Wounding induces a peroxisomal H2 O2 decrease via glycolate oxidase-catalase switch dependent on glutamate receptor-like channel-supported Ca2+ signaling in plants.

Sensing of environmental challenges, such as mechanical injury, by a single plant tissue results in the activation of systemic signaling, which attunes the plant's physiology and morphology for better survival and reproduction. As key signals, both calcium ions (Ca2+ ) and hydrogen peroxide (H2 O2 ) interplay with each other to mediate plant systemic signaling. However, the mechanisms underlying Ca2+ -H2 O2 crosstalk are not fully revealed. Our previous study showed that the interaction between glycolate oxidase and catalase, key enzymes of photorespiration, serves as a molecular switch (GC switch) to dynamically modulate photorespiratory H2 O2 fluctuations via metabolic channeling. In this study, we further demonstrate that local wounding induces a rapid shift of the GC switch to a more interactive state in systemic leaves, resulting in a sharp decrease in peroxisomal H2 O2 levels, in contrast to a simultaneous outburst of the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-derived apoplastic H2 O2 . Moreover, the systemic response of the two processes depends on the transmission of Ca2+ signaling, mediated by glutamate-receptor-like Ca2+ channels 3.3 and 3.6. Mechanistically, by direct binding and/or indirect mediation by some potential biochemical sensors, peroxisomal Ca2+ regulates the GC switch states in situ, leading to changes in H2 O2 levels. Our findings provide new insights into the functions of photorespiratory H2 O2 in plant systemic acclimation and an optimized systemic H2 O2 signaling via spatiotemporal interplay between the GC switch and NADPH oxidases.

Sorbremnoids A and B: NLRP3 Inflammasome Inhibitors Discovered from Spatially Restricted Crosstalk of Biosynthetic Pathways.

Crosstalk-oriented chemical evolution of natural products (NPs) is an efficacious strategy for generating novel skeletons through coupling reactions between NP fragments. In this study, two NOD-like receptor protein 3 (NLRP3) inflammasome inhibitors, sorbremnoids A and B (1 and 2), with unprecedented chemical architectures were identified from a fungus Penicillium citrinum. Compounds 1 and 2 exemplify rare instances of hybrid NPs formed via a major facilitator superfamily (MFS)-like enzyme by coupling reactive intermediates from two separate biosynthetic gene clusters (BGCs), pcisor and pci56. Both sorbremnoids A and B are NLRP3 inflammasome inhibitors. Sorbremnoid A demonstrated strong inhibition of IL-1ß by directly binding to the NLRP3 protein, inhibiting the assembly and activation of the NLRP3 inflammasome in vitro, with potential application in diabetic refractory wound healing through the suppression of excessive inflammatory responses. This research will inspire the development of anti-NLRP3 inflammasome agents as lead treatments and enhance knowledge pertaining to NPs derived from biosynthetic crosstalk.

Neuroprotective effect of a novel brain-derived peptide, HIBDAP, against oxygen-glucose deprivation through inhibition of apoptosis in PC12 cells.

BACKGROUND: The effect of a novel brain-derived peptide, hypoxic-ischemic brain damage associated peptide (HIBDAP), on apoptosis after oxygen-glucose deprivation (OGD) in PC12 cells was investigated. METHODS: The HIBDAP sequence (HSQFIGYPITLFVEKER) was coupled with the carrier peptide of the transactivator of transcription (TAT) sequence (YGRKKRRQRRR). FITC-labelled TAT-HIBDAP was observed by fluorescence microscopy. After TAT-HIBDAP treatment and OGD treatment, the PC12 cell apoptosis rate was analysed using lactate dehydrogenase (LDH) leakage and Annexin V-fluorescein isothiocyanate (FITC) assays. Mitochondrial membrane potential (ΔΨm) was examined by fluorescence microscopy. Protein expression of apoptotic factors was examined by Western blotting. RESULTS: FITC-labelled TAT-HIBDAP entered the PC12 cell nucleus. Compared with the OGD group, TAT-HIBDAP at low concentrations (1 µM, 5 µM, 10 µM) significantly reduced the apoptosis rate of PC12 cells (except at 20 µM); 5 µM TAT-HIBDAP had the most obvious effect. There were remarkable increases in ΔΨm at different concentrations (1 µM, 5 µM, 10 µM, 20 µM) of TAT-HIBDAP pretreatment, and 5 µM TAT-HIBDAP also had the most obvious effect. TAT-HIBDAP reversed the increased ratio of Bax/Bcl-2 and activation of Caspase-3 induced by OGD. CONCLUSION: TAT-HIBDAP is resistant to OGD-induced PC12 cell apoptosis by regulating the Bax/Bcl-2/Caspase-3 pathway, which may provide a novel therapeutic strategy for neonatal HIBD.

