Feature Fusion for Multi-Coil Compressed MR Image Reconstruction.

Magnetic resonance imaging (MRI) occupies a pivotal position within contemporary diagnostic imaging modalities, offering non-invasive and radiation-free scanning. Despite its significance, MRI's principal limitation is the protracted data acquisition time, which hampers broader practical application. Promising deep learning (DL) methods for undersampled magnetic resonance (MR) image reconstruction outperform the traditional approaches in terms of speed and image quality. However, the intricate inter-coil correlations have been insufficiently addressed, leading to an underexploitation of the rich information inherent in multi-coil acquisitions. In this article, we proposed a method called "Multi-coil Feature Fusion Variation Network" (MFFVN), which introduces an encoder to extract the feature from multi-coil MR image directly and explicitly, followed by a feature fusion operation. Coil reshaping enables the 2D network to achieve satisfactory reconstruction results, while avoiding the introduction of a significant number of parameters and preserving inter-coil information. Compared with VN, MFFVN yields an improvement in the average PSNR and SSIM of the test set, registering enhancements of 0.2622 dB and 0.0021 dB respectively. This uplift can be attributed to the integration of feature extraction and fusion stages into the network's architecture, thereby effectively leveraging and combining the multi-coil information for enhanced image reconstruction quality. The proposed method outperforms the state-of-the-art methods on fastMRI dataset of multi-coil brains under a fourfold acceleration factor without incurring substantial computation overhead.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Segmentation of lung nodules based on a refined segmentation network.

BACKGROUND: Accurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer. PURPOSE: However, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules. As regards the use of deep learning to segment lung nodules, convolutional neural networks would gradually lead to errors accumulating at the network layer due to the presence of multiple upsampling and downsampling layers, resulting in poor segmentation results. METHODS: In this study, we developed a refined segmentation network (RS-Net) for lung nodule segmentation to solve this problem. Accordingly, the proposed RS-Net was first used to locate the core region of the lung nodules and to gradually refine the segmentation results of the core region. In addition, to solve the problem of misdetection of small-sized nodules owing to the imbalance of positive and negative samples, we devised an average dice-loss function computed on nodule level. By calculating the loss of each nodule sample to measure the overall loss, the network can address the misdetection problem of lung nodules with smaller diameters more efficiently. RESULTS: Our method was evaluated based on 1055 lung nodules from Lung Image Database Consortium data and a set of 120 lung nodules collected from Shanghai Chest Hospital for additional validation. The segmentation dice coefficients of RS-Net on these two datasets were 85.90% and 81.13%, respectively. The analysis of the segmentation effect of different properties and sizes of nodules indicates that RS-Net yields a stable segmentation effect. CONCLUSIONS: The results show that the segmentation strategy based on gradual refinement can considerably improve the segmentation of lung nodules.

Noninvasive evaluation of pulmonary artery stiffness in heart failure patients via cardiovascular magnetic resonance.

Heart failure (HF) presents manifestations in both cardiac and vascular abnormalities. Pulmonary hypertension (PH) is prevalent in up 50% of HF patients. While pulmonary arterial hypertension (PAH) is closely associated with pulmonary artery (PA) stiffness, the association of HF caused, post-capillary PH and PA stiffness is unknown. We aimed to assess and compare PA stiffness and blood flow hemodynamics noninvasively across HF entities and control subjects without HF using CMR. We analyzed data of a prospectively conducted study with 74 adults, including 55 patients with HF across the spectrum (20 HF with preserved ejection fraction [HFpEF], 18 HF with mildly-reduced ejection fraction [HFmrEF] and 17 HF with reduced ejection fraction [HFrEF]) as well as 19 control subjects without HF. PA stiffness was defined as reduced vascular compliance, indicated primarily by the relative area change (RAC), altered flow hemodynamics were detected by increased flow velocities, mainly by pulse wave velocity (PWV). Correlations between the variables were explored using correlation and linear regression analysis. PA stiffness was significantly increased in HF patients compared to controls (RAC 30.92 ± 8.47 vs. 50.08 ± 9.08%, p < 0.001). PA blood flow parameters were significantly altered in HF patients (PWV 3.03 ± 0.53 vs. 2.11 ± 0.48, p < 0.001). These results were consistent in all three HF groups (HFrEF, HFmrEF and HFpEF) compared to the control group. Furthermore, PA stiffness was associated with higher NT-proBNP levels and a reduced functional status. PA stiffness can be assessed non-invasively by CMR. PA stiffness is increased in HFrEF, HFmrEF and HFpEF patients when compared to control subjects.Trial registration The study was registered at the German Clinical Trials Register (DRKS, registration number: DRKS00015615).

Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI.

BACKGROUND AND OBJECTIVES: Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI. METHODS: In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05). RESULTS: The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas. CONCLUSIONS: IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.

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.

[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.

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.

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.

Mechanical ventilation characteristics and their prediction performance for the risk of moderate and severe bronchopulmonary dysplasia in infants with gestational age <30 weeks and birth weight <1,500†g.

Introduction: Moderate and severe bronchopulmonary dysplasia (BPD) is a common pulmonary complication in premature infants, which seriously affects their survival rate and quality of life. This study aimed to describe the mechanical ventilation characteristics and evaluate their prediction performance for the risk of moderate and severe BPD in infants with gestational age <30 weeks and birth weight <1,500 g on postnatal Day 14. Methods: In this retrospective cohort study, 412 infants with gestational age <30 weeks and birth weight <1,500 g were included in the analysis, including 104 infants with moderate and severe BPD and 308 infants without moderate and severe BPD (as controls). LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomograms were developed visually using the selected variables. To validate the model, receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used. Results: From the original 28 variables studied, six predictors, namely birth weight, 5 min apgar score, neonatal respiratory distress syndrome (≥Class II), neonatal pneumonia, duration of invasive mechanical ventilation (IMV) and maximum of FiO2 (fraction of inspiration O2) were identified by LASSO regression analysis. The model constructed using these six predictors and a proven risk factor (gestational age) displayed good prediction performance for moderate and severe BPD, with an area under the ROC of 0.917 (sensitivity = 0.897, specificity = 0.797) in the training set and 0.931 (sensitivity = 0.885, specificity = 0.844) in the validation set, and was well calibrated (P Hosmer-Lemeshow test = 0.727 and 0.809 for the training and validation set, respectively). Conclusion: The model included gestational age, birth weight, 5 min apgar score, neonatal respiratory distress syndrome (≥Class II), neonatal pneumonia, duration of IMV and maximum of FiO2 had good prediction performance for predicting moderate and severe BPD in infants with gestational age <30 weeks and birth weight <1,500 g on postnatal Day 14.

A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis.

In the USA, there were about 1 |806 |590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.

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.

Transcriptomic analysis of OsRUS1 overexpression rice lines with rapid and dynamic leaf rolling morphology.

Moderate leaf rolling helps to form the ideotype of rice. In this study, six independent OsRUS1-GFP overexpression (OsRUS1-OX) transgenic rice lines with rapid and dynamic leaf rolling phenotype in response to sunlight were constructed. However, the mechanism is unknown. Here, RNA-Seq approach was utilized to identify differentially expressed genes between flag leaves of OsRUS1-OX and wildtype under sunlight. 2920 genes were differentially expressed between OsRUS1-OX and WT, of which 1660 upregulated and 1260 downregulated. Six of the 16 genes in GO: 0009415 (response to water stimulus) were significantly upregulated in OsRUS1-OX. The differentially expressed genes between WT and OsRUS1-OX were assigned to 110 KEGG pathways. 42 of the 222 genes in KEGG pathway dosa04075 (Plant hormone signal transduction) were differentially expressed between WT and OsRUS1-OX. The identified genes in GO:0009415 and KEGG pathway dosa04075 were good candidates to explain the leaf rolling phenotype of OsRUS1-OX. The expression patterns of the 15 genes identified by RNA-Seq were verified by qRT-PCR. Based on transcriptomic and qRT-PCR analysis, a mechanism for the leaf rolling phenotype of OsRUS1-OX was proposed. The differential expression profiles between WT and OsRUS1-OX established by this study provide important insights into the molecular mechanism behind the leaf rolling phenotype of OsRUS1-OX.

A Fungal Promiscuous UbiA Prenyltransferase Expands the Structural Diversity of Chrodrimanin-Type Meroterpenoids.

Prenyltransferases play important roles in the diversification of natural products and the improvement of biological activities. A UbiA-type prenyltransferase CdnC with substrate promiscuity was identified as the pivotal builder of the noncanonical chrodrimanin skeletons, which carry a benzo-cyclohexanone structure as the nonterpene part. In vitro and heterologous expression studies with CdnC led to the production of a series of novel chrodrimanin-like structures. The discovery of CdnC offers a referable strategy for the biosynthesis and structural diversification of farnesyl-derived meroterpenoids.

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.

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.