Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.

Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch's Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.

Chang-Kang-Fang alleviates diarrhea predominant irritable bowel syndrome (IBS-D) through inhibiting TLR4/NF-κB/NLRP3 pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Chang-Kang-Fang (CKF), originated from traditional Chinese medicine (TCM) formulas, has been utilized to treat diarrhea predominant irritable bowel syndrome (IBS-D) based on clinical experience. However, the underlying mechanism of CKF for treating IBS-D remains unclear and need further clarification. AIM OF THE STUDY: The objective of this present investigation was to validate the efficacy of CKF on IBS-D model rats and to uncover its potential mechanism for the treatment of IBS-D. MATERIALS AND METHODS: We first established the IBS-D rat model through neonatal maternal separation (NMS) in combination with restraint stress (RS) and the administration of senna decoction via gavage. To confirm the therapeutic effect of CKF on treating IBS-D, abdominal withdrawal reflex (AWR) scores, the quantity of fecal pellets, and the fecal water content (FWC) were measured to evaluate the influence of CKF on visceral hypersensitivity and the severity of diarrhea symptom after the intragastric administration of CKF for 14 days. Subsequently, enzyme linked immunosorbent assay (ELISA) was applied to assess the effect of CKF on neuropeptides substance P (SP) and 5-hydroxytryptamine (5-HT), as well as inflammatory cytokines in serum and in intestinal tissues. Further, colonic pathological changes, the amount of colonic mast cells, and the expression level of occludin in rat colon tissues, were investigated by hematoxylin-eosin (HE) staining, toluidine blue staining, and immunohistochemistry, respectively. To explore the underlying mechanisms, alterations in colonic RNA transcriptomics for the normal, model, and CKF treatment groups were assessed using RNA sequencing (RNA-Seq). Subsequently, quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB), and immunofluorescence (IF) assays were applied to validate the effect of CKF on predicted pathways in vivo and in vitro. In addition, to elucidate the potential active compounds in CKF, 11 representative components found in CKF were selected, and their anti-inflammation potentials were evaluated using LPS-treated RAW264.7 cell models. RESULTS: CKF treatment significantly reduced the number of fecal pellets, attenuated visceral hypersensitivity, and decreased 5-HT and SP concentrations in serum and colon tissues, along with a reduction in colonic mast cell counts, correlating with improved symptoms in IBS-D rats. Meanwhile, CKF treatment reduced the colonic inflammatory cell infiltration, lowered the levels of IL-6, TNF-α, and IL-1ß in serum and colon tissues, and increased the occludin protein expression in colon tissues to improve inflammatory response and colonic barrier function. RNA-Seq, in conjugation with our previous network pharmacology analysis, indicated that CKF might mitigate the symptoms of IBS-D rats by inhibiting the Toll like receptor 4/Nuclear factor kappa-B/NLR family pyrin domain-containing protein 3 (TLR4/NF-κB/NLRP3) pathway, which was confirmed by WB, IF, and qRT-PCR experiments in vivo and in vitro. Furthermore, coptisine, berberine, hyperoside, epicatechin, and gallic acid present in CKF emerged as potential active components for treating IBS-D, as they demonstrated in vitro anti-inflammatory effects. CONCLUSION: Our findings demonstrate that CKF effectively improves the symptoms of IBS-D rats, potentially through the inhibition of the TLR4/NF-κB/NLRP3 pathway. Moreover, this study unveils the potential bioactive components in CKF that could be applied in the treatment of IBS-D.

Continuous ultrasonic ozone coupling technology-assisted control of ceramic membrane fouling coupled enhanced multiphase mixing to treat dye wastewater and CFD flow field simulation.

