Search

Inborn errors of the signal transducer and activator of transcription 1 (STAT1) result in four types of immunodeficiency disease with varying degrees of impaired STAT1 function: autosomal recessive (AR) complete STAT1 deficiency, AR partial STAT1 deficiency, autosomal dominant (AD) STAT1 deficiency, and AD STAT1 gain-of-function (STAT1-GOF). Of which, the STAT1-GOF mutations promote a clinical syndrome of immune dysregulation characterized by recurrent infections, especially chronic mucocutaneous candidiasis (CMC) and Talaromyces marneffei infection and predisposition to humoral autoimmunity. STAT1-GOF mutations lead to enhanced phosphorylation of STAT1 (pSTAT1), delayed dephosphorylation, and impaired nuclear dephosphorylation. As a result, the development of T helper (Th) 17 cells is impaired, limiting the production of interleukin (IL)-17, which plays an important role in antifungal immunity. Additionally, mutations can also cause a decrease in the proportion of CD4+, CD8+, and natural killer (NK) cells. Recent research demonstrated that in the absence of overt infection, STAT-GOF mice can disrupt naïve CD4+ T cell homeostasis and promote expansion and differentiation of abnormal T-follicular helper/T-helper 1-like (Tfh/Th1-like) T cells and germinal center-like (GC-like) B cells, and thus reminds us of the complex molecular mechanism of autoimmune disease with/without fungal infection, which may further involve specific clinical treatment including antifungal and anti-autoimmunity therapies. In addition, sex and location of mutation were also associated with the clinical phenotype. Individuals with DNA binding domain (DBD) mutations had a higher prevalence of autoimmunity and aberrant B cell activation. Disrupted CD4+ T cell homeostasis occurred sooner and more robustly in females, highlighting the importance of specific treatment to normalize STAT1 expression and restore immune tolerance in patients with STAT1-GOF syndrome. Herein, we provide a comprehensive review of STAT1-GOF aiming to further clarify the regulatory mechanism of cellular and humoral immune deficiency in patients with fungal infection with or without autoimmunity.

Tongue cancer, a prevalent form of oral malignancy, particularly manifests as tongue squamous cell carcinoma (TSCC), which holds the highest incidence among oral squamous cell carcinoma cases, representing 43.4 % of occurrences. Presently, surgical resection stands as the primary treatment for TSCC, with no effective pharmaceutical interventions identified. This study aims to search for the potential anti-tumor properties and molecular mechanisms of a rearranged abietane diterpenoid extracted from Clerodendrum mandarinorum. Within this investigation, six known rearranged abietane diterpenoids (1–6) were isolated and characterized from C. mandarinorum. Notably, compound 1 demonstrated the most robust inhibitory effect against CAL-27 cells. Pharmacological assays further substantiated that compound 1 significantly restrained the proliferation, migration, and invasion of CAL-27 cells. Moreover, it was revealed that compound 1 directly interacted with TGFβR2, a pivotal receptor in the downstream signaling pathways of TGF-β/Smad and PI3K/AKT. Transcriptome analysis provided additional confirmation of these observations. Overall, our study highlights the promising potential of compound 1 as a therapeutic agent for the treatment of oral squamous cell carcinoma.

IntroductionDeglycosylated azithromycin (Deg-AZM), a newly developed Class I drug with good therapeutic effects on slow transit constipation, is a small-molecule transgelin agonist that has been approved for clinical trials in 2024. The preclinical pharmacokinetic profile of Deg-AZM was investigated to support further development.MethodsA LC-MS/MS method was established and validated to detected the concentration of Deg-AZM in various biological samples. In vivo tests such as pharmacokinetic studies in rats and dogs, tissue distribution studies in rats, and extraction studies in rats were conducted to investigated the preclinical pharmacokinetic behaviors of Deg-AZM comprehensively. The plasma protein rate of Deg-AZM was determined by rapid equilibrium dialysis method in vitro. The metabolic stability and metabolite profile of Deg-AZM was assessed using pooled mice, rats, dogs, monkeys and humans microsomes in vitro. The PK profiles of Deg-AZM in human was predicted based on physiologically based pharmacokinetic (PBPK) models.ResultsThe plasma protein binding rates of Deg-AZM were lower in mice and rats, higher in dogs, and moderate in humans. The metabolic process of Deg-AZM was similar in rat and human liver microsomes. From Pharmacokinetic studies in rats and dogs, Deg-AZM was rapidly absorbed into the blood and then quickly eliminated. Plasma exposure of Deg-AZM was dose dependent with no accumulation after continuous gavage administration. In addition, there is no significant gender difference in the pharmacokinetic behavior of Deg-AZM. Deg-AZM was widely distributed in the tissues without obvious accumulation, and mainly excreted from the urinary excretion pathway. Furthermore, the pharmacokinetic profiles of Deg-AZM in humans showed dose dependency.ConclusionThe pharmacokinetic profiles of Deg-AZM was fully explored, these results could provide valuable information to support the first-in-human dosage prediction and phase I clinical design.

Abstract Glioblastoma (GBM) cells require large amounts of iron for tumor growth and progression, which makes these cells vulnerable to destruction via ferroptosis induction. Mitochondria are critical for iron metabolism and ferroptosis. Sirtuin-3 (SIRT3) is a deacetylase found in mitochondria that regulates mitochondrial quality and function. This study aimed to characterize SIRT3 expression and activity in GBM and investigate the potential therapeutic effects of targeting SIRT3 while also inducing ferroptosis in these cells. We first found that SIRT3 expression was higher in GBM tissues than in normal brain tissues and that SIRT3 protein expression was upregulated during RAS-selective lethal 3 (RSL3)-induced GBM cell ferroptosis. We then observed that inhibition of SIRT3 expression and activity in GBM cells sensitized GBM cells to RSL3-induced ferroptosis both in vitro and in vivo. Mechanistically, SIRT3 inhibition led to ferrous iron and ROS accumulation in the mitochondria, which triggered mitophagy. RNA-Sequencing analysis revealed that upon SIRT3 knockdown in GBM cells, the mitophagy pathway was upregulated and SLC7A11, a critical antagonist of ferroptosis via cellular import of cystine for glutathione (GSH) synthesis, was downregulated. Forced expression of SLC7A11 in GBM cells with SIRT3 knockdown restored cellular cystine uptake and consequently the cellular GSH level, thereby partially rescuing cell viability upon RSL3 treatment. Furthermore, in GBM cells, SIRT3 regulated SLC7A11 transcription through ATF4. Overall, our study results elucidated novel mechanisms underlying the ability of SIRT3 to protect GBM from ferroptosis and provided insight into a potential combinatorial approach of targeting SIRT3 and inducing ferroptosis for GBM treatment.