Microbial Dimerization and Chlorination of Isoflavones by a Takla Makan Desert-Derived Streptomyces sp. HDN154127.

Sixteen new biisoflavones, bisoflavolins A-N (1-16), were discovered from cultures of the Takla Makan desert-derived strain Streptomyces sp. HDN154127. The chemical structures, including axial chirality, were elucidated by NMR, MS, and ECD analyses. Antibacterial activity of dimerized compounds was tested against seven different bacteria. The dimerized compounds showed better activity (MIC from 0.8 to 50.0 µM) than the corresponding monomers (daidzein and genistein, MIC > 50.0 µM). The rare dimeric and chlorinated structures in 1-16 were proved to be biotransformation products obtained from soy isoflavones and sodium chloride, which constituted the culture medium. This is the first report of an actinomycete that promotes both dimerization and chlorination utilizing natural isoflavones as skeletons sources.

A Novel Deep Learning Model Based on Multi-Scale and Multi-View for Detection of Pulmonary Nodules.

Lung cancer manifests as pulmonary nodules in the early stage. Thus, the early and accurate detection of these nodules is crucial for improving the survival rate of patients. We propose a novel two-stage model for lung nodule detection. In the candidate nodule detection stage, a deep learning model based on 3D context information roughly segments the nodules detects the preprocessed image and obtain candidate nodules. In this model, 3D image blocks are input into the constructed model, and it learns the contextual information between the various slices in the 3D image block. The parameters of our model are equivalent to those of a 2D convolutional neural network (CNN), but the model could effectively learn the 3D context information of the nodules. In the false-positive reduction stage, we propose a multi-scale shared convolutional structure model. Our lung detection model has no significant increase in parameters and computation in both stages of multi-scale and multi-view detection. The proposed model was evaluated by using 888 computed tomography (CT) scans from the LIDC-IDRI dataset and achieved a competition performance metric (CPM) score of 0.957. The average detection sensitivity per scan was 0.971/1.0 FP. Furthermore, an average detection sensitivity of 0.933/1.0 FP per scan was achieved based on data from Shanghai Pulmonary Hospital. Our model exhibited a higher detection sensitivity, a lower false-positive rate, and better generalization than current lung nodule detection methods. The method has fewer parameters and less computational complexity, which provides more possibilities for the clinical application of this method.

Accelerating image reconstruction for multi-contrast MRI based on Y-Net3.

BACKGROUND: As one of the significant preoperative imaging modalities in medical diagnosis, Magnetic resonance imaging (MRI) takes a long scanning time due to its special imaging principle. OBJECTIVE: We propose an innovative MRI reconstruction strategy and data consistency method based on deep learning to reconstruct high-quality brain MRIs from down-sampled data and accelerate the MR imaging process. METHODS: Sixteen healthy subjects undergoing T1-weighted spin-echo (SE) and T2-weighted fast spin-echo (FSE) sequences by a 1.5T MRI scanner were recruited. A Y-Net3+ network was used to facilitate the high-quality MRI reconstruction through context information. In addition, the existing data consistency fidelity method was improved. The difference between the reconstructed K-space and the original K-space was shorten by the linear regression algorithm. Therefore, the redundant artifacts derived from under-sampling were avoided. The Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) were applied to quantitatively evaluate image reconstruction performance of different down-sampling patterns. RESULTS: Compared with the classical Y-Net, Y-Net3+ network improved SSIM and PSNR of MRI images from 0.9164±0.0178 and 33.2216±3.2919 to 0.9387±0.0363 and 35.1785±3.3105, respectively, under compressed sensing reconstruction with acceleration factor of 4. The improved network increases signal-to-noise ratio and adds more image texture information in the reconstructed images. Furthermore, in the process of data consistency, linear regression analysis was used to reduce the difference between the reconstructed K-space and the original K-space, so that the SSIM and PSNR were increased to 0.9808±0.0081 and 40.9254±1.1911, respectively. CONCLUSIONS: The improved Y-Net combined with data consistency fidelity method elucidates its potential in reconstructing high-quality T2-weighted images from the down-sampled data by fully exploring the T1-weighted information. With the advantage of avoiding down-sampled artifacts, the improved network exhibits remarkable clinical promise for fast MRI applications.

