Ultra-high pressure treatment improve the content of characteristic aromatic components of melon juice from the view of physical changes.

Introduction: The effectiveness of ultra-high pressure (UHP) technology in retaining the flavor of fresh fruit and vegetable juices has been acknowledged in recent years. Along with previously hypothesized conclusions, the improvement in melon juice flavor may be linked to the reduction of its surface tension through UHP. Methods: In this paper, the particle size, free-water percentage, and related thermodynamic parameters of melon juice were evaluated in a physical point for a deeper insight. Results: The results showed that the UHP treatment of P2-2 (200 MPa for 20 min) raised the free water percentage by 7,000 times than the other treatments and both the melting enthalpy, binding constant and Gibbs free energy of P2-2 were minimized. This significantly increased the volatility of characteristic aromatic compounds in melon juice, resulting in a 1.2-5 times increase in the content of aromatic compounds in the gas phase of the P2-2 group compared to fresh melon juice.

Characterization of an agmatine N-acetyltransferase from Bactrocera dorsalis that modulates ovary development.

Agmatine N-acetyltransferase (AgmNAT), which catalyzes the formation of N-acetylagmatine from acetyl-CoA and agmatine, is a member of the GCN5-related N-acetyltransferase family. So far, knowledge of the physiological roles of AgmNAT in insects is limited. Here, we identified one gene encoding protein homologous to that of Drosophila AgmNAT using sequence information from an activity-verified Drosophila AgmNAT in a BLAST search of the Bactrocera dorsalis genome. We expressed and purified B. dorsalis AgmNAT in Escherichia coli and used the purified enzyme to define the substrate specificity for acyl-CoA and amine substrates. Our application of the screening strategy to BdorAgmNAT led to the identification of agmatine as the best amine substrate for this enzyme, with the highest kcat/Km value. We successfully obtained a BdorAgmNAT knockout strain based on a wild-type strain (WT) using the CRISPR/Cas9 technique. The ovary development of the BdorAgmNAT knockout mutants was delayed for 10 days compared with the WT specimens. Moreover, mutants had a much smaller mature ovary size and laid far fewer eggs than WT. Loss of function of BdorAgmNAT caused by RNAi with mature WT females did not affect their fecundity. These findings indicate that BdorAgmNAT is critical for oogenesis. Our data provide the first evidence for AgmNAT in regulating ovary development.

Metabolic pathway-based subtyping reveals distinct microenvironmental states associated with diffuse large B-cell lymphoma outcomes.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease that requires personalized clinical treatment. Assigning patients to different risk categories and cytogenetic abnormality and genetic mutation groups has been widely applied for prognostic stratification of DLBCL. Increasing evidence has demonstrated that dysregulated metabolic processes contribute to the initiation and progression of DLBCL. Metabolic competition within the tumor microenvironment is also known to influence immune cell metabolism. However, metabolism- and immune-related stratification has not been established. Here, 1660 genes involved in 84 metabolic pathways were selected and tested to establish metabolic clusters (MECs) of DLBCL. MECs established based on independent lymphoma datasets distinguished different survival outcomes. The CIBERSORT algorithm and EcoTyper were applied to quantify the relative abundance of immune cell types and identify variation in cell states for 13 lineages comprising the tumor micro environment among different MECs, respectively. Functional characterization showed that MECs were an indicator of the immune microenvironment and correlated with distinctive mutational characteristics and oncogenic signaling pathways. The novel immune-related MECs exhibited promising clinical prognostic value and potential for informing DLBCL treatment decisions.

Unraveling Stoichiometry Effect in Nickel-Tungsten Alloys for Efficient Hydrogen Oxidation Catalysis in Alkaline Electrolytes.

Anion-exchange membrane fuel cells provide the possibility to use platinum group metal-free catalysts, but the anodic hydrogen oxidation reaction (HOR) suffers from sluggish kinetics and its source is still debated. Here, over nickel-tungsten (Ni-W) alloy catalysts, we show that the Ni:W ratio greatly governs the HOR performance in alkaline electrolyte. Experimental and theoretical studies unravel that alloying with W can tune the unpaired electrons in Ni, tailoring the potential of zero charge and the catalytic surface to favor hydroxyl adsorption (OHad). The OHad species coordinately interact with potassium (K+) ions, which break the K+ solvation sheath to leave free water molecules, yielding an improved connectivity of hydrogen-bond networks. Consequently, the optimal Ni17W3 alloy exhibits alkaline HOR activity superior to the state-of-the-art platinum on carbon (Pt/C) catalyst and operates steadily with negligible decay after 10,000 cycles. Our findings offer new understandings of alloyed HOR catalysts and will guide rational design of next-generation catalysts for fuel cells.

