Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category- wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Magnetic Anisotropy and Relaxation in Four-Coordinate Cobalt(II) Single-Ion Magnets with a [CoIIO4] Core.

A mononuclear four-coordinate Co(II) complex with a [CoIIO4] core, namely, PPN[Li(MeOH)4][Co(L)2] (1) (PPN = bis(phosphoranediyl)iminium; H2L = perfluoropinacol), has been studied by X-ray crystallography, magnetic characterization, and theoretical calculations. This complex presents a severely distorted coordination geometry. The O-Co-O bite angle is 83.42°/83.65°, and the dihedral twist angle between the O-Co-O chelate planes is 55.6°. The structural distortion results in a large easy-axis magnetic anisotropy with D = -104(1) cm-1 and a transverse component with |E| = +4(2) cm-1. Alternating current (ac) susceptibility measurements demonstrate that 1 exhibits slow relaxation of magnetization at zero static field. However, the frequency-dependent out-of-phase (χ"M) susceptibilities of 1 at 0 Oe do not show a characteristic maximum. Upon the application of a dc field or the dilution with a diamagnetic Zn matrix, the quantum tunneling of magnetization (QTM) process can be successfully suppressed. Notably, after dilution with the Zn matrix, the obtained sample exhibits a structure different from that of the pristine complex. In this altered sample, the asymmetric unit does not contain the Li(MeOH)4+ cation, resulting in an O-Co-O bite angle of 86.05° and a dihedral twist angle of 75.84°, thereby leading to an approximate D2d symmetry. Although such differences are not desirable for magnetic studies, this study still gives some insights. Theoretical calculations reveal that the D parameter is governed by the O-Co-O bite angle, in line with our previous report for other tetrahedral Co(II) complex with a [CoIIN4] core. On the other hand, the rhombic component is found to increase as the dihedral angle deviates from 90°. These findings provide valuable guidelines for fine-tuning the magnetic properties of Co(II) complexes.

New α-Glucosidase Inhibitors from the Whole Plant of Hypericum beanii Based on Ligand Fishing and Molecular Networking Analysis.

In this work, a new rapid and targeted method for screening α-glucosidase inhibitors from Hypericum beanii was developed and verified. Ten new polycyclic polyprenylated acylphloroglucinols (PPAPs), hyperlagarol A-J (1-10), and nine known PPAPs (11-19) were obtained from H. beanii. Their structures were identified by using comprehensive analyses involving mass spectrometry, ultraviolet spectroscopy, infrared spectroscopy, nuclear magnetic resonance spectroscopy, and electron capture dissociation calculations. 1 and 2 are two new rare 2,3-seco-spirocyclic PPAPs, 3 and 4 are two novel 12,13-seco-spirocyclic PPAPs, 5 and 6 are two novel spirocyclic PPAPs, 7 and 8 are two new unusual spirocyclic PPAPs with complex bridged ring systems, and 9 and 10 are two novel nonspirocyclic PPAPs. α-GC inhibitory activities of all isolated compounds were tested. Most of them displayed inhibitory activities against α-glucosidase, with the IC50 values ranging from 6.85 ± 0.65 to 112.5 ± 9.03 µM. Moreover, the inhibitory type and mechanism of the active compounds were further analyzed using kinetic studies and molecular docking.

[Effects of Vermicomposting on Compost Quality and Heavy Metals：A Meta-analysis].

In order to comprehensively evaluate the effects of vermicomposting on compost quality and the conversion of heavy metals under different control conditions, 109 studies were reviewed. The effects of earthworm species, pre-compost time, ventilation methods, initial C/N, initial pH, and initial moisture of the raw materials on compost quality and the heavy metal toxicity were quantitatively discussed during the vermicomposting process through Meta-analysis. The results showed that the six subgroups of factors all showed obvious influences on the compost quality and heavy metal toxicity. After vermicomposting, the contents of NO3--N (116.2%), TN (29.1%), TP (31.2%), and TK (15.0%) were significantly increased, whereas NH4+-N (-14.8%) and C/N (-36.3%) were significantly decreased. Meanwhile, the total amount of Cu and Cr of the final compost and their bioavailability were significantly reduced. Considering the influences of grouping factors on compost quality and heavy metals, it is recommended to adjust the initial moisture of pile materials to 70%-80%, C/N to 30-85, and pH to 6-7 and to conduct pre-composting for 0-15 d; additionally, vermicomposting should be naturally placed when the composting is aimed at promoting the compost quality. If the main purpose is to weaken the perniciousness of heavy metals in the raw material, it is recommended to adjust the initial moisture of the material to 50%-60%, C/N to less than 30, and pH to 7-8; to conduct no pre-compost; regularly turn the piles; and use the earthworm Eudrilus eugeniae for vermicomposting.

