Puerarin alleviates diabetic nephropathy by inhibiting Caspase-1-mediated pyroptosis.

BACKGROUND AND PURPOSE: Diabetic nephropathy (DN) is an important cause of end-stage renal disease, with podocyte injury as the main feature. Pyroptosis plays a non-negligible role in the process of diabetic nephropathy. Puerarin (PR) treatment of diabetic nephropathy has great potential, but the mechanism is not very clear. This article aims to study the protective effect and mechanism of puerarin on DN. METHODS: Streptozotocin (STZ)-induced C57 BL/6J mouse model of DN was given PR, Necrosulfomide (NSA), Nigericin for 12 weeks; A 60 mM high glucose(HG) induced MPC5 cell injury model was administered to PR, NSA, and Nigericin interventions for 24 h. RESULTS: After 12 weeks of administration, PR reduced fasting blood glucose levels in DN mice, alleviated glomerular lesions, reduced podocyte damage, and protected renal function. Meanwhile, PR also inhibits the expression of pyroptosis-related proteins. In addition, PR alleviated the release of Interleukin 18 (IL-18), Interleukin 1beta (IL-1ß), and lactate dehydrogenase (LDH) in MPC5 cells under HG conditions, downregulated the expression of pyrozozois-related proteins, and improved Caspase-1-mediated pyroptosis in MPC5 cells. CONCLUSION: Our study suggests that the beneficial effects of PR in diabetic nephropathy may be associated with inhibition of Caspase-1-mediated pyroptosis.

Yulangsan polysaccharide inhibits epithelial-mesenchymal transition and invasion in NSCLC by attenuating the TGF-ß1/ERK signaling pathway.

Active polysaccharides have unique advantages in inhibiting cancer cell proliferation, invasion and metastasis and inducing apoptosis. Yulangsan polysaccharide (YLSPS) is derived from the root of Millettia pulchra var. laxior (Dunn) Z. Wei. Previous studies revealed that YLSPS exhibits bioactivities such as antibacterial, antidepressive, antitumor, hepatoprotective and immunomodulating activities. However, the anticancer effects of YLSPS on lung cancer have not yet been studied, and its mechanism of action remains unclear. The present study investigated the anti-migration/invasion effects of YLSPS and possible mechanisms in lung cancer cells (A549 and Lewis) in vitro and in vivo. The data suggested that YLSPS reversed epithelial-mesenchymal transition (EMT) and inhibited the invasion and migration of lung cancer cells by inhibiting the TGF-ß1-induced ERK signaling pathway. Furthermore, YLSPS reduced the levels of proteins associated with EMT, including vimentin, but increased those of E-cadherin, as determined by Western blotting. In vivo, YLSPS significantly inhibited the growth of xenograft tumors, and decreased the levels of TGF-ß1 and protein markers associated with EMT. Importantly, YLSPS had fewer toxic side effects than cisplatin. Overall, YLSPS significantly delayed non-small cell lung cancer (NSCLC) progression by modulating EMT and TGF-ß1/ERK signaling pathway. The present findings suggest that YLSPS may be a potential adjuvant therapy and drug for improving the tumor microenvironment of lung cancer.

Convolutional Neural Network Defect Detection Algorithm for Wire Bonding X-ray Images.

To address the challenges of complex backgrounds, small defect sizes, and diverse defect types in defect detection of wire bonding X-ray images, this paper proposes a convolutional-neural-network-based defect detection method called YOLO-CSS. This method designs a novel feature extraction network that effectively captures semantic features from different gradient information. It utilizes a self-adaptive weighted multi-scale feature fusion module called SMA which adaptively weights the contribution of detection results based on different scales of feature maps. Simultaneously, skip connections are employed at the bottleneck of the network to ensure the integrity of feature information. Experimental results demonstrate that on the wire bonding X-ray defect image dataset, the proposed algorithm achieves mAP 0.5 and mAP 0.5-0.95 values of 97.3% and 72.1%, respectively, surpassing the YOLO series algorithms. It also exhibits certain advantages in terms of model size and detection speed, effectively balancing detection accuracy and speed.

ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.

In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after packaging. However, the current traditional defect detection algorithm cannot meet the demands of high accuracy, fast speed, and real-time chip defect detection in industrial production. Therefore, this paper proposes a new multi-scale feature fusion module (ATSPPF) based on convolutional neural networks, which can more fully extract semantic information at different scales. In addition, based on this module, we design a deep learning model (ATNet) for detecting lead defects in chips. The experimental results show that at 8.2 giga floating point operations (GFLOPs) and 146 frames per second (FPS), mAP0.5 and mAP0.5-0.95 can achieve an average accuracy of 99.4% and 69.3%, respectively, while the detection speed is faster than the baseline yolov5s by nearly 50%.

A Lightweight Method for Detecting IC Wire Bonding Defects in X-ray Images.

