Human cytomegalovirus pUL97 upregulates SOCS3 expression via transcription factor RFX7 in neural progenitor cells.

Congenital human cytomegalovirus (HCMV) infection causes severe damage to the fetal brain, and the underlying mechanisms remain elusive. Cytokine signaling is delicately controlled in the fetal central nervous system to ensure proper development. Here we show that suppressor of cytokine signaling 3 (SOCS3), a negative feedback regulator of the IL-6 cytokine family signaling, was upregulated during HCMV infection in primary neural progenitor cells (NPCs) with a biphasic expression pattern. From viral protein screening, pUL97 emerged as the viral factor responsible for prolonged SOCS3 upregulation. Further, by proteomic analysis of the pUL97-interacting host proteins, regulatory factor X 7 (RFX7) was identified as the transcription factor responsible for the regulation. Depletion of either pUL97 or RFX7 prevented the HCMV-induced SOCS3 upregulation in NPCs. With a promoter-luciferase activity assay, we demonstrated that the pUL97 kinase activity and RFX7 were required for SOCS3 upregulation. Moreover, the RFX7 phosphorylation level was increased by either UL97-expressing or HCMV-infection in NPCs, suggesting that pUL97 induces RFX7 phosphorylation to drive SOCS3 transcription. We further revealed that elevated SOCS3 expression impaired NPC proliferation and migration in vitro and caused NPCs migration defects in vivo. Taken together, these findings uncover a novel regulatory mechanism of sustained SOCS3 expression in HCMV-infected NPCs, which perturbs IL-6 cytokine family signaling, leads to NPCs proliferation and migration defects, and consequently affects fetal brain development.

EphA2 is a functional entry receptor for HCMV infection of glioblastoma cells.

Human cytomegalovirus (HCMV) infection is associated with human glioblastoma, the most common and aggressive primary brain tumor, but the underlying infection mechanism has not been fully demonstrated. Here, we show that EphA2 was upregulated in glioblastoma and correlated with the poor prognosis of the patients. EphA2 silencing inhibits, whereas overexpression promotes HCMV infection, establishing EphA2 as a crucial cell factor for HCMV infection of glioblastoma cells. Mechanistically, EphA2 binds to HCMV gH/gL complex to mediate membrane fusion. Importantly, the HCMV infection was inhibited by the treatment of inhibitor or antibody targeting EphA2 in glioblastoma cells. Furthermore, HCMV infection was also impaired in optimal glioblastoma organoids by EphA2 inhibitor. Taken together, we propose EphA2 as a crucial cell factor for HCMV infection in glioblastoma cells and a potential target for intervention.

SOX2 downregulation of PML increases HCMV gene expression and growth of glioma cells.

The presence of human cytomegalovirus (HCMV) in glioblastoma (GBM) and improved outcomes of GBM patients receiving therapies targeting the virus have implicated HCMV in GBM progression. However, a unifying mechanism that accounts for the contribution of HCMV to the malignant phenotype of GBM remains incompletely defined. Here we have identified SOX2, a marker of glioma stem cells (GSCs), as a key determinant of HCMV gene expression in gliomas. Our studies demonstrated that SOX2 downregulated promyelocytic leukemia (PML) and Sp100 and consequently facilitated viral gene expression by decreasing the amount of PML nuclear bodies in HCMV-infected glioma cells. Conversely, the expression of PML antagonized the effects of SOX2 on HCMV gene expression. Furthermore, this regulation of SOX2 on HCMV infection was demonstrated in a neurosphere assay of GSCs and in a murine xenograft model utilizing xenografts from patient-derived glioma tissue. In both cases, SOX2 overexpression facilitated the growth of neurospheres and xenografts implanted in immunodeficient mice. Lastly, the expression of SOX2 and HCMV immediate early 1 (IE1) protein could be correlated in tissues from glioma patients, and interestingly, elevated levels of SOX2 and IE1 were predictive of a worse clinical outcome. These studies argue that HCMV gene expression in gliomas is regulated by SOX2 through its regulation of PML expression and that targeting molecules in this SOX2-PML pathway could identify therapies for glioma treatment.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma.

OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. CONCLUSION: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Does natural resource dependence restrict green development? An investigation from the "Belt and road" countries.

