Three stilbenes from pigeon pea with promising anti-methicillin-resistant Staphylococcus aureus biofilm formation activity.

Cajaninstilbene acid (CSA), longistylin A (LLA), and longistylin C (LLC) are three characteristic stilbenes isolated from pigeon pea. The objective of this study was to evaluate the antibacterial activity of these stilbenes against Staphylococcus aureus and even methicillin-resistant Staphylococcus aureus (MRSA) and test the possibility of inhibiting biofilm formation. The minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of these stilbenes were evaluated. And the results showed that LLA was most effective against tested strains with MIC and MBC values of 1.56 µg/mL followed by LLC with MIC and MBC values of 3.12 µg/mL and 6.25 µg/mL as well as CSA with MIC and MBC values of 6.25 µg/mL and 6.25-12.5 µg/mL. Through growth curve and cytotoxicity analysis, the concentrations of these stilbenes were determined to be set at their respective 1/4 MIC in the follow-up research. In an anti-biofilm formation assay, these stilbenes were found to be effectively inhibited bacterial proliferation, biofilm formation, and key gene expressions related to the adhesion and virulence of MRSA. It is the first time that the anti-S. aureus and MRSA activities of the three stilbenes have been systematically reported. Conclusively, these findings provide insight into the anti-MRSA mechanism of stilbenes from pigeon pea, indicating these compounds may be used as antimicrobial agents or additives for food with health functions, and contribute to the development as well as application of pigeon pea in food science.

Three new glycosides from the stems of Eurya chinensis R. Br.

Two new phenolic glycosides (1 and 2), one known analogue (3), along with a new diterpene glucoside (4) were obtained from ethanolic extract of the stems of Eurya chinensis R. Br. The structures of these isolated compounds were identified by extensive analysis of HRESIMS and NMR spectroscopic data. The cytotoxicities of these compounds were evaluated on MCF-7, A549, HepG2, CaCo2 and 5-8 F cell lines by MTT method, but no obvious activities were observed.

YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

Four new stilbenes and one new flavonoid with potential antibacterial and anti-SARS-CoV-2 activity from Cajanus cajan.

Four new stilbenes (1-4) and one new flavonoid (5), named cajanines D-H, together with three known stilbenes (6-8) were isolated from the leaves of Cajanus cajan (L.) Millsp. (pigeon pea). The structures of these compounds were elucidated unambiguously on the basis of IR, 1D, and 2D NMR, as well as HRESIMS data. Structurally, stilbenes 1-4 bore an isopentyl side chain, and further hydroxylation of compounds 1-3 generated a greater variety of structural forms. Compound 5 was a flavonoid owning an isopentyl side chain. Besides, antibacterial activity of the isolated compounds against Staphylococcus aureus, Bacillus cereus, and Escherichia coli was studied in vitro. Compounds 1-8 were endowed with profound antibacterial activity. Among them, the MIC values of compounds 4, 6, and 7 against S. aureus were 1.56, 0.78, and 0.78 µg/mL, respectively, among which 6 and 7 had better antibacterial activity than the positive control Vancomycin with the MIC values of 1.56 µg/mL. Additionally, the anti-SARS-CoV-2 main protease activity of all the isolated compounds was evaluated, and it was worth mentioning that the IC50 values of compounds 5-7 were 8.27, 4.65, and 8.30 µM, respectively, being comparable to the positive control Ebselen. Our findings may provide valuable guidance for the application of stilbenes as lead compounds in the future for the development of drugs with antibacterial or anti-COVID-19 activity.

The sesquiterpenes with the COVID-19 Mpro inhibitory activity from the Carpesium abrotanoides L.