In this study, ozone catalysts (hydrogenation-modified red mud, HM-RM) successfully prepared by hydrogenation-modification of industrial hazardous solid waste red mud (RM) as a raw material in accordance with the viewpoint of treating waste with waste and using waste. Meanwhile, as for the common phenomenon of membrane fouling, uneven distribution of multiphase solid catalysts and ozone in liquids, the addition of ultrasound can not only disperse materials, but also play a role in online cleaning of ceramic membranes and catalysts. The optimum treatment conditions for Rhodamine B (RhB) solution with volume of 2 L and concentration of 40 mg/L were catalyst concentration of 0.4 mg/L, reaction temperature of 45 °C, ultrasonic time of 1 h, ultrasonic intensity of 600 W, removal rate of RhB was up to 90 %. In addition, the computational fluid dynamics (CFD) simulation method was used to investigate the fluid flow between the two gas-liquid phases and the effect of the negative pressure of the membrane pump on the fluid by the analysis of flow, pressure and ozone flux of the ceramic membrane(CM) reaction apparatus. The CFD simulation results showed that at the inlet gas-liquid flow rate of 3 m/s and the negative pressure of 20,000 Pa, the maximum flow rates of CM-1 were 3 m/s, 0.752 m/s for CM-2, and 0.228 m/s for CM-3, respectively. Vortices, which are beneficial to solid-liquid mixing and gas-liquid mass transfer, formed between the suction port CM-1 of CM-1 and the inlets of CM-2 and CM-3. This discovery is consistent with relevant experimental research results. Significantly higher concentrations of both •OH and dissolved ozone were observed in the US/HM-RM/O3 system compared to other systems, indicating the significant improvement in ozone utilization rate through the application of ultrasound. The superiority of the US/HM-RM/O3 device was demonstrated. The real dye effluent was tested under optimum operating conditions and the results showed that COD and TOC were reduced by 81.34 % and 60.23 % respectively after 180 min of treatment. The above research can provide technical support for the treatment of dye wastewater using Ultrasound-enhanced ozone oxidation ceramic membranes.

Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network.

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.

An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks.

Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice, enabling non-experts to assess heart shunt lesions. This study aims to develop a fully automated and scalable analysis pipeline for distinguishing heart shunts, utilizing a deep neural network-based framework. The pipeline consists of three steps: (1) chamber segmentation, (2) ultrasound microbubble localization, and (3) disease classification model establishment. The study's normal control group included 91 patients with intracardiac shunts, 61 patients with extracardiac shunts, and 84 asymptomatic individuals. Participants' SC-TTE images were segmented using the U-Net model to obtain cardiac chambers. The segmentation results were combined with ultrasound microbubble localization to generate multivariate time series data on microbubble counts in each chamber. A classification model was then trained using this data to distinguish between intracardiac and extracardiac shunts. The proposed framework accurately segmented heart chambers (dice coefficient = 0.92 ± 0.1) and localized microbubbles. The disease classification model achieved high accuracy, sensitivity, specificity, F1 score, kappa value, and AUC value for both intracardiac and extracardiac shunts. For intracardiac shunts, accuracy was 0.875 ± 0.008, sensitivity was 0.891 ± 0.002, specificity was 0.865 ± 0.012, F1 score was 0.836 ± 0.011, kappa value was 0.735 ± 0.017, and AUC value was 0.942 ± 0.014. For extracardiac shunts, accuracy was 0.902 ± 0.007, sensitivity was 0.763 ± 0.014, specificity was 0.966 ± 0.008, F1 score was 0.830 ± 0.012, kappa value was 0.762 ± 0.017, and AUC value was 0.916 ± 0.006. The proposed framework utilizing deep neural networks offers a fast, convenient, and accurate method for identifying intracardiac and extracardiac shunts. It aids in shunt recognition and generates valuable quantitative indices, assisting clinicians in diagnosing these conditions.

Semi-supervised contrast learning-based segmentation of choroidal vessel in optical coherence tomography images.

Objective.Choroidal vessels account for 85% of all blood vessels in the eye, and the accurate segmentation of choroidal vessels from optical coherence tomography (OCT) images provides important support for the quantitative analysis of choroid-related diseases and the development of treatment plans. Although deep learning-based methods have great potential for segmentation, these methods rely on large amounts of well-labeled data, and the data collection process is both time-consuming and laborious.Approach.In this paper, we propose a novel asymmetric semi-supervised segmentation framework called SSCR, based on a student-teacher model, to segment choroidal vessels in OCT images. The proposed framework enhances the segmentation results with uncertainty-aware self-integration and transformation consistency techniques. Meanwhile, we designed an asymmetric encoder-decoder network called Pyramid Pooling SegFormer (APP-SFR) for choroidal vascular segmentation. The network combines local attention and global attention information to improve the model's ability to learn complex vascular features. Additionally, we proposed a boundary repair module that enhances boundary confidence by utilizing a repair head to re-predict selected fuzzy points and further refines the segmentation boundary.Main results.We conducted extensive experiments on three different datasets: the ChorVessel dataset with 400 OCT images, the Meibomian Glands (MG) dataset with 400 images, and the U2OS Cell Nucleus Dataset with 200 images. The proposed method achieved an average Dice score of 74.23% on the ChorVessel dataset, which is 2.95% higher than the fully supervised network (U-Net) and outperformed other comparison methods. In both the MG dataset and the U2OS cell nucleus dataset, our proposed SSCR method achieved average Dice scores of 80.10% and 87.26%, respectively.Significance.The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art methods. The method is designed to help clinicians make rapid diagnoses of ophthalmic diseases and has potential for clinical application.