Addressing natural resource dependence is integral to achieving the Sustainable Development Goals by promoting economic diversification, environmental sustainability, and climate resilience. This study explores the effect of natural resource dependence on green development by adopting the balanced panel dataset from the "Belt and Road" countries from 2005 to 2019. Notably, the novelty of our analysis lies in the empirical analysis using instrument-based techniques that consolidate the "green development curse hypothesis" in the Belt and Road countries. The mechanism analysis reveals that natural resource dependence curbs green development by weakening innovative capability, disturbing institutional quality, reducing population density, and crowding out human capital. Further, the dynamic panel threshold model handling endogeneity verifies the nonlinear relationship between natural resource dependence and green development. Interestingly, digital trade offers greater "resilience" than traditional trade, correcting the resource curse dilemma. Finally, heterogeneity analyses indicate that the green development curse hypothesis only exists in countries with high-level environmental regulations and resource-based countries.

Balancing growth targets and environmental regulations: An empirical analysis of dual policy impact on corporate environmental responsibility-insights from China.

Balancing economic growth with environmental conservation poses a universal challenge for governments worldwide. This study investigates the intricate interplay between governments' economic-environmental trade-offs and their implementation of policies aimed at promoting Corporate Environmental Responsibility (CER). Given the discretion of Chinese local governments in economic and environmental policy, we take China as a case study. To conduct this research, we first merge critical data on China's economic growth targets and environmental regulations with information on listed enterprises. Then, we employ a "U-shaped" relationship model to examine the impact of these trade-offs on CER implementation. The results reveal that: (1)The effective fulfillment of CER by enterprises is primarily driven by stricter environmental regulations. (2) Economic growth targets can, to some extent, diminish the policy effect of environmental regulations on CER fulfillment. (3)The crowding-out effect of economic growth targets is particularly pronounced within specific subsets of enterprises, including state-owned enterprises, heavily polluting firms, and those facing high profit pressure. These findings imply that when local governments implement contradictory policies, they must consider not only enterprises' political connections and economic contributions but also pay close attention to the survival dilemma of enterprises. This balancing act aims to harmonize conflicting policy objectives. This research deepens the understanding of how institutional and policy frameworks impact enterprise engagement in CER, especially within the context of governments' economic-environmental trade-offs. It sheds light on the strategies employed by China and other emerging economies to effectively leverage contradictory policies to foster sustainable green growth.

Enhancing New Zealand's emissions trading scheme: A comprehensive sector-level assessment for a stronger regulatory framework.

Certain nations have opted for stimulus-based regulations to curtail emissions, build a liveable, environmentally friendly setting, and work towards aspirational mitigation targets. New Zealand (NZ) prefers an Emission Trading Scheme (ETS) to taxation, mitigating emissions on one hand while retaining incentives for economic growth on the other. As a result, NZ has initiated a legal framework since 2008 to allow its economic sectors to engage in ETS and minimize emissions. Yet, selecting the appropriate sectors and effectively adjusting sector-specific regulations remain critical and complex challenges in the global design of ETS since both excessive and insufficient intervention can lead to inefficiencies in the system's functioning. Therefore, this study begins validating the NZ ETS's abatement potential regarding sectoral carbon intensity by executing the double machine learning techniques, consolidating the ETS efficacy that has robustly mitigated sectoral carbon intensity in NZ during 2006-2020. However, this conclusion seems invalid at the disaggregated level when focusing on forward-backward linkages, where NZ's input-output tables furnish a compelling scenario of sectoral dependencies and the products (residuals) they provide. Altogether, the regulatory requirements are either too strict or too lax, leaving out five of the 24 (as a whole) key sectors. Rather, the ETS could be powerful, providing these five key sectors are well tackled, necessitating a reformulation of the ongoing ETS regulatory regime.

iTRAQ-Based Proteomics Analysis of Human Cytomegalovirus Latency and Reactivation in T98G Cells.

