Multi-omics analysis provides new insights into mechanism of didymin on non-alcoholic fatty liver disease in rats.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases accompanied by lipid and glucose metabolism disorder. Didymin has been reported to have various hepatoprotective effects, however, its potential effects and mechanisms on NAFLD remain unclear from the perspective of the whole. PURPOSE: To investigate the underlying mechanism of didymin against NAFLD using multi-omics technologies. METHODS: Rats were fed with a high-fat diet (HFD) for 8 weeks to induce NAFLD, followed by didymin treatment for 8 weeks. Next, biochemical analysis and histopathological examinations were performed to evaluate the effects of didymin. The key regulating pathways were predicted using transcriptomics, metabolomics and proteomics, and the target pathways were then verified by detecting the key genes/proteins using various experiments. RESULTS: Didymin markedly mitigated liver injury and excessive lipid droplet accretion. An integrative multi-omics analysis suggested that the PPAR signaling cascade and insulin signaling pathway might serve as pivotal mechanisms underlying the modulation of lipid and glucose homeostasis by didymin. Further dissection identified five pivotal genes (PPARα, PPARß, FABP4, ANGPTL4, and PLIN2) and four genes (HK1, HK3, GCK, and PTPN1) as potential hubs within these pathways. Subsequent validation experiments, including qPCR and Western blot, demonstrated upregulated expression of PPARα and PPARß, indicating the activation of the PPAR pathway by didymin. Concurrently, didymin appeared to modulate the insulin signaling pathway, as evidenced by the upregulated expression of HK1 and downregulated expression of PTPN1. Notably, the manipulation of PPARα, PPARß, and PTPN1 expression in LO2 cells through silence or overexpression confirmed that didymin significantly reduced lipid accumulation, with its molecular targets likely being the PPAR and insulin pathways. CONCLUSIONS: Our findings demonstrate that didymin has a protective effect on NAFLD, and its underlying mechanism may be associated with the regulation of the PPAR and insulin signaling pathways.

DMDD, isolated from Averrhoa carambola L., ameliorates diabetic nephropathy by regulating endoplasmic reticulum stress-autophagy crosstalk.

BACKGROUND: Studies have shown that Averrhoa carambola L. possesses therapeutic potential for diabetes and related complications. However, the specific beneficial effects and molecular mechanisms of 2-dodecyl-6-meth-oxycyclohexa-2,5-diene-1,4-dione (DMDD) isolated from Averrhoa carambola L. on diabetic nephropathy (DN) require further investigation. METHODS: 80 C57BL/6 J male mice were subjected to a 1-week adaptive feeding, followed by a high-fat diet and intraperitoneal injection of 100 mg/kg streptozotocin (STZ) to construct an in vivo DN model. Additionally, human renal proximal tubular epithelial cells (HK-2) induced by high glucose (HG) were used as an in vitro DN model. The expression levels of epithelial-mesenchymal transition (EMT), endoplasmic reticulum stress (ERS), and autophagy-related proteins in renal tubular cells were detected by Western Blot, flow cytometry, immunofluorescence, and enzyme-linked immunosorbent assay (ELISA) staining. Transcriptome analysis revealed was conducted to elucidate the specific mechanism of by which DMDD mitigates DN by inhibiting ERS and autophagy. HK-2 cells were transfected with IRE1α overexpression lentivirus to reveal the role of IRE1α overexpression in HG-induced HK-2. RESULTS: The experimental data showed that DMDD significantly reduced blood glucose levels and improved renal pathological alterations in DN mice. Additionally, DMDD inhibited the calcium (Ca2+) pathway, manifested by decreased autophagosome formation and downregulation of LC3II/I, Beclin-1, and ATG5 expression. Moreover, in HG-induced HK-2 cells, DMDD suppressed the overexpression of GRP78, CHOP, LC3II/I, Beclin1, and ATG5. Notably, IRE1α overexpression significantly increased autophagy incidence; however, DMDD treatment subsequently reduced the expression of LC3II/I, Beclin1, and ATG5. CONCLUSION: DMDD effectively inhibits excessive ERS and autophagy, thereby reducing renal cell apoptosis through the IRE1α pathway and Ca 2+ pathway.

Presence of sputum IgG against eosinophilic inflammatory proteins in asthma.

Background: Sputum immunoglobulin G (Sp-IgG) has been discovered to induce cytolytic extracellular trap cell death in eosinophils, suggesting a potential autoimmune mechanism contributing to asthma. This study aimed to explore the potential origin of Sp-IgG and identify clinically relevant subtypes of Sp-IgG that may indicate autoimmune events in asthma. Methods: This study included 165 asthmatic patients and 38 healthy volunteers. We measured Sp-IgG and its five subtypes against eosinophil inflammatory proteins (Sp-IgGEPs), including eosinophil peroxidase, eosinophil major basic protein, eosinophil-derived neurotoxin, eosinophil cationic protein, and Charcot-Leyden Crystal protein in varying asthma severity. Clinical and Mendelian randomization (MR) analyses were conducted. A positive Sp-IgGEPs signature (Sp-IgGEPs+) was defined when any of the five Sp-IgGEPs values exceeded the predefined cutoff thresholds, calculated as the mean values of healthy controls plus twice the standard deviation. Results: The levels of Sp-IgG and Sp-IgGEPs were significantly elevated in moderate/severe asthma than those in mild asthma/healthy groups (all p < 0.05). Sp-IgG levels were positively correlated with airway eosinophil and Sp-IgGEPs. MR analysis showed causality between eosinophil and IgG (OR = 1.02, 95%CI = 1.00-1.04, p = 0.020), and elevated IgG was a risk factor for asthma (OR = 2.05, 95%CI = 1.00-4.17, p = 0.049). Subjects with Sp-IgGEPs+ exhibited worse disease severity and served as an independent risk factor contributing to severe asthma (adjusted-OR = 5.818, adjusted-95% CI = 2.193-15.431, adjusted-p < 0.001). Receiver operating characteristic curve analysis demonstrated that the combination of Sp-IgGEPs+ with non-allergic status, an ACT score < 15, and age ≥ 45 years, effectively predicted severe asthma (AUC = 0.84, sensitivity = 86.20%, specificity = 67.80%). Conclusion: This study identifies a significant association between airway eosinophilic inflammation, Sp-IgG, and asthma severity. The Sp-IgGEPs panel potentially serves as the specific biomarker reflecting airway autoimmune events in asthma.

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.

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.].

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.

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.