Associations of Sedentary Behavior with Risks of Cardiovascular Disease Events among Chinese Adults.

AIMS: Evidence regarding the modification effects of age, sex, ethnicity, socioeconomic status, or weight status on the associations of sedentary behavior (SB) with cardiovascular diseases (CVDs) is limited. Moreover, the mechanisms for the associations also remain unclear. We aimed to investigate the possible influence of these factors on the associations of SB with CVD events and whether the associations are mediated by metabolic phenotypes. METHODS: This study included 42,619 participants aged 20-74 years, recruited from the Shanghai Suburban Adult Cohort and Biobank study. SB was assessed at baseline and integrated with health information systems to predict future CVD events. Cox proportional hazards models, interaction analyses, restricted cubic splines and causal mediation analyses were used for assessments. RESULTS: Compared to those with ＜3 h/d sedentary time, participants having SB ≥ 5 h/d had significantly higher risks of CVD (HR[95%CI]: 1.27[1.12-1.44]), coronary heart disease (CHD, 1.35[1.14-1.60]), and ischemic stroke (IS, 1.30[1.06-1.60]). The association of CHD was more pronounced in the retired individuals than their counterparts (1.45[1.20-1.76] versus 1.06[0.74-1.52], pinteraction=0.046). When SB was expressed as a continuous variable, a 1 h/d increment in SB was positively associated with risks of CVD (1.03[1.01-1.05]), CHD (1.04[1.01-1.07]), and IS (1.05[1.01-1.08]). High-density lipoprotein cholesterol (HDL-C, proportion mediated: 12.54%, 12.23%, and 11.36%, all p＜0.001), followed by triglyceride (TG, 5.28%, 4.77%, and 4.86%, all p＜0.01) and serum uric acid (SUA, 3.64%, 4.24%, and 2.29%, all p＜0.05) were major mediators through metabolic phenotypes. CONCLUSIONS: Higher SB was associated with elevated risks of CVD events. The detrimental effect of SB on CHD risk was more pronounced among retired individuals. Moreover, HDL-C, TG and SUA partially mediated the relationships between SB and CVD events. Our findings may have implications for preventing and controlling CVD associated with SB.

Lipid-Lowering Meroterpenoids Penihemeroterpenoids A-F from Penicillium herquei GZU-31-6 via Targeting the AMPK/ACC/SREBP-1c Signaling Pathway.

Penihemeroterpenoids A-C, the first meroterpenoids with an unprecedented 6/5/6/5/5/6/5 heptacyclic ring system, together with precursors penihemeroterpenoids D-F, were co-isolated from the fungus Penicillium herquei GZU-31-6. Among them, penihemeroterpenoids C-F exhibited lipid-lowering effects comparable to those of the positive control simvastatin by the activation of the AMPK/ACC/SREBP-1c signaling pathway, downregulated the mRNA levels of lipid synthesis genes FAS and PNPLA3, and increased the level of mRNA expression of the lipid export gene MTTP.

The A subunit of vacuolar H+-ATPase gene (CmVHA-A) plays opposite roles in plant growth and drought tolerance of chrysanthemum under different growing conditions.