[Application of generative adversarial network in magnetic resonance image reconstruction].

Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.

Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast-Enhanced MRI.

BACKGROUND: Imaging-driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large-scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult. PURPOSE: To investigate two transfer learning algorithms for breast cancer molecular subtype prediction (luminal and non-luminal) based on unsupervised pre-training and ensemble learning: M_EL and B_EL, using malignant and benign datasets as the source domain, respectively. STUDY TYPE: Retrospective. POPULATION: Eight hundred and thirty-three female patients with histologically confirmed breast lesions (567 benign and 266 malignant cases) were selected. In the 5-fold cross-validation, the malignant cohort was randomly divided into 5 subsets to form a training set (80%) and a validation set (20%). FIELD STRENGTH/SEQUENCE: 3.0 T, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using T1-weighted high-resolution isotropic volume examination. ASSESSMENT: First, three datasets acquired at different times post-contrast were preprocessed as unlabeled source domains. Second, three baseline networks corresponding to the different MRI post-contrast phases were built, optimized by a combination of mutual information maximization between high- and low-level representations and prior distribution constraints. Next, the pre-trained networks were fine-tuned on the labeled target domain. Finally, prediction results were integrated using weighted voting-based ensemble learning. STATISTICAL TESTS: Mean accuracy, precision, specificity, and area under receiver operating characteristic curve (AUC) were obtained with 5-fold cross-validation. P < 0.05 was considered to be statistically significant. RESULTS: Compared with a convolutional long short-term memory network, pre-trained VGG-16, VGG-19, and DenseNet-121 from ImageNet, M_EL and B_EL exhibited significantly more optimized prediction performance (specificity: 90.5% and 89.9%; accuracy: 82.6% and 81.1%; precision: 91.2% and 90.9%; AUC: 0.836 and 0.823, respectively). DATA CONCLUSION: Transfer learning based on unsupervised pre-training may facilitate automatic prediction of breast cancer molecular subtypes. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Dimeric Tetracenomycin Derivatives from a Taklamakan Desert-Derived Streptomyces sp. HDN154193.

Bitetracenomycin A (1) and its diastereomers [(±)-bitetracenomycin B, (±)-2] were discovered from the cultures of Streptomyces sp. HDN154193. Compounds 1 and (±)-2 were the first tetracenomycin dimers obtained from a natural source with sp3 methine protons at the bridge positions (C-12/12'), which also exhibited broad-spectrum antibacterial activity. The racemate (±)-2 was semisynthesized and separated into enantiomers (+)-2 and (-)-2, and the absolute configurations were determined by specific rotation and ECD data. These metabolites exhibited potent antibacterial activity especially against drug-resistant strains (MRSA and MRCNS) with MIC values ranging from 1.0 to 1.9 µg/mL.

Groundwater antibiotics and microplastics in a drinking-water source area, northern China: Occurrence, spatial distribution, risk assessment, and correlation.

In recent years, antibiotics and microplastics have both received increasing attention. However, the contamination and correlation between the two pollutants in the groundwater of drinking-water source areas has not yet been considered. In this study, eight antibiotics were detected in 81 groundwater samples from a drinking-water source area. These were trimethoprim (TMP), sulfadimidine (SDD), sulfadiazine (SDZ), sulfamethoxazole (SMX), sulfachloropyridazine (SCP), norfloxacin (NOR), ciprofloxacin (CIP) and enrofloxacin (ENRO). Detection rates ranged from 1.23% to 95.06% and the maximum concentration ranged from 0.44 ng/L to 45.40 ng/L. Antibiotics in the groundwater pose no threat to human health, while only ENRO, CIP, NOR, SMX, and SDZ posed medium to low risks to the aquatic ecosystem. In contrast, the detection rate of microplastics was 100% with abundance values ranging from 4 n/L to 72 n/L, with an average of 29 n/L. Microplastic polymers were identified as polyamide, polyethylene, polypropylene, polyvinyl chloride and polystyrene. These also occurred in surface water but the particle sizes in groundwater were lower than those in the surface water. Through correlation analysis, it was found that NOR, ENRO and total antibiotic concentrations were significantly correlated with microplastic abundances. This study revealed the contamination and potential risks of antibiotics and microplastics in the groundwater of a drinking-water source area and found a correlation between them, indicating that risk management of antibiotics and microplastics in groundwater should be highly concerned.

