological pathways of autophagy regulating LFH. Protein-protein interaction (PPI) network analyses was used to obtain hub ARGs. Using transmission electron microscopy, quantitative RT-PCR, Western blotting, and immunohistochemistry, we identified six hub ARGs in clinical specimens and bipedal standing (BS) mouse model. Results: A total of 70 potential differentially expressed ARGs were screened, including 50 up-regulated and 20 down-regulated genes. According to GO enrichment and KEGG analyses, differentially expressed ARGs were mainly enriched in autophagy-related enrichment terms and signaling pathways related to autophagy. GSEA and GSVA results revealed the potential mechanisms by demonstrating the signaling pathways and biological processes closely related to LFH. Based on PPI network analysis, 14 hub ARGs were identified. Using transmission electron microscopy, we observed the autophagy process in LF tissues for the first time. Quantitative RT-PCR, Western blotting, and immunohistochemistry results indicated that the mRNA and protein expression levels of FN1, TGFß1, NGF, and HMOX1 significantly higher both in human and mouse with LFH, while the mRNA and protein expression levels of CAT and SIRT1 were significantly decreased. Conclusion: Based on bioinformatics analysis and further experimental validation in clinical specimens and the BS mouse model, six potential ARGs including FN1, TGFß1, NGF, HMOX1, CAT, and SIRT1 were found to participate in the fibrosis process of LFH through autophagy and play an essential role in its molecular mechanism. These potential genes may serve as specific therapeutic molecular targets in the treatment of LFH.

Evaluation of the safety and efficacy of a novel Anatomical classification and dUal anchoRing theory to Optimize the tavR strategy for pure severe Aortic regurgitation (AURORA): a prospective cohort study.

BACKGROUND: Success rate of transcatheter aortic valve replacement (TAVR) in aortic regurgitation (AR) patients is relatively low on account of the absence of calcified anchoring structures. Morphological classification and corresponding TAVR strategies for AR are lacking yet. METHODS: The AURORA study is a prospective, multicenter, single-arm cohort study to evaluate the safety and efficacy of transfemoral TAVR for severe AR in patients with high or prohibitive risk for surgery. Patients who are ≥ 65 years and diagnosed with severe pure AR as defined by the Echocardiographic Core Laboratory will be consecutively enrolled for further multidetector computed tomography (MDCT) scanning and multiplanar analyses. Based on a new anatomical classification and dual anchoring theory, patients will be classified into 4 types according to the level of the anchoring area. Types 1, 2 and 3 (at least 2 anchoring areas) will undergo the TAVR procedure with a domestic Chinese self-expanding valve (VitaFlow Valve, MicroPort, Shanghai, China), whereas type 4 (0 or 1 anchoring area) patients will be considered unsuitable for TAVR and will receive medical treatment. Our goal is to recruit 100 patients to account for 10% missing data or loss of patients to follow-up. Procedural, 30-day, 6-month and 12-month outcomes will be assessed according to Valve Academic Research Consortium-3 criteria. DISCUSSION: The AURORA study will establish a new AR anatomical classification based on dual anchoring theory through MDCT multiplanar measurement and assess the safety and efficacy of TAVR guided by this new classification and strategy in AR patients. TRIAL REGISTRATION: This Study was registered at Chinses Clinical Trial Registry. The registration number: ChiCTR2200055415; The date of registration: 9, January 2022; The URL of the registration: http://www.chictr.org.cn/showproj.aspx?proj=141209 .

A Time-Division Position-Sensitive Detector Image System for High-Speed Multitarget Trajectory Tracking.

High-speed trajectory tracking with real-time processing capability is particularly important in the fields of pilotless automobiles, guidance systems, robotics, and filmmaking. The conventional optical approach to high-speed trajectory tracking involves charge coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) image sensors, which suffer from trade-offs between resolution and framerates, complexity of the system, and enormous data-analysis processes. Here, a high-speed trajectory tracking system is designed by using a time-division position-sensitive detector (TD-PSD) based on a graphene-silicon Schottky heterojunction. Benefiting from the high-speed optoelectronic response and sub-micrometer positional accuracy of the TD-PSD, multitarget real-time trajectory tracking is realized, with a maximum image output framerate of up to 62 000 frames per second. Moreover, multichannel trajectory tracking and image-distortion correction functionalities are realized by TD-PSD systems through frequency-related image preprocessing, which significantly improves the capacity of real-time information processing and image quality in complicated light environments.