IntroductionNeutrophil extracellular traps (NETs) constitute a crucial element of the immune system, and dysfunction in immune responses is implicated in the susceptibility and progression of Parkinson's disease (PD). Nevertheless, the mechanism connecting PD and NETs remains unclear. This study aims to uncover potential NETs-related immune biomarkers and elucidate their role in PD pathogenesis.MethodsThrough differential gene analysis of PD and NETs in GSE7621 datasets, we identified two PD subtypes and explored potential biological pathways. Subsequently, using ClusterWGCNA, we pinpointed pertinent genes and developed clinical diagnostic models. We then optimized the chosen model and evaluated its association with immune infiltration. Validation was conducted using the GSE20163 dataset. Screening the single-cell dataset GSE132758 revealed cell populations associated with the identified gene. ResultsOur findings identified XGB as the optimal diagnostic model, with CAP2 identified as a pivotal gene. The risk model effectively predicted overall diagnosis rates, demonstrating a robust correlation between infiltrating immune cells and genes related to the XGB model. DiscussionIn conclusions, we identified PD subtypes and diagnostic genes associated with NETs, highlighting CAP2 as a pivotal gene. These findings have significant implications for understanding potential molecular mechanisms and treatments for PD.

Idiopathic pulmonary fibrosis (IPF) is one of the most fatal chronic interstitial lung diseases with unknown pathogenesis, current treatments cannot truly reverse the progression of the disease. Pulmonary macrophages, especially bone marrow derived pro-fibrotic macrophages, secrete multiple kinds of profibrotic mediators (SPP1, CD206, CD163, IL-10, CCL18…), thus further promote myofibroblast activation and fibrosis procession. IL20Rb is a cell-surface receptor that belongs to IL-20 family. The role of IL20Rb in macrophage activation and pulmonary fibrosis remains unclear. In this study, we established a bleomycin-induced pulmonary fibrosis model, used IL4/13-inducing THP1 cells to induce profibrotic macrophage (M2-like phenotype) polarization models. We found that IL20Rb is upregulated in the progression of pulmonary fibrosis, and its absence can alleviate the progression of pulmonary fibrosis. In addition, we demonstrated that IL20Rb promote the activation of bone marrow derived profibrotic macrophages by regulating the Jak2/Stat3 and Pi3k/Akt signaling pathways. In terms of therapeutic strategy, we used IL20Rb neutralizing antibodies for animal administration, which was found to alleviate the progression of IPF. Our results suggest that IL20Rb plays a profibrotic role by promoting profibrotic macrophage polarization, and IL20Rb may become a potential therapeutic target for IPF. Neutralizing antibodies against IL20Rb may become a potential drug for the clinical treatment of IPF.

Abstract The infrared (IR) absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy. The Kohonen neural network algorithm was used to identify the type of the lubricating grease. The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands, which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases. The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease.

Fibrotic skin diseases, such as keloids, are pathological results of aberrant tissue healing and are characterized by overgrowth of dermal fibroblasts. Remdesivir (RD), an antiviral drug, has been reported to have pharmacological activities in a wide range of fibrotic diseases. However, whether RD function on skin fibrosis remains unclear. Therefore, in our study, we explored the potential effect and mechanisms of RD on skin fibrosis both in vivo and in vitro. As expected, the results demonstrated that RD alleviated BLM-induced skin fibrosis and attenuates the gross weight of keloid tissues in vivo. Further studies suggested that RD suppressed fibroblast activation and autophagy both in vivo and in vitro. In addition, mechanistic research showed that RD attenuated fibroblasts activation by the TGF-β1/Smad signaling pathway and inhibited fibroblasts autophagy by the PI3K/Akt/mTOR signaling pathway. In summary, our results demonstrate therapeutic potential of RD for skin fibrosis in the future.

ABSTRACT The increasing prevalence of hypervirulent (hv) and carbapenem-resistant (CR) Klebsiella pneumoniae (Kp) highlights the importance of timely and accurate differential diagnosis for epidemiological investigation and clinical management. A multiplex quantitative real-time PCR (qRT-PCR) assay for the simultaneous identification of hvKp and CR-Kp was developed and validated with excellent performance in sensitivity and specificity. Generally, the gltA gene for Kp, the iucA, rmpA and rmpA2 genes for hvKp, and the Klebsiella pneumoniae carbapenemases (KPC) gene for CR-Kp were included in the qRT-PCR assay. The detection limits for classic Kp (cKp), CR-cKp, hvKp, and CR-hvKp strains could all reach 50 genome equivalent copies and 20 CFUs per reaction with high accuracy (R 2 > 0.99) and reliability (CV values < 3%). Detection results from 84 Kp positive clinical samples showed 31 hvKp with 8 CR-hvKp and 53 cKp with 1 CR-cKp strains. The presence of virulence-associated factors for the identified hvKp and KPC genes for CR-Kp was confirmed by previously developed conventional PCR and antimicrobial susceptibility tests, respectively. Furthermore, the qRT-PCR identified hvKp strains showed mortality rates of ≥40% in the outbred murine infection model, while no death for the identified cKp strains. These results indicated that our multiplex qRT-PCR assay could accurately identify hvKp and CR-Kp strains, which will be of great use for the rapid and accurate diagnosis in a clinical setting and the surveillance of the circulating Kp. IMPORTANCE Globally, the increasing number of hypervirulent Klebsiella pneumoniae (hvKp) and carbapenem-resistant Kp (CR-Kp) infections poses a huge public health challenge with high morbidity and mortality. Worrisomely, due to the mobility of elements carrying virulence and drug-resistance genes, the increasing prevalence of CR-hvKp has also been found with an overwhelming mortality rate in recent years. However, the current detection methods for hvKp and CR-Kp have many disadvantages, such as long turnaround time, complex operation, low sensitivity, and specificity. Herein, a more sensitive, rapid, single-reaction, and multiplex quantitative real-time PCR was developed and validated to differentiate the circulating lineages of Kp with excellent performance in sensitivity and specificity, providing a useful tool for the differential diagnosis and the surveillance of the circulating Kp.