Proficiency testing for event-specific quantification of genetically modified maize MON87427: Performance assessment based on the metrologically traceable reference values as assigned values.

The Asia Pacific Metrology Program and the Accreditation Cooperation joint Proficiency Testing (PT) program for the quantification of genetically modified maize MON87427 was organized by the National Institute of Metrology, China, to enhance the measurement accuracy and metrological traceability in the region. Certified reference materials were employed as test samples; metrologically traceable certified reference values served as PT reference values (PTRVs) for evaluating the participants results. The consensus values obtained from the participants were higher than the assigned values, potentially due to the systematic effects of DNA extraction process. The participants' relatively poor overall performance by the ζ-score compared with z-score demonstrates their need to thoroughly investigate quantification bias to elevate the measurement capability of genetically modified (GM) content and deepen their understanding of uncertainty estimation. This program confirmed the importance of using metrologically traceable reference values instead of consensus values as PTRV for reliable performance assessment.

Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces.

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the "6S" goals of parallel driving.

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer.

BACKGROUND: Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties. AIM: To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer. METHODS: We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram. RESULTS: Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834). CONCLUSION: The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.

Treatment patterns, clinical outcomes and gene mutation characteristics of hepatitis B virus-associated mantle cell lymphoma.

Mantle cell lymphoma (MCL) is an uncommon and incurable B-cell lymphoma subtype that has an aggressive course. Hepatitis B virus (HBV) infection has been associated with an increased risk for B-cell lymphomas, and is characterized by distinct clinical and genetic features. Here, we showed that 9.5% of MCL Chinese patients were hepatitis B surface antigen positive (HBsAg+). Compared to HBsAg-negative (HBsAg-) patients, HBsAg+ MCL patients had a greater incidence of elevated lactate dehydrogenase (LDH), but no difference was observed in the other clinical characteristics, including sex, age, ECOG ps, Ann Arbor stage, MIPI, extranodal involvement and Ki-67. The HD-AraC (high-dose cytarabine) regimen was the main first-line induction regimen for younger HBsAg+ patients, and cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) were used for elderly patients. HBsAg seropositivity was associated with a significantly shorter PFS than HBsAg seronegativity when patients were treated with rituximab or CHOP-based regimens. Compared with CHOP, the HD-AraC regimen was associated with longer PFS in HBsAg+ patients. Treatment with a Bruton tyrosine kinase inhibitor (BTKi) alone can also cause HBV reactivation. Among the 74 patients who underwent targeted deep sequencing (TDS), the nonsynonymous mutation load of HBsAg+ MCL patients was greater than that of HBsAg- MCL patients. HDAC1, TRAF5, FGFR4, SMAD2, JAK3, SMC1A, ZAP70, BLM, CDK12, PLCG2, SMO, TP63, NF1, PTPR, EPHA2, RPTOR and FIP1L1 were significantly enriched in HBsAg+ MCL patients.

Study on sex differences and potential clinical value of three-dimensional computerized tomography pelvimetry in rectal cancer patients.

BACKGROUND: Laparoscopic rectal cancer radical surgery is a complex procedure affected by various factors. However, the existing literature lacks standardized parameters for the pelvic region and soft tissues, which hampers the establishment of consistent conclusions. AIM: To comprehensively assess 16 pelvic and 7 soft tissue parameters through computerized tomography (CT)-based three-dimensional (3D) reconstruction, providing a strong theoretical basis to address challenges in laparoscopic rectal cancer radical surgery. METHODS: We analyzed data from 218 patients who underwent radical laparoscopic surgery for rectal cancer, and utilized CT data for 3D pelvic reconstruction. Specific anatomical points were carefully marked and measured using advanced 3D modeling software. To analyze the pelvic and soft tissue parameters, we employed statistical methods including paired sample t-tests, Wilcoxon rank-sum tests, and correlation analysis. RESULTS: The investigation highlighted significant sex disparities in 14 pelvic bone parameters and 3 soft tissue parameters. Males demonstrated larger measurements in pelvic depth and overall curvature, smaller measurements in pelvic width, a larger mesorectal fat area, and a larger anterior-posterior abdominal diameter. By contrast, females exhibited wider pelvises, shallower depth, smaller overall curvature, and an increased amount of subcutaneous fat tissue. However, there were no significant sex differences observed in certain parameters such as sacral curvature height, superior pubococcygeal diameter, rectal area, visceral fat area, waist circumference, and transverse abdominal diameter. CONCLUSION: The reconstruction of 3D CT data enabled accurate pelvic measurements, revealing significant sex differences in both pelvic and soft tissue parameters. This study design offer potential in predicting surgical difficulties and creating personalized surgical plans for male rectal cancer patients with a potentially "difficult pelvis", ultimately improving surgical outcomes. Further research and utilization of these parameters could lead to enhanced surgical methods and patient care in laparoscopic rectal cancer radical surgery.