Preparation of fish collagen and vancomycin microspheres based on microfluidic technology and its application in osteomyelitis.

At present, the clinical treatment of osteomyelitis and osteomyelitis-induced bone defects is challenging, easy to recur, drug toxic side effects, secondary or multiple surgeries, etc. The design of biodegradable composite biomaterials to improve antibiotics in the local precise anti-infection at the same time to complete the repair of bone defects is the current research hot spot. Herein, a composite hydrogel with a double bond at the end (FA-MA) was prepared by affinity addition reaction between fish collagen (FA) and methacrylic anhydride (MA) under photoinitiator initiation conditions, then, FA-MA was amino-activated by EDC/NHC, and vancomycin was attached to FA-MA via amide bonding to prepare FA-MA-Van hydrogels, and finally, the composite hydrogel microspheres were prepared by microfluidic technology. The structure of the hydrogel was confirmed by SEM (elemental analysis), optical microscopy, FTIR, and XPS to confirm the successful preparation. The composite hydrogel microspheres showed the better antimicrobial effect of hydrogel microspheres by bacterial coated plate experiments and SEM morphology results, with the antimicrobial class reaching 99.8%. The results of immunofluorescence staining and X-ray experiments showed that the hydrogel microspheres had a better effect on promoting bone repair. This engineered design of hydrogel microspheres provides clinical significance for treating osteomyelitis at a later stage.

A phase 1 study of dimdazenil to evaluate the pharmacokinetics, food effect and safety in Chinese healthy subjects.

Background and objective: As a partial positive allosteric modulator of the gamma-aminobutyric acid A (GABAA) receptor, dimdazenil was used for the treatment of insomnia with the potential to alleviate associated side effects compared to full agonists. The objective of this trial is to assess the safety, tolerability, food effect and pharmacokinetics following single and multiple doses of dimdazenil in Chinese healthy subjects. Methods: In this phase 1 trial, 36 healthy subjects aged ≥18 years were assigned to receive a single dose of 1.5, 2.5, or 5 mg dimdazenil, with each dose cohort consisting of 12 subjects, and 14 subjects were assigned to receive a multiple 2.5 mg daily dose of dimdazenil for 5 days. Safety, tolerability, and pharmacokinetic characteristics were evaluated. Results: Of the 50 subjects enrolled and 49 completed the trial, the incidences of treatment-emergent adverse events (AEs) in the single-dose groups of 1.5, 2.5, and 5 mg were 16.7%, 58.3% and 66.7% respectively, while 61.5% in the multiple-dose group. There were no serious AEs, deaths, AEs leading to discontinuation or AEs of requiring clinical intervention in any treatment groups. The most treatment-emergent AEs were dizziness (n = 4, 8.2%), hyperuricemia (n = 2, 6.1%), upper respiratory tract infection (n = 2, 6.1%), diastolic blood pressure decreased (n = 2, 6.1%), blood TG increased (n = 2, 6.1%) and RBC urine positive (n = 2, 6.1%). All AEs were mild-to-moderate and transient, and no severe AEs were documented in any study phase. The PK profile of dimdazenil and its active metabolite Ro46-1927 was linear across 1.5-5 mg oral doses in humans. The median Tmax for dimdazenil was in the range of 0.5-1.5 h, and the apparent terminal t1/2z ranged from 3.50 to 4.32 h. Taking Dimdazenil with food may delay Tmax and decrease Cmax, without affecting the total exposure (AUC). No relevant accumulations of dimdazenil and Ro 46-1927 were observed in multiple-dose group. Conclusion: Dimdazenil was generally well tolerated in healthy Chinese subjects after single and 5 days-multiple dosing. The pharmacokinetic properties of dimdazenil are compatible with a drug for the treatment of insomnia. Clinical Trial Registration: chinadrugtrials.org.cn, identifier CTR20201978.