Idiopathic pulmonary fibrosis (IPF) is an incurable chronic progressive disease with a low survival rate and ineffective therapeutic options. We examined the effects of imrecoxib, a nonsteroidal anti-inflammatory drug, on experimental pulmonary fibrosis. The mouse IPF model was established by intratracheal instillation of bleomycin. From Day 0 to Day 13, the mice were orally administered imrecoxib (100 mg/kg) and pirfenidone (200 mg/kg) daily, and from Day 7 to Day 13, the mice were orally administered pirfenidone and imrecoxib daily. The tissues were dissected on the 14th day. Mouse body weight was measured, and histopathological examination and hydroxyproline content analysis confirmed that the administration of imrecoxib exerted a similar effect to pirfenidone. Compared with bleomycin-induced mice, imrecoxib-treated mice showed significantly reduced inflammatory factor expression (IL-1 and TNF-α) and inflammatory cell numbers (macrophages, lymphocytes, and neutrophils) in BALF (bronchoalveolar lavage fluid). Our experiment tested the ability of imrecoxib to inhibit the signal pathway involved in gene expression induced by TGF-β1 in the NIH-3T3 cell line in vitro. Western blotting showed that imrecoxib (20 μM and 40 μM) inhibited the expression of fibronectin, type I collagen and CTGF. In addition, imrecoxib reduced the levels of p-ERK1/2. The changes in the expression of related proteins in mouse lung tissue were similar to those in cells. In summary, our findings suggested that the administration of imrecoxib prevented and treated murine IPF by inhibiting inflammation and the TGF-β1-ERK1/2 signaling pathway.

Abstract Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease characterized by alveolar epithelial cell injury and lung fibroblast overactivation. At present, only two drugs are approved by the FDA for the treatment of IPF, including the synthetic pyridinone drug, pirfenidone, and the tyrosine kinase inhibitor, nintedanib. Avitinib (AVB) is a novel oral and potent third-generation tyrosine kinase inhibitor for treating non-small cell lung cancer (NSCLC). However, the role of avitinib in pulmonary fibrosis has not yet been established. In the present study, we used in vivo and in vitro models to evaluate the role of avitinib in pulmonary fibrosis. In vivo experiments first verified that avitinib significantly alleviated bleomycin-induced pulmonary fibrosis in mice. Further in vitro molecular studies indicated that avitinib inhibited myofibroblast activation, migration and extracellular matrix (ECM) production in NIH-3T3 cells, mainly by inhibiting the TGF-β1/Smad3 signalling pathways. The cellular experiments also indicated that avitinib improved alveolar epithelial cell injury in A549 cells. In conclusion, the present findings demonstrated that avitinib attenuates bleomycin-induced pulmonary fibrosis in mice by inhibiting alveolar epithelial cell injury and myofibroblast activation.

The crystal habit of pentaerythritol (PE) crystals is usually rod-shaped, which may lead to low bulk density and bad flowability compared with low aspect ratio crystals. In this study, dipentaerythritol (DPE), methylcellulose (MC), and hydroxypropyl methylcellulose (HPMC) were selected as additives to modify the morphology of PE crystals. In the presence of DPE, the bulk density of PE crystals was improved, and the aspect ratio was decreased. The modified attachment energy (AE) model was conducted to analyze changes in PE crystal habits in the presence of DPE, which characterizes the intensity of the interaction between DPE molecules and PE crystal faces. Spherical pentaerythritol crystals can be successfully prepared by adding MC and HPMC solution, and the formation mechanism can be divided into five steps.

Abstract Metabolic‐associated fatty liver disease (MAFLD) is a series of liver diseases based on liver steatosis and metabolic disorders. Steatosis, as the core factor in MAFLD diagnosis, and fibrosis, as the major determinant of adverse outcomes of MAFLD, need to be assessed simply and accurately. In this study, we explored the significance of mid‐upper arm circumference (MUAC) in evaluating liver steatosis and fibrosis in patients with MAFLD. We included 2397 cases with MAFLD from the 2017–2018 National Health and Nutrition Examination Surveys (NHANES) database. Liver steatosis and fibrosis were measured by vibration controlled transient elastography. Anthropometric parameters and demographic and serological data were obtained from the NHANES database. The association between MUAC and liver steatosis and fibrosis were evaluated by a multivariable linear regression model, a weighted generalized additive model, and smooth curve fitting using R. MUAC was positively associated with liver steatosis in every multivariate linear regression model (model 1: β = 3.3513; 95% confidence interval [CI], 2.7722–3.9304; model 2: β = 3.8492; 95% CI, 3.2441–4.4542; model 3: β = 2.4987; 95% CI, 1.8371–3.1604), and this positive association was consistent in both men and women and among different race groups (Mexican American, other Hispanic, non‐Hispanic White, Black, Asian, and other race). On the other hand, MUAC was positively associated with liver fibrosis in every multivariate linear regression model, and this positive association also was consistent in both men and women and among non‐Hispanic White and Black populations. Increased MUAC was positively associated with liver steatosis and fibrosis in patients with MAFLD. This was particularly true for MUAC ≥ 42.0 cm. MUAC might be a simple and convenient evaluation tool for MAFLD.

Background: Complex bone plateau fractures have been treated with bilateral plate fixation, but previous research has overemphasized evaluating the effects of internal fixation design, plate position, and screw orientation on fracture fixation stability, neglecting the internal fixation system’s biomechanical properties in postoperative rehabilitation exercises. This study aimed to investigate the mechanical properties of tibial plateau fractures after internal fixation, explore the biomechanical mechanism of the interaction between internal fixation and bone, and make suggestions for early postoperative rehabilitation and postoperative weight-bearing rehabilitation.Methods: By establishing the postoperative tibia model, the standing, walking and running conditions were simulated under three axial loads of 500 N, 1000 N, and 1500 N. Accordingly, finite element analysis (FEA) was performed to analyze the model stiffness, displacement of fractured bone fragments, titanium alloy plate, screw stress distribution, and fatigue properties of the tibia and the internal fixation system under various conditions.Results: The stiffness of the model increased significantly after internal fixation. The anteromedial plate was the most stressed, followed by the posteromedial plate. The screws at the distal end of the lateral plate, the screws at the anteromedial plate platform and the screws at the distal end of the posteromedial plate are under greater stress, but at a safe stress level. The relative displacement of the two medial condylar fracture fragments varied from 0.002–0.072 mm. Fatigue damage does not occur in the internal fixation system. Fatigue injuries develop in the tibia when subjected to cyclic loading, especially when running.Conclusion: The results of this study indicate that the internal fixation system tolerates some of the body’s typical actions and may sustain all or part of the weight early in the postoperative period. In other words, early rehabilitative exercise is recommended, but avoid strenuous exercise such as running.