Biotransformation of Bioactive Metabolites in Cassiae Semen by Endogenous Enzymes and Probiotics.

The present study evaluated the potential of endogenous enzymes and probiotics in transforming bioactive metabolites to reduce the purgative effect and improve the functional activity of Cassiae Semen and verified and revealed the biotransformation effect of endogenous enzymes. Although probiotics, especially Lactobacillus rhamnosus, exerted the transformation effect, the endogenous enzymes proved to be more effective in transforming the components of Cassiae Semen. After biotransformation by endogenous enzymes for 12 h, the levels of six anthraquinones in Cassiae Semen increased by at least 2.98-fold, and free anthraquinones, total phenolics, and antioxidant activity also showed significant improvement, accompanied by an 82.2% reduction in combined anthraquinones responsible for the purgative effect of Cassiae Semen. Further metabolomic analysis revealed that the biotransformation effect of endogenous enzymes on the bioactive metabolites of Cassiae Semen was complex and diverse, and the biotransformation of quinones and flavonoids was particularly prominent and occurred by three primary mechanisms, hydrolyzation, methylation, and dimerization, might under the action of glycosyl hydrolases, SAM-dependent methyltransferases, and CYP450s. Accordingly, biotransformation by endogenous enzymes emerges as a mild, economical, food safety risk-free, and effective strategy to modify Cassiae Semen into an excellent functional food.

Role of Edge Reconstruction in the Synthesis of Few-Layer Black Phosphorene.

Recent advancements in preparing few-layer black phosphorene (BP) are hindered by edge reconstruction challenges. Our previous studies have revealed the factors contributing to the difficulty of growing few-layer BP. In this study, we have successfully identified three reconstructed edges in bi- and multilayer BP through a combination of the crystal structure analysis by particle swarm optimization (CALYPSO) global structure search and density functional theory (DFT). Notably, the reconstruction between adjacent layers proves more beneficial than self-passivation or maintaining pristine edges. Among the reconstructed edges, the reconstructed ZZ edge is the most stable, regardless of the number of layers. Calculated electronic band structures reveal a significant transition in the electronic properties of black phosphorus nanoribbons (BPNRs), changing from metallic to semiconducting. This insight not only enhances the understanding of the fundamental properties of BP but also provides valuable theoretical guidance for the experimental growth of BPNRs or black phosphorus nanowires (BPNWs).

A review of type 3 Gaucher disease: unique neurological manifestations and advances in treatment.

Gaucher disease (GD) is a rare lysosomal storage disease that is caused by mutations in the GBA gene. It is classified into three main phenotypes according to the patient's clinical presentation. Of these, chronic neuronopathic GD (GD3) is characterized by progressive neurological damage. Understanding the unique neurological manifestations of GD3 has important diagnostic and therapeutic implications. Our article summarizes the neurological symptoms specific to GD3 and related therapeutic advances, and it highlights the relevance of the gene to clinical symptoms, so as to provide a reference for the diagnosis and treatment of GD3.

Association of hypoglycemic events with cognitive impairment in patients with type 2 diabetes mellitus: Protocol for a dose-response meta-analysis.