The extract of the whole plant of Carpesium abrotanoides L. yielded four new sesquiterpenes including a novel skeleton (claroguaiane A, 1), two guaianolides (claroguaianes B-C, 2-3), and one eudesmanolide (claroeudesmane A, 4), together with three known sesquiterpenoids (5-7). The structures of the new compounds were elucidated by spectroscopic analysis especially 1D and 2D NMR spectroscopy and HRESIMS data. Additionally, all the isolated compounds were preliminarily evaluated for the inhibitive activity of COVID-19 Mpro. As a result, compound 5 showed moderate activity with an IC50 value of 36.81 µM and compound 6 exhibited a potent inhibitory effect with an IC50 value of 16.58 µM, while other compounds were devoid of noticeable activity (IC50 > 50 µM).

Versatile Biocatalytic C(sp3 )-H Oxyfunctionalization for the Site- Selective and Stereodivergent Synthesis of α- and ß-Hydroxy Acids.

C(sp3 )-H oxyfunctionalization, the insertion of an O-atom into C(sp3 )-H bonds, streamlines the synthesis of complex molecules from easily accessible precursors and represents one of the most challenging tasks in organic chemistry with regard to site and stereoselectivity. Biocatalytic C(sp3 )-H oxyfunctionalization has the potential to overcome limitations inherent to small-molecule-mediated approaches by delivering catalyst-controlled selectivity. Through enzyme repurposing and activity profiling of natural variants, we have developed a subfamily of α-ketoglutarate-dependent iron dioxygenases that catalyze the site- and stereodivergent oxyfunctionalization of secondary and tertiary C(sp3 )-H bonds, providing concise synthetic routes towards four types of 92 α- and ß-hydroxy acids with high efficiency and selectivity. This method provides a biocatalytic approach for the production of valuable but synthetically challenging chiral hydroxy acid building blocks.

Four new eudesmane-type and one new eremophilane-type sesquiterpenes from the whole plant of Carpesium abrotanoides L.

The extract of the whole plant of Carpesium abrotanoides L. yielded five new sesquiterpenes including four eudesmanes (1-4) and one eremophilane (5). The new compounds were characterized by spectroscopic analysis especially 1D and 2D NMR spectroscopy and HRESIMS data. Structurally, both compounds 1 and 2 were sesquiterpene epoxides and 2 owned an epoxy group at C-4/C-15 position to form a spiro skeleton. Compounds 4 and 5 were two sesquiterpenes without lactones and 5 possessed a carboxy group in the molecule. Additionally, all the isolated compounds were preliminarily evaluated for the inhibitory activity against SARS-CoV-2 main protease. As a result, compound 2 showed moderate activity with an IC50 value of 18.79 µM, while other compounds were devoid of noticeable activity (IC50 > 50 µM).

Euryachincoside, a Novel Phenolic Glycoside with Anti-Hepatic Fibrosis Activity from Eurya chinensis.

Eurya chinensis has been recorded as a folk medicine traditionally used for treatment of a variety of symptoms. However, the phytochemical and pharmacological investigations of this plant are still scarce. A novel phenolic glycoside named Euryachincoside (ECS) was isolated by chromatographic separation from E. chinensis, and its chemical structure was identified by analysis of HRMS and NMR data. Its anti-hepatic fibrosis effects were evaluated in both HSC-T6 (rat hepatic stellate cells) and carbon tetrachloride (CCl4)-induced mice with Silybin (SLB) as the positive control. In an in vitro study, ECS showed little cytotoxicity and inhibited transforming growth factor-beta (TGF-ß)-induced Collagen I (Col1) along with alpha-smooth muscle actin (α-SMA) expressions in HSC-T6. An in vivo study suggested ECS significantly ameliorated hepatic injury, secretions of inflammatory cytokines, and collagen depositions. Moreover, ECS markedly mediated Smad2/3, nuclear factor kappa B (NF-κB) and nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathways both in vitro and vivo. These present findings confirmed that ECS is a novel phenolic glycoside from E. chinensis with promising curative effects on hepatic fibrosis, and its mechanisms may include decreasing extracellular matrix accumulation, reducing inflammation and attenuating free radicals via Smad2/3, NF-κB and Nrf2 signaling pathways, which may shed light on the exploration of more effective phenolic glycoside-based anti-fibrotic agents.

Two new withanolides from the whole plants of Physalis peruviana.