Human cytomegalovirus (HCMV) establishes a persistent/latent infection after primary infection, and the host factor(s) plays a key role in regulating HCMV infection status. The spread of reactivated HCMV via the hematogenous or neural route usually results in severe diseases in newborns and immunocompromised individuals. As the primary reservoirs in vivo, cells of myeloid lineage have been utilized extensively to study HCMV infection. However, the molecular mechanism of HCMV latency/reactivation in neural cells is still poorly understood. We previously showed that HCMV-infected T98G cells maintain a large number of viral genomes and support HCMV reactivation from latency upon cAMP/IBMX treatment. Here, we employed an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomics to characterize cellular protein changes during HCMV latency and reactivation in T98G cells. A total of 168 differentially expressed proteins (DEPs) were identified, including 89 proteins in latency and 85 proteins in reactivation. Bioinformatics analysis showed that a few biological pathways were associated with HCMV latency or reactivation. Moreover, we validated 16 DEPs by both mRNA and protein expression profiles and further evaluated the effects of ApoE and the phosphatidylinositol 3-kinase (PI3K) pathway on HCMV infection. ApoE knockdown reduced HCMV loads and virus release, whereas overexpressing ApoE hampered HCMV latent infection, indicating a role in HCMV latency establishment/maintenance. Blocking the PI3K pathway by LY294002, a PI3K inhibitor, induced HCMV reactivation from latency in T98G cells. Overall, this comparative proteomics analysis delineates the cellular protein changes during HCMV latency and reactivation and provides a road map to advance our understanding of the mechanism(s) in the context of neural cells. IMPORTANCE Human cytomegalovirus (HCMV) is a highly transmissible betaherpesvirus that has a prevalence of 60% to 90% worldwide. This opportunist pathogen poses a significant threat to newborns and immunosuppressed individuals. One major obstacle for developing effective therapeutics is a poor understanding of HCMV latency/reactivation mechanisms. This study presents, for the first time, a systemic analysis of host cell protein expression changes during HCMV latency establishment and reactivation processes in neural cells. We showed that ApoE was downregulated by HCMV to facilitate latent infection. Also, the proteomics analysis has associated a few PI3K pathway-related proteins with HCMV reactivation. Altogether, this study highlights multiple host proteins and signaling pathways that can be further investigated as potential druggable targets for HCMV-related diseases, especially brain disorders.

How does financial development influence carbon emission intensity in the OECD countries: Some insights from the information and communication technology perspective.

Based on an extended STIRPAT framework, this paper investigates the effects of financial development on carbon emission intensity in OECD countries from linear and non-linear perspectives, where financial development is proxied by three dimensions: financial deepening, financial deepening, and financial size, and financial efficiency. Fortunately, three types of financial development significantly alleviate carbon emission intensity. An extended moderation effect model is built to estimate the effect of financial development via information and communication technology on carbon emission intensity. The results reveal that internet-based information and communication technology and service-based information and communication technology are positively correlated with carbon emission intensity. To effectively handle the endogeneity issue triggered by causal relationships between variables and allow potential non-linear nexus, an advanced dynamic panel threshold model incorporating the generalised method of moments is employed to investigate how financial development affects carbon emission intensity under different types of information and communication technology. Empirical evidence demonstrates the significance of the non-linear nexus between financial development and carbon emission intensity. Lastly, heterogeneity analysis demonstrates the existence of heterogeneity associated with institutional quality, degree of economic development, and resource endowment concerning the effect of financial development on carbon emission intensity among the OECD countries.

Impact of regional energy allocation distortion on carbon emission efficiency: Evidence from China.

The free flow of energy cannot be fully achieved in China's energy market because of incomplete market-oriented reform, resulting in energy allocation distortion, which has hampered carbon emissions reduction. However, the extent of energy allocation distortion and its role in carbon emission efficiency remain unexplored. Therefore, this study aims to measure energy allocation distortion and investigate its impact on carbon emission efficiency. For this purpose, first, we derive energy allocation distortion based on a production function and carbon emission efficiency using a meta-frontier non-radial Malmquist index. To effectively address the endogeneity issue, we use a generalized method of moments model to estimate the impact of energy allocation distortion on carbon emission efficiency. Second, we further explore the distortionary mechanism of carbon emission efficiency associated with energy allocation and analyze the asymmetric effect of energy allocation distortion on carbon emission efficiency. The results show a certain degree of energy allocation distortion throughout the country, and disparity exists among different regions. The average value of carbon emission efficiency in the eastern region is 1.0286, well ahead of the national average, demonstrating better performance than other regions. Energy allocation distortion negatively affects carbon emission efficiency, with a 1% increase in energy allocation distortion leading to a 0.251% decrease in carbon emission efficiency. Technological progress, the structure of energy consumption, and industrial structure are important transmission channels through which energy allocation affects carbon emission efficiency. The study contributes to uncovering regional energy allocation distortion and its impacts on carbon emission efficiency and providing strategic policy recommendations for improving energy allocation efficiency.