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes.

In this work, we propose a deep-learning-based mapping model for simulating and predicting the flow field of Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) of propellers. The model employs image processing and computer vision methods to process the two-dimensional propeller RANS and LES simulation data. First, images are obtained by simulating the flow fields with the location data used to acquire a set of features specific to the corresponding positions. Second, the regression models for the flow fields and the mapping between the two different flow fields are established to predict the LES flow field at that position. Specifically, we utilize a deep convolutional neural network (CNN) for feature extraction from the flow field, which is then integrated with a nonlinear module for the purposes of regression and mapping. The effectiveness and accuracy of the proposed model in flow field prediction are demonstrated by its application to propeller RANS and LES simulations. It is shown that the overall error rate between the LES flow field predictions generated using this method and actual flow field data is 7.92%. Additionally, we also evaluate the model’s generalization ability, stability, and robustness by testing it on the data of propeller flow fields at different Reynolds numbers. The results verify the applicability of the proposed model in various problems of flow field simulation and prediction.

BackgroundThe monocyte-macrophage-dendritic cell (DC) (MMD) system exerts crucial functions that may modulate fibrogenesis in nonalcoholic steatohepatitis (NASH). In this study, we explored the cell characteristics, distribution and developmental trajectory of the liver MMD system in NASH mice with fibrosis and clarified characteristic genes of the MMD system involved in liver fibrosis progression in NASH mice and patients.MethodsSingle cells in liver tissue samples from NASH and normal mice were quantified using single-cell RNA sequencing (scRNA-seq) analysis. Differentially expressed genes (DEGs) in the MMD system by pseudotime analysis were validated by tyramide signal amplification (TSA)-immunohistochemical staining (IHC) and analyzed by second harmonic generation (SHG)/two-photon excitation fluorescence (TPEF).ResultsCompared with control mice, there were increased numbers of monocytes, Kupffer cells, and DCs in two NASH mouse models. From the transcriptional profiles of these single cells, we identified 8 monocyte subsets (Mono1-Mono8) with different molecular and functional properties. Furthermore, the pseudotime analysis showed that Mono5 and Mono6 were at the beginning of the trajectory path, whereas Mono2, Mono4, Kupffer cells and DCs were at a terminal state. Genes related to liver collagen production were at the late stage of this trajectory path. DEGs analysis revealed that the genes Fmnl1 and Myh9 in the MMD system were gradually upregulated during the trajectory. By TSA-IHC, the Fmnl1 and Myh9 expression levels were increased and associated with collagen production and fibrosis stage in NASH mice and patients.ConclusionsOur transcriptome data provide a novel landscape of the MMD system that is involved in advanced NASH disease status. Fmnl1 and Myh9 expression in the MMD system was associated with the progression of NASH fibrosis.

Lung adenocarcinoma (LUAD) is the most common lung cancer, which accounts for about 35–40% of all lung cancer patients. Despite therapeutic advancements in recent years, the overall survival time of LUAD patients still remains poor, especially KRAS mutant LUAD. Therefore, it is necessary to further explore novel targets and drugs to improve the prognos is for LUAD. Ferroptosis, an iron-dependent regulated cell death (RCD) caused by lipid peroxidation, has attracted much attention recently as an alternative target for apoptosis in LUAD therapy. Ferroptosis has been found to be closely related to LUAD at every stage, including initiation, proliferation, and progression. In this review, we will provide a comprehensive overview of ferroptosis mechanisms, its regulation in LUAD, and the application of targeting ferroptosis for LUAD therapy.

(1) Background: Heart failure (HF) is the final stage of multiple cardiac diseases, which have now become a severe public health problem worldwide. β-Adrenergic receptor (β-AR) overactivation is a major pathological factor associated with multiple cardiac diseases and mediates cardiac fibrosis and inflammation. Previous research has demonstrated that Bruton’s tyrosine kinase (BTK) mediated cardiac fibrosis by TGF-β related signal pathways, indicating that BTK was a potential drug target for cardiac fibrosis. Zanubrutinib, a second-generation BTK inhibitor, has shown anti-fibrosis effects in previous research. However, it is unclear whether Zanubrutinib can alleviate cardiac fibrosis induced by β-AR overactivation; (2) Methods: In vivo: Male C57BL/6J mice were treated with or without the β-AR agonist isoproterenol (ISO) to establish a cardiac fibrosis animal model; (3) Results: In vivo: Results showed that the BTK inhibitor Zanubrutinib (ZB) had a great effect on cardiac fibrosis and inflammation induced by β-AR. In vitro: Results showed that ZB alleviated β-AR-induced cardiac fibroblast activation and macrophage pro-inflammatory cytokine production. Further mechanism studies demonstrated that ZB inhibited β-AR-induced cardiac fibrosis and inflammation by the BTK, STAT3, NF-κB, and PI3K/Akt signal pathways both in vivo and in vitro; (4) Conclusions: our research provides evidence that ZB ameliorates β-AR-induced cardiac fibrosis and inflammation.

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R2 values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis. However, important information about weather and sea conditions the ship sails through, such as waves, sea currents, and sea water temperature, is often absent from sensor data. This study addresses this issue by fusing sensor data and publicly accessible meteorological data, constructing nine datasets accordingly, and experimenting with widely adopted machine learning (ML) models to quantify the relationship between a ship's fuel consumption rate (ton/day, or ton/h) and its voyage-based factors (sailing speed, draft, trim, weather conditions, and sea conditions). The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification. The best ML models found are consistent with our previous studies, including Extremely randomized trees (ET), Gradient Tree Boosting (GB) and XGBoost (XG). Given the best dataset from data fusion, their R2 values over the training set are 0.999 or 1.000, and their R2 values over the test set are all above 0.966. Their fit errors with RMSE values are below 0.75 ton/day, and with MAT below 0.52 ton/day. These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis. The applicability of the selected datasets and ML models is also verified in a rolling horizon approach, resulting in a conjecture that a rolling horizon strategy of “5-month training + 1-month test/applicatoin” could work well in practice and sensor data of less than five months could be insufficient to train ML models.

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources. These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models. Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments. For each ship, nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested. Experimental results revealed the benefits of fusing voyage report data, AIS data, and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate. Over the best datasets, the performances of several decision tree-based models are promising, including Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG). With the best datasets, their R2 values over the training sets are all above 0.96 and mostly reach the level of 0.99–1.00, while their R2 values over the test sets are in the range from 0.75 to 0.90. Fit errors of ET, AB, GB, and XG on daily bunker fuel consumption, measured by RMSE and MAE, are usually between 0.8 and 4.5 ton/day. These results are slightly better than our previous study, which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data, compared with the estimated geographical positions derived from the great circle route, in retrieving weather and sea conditions the ship sails through.