Integrated circuit (IC) X-ray wire bonding image inspections are crucial for ensuring the quality of packaged products. However, detecting defects in IC chips can be challenging due to the slow defect detection speed and the high energy consumption of the available models. In this paper, we propose a new convolutional neural network (CNN)-based framework for detecting wire bonding defects in IC chip images. This framework incorporates a Spatial Convolution Attention (SCA) module to integrate multi-scale features and assign adaptive weights to each feature source. We also designed a lightweight network, called the Light and Mobile Network (LMNet), using the SCA module to enhance the framework's practicality in the industry. The experimental results demonstrate that the LMNet achieves a satisfactory balance between performance and consumption. Specifically, the network achieved a mean average precision (mAP50) of 99.2, with 1.5 giga floating-point operations (GFLOPs) and 108.7 frames per second (FPS), in wire bonding defect detection.

Strip Surface Defect Detection Algorithm Based on YOLOv5.

In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis.

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1ß, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4+ T/CD8+ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3+ T,CD4+T and CD8+T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1ß, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4+ and CD8+ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

Nickel-Catalyzed Oxidative Carbonylation of Alkylarenes to Arylacetic Acids.

A catalytic system combined with NiBr2 and diphenylphosphine oxide that enabled direct access to the valuable arylacetic acids from inexpensive alkylarenes and H2O via oxidative carbonylation was developed. Alkylarenes with primary and secondary benzylic C-H bonds were compatible with this method. Remarkably, the marketed drugs ibuprofen and diclofenac could be easily obtained by this procedure straightforwardly.

Correction: The Effects of 17-Methoxyl-7-Hydroxy-Benzene-Furanchalcone on the Pressure Overload-Induced Progression of Cardiac Hypertrophy to Cardiac Failure.

[This corrects the article DOI: 10.1371/journal.pone.0091834.].

Tormentic Acid Ameliorates Hepatic Fibrosis in vivo by Inhibiting Glycerophospholipids Metabolism and PI3K/Akt/mTOR and NF-κB Pathways: Based on Transcriptomics and Metabolomics.

This study aimed to investigate the effects and underlying mechanisms of tormentic acid (TA) on carbon tetrachloride (CCl4)-induced liver fibrosis in rats. The rats were intragastrically administered with 50% CCl4 for 9 weeks to induce hepatic fibrosis, followed by various agents for 6 weeks. Transcriptomic analysis was carried out to predict the potential targets, and then multiple examinations were performed to verify the prediction. The results showed that TA significantly alleviated liver injury and fibrosis, as evidenced by the ameliorative pathological tissue, low transaminase activity, and decreased collagen accumulation. Besides, TA markedly reduced hepatocyte apoptosis by regulating the expression of caspase-3 and Bcl-2 families. The transcriptomic analysis revealed 2,173 differentially expressed genes (DEGs) between the TA and model groups, which could be enriched in the metabolic pathways and the PI3K/Akt and NF-κB signaling pathways. The metabolomics analysis showed that TA could regulate the glycerophospholipid metabolism pathway by regulating the synthesis of phosphatidylserines, phosphatidylethanolamines and phosphatidylcholines. Moreover, the integrative analysis of the transcriptomics and metabolomics data indicated that TA inhibited the glycerophospholipid metabolism pathway by inhibiting the expression of LPCAT4, PTDSS2, PLA2G2A and CEPT1. In addition, the relevant signaling pathways analysis confirmed that TA inhibited HSCs activation by blocking the PI3K/Akt/mTOR pathway and ameliorated inflammatory injury by inhibiting the NF-κB pathway. In conclusion, TA significantly alleviates liver fibrosis in vivo by inhibiting the glycerophospholipid metabolism pathway and the PI3K/Akt/mTOR and NF-κB signaling pathways.

Silver-catalyzed chemodivergent assembly of aminomethylated isochromenes and naphthols.

A silver-catalyzed chemodivergent cyclization of alkyne-tethered aldehydes with aminals to aminomethylated 1H-isochromenes and naphthols is described by tuning the reaction conditions. The reaction exhibits broad substrate generality and functional group compatibility. Mechanistic studies have disclosed that the aminomethylated naphthols are generated from the resulting N,O-aminal containing isochromenes via a silver-catalyzed unusual rearrangement process.

Palladium-Catalyzed Aminomethylative Oppolzer-Type Cyclization of Enynes: Access to Aminomethylated Benzofulvenes.

A novel palladium-catalyzed Oppolzer-type cyclization reaction aided by the aminomethyl cyclopalladated complex has been developed, which provides rapid access to functionalized benzofulvenes with excellent stereoselectivity. The corresponding products can undergo Diels-Alder reaction with maleimides, providing a series of complex polycyclic compounds with excellent regio- and stereoselectivities.

FEF25-75% Is a More Sensitive Measure Reflecting Airway Dysfunction in Patients with Asthma: A Comparison Study Using FEF25-75% and FEV1.