How financial clustering influences China's green development: Mechanism investigation and empirical discussion.

Understanding the significance of financial clustering in the context of green development holds immense importance for China as it strives towards achieving high-quality green development. Using a balanced panel dataset encompassing China's 283 cities from 2009 to 2020, we aim to explore the impact of financial clustering on green development from both linear and nonlinear perspectives. Empirical evidence suggests that when the level of financial clustering increases by 1%, the city-level green development increases by 0.1012%. A mediation effect model certifies that there are three essential channels through which financial clustering robustly boosts green development: technical, structural and scale effects. Subsequently, a novel program handling endogeneity is designed and verifies the nonlinear nexus between financial clustering and green development. Moreover, the spatial Durbin model demonstrates that financial clustering significantly sustains local green development, despite its relatively weak spill over effects. Heterogeneity analysis presents that the promoting effect is particularly predominant in Central China, as well as in cities characterized by high levels of financial clustering and large population sizes.

Appraising the role of the digital economy in global decarbonization: A spatial non-linear perspective on globalization.

In the context of globalization, the role of the digital economy in carbon emissions may generate spatial spill over effects. This study comprehensively applies a spatial model to understand the nexus between the digital economy and carbon emissions in 67 economies from 2010 to 2019. Specifically, this study contributes by introducing a spatial panel threshold model, which helps to present the new evidence regarding decarbonization process. Empirical findings exemplify that the digital economy remarkably reduces local carbon emissions, with the positive spatial spill over effects being salient. The spatial moderating effect model uncover that globalization positively affects the nexus between the digital economy and carbon emissions. Interestingly, the spatial panel threshold model designates that the digital economy's reduction effect on local carbon emissions will be tightened, whereas the positive spatial spill over effects turn negative only when globalization surpasses a threshold. Our model has the potential to explain some results that traditional models cannot reach.

Strike Velocity Prediction of Stick Blunt Instruments Based on Backpropagation Neural Network.

OBJECTIVES: To analyze and predict the striking velocity range of stick blunt instruments in different populations, and to provide basic data for the biomechanical analysis of blunt force injuries in forensic identification. METHODS: Based on the Photron FASTCAM SA3 high-speed camera, Photron FASTCAM Viewer 4.0 and SPSS 26.0 software, the tester's maximum striking velocity of stick blunt instruments and related factors were calculated and analyzed, and inputed to the backpropagation (BP) neural network for training. The trained and verified BP neural network was used as the prediction model. RESULTS: A total of 180 cases were tested and 470 pieces of data were measured. The maximum striking velocity range was 11.30-35.99 m/s. Among them, there were 122 female data, the maximum striking velocity range was 11.63-29.14 m/s; there were 348 male data, the maximum striking velocity range was 20.11-35.99 m/s. The maximum striking velocity of stick blunt instruments increased with the increase of weight and height, but there was no obvious increase trend in the male group; the maximum striking velocity decreased with age, but there was no obvious downward trend in the female group. The maximum striking velocity of stick blunt instruments has no significant correlation with the material and strike posture. The root mean square error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) of the prediction results by using BP neural network were 2.16, 1.63 and 0.92, respectively. CONCLUSIONS: The prediction model of BP neural network can meet the demand of predicting the maximum striking velocity of different populations.

Reconstruction and Quantitative Evaluation of Blunt Injury Cases by Finite Element Method.