Unusual Tetrahydropyridoindole-Containing Tetrapeptides with Human Nicotinic Acetylcholine Receptors Targeting Activity Discovered from Antarctica-Derived Psychrophilic Pseudogymnoascus sp. HDN17-933.

Chemical investigation of the psychrophilic fungus Pseudogymnoascus sp. HDN17-933 derived from Antarctica led to the discovery of six new tetrapeptides psegymamides A-F (1-6), whose planar structures were elucidated by extensive NMR and MS spectrometric analyses. Structurally, psegymamides D-F (4-6) possess unique backbones bearing a tetrahydropyridoindoles unit, which make them the first examples discovered in naturally occurring peptides. The absolute configurations of structures were unambiguously determined using solid-phase total synthesis assisted by Marfey's method, and all compounds were evaluated for their inhibition of human (h) nicotinic acetylcholine receptor subtypes. Compound 2 showed significant inhibitory activity. A preliminary structure-activity relationship investigation revealed that the tryptophan residue and the C-terminal with methoxy group were important to the inhibitory activity. Further, the high binding affinity of compound 2 to hα4ß2 was explained by molecular docking studies.

The influence of white matter hyperintensities severity on functional brain activity in cerebral small vessel disease: An rs-fMRI study.

OBJECTIVE: To investigate relationships between the severity of white matter hyperintensities (WMH), functional brain activity, and cognition in cerebral small vessel disease (CSVD) based on resting-state functional magnetic resonance imaging (rs-fMRI) data. METHODS: A total of 103 subjects with CSVD were included. The amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC) and their graph properties were applied to explore the influence of WMH burden on functional brain activity. We also investigated whether there are correlations between different functional brain characteristics and cognitive assessments. Finally, we selected disease-related rs-fMRI features in combination with ensemble learning to classify CSVD patients with low WMH load and with high WMH load. RESULTS: The high WMH load group demonstrated significantly abnormal functional brain activity based on rs-MRI data, relative to the low WMH load group. ALFF and graph properties in specific brain regions were significantly correlated with patients' cognitive assessments in CSVD. Moreover, altered rs-fMRI signal can help predict the severity of WMH in CSVD patients with an overall accuracy of 92.23%. CONCLUSIONS: This study provided a comprehensive analysis and evidence for a pattern of altered functional brain activity under different WMH load in CSVD based on rs-fMRI data, enabling accurately individual prediction of status of WMH.

[Segmentation of ground glass pulmonary nodules using full convolution residual network based on atrous spatial pyramid pooling structure and attention mechanism].

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.

A One-Dimensional U-Net-Based Calibration-Transfer Method for Low-Field Nuclear Magnetic Resonance Signals.

The reconstruction of the statistical analysis model of an instrument is a time-consuming and expensive process. Herein, the feasibility of spectral model calibration-transfer application to the same type of low-field nuclear magnetic resonance (LF-NMR) instrument was investigated using a one-dimensional U-net (1D U-net). Unlike conventional calibration-transfer algorithms such as direct standardization (DS), the 1D U-net network can reduce the error between the master and slave instruments through iterative cycles. The calibration-transfer ability was verified; three experiments that entailed the use of edible oil and copper sulfate (CuSO4) samples were implemented. The analysis of the spectral responses and feature analysis of the edible oil samples revealed that the signal of the slave instrument calibrated using the 1D U-net most resembled the signal of the master instrument, and its relative residual value was reduced to 0.0045. Further analysis of the CuSO4 concentration prediction showed that on the support vector regression (SVR) model constructed using the master instrument, the signal of the slave instrument calibrated by the 1D U-net was more similar to the response of the master instrument, and its root mean square error (RMSE) was only 0.01606 mmol/L. Thus, 1D U-net is a viable candidate for calibration-transfer applications to LF-NMR instruments.

Ascandinines A-D, Indole Diterpenoids, from the Sponge-Derived Fungus Aspergillus candidus HDN15-152.