Novel citrinin derivatives from fungus Penicillium sp. TW131-64 and their antimicrobial activities.

Six new citrinin derivatives (1, 2, 4, 10, 11, and 16), along with fourteen known analogues, were acquired from Penicillium sp. TW131-64, a marine-derived fungus strain. The chemical structures of new compounds were identified through adopting various spectroscopic methods in combination with X-ray diffraction technology and comparison of the experimental electronic circular dichroism (ECD) with calculated ones. Among them, compounds 1-4 were nitrogen-containing citrinin derivatives existing in enantiomers which were resolved by chiral chromatography. A putative biosynthetic pathway for compounds 1-4 was proposed. Additionally, the antimicrobial activities of these compounds were detected by the broth microdilution assays. Citrinin derivatives 1, 2, 4 and their corresponding enantiomers (1a, 2a, 4a, 1b, 2b, and 4b) exhibited potent antimicrobial activities towards Helicobacter pylori standard strains and multidrug-resistant strains (MIC values ranging from 0.25 to 8 µg/mL), which were comparable or even better than metronidazole. Moreover, compounds 1a and 1b also showed remarkable broad antimicrobial effects towards Staphylococcus aureus, Enterococcus faecalis, methicillin-resistant Staphylococcus aureus (MRSA), Bacillus subtilis, vancomycin-resistant Enterococcus faecium (VRE), and Candida albicans. In summary, our studies demonstrated that citrinin enantiomers 1a-4a and 1b-4b, especially 1a and 1b, can be lead compounds in the research and development (R & D) of novel antimicrobial drugs. KEY POINTS: • 3 novel nitrogen-containing citrinin derivatives (1, 2, 4) were isolated. • citrinin derivatives 1-4 in enantiomers were resolved by chiral chromatography. • citrinin derivatives 1a and 1b showed broad and significant antimicrobial effects.

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.

BACKGROUND: Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD). OBJECTIVE: In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images. METHODS: A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions. The encoder architecture was based on the residual and inception modules to obtain multi-scale features from multiple convolutional layers with different window shapes. Transfer learning was utilized to improve both the initial performance and learning efficiency. A hybrid loss function was employed to further optimize the segmentation model. RESULTS: The model was tested on a data set of 616 ICAs obtained from 210 patients, composed of 437 images for training, 49 images for validation, and 130 images for testing. The segmentation model achieved a Dice score of 0.8942, a sensitivity of 0.8735, a specificity of 0.9954, and a Hausdorff distance of 6.0794 mm; it could predict arteries for a single ICA frame in 0.2114 seconds. CONCLUSIONS: The results showed that our model outperformed the state-of-the-art deep-learning models. Our new method has great potential for clinical use.

Deciphering chemical and metabolite profiling of Chang-Kang-Fang by UPLC-Q-TOF-MS/MS and its potential active components identification.

Chang-Kang-Fang (CKF) formula, a Traditional Chinese Medicine (TCM) prescription, has been widely used for the treatment of irritable bowel syndrome (IBS). However, its potential material basis and underlying mechanism remain elusive. Therefore, this study employed an integrated approach that combined ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) with network pharmacology to systematically characterize the phytochemical components and metabolites of CKF, as well as elucidating its underlying mechanism. Through this comprehensive analysis, a total of 150 components were identified or tentatively characterized within the CKF formula. Notably, six N-acetyldopamine oligomers from CicadaePeriostracum and eight resin glycosides from Cuscutae Semen were characterized in this formula for the first time. Meanwhile, 149 xenobiotics (58 prototypes and 91 metabolites) were detected in plasma, urine, feces, brain, and intestinal contents, and the in vivo metabolic pathways of resin glycosides were elaborated for the first time. Furthermore, network pharmacology and molecular docking analyses revealed that alkaloids, flavonoids, chromones, monoterpenes, N-acetyldopamine dimers, p-hydroxycinnamic acid, and Cus-3/isomer might be responsible for the beneficial effects of CKF in treating IBS, and CASP8, MARK14, PIK3C, PIK3R1, TLR4, and TNF may be its potential targets. These discoveries offer a comprehensive understanding of the potential material basis and clarify the underlying mechanism of the CKF formula in treating IBS, facilitating the broader application of CKF in the field of medicine.

ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

Molecular Networking-Guided Isolation of Cyclopentapeptides from the Hydrothermal Vent Sediment Derived Fungus Aspergillus pseudoviridinutans TW58-5 and Their Anti-inflammatory Effects.