As the most prominent proton pumps in plants, vacuolar H+-ATPases (VHAs) comprise multiple subunits that are important for physiological processes and stress tolerance in plants. However, few studies on the roles of subunit genes of VHAs in chrysanthemum have been reported to date. In this study, the gene of A subunit of V-ATPase in chrysanthemum (CmVHA-A) was cloned and identified. CmVHA-A was conserved with VHA-A proteins from other plants. Expression analysis showed that CmVHA-A was highly expressed in most tissues of chrysanthemum except for the flower bud, and was readily induced by polyethylene glycol (PEG) treatment. Functional analysis demonstrated that CmVHA-A exerted a negative influence on the growth and development of shoot and root of chrysanthemum under normal conditions. RNA-sequencing (RNA-seq) analysis revealed the possible explanations for phenotypic differences between transgenic and wild-type (WT) plants. Under drought conditions, CmVHA-A positively affected the drought tolerance of chrysanthemum by enhancing antioxidase activity and alleviating photosynthetic disruption. Overall, CmVHA-A plays opposite roles in plant growth and drought tolerance of chrysanthemums under different growing conditions.

Cytotoxic indole diterpenoids and rare pyridoxatin atropisomers from the endophytic fungus Tolypocladium sp. SHJJ1.

Phytochemical investigation on the extract of endophytic fungus Tolypocladium sp. SHJJ1 resulted in the identification of a pair of previously undescribed pyridoxatin atropisomers [1 (M/P)] and three new indole diterpenoids (3-5), together with a pair of known pyridoxatin atropisomers [2 (M/P)] and ten known indole diterpenoids (6-15). Their structures, including their absolute configurations were elucidated by extensive spectroscopic analysis, quantum chemical calculations, and X-ray diffraction. Among the undescribed natural products, [1 (M/P)] that two rapidly interconverting atropisomers are the third example to report in the pyridoxatin atropisomers. Except for compounds 1 (M/P) and 2 (M/P), all other compounds were tested for their cytotoxicity using HepG2, A549, and MCF-7 human cell lines. Compound 9 displayed moderate cytotoxicity against the HepG2, A549, and MCF-7 cell lines with IC50 values of 32.39 ± 1.48 µM, 26.06 ± 1.14 µM, and 31.44 ± 1.94 µM, respectively, which was similar to the positive drug cisplatin (with IC50 values of 32.55 ± 1.76 µM, 18.40 ± 1.43 µM, and 27.31 ± 1.22 µM, respectively).

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis using Histopathological Images.

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning methods have not adequately exploited geometric features for pathological image classification due to the absence of effective descriptors that can capture both cell distribution and gathering patterns, which often serve as potent indicators. In this paper, inspired by clinical practice, a Hierarchical Graph Pyramid Transformer (HGPT) is proposed to guide pathological image classification by effectively exploiting a geometric representation of tissue distribution which was ignored by existing state-of-the-art methods. First, a graph representation is constructed according to morphological feature of input pathological image and learn geometric representation through the proposed multi-head graph aggregator. Then, the image and its graph representation are feed into the transformer encoder layer to model long-range dependency. Finally, a locality feature enhancement block is designed to enhance the 2D local representation of feature embedding, which is not well explored in the existing vision transformers. An extensive experimental study is conducted on Kather-5K, MHIST, NCT-CRC-HE, and GasHisSDB for binary or multi-category classification of multiple cancer types. Results demonstrated that our method is capable of consistently reaching superior classification outcomes for histopathological images, which provide an effective diagnostic tool for malignant tumors in clinical practice.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Biological Interactions Mediate Soil Functions by Altering Rare Microbial Communities.

Soil microbes, the main driving force of terrestrial biogeochemical cycles, facilitate soil organic matter turnover. However, the influence of the soil fauna on microbial communities remains poorly understood. We investigated soil microbiota dynamics by introducing competition and predation among fauna into two soil ecosystems with different fertilization histories. The interactions significantly affected rare microbial communities including bacteria and fungi. Predation enhanced the abundance of C/N cycle-related genes. Rare microbial communities are important drivers of soil functional gene enrichment. Key rare microbial taxa, including SM1A02, Gammaproteobacteria, and HSB_OF53-F07, were identified. Metabolomics analysis suggested that increased functional gene abundance may be due to specific microbial metabolic activity mediated by soil fauna interactions. Predation had a stronger effect on rare microbes, functional genes, and microbial metabolism compared to competition. Long-term organic fertilizer application increased the soil resistance to animal interactions. These findings provide a comprehensive understanding of microbial community dynamics under soil biological interactions, emphasizing the roles of competition and predation among soil fauna in terrestrial ecosystems.

HPPO-Derived Meroterpenoids from the Marine-Derived Fungus Penicillium sp. SCSIO 41691.