Abstract Background Thousands of Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals Persistent follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis. Methods This study involves 462 laboratory-confirmed patients with COVID-19 who were admitted to Shenzhen Third People’s Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 287 patients were followed up from 90 to 150 days after the onset of the disease, and lung function tests were conducted about three months after the onset. The risk factors affecting the persistence of pulmonary fibrosis were identified through regression analysis and the prediction model of the persistence of pulmonary fibrosis was established. Results Parenchymal bands, irregular interfaces, reticulation and traction bronchiectasis were the most common CT features in all COVID-19 patients. During the 0–30, 31–60, 61–90, 91–120 and > 120 days after onset, 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. More than half of the COVID-19 patients revealed abnormal conditions in lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups. Conclusions Persistent pulmonary fibrosis was more likely to develop in patients with older age, higher BMI, severe/critical condition, fever, a longer viral clearance time, pre-existing disease and delayed hospitalization. Fibrosis developed in COVID-19 patients could be reversed in about a third of the patients after 120 days from onset. The pulmonary function of less than half of COVID-19 patients could turn to normal condition after three months from onset. An effective prediction model with an average area under the curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.

Background and Aim(s)Liver steatosis, as the main feature of metabolic associated fatty liver disease (MAFLD), was associated with the progression of liver fibrosis and metabolic syndrome, which needed to be estimated accurately. In this study, we explored the significance of appendicular skeletal muscle index (ASMI) in evaluating liver steatosis of MAFLD patients.MethodsEight hundred and ninety-nine cases with MAFLD from 2017 to 2018 National Health and Nutrition Examination Surveys (NHANES) database were included. All the analyzed data were obtained from NHANES database. The association between ASMI and liver steatosis were evaluated using R and EmpowerStats.ResultsMAFLD individuals were randomly divided into a training (n = 450) and validation cohort (n = 449). In univariate analysis, HbA1c, arms fat, arms lean mass, legs lean mass, trunk lean mass, total fat, total lean mass and ASMI were significantly associated with liver steatosis (p < 0.05). Multivariate analysis showed that HbA1c (OR: 1.6732; 95% CI: 1.2753–2.1929, p = 0.0002) and ASMI (OR: 1.6723; 95% CI: 1.1760–2.5204, p = 0.0052) were independently associated with severe liver steatosis. ASMI accurately evaluated severe liver steatosis with an AUROC of 0.73 and 0.81 in training and validation cohort, respectively. Compared with ASMI only, ASMI combined with HbA1c improved the AUROC to 0.85 and 0.88. Furthermore, the AUROC of our model was superior to FLI in the evaluation of liver steatosis.ConclusionASMI combined with HbA1c has good evaluation value for liver steatosis in MAFLD patients, which might be beneficial for the management of MAFLD clinically.

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.

The conversion of lignocellulose into valuable chemicals has been recognized as the key technology in green chemistry. However, selective degradation of hemicellulose and cellulose with the production of lignin is still a challenge. Therefore, a two-step process has been developed to degrade corncob into xylose and glucose under mild conditions. At first, the corncob was treated with the lower concentration of zinc chloride aqueous solution (30–55 w%) at 95 °C with a short reaction time (8–12 min) and 30.4 w% (selectivity = 89%) of xylose obtained with a solid residue of the composite of cellulose and lignin. Next, the solid residue was treated with a high concentration of zinc chloride aqueous solution (65–85 w%) at 95 °C for about 10 min, and 29.4 w% (selectivity = 92%) of glucose can be obtained. Combining the two steps, the total yield of xylose is 97%, while glucose is 95%. In addition, high pure lignin can be obtained simultaneously, which was confirmed using HSQC studies. Furthermore, for the solid residue of the first-step reaction, a ternary deep eutectic solvent (DES) (choline chloride/oxalic acid/1,4-butanediol, ChCl/OA/BD) has been used to separate the cellulose and lignin efficiently, and high-quality cellulose (Re-C) and lignin (Re-L) were obtained. Furthermore, it provides a simple method to disassemble the lignocellulose for monosaccharides, lignin, and cellulose.

Numerical methods, such as finite element or finite difference, have been widely used in the past decades for modeling solid mechanics problems by solving partial differential equations (PDEs). Differently from the traditional computational paradigm employed in numerical methods, physics-informed deep learning approximates the physics domains using a neural network and embeds physics laws to regularize the network. In this work, a physics-informed neural network (PINN) is extended for application to linear elasticity problems that arise in modeling non-uniform deformation for a typical open-holed plate specimen. The main focus will be on investigating the performance of a conventional PINN approach to modeling non-uniform deformation with high stress concentration in relation to solid mechanics involving forward and inverse problems. Compared to the conventional finite element method, our results show the promise of using PINN in modeling the non-uniform deformation of materials with the occurrence of both forward and inverse problems.

Villager participation has become a key breakthrough in rural landscape governance. Using the theory of planned behavior and the norm activation theory as frameworks, this study adopts the structural equation model to explore the influencing mechanism of villager participation in rural micro-landscapes based on data gathered from 414 villagers in a rural micro-landscape construction survey in Jinjiang, China. The results indicate that (1) integrated planned behavior theory and norm activation theory can better explain the influencing mechanism of villagers’ participation in rural micro-landscape construction; (2) perception, norm, attitude, and control dimensions significantly influence villagers’ participation behavior intention. The attitude dimension had the greatest influence, followed by the normative and control dimensions, while the perception dimension had the least influence on the procedure; and (3) according to the mediation results, natural environment perception, social environment perception, personal norm, social norm, participation attitude, result awareness, and self-efficacy all exert indirect effects on participation behavior based on villagers’ participation behavioral intention. The largest median effect value was result awareness, followed by personal norm, participation attitude, natural environment awareness, self-efficacy, and social norm. This study expands the theoretical framework and research content of planned behavior and clarifies the mechanism of the influencing factors of villagers’ participation in rural micro-landscapes, extending the theory of planned behavior to the research field of villagers’ participation, which has a guiding role in promoting the co-construction, co-governance, and sharing of rural landscapes.