Repetitive isolation of known compounds remains a major challenge in natural-product-based drug discovery. LC-MS/MS-based molecular networking has become a highly efficient strategy for the discovery of new natural products from complex mixtures. Herein, we report a molecular networking-guided isolation procedure, which resulted in the discovery of seven new cyclopentapeptides, namely, pseudoviridinutans A-F (1-7), from the marine-derived fungus Aspergillus pseudoviridinutans TW58-5. Compounds 1-7 feature a rare amino acid moiety, O,ß-dimethyltyrosine, observed for the first time from a marine-derived fungus. The planar structures of 1-7 were elucidated by detailed analyses of IR, UV, HR ESI-Q-TOF MS, and 1D and 2D NMR spectroscopic data. Meanwhile, their absolute configurations were determined through a combination of Marfey's method and X-ray diffraction. Subsequent bioassay revealed the anti-inflammation potential of 1-7, especially 6, which inhibited the production of nitric oxide (NO), a vital inflammatory mediator, in LPS-induced murine macrophage RAW264.7 cells by regulating the expression level of NLRP3 and iNOS.

Nur77 Serves as a Potential Prognostic Biomarker That Correlates with Immune Infiltration and May Act as a Good Target for Prostate adenocarcinoma.

Prostate adenocarcinoma (PRAD) is the most frequent malignancy, and is the second leading cause of death due to cancer in men. Thus, new prognostic biomarkers and drug targets for PRAD are urgently needed. As we know, nuclear receptor Nur77 is important in cancer development and changes in the tumor microenvironment; whereas, the function of Nur77 in PRAD remains to be elucidated. The TCGA database was used to explore the Nur77 expression and its role in the prognosis of PRAD. It was shown that Nur77 was down regulated in PRAD, and low Nur77 expression was correlated with advanced clinical pathologic characteristics (high grade, histological type, age) and poor prognosis. Furthermore, key genes screening was examined by univariate Cox analysis and Kaplan-Meier survival. Additionally, Nur77 was closely related to immune infiltration and some anti-tumor immune functions. The differentially expressed genes (DEGs) were presented by protein-protein interaction (PPI) network analysis. Therefore, the expression level of Nur77 might help predict the survival of PRAD cases, which presents a new insight and a new target for the treatment of PRAD. In vitro experiments verified that natural product malayoside targeting Nur77 exhibited significant therapeutic effects on PRAD and largely induced cell apoptosis by up-regulating the expression of Nur77 and its mitochondrial localization. Taken together, Nur77 is a prognostic biomarker for patients with PRAD, which may refresh the profound understanding of PRAD individualized treatment.

A novel sorbicillinoid compound as a potent anti-inflammation agent through inducing NLRP3 protein degradation.

BACKGROUND AND PURPOSE: Chronic inflammation is pathogenic and contributes to human diseases, causing a significant threat to public health. The NLR family pyrin domain-containing protein 3 (NLRP3) is the best-characterized factor regulating inflammation. Therefore, targeting NLRP3 has the potential to treat inflammatory diseases and improve human health. EXPERIMENTAL APPROACH: Lipopolysaccharide was used to induce inflammation in cell cultures. Lipopolysaccharide/d-galactosamine and dextran sulfate sodium salt were used to induce acute liver inflammation and ulcerative colitis respectively in C57BL/6J mice. Western blotting, immunofluorescence, immunoprecipitation, quantitative PCR and enzyme-linked immunosorbent assay (ELISA) were used to evaluate the activation of the inflammatory response in cell cultures and in mice. KEY RESULTS: JNUTS013, a novel sorbicillinoid compound recently synthesized by us, significantly inhibited inflammation both in cell cultures and in mouse models. Mechanistically, JNUTS013 induced proteasome-dependent degradation of NLRP3. Hence, it suppressed the formation of the NLRP3 inflammasome and the production of downstream inflammatory cytokines and chemokines. The inhibitory effect of JNUTS013 on NLRP3 protein expression was confirmed in mice. Importantly, JNUTS013 failed to ameliorate bowel inflammation in Nlrp3-/- knockout mice, supporting NLRP3 as the biological target by which JNUTS013 inhibits inflammation. Further studies revealed critical chemical moieties of JNUTS013 required for inducing NLRP3 degradation. CONCLUSION AND IMPLICATIONS: This study identifies a novel compound JNUTS013 that inhibits inflammation through inducing NLRP3 protein degradation in vitro and in vivo, which not only supports the development of JNUTS013 as an anti-inflammation agent but also creates a new way for the treatment of inflammation by chemically inducing NLRP3 degradation.