BACKGROUND: Reduced forced expiratory flow between 25% and 75% of vital capacity percent predicted (FEF25-75%) representing small airway dysfunction (SAD) was associated with asthma development and progression. OBJECTIVE: To investigate whether FEF25-75% was superior to forced expiratory volume in 1 second in predicted (FEV1%) in reflecting asthma features in adult patients. METHODS: A retrospective spirometry dataset comprising 1801 adult patients with confirmed asthma and a subgroup of 332 patients having detailed clinical data were used to explore the association of FEF25-75% and/or FEV1% with clinical features of asthma. RESULTS: Of the 1801 subjects, FEV1% and FEF25-75% ranged from 136.8% to 10.2% and 127.3% to 3.1%, respectively. FEF25-75% < 65% was present in 1,478 (82.07%) of patients. FEF25-75% was strongly correlated with matched FEV1% (r = 0.900, P < .001). FEF25-75% and FEV1% were both correlated with airway hyperresponsiveness (r = 0.436, P < .001; r = 0.367, P < .001), asthma control test score (r = 0.329, P < .001; r = 0.335, P < .001), and sputum eosinophil count (r = -0.306, P < .001; r = -0.307, P < .001). Receiver-operating characteristic curves showed that FEF25-75% had greater value in predicting severe asthma (area under the curve: 0.84 vs 0.81, P = .018), airflow obstruction (0.97 vs 0.89, P < .001), and severe bronchial hyperresponsiveness (0.74 vs 0.69, P = .012) as compared with FEV1%. Patients with SAD (FEF25-75% < 65%) in the presence of normal FEV1% exhibited higher sputum eosinophil counts and had an increased dosage of daily inhaled corticosteroids (P < .001 and P = .010) than patients with normal lung function and their FEF25-75% values correlated with sputum eosinophil count (r = -0.419, P = .015), but not FEV1%. CONCLUSION: FEF25-75% represented small airway function and was more sensitive at reflecting airway hyperresponsiveness, inflammation, and disease severity as compared with FEV1% in patients with asthma. Our data suggest further assessment of FEF25-75% in asthma management, particularly for those with SAD who present normal FEV1%.

Correction: Yulangsan polysaccharide improves redox homeostasis and immune impairment in D-galactose-induced mimetic aging.

Correction for 'Yulangsan polysaccharide improves redox homeostasis and immune impairment in d-galactose-induced mimetic aging' by Van Minh Doan et al., Food Funct., 2015, 6, 1712-1718, DOI: 10.1039/C5FO00238A.

Uncovering the Pharmacological Mechanism of 2-Dodecyl-6-Methoxycyclohexa-2,5 -Diene-1,4-Dione Against Lung Cancer Based on Network Pharmacology and Experimental Evaluation.

Background: 2-Dodecyl-6-Methoxycyclohexa-2, 5-Diene-1,4-Dione (DMDD) was purified from the roots of Averrhoa carambola L. Previous research demonstrated that DMDD is a small molecular compound with significant therapeutic potential for tumors. However, the potential targets and pharmacological mechanism of DMDD to treat lung cancer has not been reported. Methods: We employed network pharmacology and experimental evaluation to reveal the pharmacological mechanism of DMDD against lung cancer. Potential therapeutic targets of DMDD were screened by PharmMapper. Differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA) lung cancer data sets were extracted and analyzed by GEPIA2. The mechanism of DMDD against lung cancer was determined by PPI, gene ontology (GO) and KEGG pathway enrichment analysis. Survival analysis and molecular docking were employed to obtain the key targets of DMDD. Human lung cancer cell lines H1975 and PC9 were used to detect effects of DMDD treatment in vitro. The expression of key targets after DMDD treated was validated by Western Blot. Results: A total of 60 Homo sapiens potential therapeutic targets of DMDD and 3,545 DEGs in TCGA lung cancer datasets were identified. Gene ontology and pathway analysis revealed characteristic of the potential targets of DMDD and DEGs in lung cancer respectively. Cell cycle and pathways in cancer were overlapping with DMDD potential targets and lung cancer DEGs. Eight overlapping genes were found between DMDD potential therapeutic targets and lung cancer related DEGs. Survival analysis showed that high expression of DMDD potential targets CCNE1 and E2F1 was significantly related to poor patient survival in lung cancer. Molecular docking found that DMDD exhibited significant binding affinities within the active site of CCNE1 and E2F1. Further tests showed that DMDD inhibited the proliferation, migration and clone formation in lung cancer cell lines (H1975 and PC9) in a dose and time dependent manner. Mechanistically, DMDD treatment decreased the expression of CDK2, CCNE1, E2F1 proteins and induced cell cycle arrest at the G1/S phase in H1975 and PC9 cells. Conclusion: These results delineated that DMDD holds therapeutic potential that blocks tumorigenesis by cell cycle regulation in lung cancer, and may provide potential therapies for lung cancer.