Four new indole diterpenoids, ascandinines A-D (1-4), were isolated from an Antarctic sponge-derived fungus Aspergillus candidus HDN15-152. Their structures, including absolute configurations, were established based on NMR data, computational calculations, and biosynthetic considerations. Ascandinine A (1) possesses an unprecedented 2-oxabicyclo[2.2.2]octan-3-ol motif embedded in a pentacyclic ring system, while compounds 2-4 represent a rare type of indole diterpenoid featuring the 6/5/5/6/6/6/6-fused ring system. Compound 3 displayed anti-influenza virus A (H1N1) activity with an IC50 value of 26 µM, while compound 4 showed cytotoxicity against HL-60 cells with an IC50 value of 7.8 µM.

Variety-Selective Rhizospheric Activation, Uptake, and Subcellular Distribution of Perfluorooctanesulfonate (PFOS) in Lettuce (Lactuca sativa L.).

Perfluorooctanesulfonate (PFOS) as an accumulative emerging persistent organic pollutant in crops poses severe threats to human health. Lettuce varieties that accumulate a lower amount of PFOS (low-accumulating crop variety, LACV) have been identified, but the regarding mechanisms remain unsolved. Here, rhizospheric activation, uptake, translocation, and compartmentalization of PFOS in LACV were investigated in comparison with those of high-accumulating crop variety (HACV) in terms of rhizospheric forms, transporters, and subcellular distributions of PFOS. The enhanced PFOS desorption from the rhizosphere soils by dissolved organic matter from root exudates was observed with weaker effect in LACV than in HACV. PFOS root uptake was controlled by a transporter-mediated passive process in which low activities of aquaporins and rapid-type anion channels were corrected with low expression levels of PIPs (PIP1-1 and PIP2-2) and ALMTs (ALMT10 and ALMT13) genes in LACV roots. Higher PFOS proportions in root cell walls and trophoplasts caused lower root-to-shoot transport in LACV. The ability to cope with PFOS toxicity to shoot cells was poorer in LACV relative to HACV since PFOS proportions were higher in chloroplasts but lower in vacuoles. Our findings provide novel insights into PFOS accumulation in lettuce and further understanding of multiprocess mechanisms of LACV.

Prognostic value of initial chest CT findings for clinical outcomes in patients with COVID-19.

Rationale: To identify whether the initial chest computed tomography (CT) findings of patients with coronavirus disease 2019 (COVID-19) are helpful for predicting the clinical outcome. Methods: A total of 224 patients with laboratory-confirmed COVID-19 who underwent chest CT examination within the first day of admission were enrolled. CT findings, including the pattern and distribution of opacities, the number of lung lobes involved and the chest CT scores of lung involvement, were assessed. Independent predictors of adverse clinical outcomes were determined by multivariate regression analysis. Adverse outcome were defined as the need for mechanical ventilation or death. Results: Of 224 patients, 74 (33%) had adverse outcomes and 150 (67%) had good outcomes. There were higher frequencies of more than four lung zones involved (73% vs 32%), both central and peripheral distribution (57% vs 42%), consolidation (27% vs 17%), and air bronchogram (24% vs 13%) and higher initial chest CT scores (8.6±3.4 vs 5.4±2.1) (P < 0.05 for all) in the patients with poor outcomes. Multivariate analysis demonstrated that more than four lung zones (odds ratio [OR] 3.93; 95% confidence interval [CI]: 1.44 to 12.89), age above 65 (OR 3.65; 95% CI: 1.11 to 10.59), the presence of comorbidity (OR 5.21; 95% CI: 1.64 to 19.22) and dyspnea on admission (OR 3.19; 95% CI: 1.35 to 8.46) were independent predictors of poor outcome. Conclusions: Involvement of more than four lung zones and a higher CT score on the initial chest CT were significantly associated with adverse clinical outcome. Initial chest CT findings may be helpful for predicting clinical outcome in patients with COVID-19.

Rapid screening for hazelnut oil and high-oleic sunflower oil in extra virgin olive oil using low-field nuclear magnetic resonance relaxometry and machine learning.