Seven new 4-hydroxy-6-phenyl-2H-pyran-2-one (HPPO) derived meroterpenoids, 1-methyl-12a,12b-epoxyarisugacin M (1), 1-methyl-4a,12b-epoxyarisugacin M (2), 2,3-dihydroxy-3,4a-epoxy-12a-dehydroxyisoterreulactone A (3), 2-hydroxy-12a-dehydroxyisoterreulactone A (4), 3'-demethoxyterritrems B' (5), 4a-hydroxyarisugacin P (6), and 1-epi-arisugacin H (7), together with two known analogues (8 and 9), were isolated from the marine-derived fungal strain Penicillium sp. SCSIO 41691. Their structures were elucidated by spectroscopic methods, and the absolute configurations of compounds 1 and 3 were determined by single-crystal X-ray diffraction. Among them, 1 and 2 had a unique methyl migration in the basic meroterpenoid skeleton with a 12a,12b-epoxy or 4a,12b-epoxy group, and 3 was a highly oxygenated HPPO-derived meroterpenoid featuring a rare 6/5/6/6/6/6 hexacyclic system with a 3,4a-epoxy group. Biologically, 5 exhibited inhibitory activity against lipopolysaccharide-induced nitric oxide production in RAW 264.7 cells with an IC50 value of 21 µM, more potent than the positive control indomethacin.

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images.

Objective.The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels.Approach.We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor CT segmentation. First, a feature map was obtained from segmentation backbone encoder, which contained many image region nodes. An attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node and enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to integrate the channel context relationship. Finally, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate the mutual information among the multiple views.Main results.The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. Utilizing MEMI on multiple segmentation backbones also demonstrated MEMI's generalization ability.Significance.Our model improved the lung tumor segmentation performance by learning the correlations among multiple region nodes, integrating the channel context relationship, and mutual information enhancement from multiple views.

The BTB/TAZ domain-containing protein CmBT1-mediated CmANR1 ubiquitination negatively regulates root development in chrysanthemum.

Roots are fundamental for plants to adapt to variable environmental conditions. The development of a robust root system is orchestrated by numerous genetic determinants and, among them, the MADS-box gene ANR1 has garnered substantial attention. Prior research has demonstrated that, in chrysanthemum, CmANR1 positively regulates root system development. Nevertheless, the upstream regulators involved in the CmANR1-mediated regulation of root development remain unidentified. In this study, we successfully identified bric-a-brac, tramtrack and broad (BTB) and transcription adapter putative zinc finger (TAZ) domain protein CmBT1 as the interacting partner of CmANR1 through a yeast-two-hybrid (Y2H) screening library. Furthermore, we validated this physical interaction through bimolecular fluorescence complementation and pull-down assays. Functional assays revealed that CmBT1 exerted a negative influence on root development in chrysanthemum. In both in vitro and in vivo assays, it was evident that CmBT1 mediated the ubiquitination of CmANR1 through the ubiquitin/26S proteasome pathway. This ubiquitination subsequently led to the degradation of the CmANR1 protein and a reduction in the transcription of CmANR1-targeted gene CmPIN2, which was crucial for root development in chrysanthemum. Genetic analysis suggested that CmBT1 modulated root development, at least in part, by regulating the level of CmANR1 protein. Collectively, these findings shed new light on the regulatory role of CmBT1 in degrading CmANR1 through ubiquitination, thereby repressing the expression of its targeted gene and inhibiting root development in chrysanthemum.

Two successful pregnancies in a membranous nephropathy patient: Case report and literature review.

BACKGROUND: Pregnancy in patients with nephrotic syndrome presents enormous challenges to both the mother and fetus, and there are no treatment guidelines for these patients. METHODS: We show a case of a woman with anti-PLA2R antibody-positive membranous nephropathy who did not have a kidney biopsy. Her clinical course during both pregnancies was closely followed and her medications were guided. RESULTS: She gave birth to 2 healthy babies and her condition was very well controlled with the help of medication. CONCLUSION: Patients with nephrotic syndrome can have successful pregnancies after drug treatment. In addition, similar to the non-pregnant population, percutaneous kidney biopsy is not required for the diagnosis of idiopathic membranous nephropathy (IMN) in pregnant nephrotic syndrome patients with anti-PLA2R antibody positive, but the etiology of secondary MN should be excluded.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs.