An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2 also can reach 0.9954, which can provide decision support for the operation of shipping companies.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease with unknown etiology, high mortality and limited treatment options. It is characterized by myofibroblast proliferation and extensive deposition of extracellular matrix (ECM), which will lead to fibrous proliferation and the destruction of lung structure. Transforming growth factor-β1 (TGF-β1) is widely recognized as a central pathway of pulmonary fibrosis, and the suppression of TGF-β1 or the TGF-β1-regulated signaling pathway may thus offer potential antifibrotic therapies. JAK-STAT is a downstream signaling pathway regulated by TGF-β1. JAK1/2 inhibitor baricitinib is a marketed drug for the treatment of rheumatoid arthritis, but its role in pulmonary fibrosis has not been reported. This study explored the potential effect and mechanism of baricitinib on pulmonary fibrosis in vivo and in vitro. The in vivo studies have shown that baricitinib can effectively attenuate bleomycin (BLM)-induced pulmonary fibrosis, and in vitro studies showed that baricitinib attenuates TGF-β1-induced fibroblast activation and epithelial cell injury by inhibiting TGF-β1/non-Smad and TGF-β1/JAK/STAT signaling pathways, respectively. In conclusion, baricitinib, a JAK1/2 inhibitor, impedes myofibroblast activation and epithelial injury via targeting the TGF-β1 signaling pathway and reduces BLM-induced pulmonary fibrosis in mice.

There is currently an outbreak of respiratory disease caused by a novel coronavirus. The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it causes has been named coronavirus disease 2019 (COVID-19). More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was 1%–2%. No specific treatment has been reported. Herein, we examined the effects of favipiravir (FPV) versus lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million international unit (IU) twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million IU twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance median time was found for the FPV arm versus the control arm (4 d (interquartile range (IQR): 2.5–9) versus 11 d (IQR: 8–13), P < 0.001). The FPV arm also showed significant improvement in chest CT compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest CT. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.

The movement of the cervical spine should be restricted throughout the rehabilitation phase after it has been injured. Cervical orthosis is commonly utilized in clinical settings to guarantee cervical spine stability. However, to date, the investigations are limited to patient-specific cervical fixation orthoses. This study provides a new idea for making personalized orthoses. The CT data of the patient’s cervical spine were collected, then mimics were used for reconstructing the skin of the cervical spine, the Geomagic Studio was used for surface fitting, the Inspire Studio was used for structural topology optimization, redundant structures were removed, the resulting orthotics were postprocessed, and finally, it was printed with a 3D printer. No signs of pain or discomfort were observed during the wearing. The cervical spine range of motion in flexion, extension, lateral flexion, and rotation is all less than 8° after using the device. Low cost, quick manufacturing time, high precision, attractive appearance, lightweight structure, waterproof design, and practical customized orthotics for patients are all advantages of 3D printing technology in the field of orthopedics. Many possible benefits of using 3D printing to build new orthotics include unique design, stiffness, weight optimization, and improved biomechanical performance, comfort, and fit. Personalized orthotics may be designed and manufactured utilizing 3D printing technology.

Abstract Introduction In December, 2019, an outbreak of the coronavirus disease 2019 (COVID-19), which was caused by a novel coronavirus, started in Wuhan, China. So far, there is limited clinical evidence on the effect of corticosteroid therapy for this disease. This study aims to investigate the association between corticosteroid therapy and the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance among patients with mild COVID-19. Methods Patients with mild COVID-19 were enrolled from two medical centers in China between January 13, 2020 and February 29, 2020. Baseline characteristics and durations of RNA clearance were compared between the corticosteroid and non-corticosteroid therapy groups. The independent effects of corticosteroid therapy on the duration of RNA clearance were estimated by generalized linear models. Results Of 82 patients with a mild infection, 40 patients were male (48.8%), with a median age of 49 years (interquartile range, IQR 36–61). Among those patients, 36 patients (43.9%) received corticosteroid therapy. The adjusted multivariate models showed that the effects of corticosteroids were non-significant on the durations of onset to first RNA clearance [β 2.48, 95% CI (95% confidence interval) − 0.42 to 5.38, P = 0.0926] and to persistent RNA clearance (β 1.54, 95% CI − 1.41 to 4.48, P = 0.3016), and durations of therapy to first RNA clearance (β 2.16, 95% CI − 0.56 to 4.89, P = 0.1184) and to persistent RNA clearance (β 1.22, 95% CI − 1.52 to 3.95, P = 0.3787). Conclusions Corticosteroid therapy in patients with mild COVID-19 was not associated with the duration of SARS-CoV-2 clearance, suggesting that the use of corticosteroids may not be beneficial for patients with mild COVID-19 and should be prudently recommended in clinical practice. However, further studies are needed to verify the findings.

As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering the influence of weather conditions, route segmentation and weather data loading methods significantly affect the reliability of speed optimization results. Therefore, taking the ocean-going container ship as the research object, on the basis of constructing the main engine fuel consumption prediction model and shaft speed prediction model based on machine learning methods, a route segmentation and weather loading-speed optimization iterative algorithm is proposed in this study. Single-objective speed optimization research is then conducted based on the algorithm. The research results show that the proposed algorithm effectively reduces the difference between optimized fuel consumption and actual fuel consumption, and can achieve a fuel-saving rate between 2.1% and 5.2%. This study achieves an accurate and reliable prediction of ship fuel consumption and shaft speed, and solves the strong coupling problem between route segmentation, weather loading, and speed optimization by iterative optimization of ship speed. The proposed algorithm provides strong technical support for ships to achieve the goal of energy saving and emission reduction.

At present, there are many aerial-view datasets that contain motion data from vehicles in a variety of traffic scenarios. However, there are few datasets that have been collected under different weather conditions in an urban mixed-traffic scenario. In this study, we propose a framework for extracting vehicle motion data from UAV videos captured under various weather conditions. With this framework, we improve YOLOv5 (you only look once) with image-adaptive enhancement for detecting vehicles in different environments. In addition, a new vehicle-tracking algorithm called SORT++ is proposed to extract high-precision vehicle motion data from the detection results. Moreover, we present a new dataset that includes 7133 traffic images (1311 under sunny conditions, 961 under night, 3366 under rainy, and 1495 under snowy) of 106,995 vehicles. The images were captured by a UAV to evaluate the proposed method for vehicle orientation detection. In order to evaluate the accuracy of the extracted traffic data, we also present a new dataset of four UAV videos, each having 30,000+ frames, of approximately 3K vehicle trajectories collected under sunny, night, rainy, and snowy conditions, respectively. The experimental results show the high accuracy and stability of the proposed methods.