Two new withaphysalin-type withanolides (18-O-ethylwithaphysalin R and 5-O-ethylphysaminimin C, 1 and 2), along with twelve known withanolides (3-14), were purified and identified from Physalis peruviana L. The chemical structures of these new isolates were elucidated through analyzing spectroscopic and HRESIMS data. All the obtained metabolites were appraised for their potential antiproliferative activity against the human breast cancer cell line MCF-7. Compound 7 was discovered to exhibit potent activity with an IC50 value of 3.51 µM and compounds 2, 6 and 14 showed weak cytotoxic effect.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Peruranolides A-D, four new withanolides with potential antibacterial and cytotoxic activity from Physalis peruviana L.

BACKGROUND: Many drugs for anti-tumour have been developed, nevertheless, seeking new anticancer drug is the focus of ongoing investigation. Withanolides have been reported to possess potent antiproliferative activity. Literature findings revealed that a diversity of withanolides were obtained from Physalis peruviana, however, the antitumor activity of these bioactive compounds is still unclear. METHODS: The EtOAc fraction of P. peruviana were decolorized on Middle Chromatogram Isolated (MCI) Gel column, repeatedly subjected to column chromatography (CC) over sephadex LH-20, preparative High Performance Liquid Chromatography (HPLC) and silica gel to afford compounds. Their chemical structures of the new isolates were elucidated through analyzing spectroscopic and HRESIMS data. All these obtained metabolites were appraised for their potential antiproliferative activity against the human breast cancer cell line MCF-7 by MTT assay, and in vitro antibacterial activity of the isolated compounds (1-7) were evaluated against E. coli, B. cereus and S. aureus. Results: Four new withanolides, including one withaphysalin-type withanolide (peruranolide A, 1), two 13,14-seco-withaphysalins (peruranolides B-C, 2-3), as well as one normal withanolide (peruranolide D, 4), were purified and separated from P. peruviana L.. Compound 5 was discovered to exhibit potent cytotoxic effect with an IC50 value of 3.51 µM. In vitro antibacterial activities, compounds 1-7 had no obvious inhibitory activity against E. coli, but had moderate inhibitory activities against B. cereus and S. aureus. CONCLUSIONS: Our findings might offer valuable clues for the utilization of withanolides as lead compounds for antineoplastic or antibacterial drug development.

Two new 7,20-epoxy-ent-kaurane diterpenoids from the aerial parts of Isodon serra.

Two new compounds (1 and 2), belonging to C-20 oxygenated ent-kauranes-type diterpenoids, were identified from the aerial parts of Isodon serra. Their structures were elucidated by extensive analysis of HRESI-MS and NMR spectroscopic data. Both these two compounds possess a common 7,20-epoxy-ent-kauranes skeleton with a hydroxyl group rarely occurring at C-13. Compounds 1 and 2 were evaluated for their cytotoxic activity against Hela-60 and HepG2 as well as the antibacterial activity against Staphylococcus aureus, Bacillus cereus and Escherichia coli.

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.

Background. Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. Objective. In this work, we aimed to investigate whether machine learning techniques, when applied to a combination of baseline whole brain volumes and clinical data, can accurately predict individual motor outcome after stroke. Methods. Upper extremity Fugl-Meyer Assessments (FMA-UE) were conducted 1 week and 12 weeks, and structural MRI was performed 1 week, after onset in 56 patients with subcortical infarction. Proportional recovery model residuals were employed to assign patients to proportional and poor recovery groups (34 vs 22). A sophisticated machine learning scheme, consisting of conditional infomax feature extraction, synthetic minority over-sampling technique for nominal and continuous, and bagging classification, was employed to predict motor outcomes, with the input features being a combination of baseline whole brain volumes and clinical data (FMA-UE scores). Results. The proposed machine learning scheme yielded an overall balanced accuracy of 87.71% in predicting proportional vs poor recovery outcomes, a sensitivity of 93.77% in correctly identifying poor recovery outcomes, and a ROC AUC of 89.74%. Compared with only using clinical data, adding whole brain volumes can significantly improve the classification performance, especially in terms of the overall balanced accuracy (from 80.88% to 87.71%) and the sensitivity (from 92.23% to 93.77%). Conclusions. Experimental results suggest that a combination of baseline whole brain volumes and clinical data, when equipped with appropriate machine learning techniques, may provide valuable information for personalized rehabilitation planning after subcortical infarction.