Deciphering in vivo metabolic profile and pharmacological mechanisms of Jitongning Tablet for the treatment of Ankylosing spondylitis.

Jitongning tablet (JTNT) is a Traditional Chinese Medicine (TCM) prescription used for the treatment of Ankylosing spondylitis (AS). Currently, it is in phase II clinical trial (NCT03932019) for patients with active axial Spondyloarthritis (axSpA), showing great promise for the treatment of AS. However, the potential material basis and the underlying mechanisms for JTNT to treat AS remain elusive. Here, we performed UPLC-Q-TOF-MS to determine the in vivo metabolic profile of JTNT in rats and conducted in vivo studies including acetic acid-induced writhing, hot plate models, and collagen-induced arthritis (CIA) in rats to evaluate and validate the analgesic and anti-inflammatory effects of JTNT, two main symptoms for AS. Additionally, network pharmacology combined with molecular docking was performed to investigate the potential underlying mechanisms. As a result, a total of 116 xenobiotics were identified from the plasma, urine, and brain tissues of rats after oral administration of JTN extracts. Pharmacological evaluation revealed that fractions JTN-3 and JTN-4 exerted significant analgesic activities by reducing the number of writhes in an acetic acid-induced writhing mice model. JTN extract also exerted excellent therapeutic effects in the CIA model by ameliorating paw edema and decreasing systemic manifestation of inflammation and the level of circulating immune complex (CIC) and interferon γ (IFN-γ). Fractions of JTN extract, especially JTN-2 and JTN-4, on the other hand, ameliorated the secondary lesions caused by chicken type II collagen (CII) to a certain extent. Further, network pharmacology combined with molecular docking suggested crucial roles of inflammation and immune-related genes such as MAPK1, MAPK14, NOS3, and RELA in the treatment of AS by JTNT. In conclusion, our studies suggest that the isoquinoline and diterpenoid alkaloids from Corydalis Rhizoma and Aconiti Radix Cocta, along with coumarins from Angelicae Pubescentis Radix, may be the main bioactive components, and the AS treatment mechanism may mainly involve immune regulation of JTNT. These results help clarify the potential material basis and underlying mechanisms of JTNT for the treatment of AS, facilitating the broad application of this TCM in clinical practice.

TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images.

The localization and segmentation of biomarkers in OCT images are critical steps in retina-related disease diagnosis. Although fully supervised deep learning models can segment pathological regions, their performance relies on labor-intensive pixel-level annotations. Compared with dense pixel-level annotation, image-level annotation can reduce the burden of manual annotation. Existing methods for image-level annotation are usually based on class activation maps (CAM). However, current methods still suffer from model collapse, training instability, and anatomical mismatch due to the considerable variation in retinal biomarkers' shape, texture, and size. This paper proposes a novel weakly supervised biomarkers localization and segmentation method, requiring only image-level annotations. The technique is a Teacher-Student network with joint Self-supervised contrastive learning and Knowledge distillation-based anomaly localization, namely TSSK-Net. Specifically, we treat retinal biomarker regions as abnormal regions distinct from normal regions. First, we propose a novel pre-training strategy based on supervised contrastive learning that encourages the model to learn the anatomical structure of normal OCT images. Second, we design a fine-tuning module and propose a novel hybrid network structure. The network includes supervised contrastive loss for feature learning and cross-entropy loss for classification learning. To further improve the performance, we propose an efficient strategy to combine these two losses to preserve the anatomical structure and enhance the encoding representation of features. Finally, we design a knowledge distillation-based anomaly segmentation method that is effectively combined with the previous model to alleviate the challenge of insufficient supervision. Experimental results on a local dataset and a public dataset demonstrated the effectiveness of our proposed method. Our proposed method can effectively reduce the annotation burden of ophthalmologists in OCT images.

An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms.