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much easier to acquire and process. To tackle this problem, we propose a cross-modality deep learning method based on deep canoncial correlation analysis (CDCCA) to transform each modality separately and coordinate different modalities into a hyperspace by using specific canonical correlation analysis constraints. In our proposed method, only eye movement signals are used as inputs in the test phase and the knowledge from EEG signals is learned in the training stage. Experimental results on two human decision confidence datasets demonstrate that our proposed method achieves advanced performance compared with the existing single-modal approaches trained and tested on eye movement signals and maintains a competitive accuracy in comparison with multimodal models.

Factors driving the biomass and species richness of desert plants in northern Xinjiang China.

The desert ecosystem is an important part of the terrestrial ecosystem. Accurate estimations of the biomass and species richness of desert plants are of great value for maintaining ecosystem stability; however, current assessments remain a challenge due to the large spatial heterogeneity in biomass and species richness and difficulties posed by time-consuming field surveys, particularly in remote areas. In the present study, There were 527 sampling sites, and each sampling site contained approximately 9 quadrats. Approximately 4500 quadrats in total were taken from the Junggar Desert of northern Xinjiang, and the spatial distribution and factors driving the biomass and species richness of the desert ecosystem were quantitatively analyzed. The results showed that the average aboveground biomass, belowground biomass, litter, and the Patrick index of the Junggar Desert in northern Xinjiang were 115.42 gm-2, 924.77 gm-2, 13.06 gm-2, and 63, with values ranging from 2-708.12 gm-2, 120.25-3537.3 gm-2, 2-56.46 gm-2, and 0-377, respectively, The mean of the variation coefficient was 56.19%, 41.16%, 62.16% and 73.83%, suggesting moderate variation. The result is affected by the differences between the desert environment and climate. Climate factors had a relatively large impact on species richness, and the variation coefficient of species richness was large, indicating a large degree of dispersion of species richness. The direct influence of environmental and climatic factors on underground biomass (BGB) is relatively small, and its coefficient of variation is small. The spatial distribution of biomass and species richness in northern Xinjiang gradually decreased from west to east. Redundancy analysis showed that climate was the main factor driving desert biomass and species richness in northern Xinjiang, with an average independent explanatory power of 20.38% and 18.57%, respectively. Structural equation modeling indicated that climate factors, elevation, and community coverage had a direct positive effect on the aboveground biomass of the desert plants in northern Xinjiang and a direct negative effect on the belowground biomass. Moreover, climate factors and biological factors showed a direct positive effect on the species richness in northern Xinjiang.

[Rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba based on Heracles NEO ultra-fast gas phase electronic nose].

Since the current identification method for Paeoniae Radix Alba is complex in operation and long time-consuming with high requirements for technicians, the present study employed Heracles NEO ultra-fast gas phase electronic nose(E-nose) technology to identify raw and sulfur-fumigated Paeoniae Radix Alba decoction pieces in order to establish a rapid identification method for sulfur-fumigated Paeoniae Radix Alba. The odors of raw Paeoniae Radix Alba and its sulfur-fumigated products were analyzed by Heracles NEO ultra-fast gas phase E-nose to obtain the odor chromatographic information. The chemometric model was established, and the data were processed by principal component analysis(PCA), discriminant function analysis(DFA), soft independent modeling of class analogy(SIMCA), and partial least squares discriminant analysis(PLS-DA). The differential compounds of raw and sulfur-fumigated samples were qualitatively analyzed based on the Kovats retention index and Arochembase. As revealed by the comparison of gas chromatograms of raw and sulfur-fumigated Paeoniae Radix Alba, the heights of several peaks in the chromatograms before and after sulfur fumigation changed significantly. The peak(No.8) produced by ethylbenzene disappeared completely due to sulfonation reaction in the process of sulfur fumigation, indicating that ethylbenzene may be the key component in the identification of Paeoniae Radix Alba and its sulfur-fumigated products. In PCA, DFA, SIMCA, and PLS-DA models, the two types of samples were separated into two different regions, indicating that the established models can clearly distinguish between raw and sulfur-fumigated Paeoniae Radix Alba. The results showed that Heracles NEO ultra-fast gas phase E-nose technology could realize the rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba, which provides a new method and idea for the rapid identification of sulfur-fumigated Chinese medicine.

[Prediction of material basis and mechanism of Curcumae Rhizoma in treatment of coronary heart disease].