Inferring the latent disease-related miRNAs is helpful for providing a deep insight into observing the disease pathogenesis. We propose a method, CMMDA, to encode and integrate the context relationship among multiple heterogeneous networks, the complementary information across these networks, and the pairwise multimodal attributes. We first established multiple heterogeneous networks according to the diverse disease similarities. The feature representation embedding the context relationship is formulated for each miRNA (disease) node based on transformer. We designed a co-attention fusion mechanism to encode the complementary information among multiple networks. In terms of a pair of miRNA and disease nodes, the pairwise attributes from multiple networks form a multimodal attribute embedding. A module based on depthwise separable convolution is constructed to enhance the encoding of the specific features from each modality. The experimental results and the ablation studies show that CMMDA's superior performance and the effectiveness of its major innovations.

Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.

MOTIVATION: The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph. RESULTS: We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes. AVAILABILITY AND IMPLEMENTATION: Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.

Meroterpenoids from the marine-derived fungus Aspergillus terreus GZU-31-1 exerts anti-liver fibrosis effects by targeting the Nrf2 signaling in vitro.

Six undescribed meroterpenoids aspertermeroterpenes A-F and four known analogues were isolated from the marine-derived fungus Aspergillus terreus GZU-31-1. Their structures were elucidated based on spectroscopic methods and electronic circular dichroism calculations. All meroterpenoids possessed the unique acetyl group at C-11, and also aspertermeroterpene A featured the rare C-14 decarboxylated in DMOA meroterpenoids. In the bioassays, aspermeroterpene B exhibited a potent inhibitory effect on the activation of hepatic stellate cells at the concentration of 5 µM via targeting the Nrf2 signaling. This is the first time reported that aspermeroterpene B as a previously undescribed carbon skeleton of meroterpenoid possessed anti-liver fibrosis effect.

Two new 2-(2-phenylethyl)chromone derivatives and two sesquiterpenes from agarwood of Aquilaria sinensis with anti-inflammatory activity.

Two new 2-(2-phenylethyl)chromones (1-2), two new sesquiterpenes (12-13), and twelve known compounds (3-11, 14-16) were isolated from agarwood of Aquilaria sinensis. These structures were confirmed by HRESIMS, 1D and 2D NMR spectra. The absolute configurations of two new sesquiterpenes were determined by comparing the experimental and calculated ECD spectra. Among them, 7,8-dihydroxy-2-[2-(4'-methoxyphenyl)ethyl]chromone (2) was the first time found that the hydroxyl groups at both C-7/C-8 in agarwood. And Aseudesm B (13), the aldehyded methyl group at C-5 of eucalyptane sesquiterpenes was first discovered in natural products. In the bioassays, all compounds were evaluated for their inhibitory activity against lipopolysaccharide-activated nitric oxide (NO) production in RAW264.7 cells. Compounds 2-5, 7, 9-10, and 13-14 revealed notable inhibitory effects against NO production with IC50 values ranging from 4.0 to 13.0 µM.

Novel 6/7/6 ring system diterpenoids and cytochalasins from the fungus Eutypella scoparia GZU-4-19Y and their anti-inflammatory activity.

Two new compounds eutyditerpenoid A (1) and seco-phenochalasin B (5), together with seven known compounds diaporthein A (2), aspergillon A (3), phenochalasin B (4), cytochalasins Z24 and Z25 (6 and 7), scoparasins A and B (8 and 9) were isolated from marine-derived Eutypella scoparia GZU-4-19Y. Among them, eutyditerpenoid A (1) with a rare 6/7/6 ring system possesing an anhydride moiety was the first example in the pimarane-type diterpenoids. Their structures were determined based on spectroscopic methods and the electronic circular dichroism (ECD) calculations. In the bioassays, all of the isolates were evaluated for their inhibitory activity against NO production induced by lipopolysaccharide in RAW 264.7 cells. Compounds 3 and 7 showed potent NO inhibition activity with IC50 values of 2.1 and 17.1 µM respectively, and the former also significantly suppressed the protein expression of iNOS and COX-2 at the concentration of 2.5 µM.