A Hybrid Learning Pipeline for Automated Diagnosis of First-Episode Schizophrenia Utilizing T1-weighted Images.

In this work, we proposed and validated a hybrid learning pipeline for automated diagnosis of first-episode schizophrenia (FES) utilizing T1-weighted images. Amygdalar and hippocampal shape abnormalities in FES have been observed in previous studies. In this work, we jointly made use of two types of features, together with advanced machine learning techniques, for an automated discrimination of FES and healthy control (96 versus 102). Specifically, we first employed a ResNet34 model to extract convolutional neural network (CNN) features. We then combined these CNN features with shape features of the bilateral hippocampi and the bilateral amygdalas, before being inputted to advanced classification algorithms such as the Gradient Boosting Decision Tree (GBDT) for classifying between FES and healthy control. Shape features were represented using log Jacobian determinants, through a well-established statistical shape analysis pipeline. When combining CNN with hippocampal shape, the best results came from utilizing GBDT as the classifier, with an overall accuracy of 75.15%, a sensitivity of 69.35%, a specificity of 80.19%, an F1 of 72.16%, and an AUC of 79.68%. When combing CNN and amygdalar shape, the best results came from utilizing Bagging as the classifier, with an overall accuracy of 74.39%, a sensitivity of 67.93%, a specificity of 80%, an F1 of 71.11%, and an AUC of 80.98%. Compared with using each single set of features, either CNN or shape, significant improvements have been observed, in terms of FES discrimination. To the best of our knowledge, this is the first work that has tried to combine CNN features and hippocampal/amygdalar shape features for automated FES identification.

Multi-task Learning Based Ocular Disease Discrimination and FAZ Segmentation Utilizing OCTA Images.

In this paper, we proposed and validated a multi-task based deep learning method for simultaneously segmenting the foveal avascular zone (FAZ) and classifying three ocular disease related states (normal, diabetic, and myopia) utilizing optical coherence tomography angiography (OCTA) images. The essential motivation of this work is that reliable predictions on disease states may be made based on features extracted from a segmentation network, by sharing a same encoder between the classification network and the segmentation network. In this study, a cotraining network structure was designed for simultaneous ocular disease discrimination and FAZ segmentation. Specifically, we made use of a classification head following a segmentation network's encoder, so that the classification branch used the feature information extracted in the segmentation branch to improve the classification results. The performance of our proposed network structure has been tested and validated on the FAZID dataset, with the best Dice and Jaccard being 0.9031±0.0772 and 0.8302 ±0.0990 for FAZ segmentation, and the best Accuracy and Kappa being 0.7533 and 0.6282 for classifying three ocular disease related states.Clinical Relevance- This work provides a useful tool for segmenting FAZ and discriminating three ocular disease related states utilizing OCTA images, which has a great clinical potential in ocular disease screening and biomarker delivering.

Three new carabrane sesquiterpenoid derivatives from the whole plant of Carpesium abrotanoides L.

Dicarabrols B and C (1 and 2), two new carabrane sesquiterpenoid dimers, along with one new carabrane sesquiterpenoid (3) were isolated from the whole plant of Carpesium abrotanoides L. Their full structures were established by extensive analysis of HR-ESI-MS and NMR spectroscopic data, and time-dependent density functional theory (TDDFT) electronic circular dichroism (ECD) calculations. Dicarabrol B possesses a novel C30 skeleton featuring a methylene-tethered bridge between two sesquiterpene moieties, while dicarabrol C presents the unique linkage of a cyclopentane ring in the molecule. Dicarabrol C exhibited potent inhibitory effects on HL-60 cells with an IC50 value of 3.7 µmol·L-1.