OBJECTIVE: Intracranial aneurysms (IA) are lethal, with high morbidity and mortality rates. Reliable, rapid, and accurate segmentation of IAs and their adjacent vasculature from medical imaging data is important to improve the clinical management of patients with IAs. However, due to the blurred boundaries and complex structure of IAs and overlapping with brain tissue or other cerebral arteries, image segmentation of IAs remains challenging. This study aimed to develop an attention residual U-Net (ARU-Net) architecture with differential preprocessing and geometric postprocessing for automatic segmentation of IAs and their adjacent arteries in conjunction with 3D rotational angiography (3DRA) images. METHODS: The proposed ARU-Net followed the classic U-Net framework with the following key enhancements. First, we preprocessed the 3DRA images based on boundary enhancement to capture more contour information and enhance the presence of small vessels. Second, we introduced the long skip connections of the attention gate at each layer of the fully convolutional decoder-encoder structure to emphasize the field of view (FOV) for IAs. Third, residual-based short skip connections were also embedded in each layer to implement in-depth supervision to help the network converge. Fourth, we devised a multiscale supervision strategy for independent prediction at different levels of the decoding path, integrating multiscale semantic information to facilitate the segmentation of small vessels. Fifth, the 3D conditional random field (3DCRF) and 3D connected component optimization (3DCCO) were exploited as postprocessing to optimize the segmentation results. RESULTS: Comprehensive experimental assessments validated the effectiveness of our ARU-Net. The proposed ARU-Net model achieved comparable or superior performance to the state-of-the-art methods through quantitative and qualitative evaluations. Notably, we found that ARU-Net improved the identification of arteries connecting to an IA, including small arteries that were hard to recognize by other methods. Consequently, IA geometries segmented by the proposed ARU-Net model yielded superior performance during subsequent computational hemodynamic studies (also known as "patient-specific" computational fluid dynamics [CFD] simulations). Furthermore, in an ablation study, the five key enhancements mentioned above were confirmed. CONCLUSIONS: The proposed ARU-Net model can automatically segment the IAs in 3DRA images with relatively high accuracy and potentially has significant value for clinical computational hemodynamic analysis.

New Polyketides from a Hydrothermal Vent Sediment Fungus Trichoderma sp. JWM29-10-1 and Their Antimicrobial Effects.

Marine fungi-derived secondary metabolites are still an important source for the discovery of potential antimicrobial agents. Here, five new polyketides (1, 2, and 6-8) and seven known compounds (3-5 and 9-12) were obtained from the culture of the marine-derived fungus Trichoderma sp. JWM29-10-1. Their structures were identified by extensive spectrographic data analyses, including 1D and 2D NMR, UV, IR, and HR-ESI-MS. Further, the absolute configurations of new compounds were determined by circular dichroism (CD) spectrum and alkali-hydrolysis in combination with the in situ dimolybdenum CD method. Subsequently, the antimicrobial effects of these isolated compounds were assessed by examining the minimal inhibition concentration (MIC) with the broth microdilution assay. Compounds 1 and 2 exhibited potent antimicrobial activity against Helicobacter pylori, including multidrug-resistant strains, with MIC range values of 2-8 µg/mL. Moreover, compound 1 showed significant inhibitory effects on the growth of Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus (MRSA), Enterococcus faecalis, and vancomycin-resistant Enterococcus faecium, which greatly threaten human health. This study demonstrates that chromone derivatives 1-2, especially for 1, could be potential lead compounds for the development of new antimicrobial agents and provides insight for future medicinal chemistry research.

Estimation of Caenorhabditis Elegans Lifespan Stages Using a Dual-Path Network Combining Biomarkers and Physiological Changes.

Assessing individual aging has always been an important topic in aging research. Caenorhabditis elegans (C. elegans) has a short lifespan and is a popular model organism widely utilized in aging research. Studying the differences in C. elegans life stages is of great significance for human health and aging. In order to study the differences in C. elegans lifespan stages, the classification of lifespan stages is the first task to be performed. In the past, biomarkers and physiological changes captured with imaging were commonly used to assess aging in isogenic C. elegans individuals. However, all of the current research has focused only on physiological changes or biomarkers for the assessment of aging, which affects the accuracy of assessment. In this paper, we combine two types of features for the assessment of lifespan stages to improve assessment accuracy. To fuse the two types of features, an improved high-efficiency network (Att-EfficientNet) is proposed. In the new EfficientNet, attention mechanisms are introduced so that accuracy can be further improved. In addition, in contrast to previous research, which divided the lifespan into three stages, we divide the lifespan into six stages. We compared the classification method with other CNN-based methods as well as other classic machine learning methods. The results indicate that the classification method has a higher accuracy rate (72%) than other CNN-based methods and some machine learning methods.