OBJECTIVES: To reconstruct the cases of acceleration craniocerebral injury caused by blunt in forensic cases by finite element method (FEM), and to study the biomechanical mechanism and quantitative evaluation method of blunt craniocerebral injury. METHODS: Based on the established and validated finite element head model of Chinese people, the finite element model of common injury tool was established with reference to practical cases in the forensic identification, and the blunt craniocerebral injury cases were reconstructed by simulation software. The cases were evaluated quantitatively by analyzing the biomechanical parameters such as intracranial pressure, von Mises stress and the maximum principal strain of brain tissue. RESULTS: In case 1, when the left temporal parietal was hit with a round wooden stick for the first time, the maximum intracranial pressure was 359 kPa; the maximum von Mises stress of brain tissue was 3.03 kPa at the left temporal parietal; the maximum principal strain of brain tissue was 0.016 at the left temporal parietal. When the right temporal was hit with a square wooden stick for the second time, the maximum intracranial pressure was 890 kPa; the maximum von Mises stress of brain tissue was 14.79 kPa at the bottom of right temporal lobe; the maximum principal strain of brain tissue was 0.103 at the bottom of the right temporal lobe. The linear fractures occurred at the right temporal parietal skull and the right middle cranial fossa. In case 2, when the forehead and left temporal parietal were hit with a round wooden stick, the maximum intracranial pressure was 370 kPa and 1 241 kPa respectively, the maximum von Mises stress of brain tissue was 3.66 kPa and 26.73 kPa respectively at the frontal lobe and left temporal parietal lobe, and the maximum principal strain of brain tissue was 0.021 and 0.116 respectively at the frontal lobe and left temporal parietal lobe. The linear fracture occurred at the left posterior skull of the coronary suture. The damage evaluation indicators of the simulation results of the two cases exceeded their damage threshold, and the predicted craniocerebral injury sites and fractures were basically consistent with the results of the autopsy. CONCLUSIONS: The FEM can quantitatively evaluate the degree of blunt craniocerebral injury. The FEM combined with traditional method will become a powerful tool in forensic craniocerebral injury identification and will also become an effective means to realize the visualization of forensic evidence in court.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.

BACKGROUND: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. PURPOSE: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. STUDY TYPE: Retrospective. POPULATION: A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). FIELD STRENGTH/SEQUENCE: Axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. ASSESSMENT: Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. RESULTS: Seven texture features, three from T1 -w, two from T2 -w, and two from CET1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. DATA CONCLUSION: The model based on radiomic features from T1 -w, T2 -w, and CET1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 2.

Sulphide activity-dependent multicolor emission dye and its applications in in vivo imaging.

Reactive sulfur species (RSS) play pivotal roles in various pathological and physiological processes. There exists an intricate relevance in generation and metabolism among these substances. Although they are nucleophilic, there are still some differences in their reactivity. There are many methods to detect them by using reactive fluorescent probes, but the systematic study of their reactivity is still lacking. In our study, we designed a multiple reaction site fluorescent probe based on benzene conjugated benzopyrylium and NBD. The study revealed that besides both biothiols and hydrogen sulfide, sulfur dioxide (SO2) can cleave the ether bond. There are two reaction forms for GSH with low reactivity: cutting the ether bond and adding the conjugated double bond of benzopyrylium. Nevertheless, Cys/Hcy with higher activity can further rearrange with NBD after cutting the ether bond. In addition, SO2 can not only cleave the ether bond, but also continue to add the conjugated double bond of benzopyrylium. The above processes lead to multicolor emission of the probe, thus realizing the characteristic analysis of different sulfides. Thus the probe can be used for the detection of sulfide in mitochondria, and further for the imaging of sulfide in cells and zebrafish. This effective analysis method will provide a broad application prospect for practical applications.

A turn-on homodimer fluorescent probe based on homo-FRET for the sensing of biothiols in lysosome: a trial of a new turn-on strategy.

Fluorescence resonance energy transfer (FRET) is often applied to construct fluorescent probes for acquiring high selectivity and sensitivity. According to the FRET theory, a homodimer composed of two identical fluorophores with a small Stokes shift has only weak fluorescence due to homo-FRET between fluorophores, and the fluorescence could be recovered after the destruction of the homodimer. In this study, we designed and synthesized a homodimer fluorescent probe, namely 1,3,5,7-tetramethyl-8-(4'-phenylthiophenol)-boron difluoride-dipyrrole methane dimer (D-TMSPB), based on this turn-on strategy. In D-TMSPB, the disulfide moiety was selected as the response moiety of biothiols, and BODIPY fluorophore was chosen as both donor and acceptor in FRET due to the ultra-small Stokes shifts and obvious overlap of its excitation/emission peak. D-TMSPB exhibited only weak fluorescence. After selective reaction with biothiols, FRET was destroyed and the derivative exhibited strong fluorescence at 514 nm with the limit of detection of about 0.15 µM for GSH. Notably, the derivative of biothiols shows remarkable fluorescence only in acidic conditions, which accords with the internal environment of lysosome. Thus, D-TMSPB was applied to image the biothiols of lysosome in living cells. The turn-on fluorescence of D-TMSPB indicated that homo-FRET is a practical strategy to design turn-on fluorescent probes, particularly for the sensing mechanism based on leaving groups.