A Review on the Phytochemical and Pharmacological Properties of Rosa laevigata: A Medicinal and Edible Plant.

Rosa laevigata Michx., a medicinal and edible plant in China, has exerted a variety of medicinal values and health benefits. This present review aims to achieve a comprehensive and up-to-date investigation in the phytochemistry and pharmacology of R. laevigata. According to these findings in the literature, approximately 123 chemical ingredients covering triterpenoids, flavonoids, tannis, lignans and polysaccharides, have been characterized from various parts of this species. Among these isolates, 77 triterpenoids have been isolated and thus regarded as the primary and characteristic substance. Based on the chemical structures, most of the obtained triterpenoids can be classified into polyhydroxy triterpenoids and readily divided into four categories: ursane-type, oleanane-type, lupinane-type, as well as seco-triterpenoids. The crude extracts and the purified compounds have demonstrated various pharmacological effects in vitro and in vivo, such as antioxidant activity, immunomodulatory effect, anti-inflammatory effect, liver protection, kidney protection, cardiovascular protection, neuroprotective effect and improvement of diabetic cataract. Noticeably, these pharmacological results of R. laevigata provide evidences for its traditional uses. In addition, these different chemical ingredients existing in the title plant may have synergistic effects. In conclusion, the chemical profiles, including ingredients and structures, together with the modern pharmacological properties have been adequately summarized. These evidences have revealed this plant to be a valuable source for therapeutic foodstuff and more attention should be paid to a better utilization of this plant.

Rosanortriterpenes A-B, two new nortriterpenes from the fruits of Rosa laevigata var. leiocapus.

One new oleanane-type nortriterpene, rosanortriterpene A (1), and one new ursane-type nortriterpene, rosanortriterpene B (2), were isolated from the fruits of Rosa laevigata var. leiocapus. The structures of 1-2 were fully characterised on the basis of extensive spectroscopic analysis, including IR, HRESIMS, as well as 1D and 2D NMR spectral data (HSQC, 1H-1H COSY, and HMBC). To the best of our knowledge, this represents the first study on the chemical constituents of R. laevigata var. leiocapus. Compounds 1-2 exhibited moderate inhibitory effects on NO production in LPS stimulated RAW264.7 cells with IC50 values of 29.29 ± 3.64 and 14.28 ± 1.20 µM, respectively.

Brain Tumor Segmentation From Multi-Modal MR Images via Ensembling UNets.

Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery planning and progression evaluation. Based on different biological properties, the glioma can be divided into three partially-overlapping regions of interest, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET). Recently, UNet has identified its effectiveness in automatically segmenting brain tumor from multi-modal magnetic resonance (MR) images. In this work, instead of network architecture, we focus on making use of prior knowledge (brain parcellation), training and testing strategy (joint 3D+2D), ensemble and post-processing to improve the brain tumor segmentation performance. We explore the accuracy of three UNets with different inputs, and then ensemble the corresponding three outputs, followed by post-processing to achieve the final segmentation. Similar to most existing works, the first UNet uses 3D patches of multi-modal MR images as the input. The second UNet uses brain parcellation as an additional input. And the third UNet is inputted by 2D slices of multi-modal MR images, brain parcellation, and probability maps of WT, TC, and ET obtained from the second UNet. Then, we sequentially unify the WT segmentation from the third UNet and the fused TC and ET segmentation from the first and the second UNets as the complete tumor segmentation. Finally, we adopt a post-processing strategy by labeling small ET as non-enhancing tumor to correct some false-positive ET segmentation. On one publicly-available challenge validation dataset (BraTS2018), the proposed segmentation pipeline yielded average Dice scores of 91.03/86.44/80.58% and average 95% Hausdorff distances of 3.76/6.73/2.51 mm for WT/TC/ET, exhibiting superior segmentation performance over other state-of-the-art methods. We then evaluated the proposed method on the BraTS2020 training data through five-fold cross validation, with similar performance having also been observed. The proposed method was finally evaluated on 10 in-house data, the effectiveness of which has been established qualitatively by professional radiologists.