Antioxidant transcription factor NRF2 plays a pivotal role in cell ferroptosis. KLK lung adenocarcinoma (LUAD) is a specific molecular subtype of Kras-mutant LUAD. The activation of mutant Kras in combination with the inactivation of Lkb1 and Keap1 abnormally increases NRF2 expression, while high NRF2 confers KLK LUAD cell resistance to ferroptosis. This study assessed the inhibition of NRF2-GSH axis to sensitize a small molecule RSL3 to induce KLK LUAD cell ferroptosis and then explored the underlying molecular mechanisms. The data showed that the NRF2-GSH inhibition sensitized RSL3 induction of KLK LUAD cell ferroptosis in vitro, while RSL3 treatment reduced level of NRF2 protein in KLK LUAD during ferroptosis. Moreover, RSL3 treatment inhibited activity of the NRF2-GSH signaling during in KLK LUAD cell ferroptosis in vitro and in vivo. Mechanistically, the RSL3 reduction of NRF2 expression was through the promotion of NRF2 ubiquitination in KLK LUAD cells. In addition, RSL3 was able to directly bind to USP11, a recently identified de-ubiquitinase of NRF2, and inactivate USP11 protein to induce NRF2 protein ubiquitination and degradation in KLK LUAD cells. These data revealed a novel mechanism of RSL3 induction in KLK LUAD cell ferroptosis by suppression of the USP11-NRF2-GSH signaling. Future study will confirm RSL3 as a novel therapeutic approach in control of KLK lung adenocarcinoma.

Idiopathic pulmonary fibrosis is a progressive lung disease with high mortality and limited therapy that is characterized by epithelial cell damage and fibroblast activation. Ellagic acid is a natural polyphenol compound widely found in fruits and nuts that has multiple pharmacological activities. In this study, we explored the potential effects and mechanisms of Ellagic acid on pulmonary fibrosis in vivo and in vitro. In vivo studies showed that Ellagic acid significantly alleviated bleomycin (BLM)-induced pulmonary fibrosis in mice. In vitro experiments indicated that Ellagic acid could suppress Wnt signaling and attenuate Wnt3a-induced myofibroblast activation and the phosphorylation of Erk2 and Akt. Further studies showed that Ellagic acid could induce autophagy formation in myofibroblasts mainly by suppressing mTOR signaling and promoting apoptosis of myofibroblasts. In vivo experiments revealed that Ellagic acid significantly inhibited myofibroblast activation and promoted autophagy formation. Taken together, our results show that Ellagic acid effectively attenuates BLM-induced pulmonary fibrosis in mice by suppressing myofibroblast activation and promoting autophagy and apoptosis of myofibroblasts by inhibiting the Wnt signaling pathway.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterized by epithelial cell damage, myofibroblast activation, and collagen deposition. Multiple studies have documented that the Wnt/β-catenin pathway is aberrantly activated in IPF and plays a vital role in myofibroblast differentiation and activation. Kinases such as Src initiate Wnt/β-catenin signaling by phosphorylating β-catenin at tyrosine residues, which facilitates β-catenin accumulation in the nucleus and promotion of fibrosis progression. Nintedanib has been approved for the treatment of IPF as a multitargeted tyrosine kinase inhibitor. Nintedanib has been demonstrated to directly block Src, and whether it attenuates pulmonary fibrosis through regulating the Wnt/β-catenin pathway remains unclear. In this study, we found that nintedanib attenuated myofibroblast activation through inhibiting the expression of genes downstream of Wnt signaling such as Cyclin D1, Wisp1, and S100a4. Further experiments showed that nintedanib inhibited Wnt3a-induced β-catenin nuclear translocation through suppressing Src kinase activation and β-catenin Y654 phosphorylation. Additionally, Src knockdown fibroblasts exhibited a phenotype similar to that of the nintedanib treatment group, and the inhibitory effects of nintedanib were consistent with those of the Src kinase inhibitor KX2-391. In summary, our study shows that nintedanib exhibits an anti-fibrosis effect, partly by inhibiting the Src/β-catenin pathway.

Skin fibrotic diseases, such as keloids, are mainly caused by pathologic scarring of wounds during healing and characterized by benign cutaneous overgrowths of dermal fibroblasts. Current surgical and therapeutic modalities of skin fibrosis are unsatisfactory. Pinocembrin, a natural flavonoid, has been shown to possess a vast range of pharmacological activities including antimicrobial, antioxidant, anti-inflammatory, and anti-tumor activities. In this study we explored the potential effect and mechanisms of pinocembrin on skin fibrosis in vitro and in vivo. In vitro studies indicated that pinocembrin dose-dependently suppressed proliferation, migration, and invasion of keloid fibroblasts and mouse primary dermal fibroblasts. The in vivo studies showed that pinocembrin could effectively alleviate bleomycin (BLM)-induced skin fibrosis and reduce the gross weight and fibrosis-related protein expression of keloid tissues in xenograft mice. Further mechanism studies indicated that pinocembrin could suppress TGF-β1/Smad signaling and attenuate TGF-β1-induced activation of skin fibroblasts. In conclusion, our results demonstrate the therapeutic potential of pinocembrin for skin fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease with multiple causes, characterized by excessive myofibrocyte aggregation and extracellular matrix deposition. Related studies have shown that transforming growth factor-β1 (TGF-β1) is a key cytokine causing fibrosis, promoting abnormal epithelial–mesenchymal communication and fibroblast-to-myofibroblast transition. Fedratinib (Fed) is a marketed drug for the treatment of primary and secondary myelofibrosis, targeting selective JAK2 tyrosine kinase inhibitors. However, its role in pulmonary fibrosis remains unclear. In this study, we investigated the potential effects and mechanisms of Fed on pulmonary fibrosis in vitro and in vivo. In vitro studies have shown that Fed attenuates TGF-β1- and IL-6-induced myofibroblast activation and inflammatory response by regulating the JAK2/STAT3 signaling pathway. In vivo studies have shown that Fed can reduce bleomycin-induced inflammation and collagen deposition and improve lung function. In conclusion, Fed inhibited inflammation and fibrosis processes induced by TGF-β1 and IL-6 by targeting the JAK2 receptor.