Combination of emerging technologies for the extraction of bioactive compounds.

With the growing consumer demands for greener alternatives that do not involve toxic chemicals as well as the industry concerns of sustainable, nontoxic routes of extraction, the applications of novel extraction technologies in the food industry have been widely studied. This review discussed the novel extraction technologies including their mechanisms, protocols, influencing factors, advantages and drawbacks, as well as a comprehensive summary of the combination of the novel extraction technologies for phyto-bioactive compounds. Novel extraction methods, including ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE) and enzyme-assisted extraction (EAE), are considered as clean, green and efficient alternative to conventional extraction technologies. Their combinations, ultrasound-assisted enzymatic extraction (UAEE), microwave-assisted enzymatic extraction (MAEE) and ultrasonic microwave-assisted extraction (UMAE), can exhibit higher potential extraction ability. However, some of them need specific equipment. The food industry in the extraction sector should choose a proper extraction method which has a balance between product quality, process efficiency, production costs and environmentally friendly processes. The current review presented comprehensive references for future research on the novel extraction of phyto-bioactive compounds extraction.HighlightsNovel clean, green and efficient alternative to conventional extraction technologies are discussed.Combination of the novel extraction technologies for synergistic effects.Minimal degradation and enhanced extraction yields.Extraction mechanisms, advantages and drawbacks associated with novel extraction technologies.

Chemical Space Charting of Different Parts of Inula nervosa Wall.: Upregulation of Expression of Nrf2 and Correlated Antioxidants Enzymes.

The edible and medicinal part of Inula nervosa Wall. (Xiaoheiyao) is confined to its root without sufficient phytochemical and biological investigation. In this study, the secondary metabolites of root, stem, leaf, and flower of I. nervosa Wall. were visualized using Global Natural Products Social Molecular Networking (GNPS), MolNetEnhancer, XCMS(xcmsonline.scripps.edu) analysis, and `ili mapping based on high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) data to reveal their chemical differences. Among the 11 kinds of chemical repertoires annotated by MolNetEnhancer and 16 hits against the GNPS library, 10-isobutyryloxy-8,9-epoxythymol isobutyrate (1) was revealed as the most dominant and responsible marker between the roots and the other parts. Moreover, a battery of unique MS features as well as differential markers were discovered from different parts of the plant. The chemical differences contribute to the bioactivity differences, which presented in the 2,2-diphenyl-1-picryl-hydrazyl (DPPH)assay and H2O2-insulted HepG2 cells and were in significant correlations with the contents of 1. real-time reverse transcription polymerase chain reaction (RT-PCR)results demonstrated that I. nervosa Wall. extracts upregulated the mRNA expression of nuclear factor E2-related factor 2(Nrf2), heme oxygenase 1(HO-1), NAD(P)H quinone dehydrogenase 1 (NQO1), manganese superoxide dismutase (MnSOD), and glutamate-cysteine ligase catalytic subunit (GCLC) actors involved in antioxidative response in H2O2-challenged HepG2 cells. These findings support the roots of I. nervosa Wall. as active parts of Xiaoheiyao, and also indicate the potential antioxidant activities of other parts.

In Vitro Susceptibility of Berberine Combined with Antifungal Agents Against the Yeast Form of Talaromyces marneffei.

Talaromyces (Penicillium) marneffei can cause fatal disseminated infection in immunocompromised hosts. However, therapeutic strategies for the mycosis are limited. Reports of the other fungi suggest that berberine, a component of traditional herb, inhibitors interact with antifungal agents to improve the treatment outcomes. In the study, we evaluated the in vitro efficacy of berberine in combination with conventional antifungal agents against the pathogenic yeast form of T. marneffei. We demonstrate the synergistic effect of combination of berberine with fluconazole (52.38%), itraconazole (66.67%), voriconazole (71.43%), amphotericin B (71.43%) or caspofungin (52.38%) of T. marneffei strains, respectively. Time-kill curves confirmed the synergistic interaction, and no antagonistic was observed in all of the combinations. In conclusion, berberine could enhance the efficacy of conventional antifungal agents against the yeast form of T. marneffei in vitro. The results indicated berberine might have a potential role in combination therapy for talaromycosis.