Coronary heart disease(CHD) is a common cardiovascular disease in clinical practice. Curcumae Rhizoma(CR), an important herbal medicine for breaking blood stasis and resolving mass, is often used for the treatment of CHD caused by blood stasis syndrome. However, the anti-CHD components, targets, and mechanism are still unclear. Therefore, in this study, the chemical components of CR were separated and identified by ultra high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS). Based on the identified components, network pharmacology analysis, including target prediction and functional enrichment, was applied to screen out the main active components against CHD, and the potential mechanism was discussed. Finally, molecular docking was performed to verify the binding between the active components and the targets. The results showed that among the 52 chemical components identified in CR, 28 were related to CHD, involving 75 core targets. The core components included(4S)-4-hydroxy-gweicurculactone, curcumadione, and curcumenone, and the core targets included phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha(PIK3 CA), mitogen-activated protein kinase 1(MAPK1), and mitogen-activated protein kinase 3(MAPK3). In summary, through the active components, such as(4S)-4-hydroxy-gweicurculactone, curcumadione, and curcumenone, CR regulates the nerve repair, vasoconstriction, lipid metabolism, and inflammatory response, thereby exerts therapeutic effect on CHD.

Imaging manifestations of idiopathic granulomatous lobular mastitis on cone-beam breast computed tomography.

PURPOSE: To evaluate the imaging manifestations of idiopathic granulomatous mastitis (IGM) on cone-beam breast computed tomography (CBBCT). METHODS: We retrospectively evaluated the clinical data of 22 female patients (mean age, 34.73 ± 10.41 years; range 20-58 years) with IGM pathologically confirmed by biopsy or resection. The non-contrast-enhanced CBBCT features, contrast-enhanced CBBCT features, contrast enhancement rate, time-density curve (TDC) and Breast Imaging-Reporting and Data System category of IGM were assessed. The contrast enhancement rates of IGM lesions at 60 s, 120 s and 180 s after injection of contrast agent were compared using ANOVA. RESULTS: All 22 patients with IGM showed non-mass enhancement on CBBCT. Approximately 40.9% (9/22) of IGM lesions displayed diffuse patchy or focal nodular enhancement on CBBCT, 31.8% (7/22) showed mammary duct dilation, 13.6% (3/22) showed pseudocystic appearance, and 13.6% (3/22) manifested as honeycomb cysts. Among the 22 patients, 72.7% (16/22) displayed type I TDC (persistently enhancing pattern) and 27.3% showed type II TDC (plateau pattern) on contrast-enhanced CBBCT. CONCLUSIONS: IGM mainly manifests as non-mass enhancement on CBBCT, with persistently enhancing or plateau TDC. CBBCT efficiently displays detailed features of IGM with high-density resolution and hemodynamic characteristics.

Targeting AKT and CK2 represents a novel therapeutic strategy for SMO constitutive activation-driven medulloblastoma.

AIMS: Sonic hedgehog subtype medulloblastoma is featured with overactivation of hedgehog pathway and can be targeted by SMO-specific inhibitors. However, the resistance is frequently developed leading to treatment failure of SMO inhibitors. W535L mutation of SMO (SMOW535L ) is thought to be an oncogenic driver for Sonic hedgehog subtype MB and confer resistance to SMO inhibitors. The regulation network of SMOW535L remains to be explored in comparison with wild-type SMO (SMOWT ). METHODS: In this study, we profiled transcriptomes, methylomes, and interactomes of MB cells expression SMOWT or SMOW535L in the treatment of DMSO or SMO inhibitor, respectively. RESULTS: Analysis of transcriptomic data indicated that SMO inhibitor disrupted processes of endocytosis and cilium organization in MB cells with SMOWT , which are necessary for SMO activation. In MB cells with SMOW535L , however, SMO inhibitor did not affect the two processes-related genes, implying resistance of SMOW535L toward SMO inhibitor. Moreover, we noticed that SMO inhibitor significantly inhibited metabolism-related pathways. Our metabolic analysis indicated that nicotinate and nicotinamide metabolism, glycerolipid metabolism, beta-alanine metabolism, and synthesis and degradation of ketone bodies might be involved in SMOW535L function maintenance. Interactomic analysis revealed casein kinase II (CK2) as an important SMO-associated protein. Finally, we linked CK2 and AKT together and found combination of inhibitors targeting CK2 and AKT showed synergetic effects to inhibit the growth of MB cells with SMO constitutive activation mutation. CONCLUSIONS: Taken together, our work described SMO-related transcriptomes, metabolomes, and interactomes under different SMO status and treatment conditions, identifying CK2 and AKT as therapeutic targets for SHH-subtype MB cells with SMO inhibitor resistance.