Idiopathic pulmonary fibrosis (IPF) is a progressive, life-threatening lung disease characterized by the proliferation of myofibroblasts and deposition of extracellular matrix that results in irreversible distortion of the lung structure and the formation of focal fibrosis. The molecular mechanism of IPF is not fully understood, and there is no satisfactory treatment. However, most studies suggest that abnormal activation of transforming growth factor-β1 (TGF-β1) can promote fibroblast activation and epithelial to mesenchymal transition (EMT) to induce pulmonary fibrosis. Deglycosylated azithromycin (Deg-AZM) is a compound we previously obtained by removing glycosyls from azithromycin; it was demonstrated to exert little or no antibacterial effects. Here, we discovered a new function of Deg-AZM in pulmonary fibrosis. In vivo experiments showed that Deg-AZM could significantly reduce bleomycin-induced pulmonary fibrosis and restore respiratory function. Further study revealed the anti-inflammatory and antioxidant effects of Deg-AZM in vivo. In vitro experiments showed that Deg-AZM inhibited TGF-β1 signaling, weakened the activation and differentiation of lung fibroblasts, and inhibited TGF-β1-induced EMT in alveolar epithelial cells. In conclusion, our findings show that Deg-AZM exerts antifibrotic effects by inhibiting TGF-β1-induced myofibroblast activation and EMT.

Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.

Abstract Occupational inhalation of dust, such as crystalline silica, for prolonged periods in the workplace leads to fibrotic lung diseases worldwide. The mechanisms underlying the diseases are unknown, so that no effective treatment exists for these conditions. We found elevated levels of follistatin like 1 (FSTL1) in serum from patients with silicosis and in lungs from silica-induced mouse model. The induced Fstl1 regulated inflammation response via activation of nod-like receptor family, pyrin domain containing 3v (NLRP3) inflammasome-mediated IL-1β production from macrophages. Meanwhile, Fstl1 promoted fibrosis via positive regulation of TGF-β1 signaling. Haploinsufficiency of Fstl1 or blockage of FSTL1 with a neutralizing antibody was protective from silica-induced lung injury in mice in vivo. Our data suggest that Fstl1 plays an important role in lung fibrosis, and may serve as a novel therapeutic target for treatment of silicosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive and usually fatal lung disease that is characterized by fibroblast proliferation and extracellular matrix remodeling, which result in irreversible distortion of the lung’s architecture and the formation of focal fibrous hyperplasia. The molecular mechanism by which pulmonary fibrosis develops is not fully understood, and no satisfactory treatment currently exists. However, many studies consider that aberrant activation of TGF-β1 frequently promotes epithelial-mesenchymal transition (EMT) and fibroblast activation in pulmonary fibrosis. Cinobufagin (CBG), a traditional Chinese medicine, has been widely used for long-term pain relief, cardiac stimulation, and anti-inflammatory and local anesthetic treatments. However, its role in pulmonary fibrosis has not yet been established. We investigated the hypothesis that cinobufagin plays an inhibitory role on TGF-β1 signaling using a luciferase-reporter assay. We further explored the effect of cinobufagin on pulmonary fibrosis both in vitro and in vivo. The in vitro experiments showed that cinobufagin suppresses TGF-β1/Smad3 signaling in a dose-dependent manner, attenuates the activation and differentiation of lung fibroblasts and inhibits EMT induced by TGF-β1 in alveolar epithelial cells. The in vivo experiments indicated that cinobufagin significantly alleviates bleomycin-induced collagen deposition and improves pulmonary function. Further study showed that cinobufagin could attenuate bleomycin-induced inflammation and inhibit fibroblast activation and the EMT process in vivo. In summary, cinobufagin attenuates bleomycin-induced pulmonary fibrosis in mice via suppressing inflammation, fibroblast activation and epithelial–mesenchymal transition.

Idiopathic pulmonary fibrosis (IPF) is a fatal and age-related pulmonary disease. Nintedanib is a receptor tyrosine kinase inhibitor, and one of the only two listed drugs against IPF. Regorafenib is a novel, orally active, multi-kinase inhibitor that has similar targets to nintedanib and is applied to treat colorectal cancer and gastrointestinal stromal tumors in patients. In this study, we first identified that regorafenib could alleviate bleomycin-induced pulmonary fibrosis in mice. The in vivo experiments indicated that regorafenib suppresses collagen accumulation and myofibroblast activation. Further in vitro mechanism studies showed that regorafenib inhibits the activation and migration of myofibroblasts and extracellular matrix production, mainly through suppressing the transforming growth factor (TGF)-β1/Smad and non-Smad signaling pathways. In vitro studies have also indicated that regorafenib could augment autophagy in myofibroblasts by suppressing TGF-β1/mTOR (mechanistic target of rapamycin) signaling, and could promote apoptosis in myofibroblasts. In conclusion, regorafenib attenuates bleomycin-induced pulmonary fibrosis by suppressing the TGF-β1 signaling pathway.

Fstl1, a secreted protein of the BMP antagonist class, has been implicated in the regulation of lung development and alveolar maturation. Here we generated a Fstl1-lacZ reporter mouse line as well as a Fstl1 knockout allele. We localized Fstl1 transcript in lung smooth muscle cells and identified Fstl1 as essential regulator of lung smooth muscle formation. Deletion of Fstl1 in mice led to postnatal death as a result of respiratory failure due to multiple defects in lung development. Analysis of the mutant phenotype showed impaired airway smooth muscle (SM) manifested as smaller SM line in trachea and discontinued SM surrounding bronchi, which were associated with decreased transcriptional factors myocardin/serum response factor (SRF) and impaired differentiation of SM cells. Fstl1 knockout mice also displayed abnormal vasculature SM manifested as hyperplasia SM in pulmonary artery. This study indicates a pivotal role for Fstl1 in early stage of lung airway smooth muscle development.

Background: To understand cartilage oligomeric matrix protein (COMP) mechanism network from human normal adjacent tissues to lung adenocarcinoma.

Follistatin-like 1 (FSTL1) is a secreted bone morphogenetic protein (BMP) antagonist, and it plays a crucial role in normal lung development. Deletion of Fstl1 leads to postnatal death in mice due to respiratory failure. To further explore the role of FSTL1 in mouse lung development, we created a transgene SFTPC-Fstl1 allele mouse displaying significant epithelial overexpression of Fstl1 in all stages of lung development. However, epithelial overexpression of Fstl1 did not alter lung morphogenesis, epithelial differentiation and lung function. Moreover, we found that FSTL1 function was blocked by the epithelial polarization, which was reflected by the remarkable apical secretion of FSTL1 and the basolateral BMP signaling. Taken together, this study demonstrates that tightly spatial interaction of FSTL1 and BMP signaling plays an essential role in lung development.