BACKGROUND: As extra virgin olive oil (EVOO) has high commercial value, it is routinely adulterated with other oils. The present study investigated the feasibility of rapidly identifying adulterated EVOO using low-field nuclear magnetic resonance (LF-NMR) relaxometry and machine learning approaches (decision tree, K-nearest neighbor, linear discriminant analysis, support vector machines and convolutional neural network (CNN)). RESULTS: LF-NMR spectroscopy effectively distinguished pure EVOO from that which was adulterated with hazelnut oil (HO) and high-oleic sunflower oil (HOSO). The applied CNN algorithm had an accuracy of 89.29%, a precision of 81.25% and a recall of 81.25%, and enabled the rapid (2 min) discrimination of pure EVOO that was adulterated with HO and HOSO in the volumetric ratio range of 10-100%. CONCLUSIONS: LF-NMR coupled with the CNN algorithm is a viable candidate for rapid EVOO authentication. © 2020 Society of Chemical Industry.

Analysis of long non-coding RNA expression profiles in neonatal rats with hypoxic-ischemic brain damage.

Hypoxic-ischemic brain damage (HIBD) which is a common cause of acute mortality and neurological dysfunction in neonates still lacks effective therapeutic methods. Long non-coding RNAs (lncRNAs) were demonstrated to play a crucial role in many diseases. To give a foundation for subsequent functional studies of lncRNAs in HIBD, we investigated the profiling of lncRNAs and messenger RNAs (mRNAs) using neonatal HIBD rat model. Six neonatal rats were divided into sham-operated group (n = 3) and HIBD group (n = 3) randomly. Deep RNA sequencing was implemented to find out the meaningful lncRNAs and mRNAs. Quantitative real-time PCR was used to validate expressions of lncRNAs and mRNAs. The Gene Ontology (GO) and kyoto encyclopedia of genes a genomes (KEGG) database were used to predict functions of lncRNAs. A total of 328 differentially expressed lncRNAs (177 down-regulated vs 151 up-regulated) and 7157 differentially expressed mRNAs (2552 down-regulated vs 4605 up-regulated) were identified. The Quantitative real-time PCR results showed significant differential expressions of five lncRNAs and five mRNAs which were consistent with the RNA-Seq data. Gene ontology and KEGG analysis showed these lncRNAs and their expression-correlated mRNAs were closely related to the Janus tyrosine kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, calcium signaling pathway, Notch signaling pathway, mitogen activated protein kinase signaling pathway, neuroactive ligand-receptor interaction pathway and more. The results of our study identified the characterization and expression profiles of lncRNAs in neonatal HIBD and may be a basis for further therapeutic research. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* and *Open Data* because it provided all relevant information to reproduce the study in the manuscript and because it made the data publicly available. The data can be accessed at https://osf.io/yf3da/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Yuanhuatine from Daphne genkwa selectively induces mitochondrial apoptosis in estrogen receptor α-positive breast cancer cells in vitro.

Breast cancer is one of the most common cancers diagnosed among women worldwide. Estrogen receptor alpha (ERα) is a transcriptional factor that plays an important role in the development and progression of breast cancer. Yuanhuatine, a natural daphnane-type diterpenoid extracted from Daphne genkwa, was reported to exhibit significant cytotoxicity against breast cancer cells. However, the underlying mechanism is still unclear. In this study, we evaluated the cytotoxicity of yuanhuatine on two breast cancer cell lines that are ERα-positive and -negative. The results show that yuanhuatine inhibits the growth of ERα-positive cells (MCF-7) with much stronger inhibitory activity (IC50 = 0.62 µM) compared with positive control tamoxifen (IC50 = 14.43 µM). However, no obvious cytotoxicity was observed in ERα-negative cells (MDA-MB-231). Subsequent experiment also indicated that yuanhuatine markedly induced mitochondrial dysfunction, leading to apoptosis in MCF-7 cells. Molecular docking studies suggest the potential interactions between yuanhuatine and ERα. Immunofluorescence staining and Western blot analysis indicated that yuanhuatine down-regulated the expression of ERα in MCF-7 cells. MPP, a specific ERα inhibitor, significantly enhanced yuanhuatine-induced mitochondrial dysfunction and apoptosis in MCF-7 cells. On the contrary, the treatment with yuanhuatine causes no apoptosis in MM231 cells. Altogether, in vitro and in silico results suggested that ERα down-regulation was involved in yuanhuatine-induced mitochondrial dysfunction and apoptosis in ERα-positive breast cancer cells. Thus, yuanhuatine could be a potential candidate for treating ERα-positive breast cancer.