INTRODUCTION: With an incidence rate as high as 46%-58%, hypoglycemia is a common complication of glycemic management among those suffering from type 2 diabetes mellitus(T2DM). According to preclinical research, hypoglycemia episodes may impair cognition by harming neurons. However, there is still controversy regarding the clinical evidence for the relationship between hypoglycemic events and the likelihood of cognitive impairment. Furthermore, little research has been done on the dose-response association between hypoglycemia incidents and the possibility of cognitive impairment. To address these knowledge gaps, the present research intends to update the comprehension of the association among hypoglycemic events and the risk of cognitive impairment and to clarify the correlation between dose and response by incorporating the most recent investigations. METHOD AND ANALYSIS: This work has developed a protocol for a systematic review and meta-analysis that will examine, via a well-organized assessment of several databases, the relationship between the incidence of hypoglycemia and the probability of cognitive impairment. Observational studies investigating the connection between hypoglycemia episodes and cognitive impairment will be included. The databases that will be searched are PubMed, Web of Science, the Chinese Biomedical Literature Database (CBM), Cochrane Library, Embase, the China National Knowledge (CNKI), Wan Fang, the Chinese Science and Technology Periodical Database (VIP), and Du Xiu. Literature from the establishment of each database to December 2023 will be included in the search. Two researchers will independently screen the studies that satisfy the requirements for both inclusion and exclusion. A third researcher will be asked to mediate any disputes. The methodological caliber of the studies included will be assessed utilizing the Newcastle-Ottawa Scale (NOS) or the Joanna Briggs Institute (JBI) critical appraisal method. With regard to GRADE, which stands for Grading of Recommendations, Assessment, Development, and Evaluation, the quality of the evidence will be evaluated. ROBIS Tool will be used to evaluate the risk of bias in the development of the systematic review. If the data is accessible, meta-analysis and dose-response curve analysis will be employed by Stata software. However, if the data does not allow for such analysis, a descriptive review will be performed. DISCUSSION AND CONCLUSION: Hypoglycemic episodes may raise the likelihood of cognitive impairment, according to earlier investigations. This study will update the relevant evidence and explore the dose-response connection between hypoglycemic episodes and cognitive impairment. The results of this review will have significant effects on decision-making by individuals with diabetes, healthcare providers, and government policy institutions. TRIAL REGISTRATION: Prospero registration number: CRD42023432352.

Upregulation of tumor suppressor PIAS3 by Honokiol promotes tumor cell apoptosis via selective inhibition of STAT3 tyrosine 705 phosphorylation.

The natural product Honokiol exhibits robust antitumor activity against a range of cancers, and it has also received approval to undergo phase I clinical trial testing. We confrmed that honokiol can promote the apoptotic death of tumor cells through cell experiments. Then siRNA constructs specific for PIAS3, PIAS3 overexpression plasmid and the mutation of the STAT3 Tyr705 residue were used to confirm the mechanism of Honokiol-induced apoptosis. Finally, we confrmed that honokiol can promote PIAS3 upregulation, in turn suppressing STAT3 Tyr705 phosphorylation through the in vivo and in vitro experiments. Honokiol was ultimately found to reduce tumor cell viability by promoting apoptosis through a mechanism dependent on the ability of Honokiol to promote PIAS3 upregulation and the selective inhibition of p-STAT3 (Tyr705) without affecting p-STAT3 (Ser727) or p-STAT1 (Tyr701) levels. PIAS3 knockdown and overexpression in tumor cells altered STAT3 activation and associated DNA binding activity through the control of Tyr705 phosphorylation via PIAS3-STAT3 complex formation, ultimately shaping Honokiol-induced tumor cell apoptosis. Honokiol was also confirmed to significantly prolong the survival of mice bearing xenograft tumors in a PIAS3-dependent fashion. Together, these findings highlight a novel pathway through which Honokiol can promote PIAS3 upregulation, in turn suppressing STAT3 Tyr705 phosphorylation and promoting the apoptotic death of tumor cells.

Knowledge-Embedded Mutual Guidance for Visual Reasoning.

Visual reasoning between visual images and natural language is a long-standing challenge in computer vision. Most of the methods aim to look for answers to questions only on the basis of the analysis of the offered questions and images. Other approaches treat knowledge graphs as flattened tables to search for the answer. However, there are two major problems with these works: 1) the model disregards the fact that the world we surrounding us interlinks our hearing and speaking of natural language and 2) the model largely ignores the structure of the acrlong KG. To overcome these challenging deficiencies, a model should jointly consider two modalities of vision and language, as well as the rich structural and logical information embedded in knowledge graphs. To this end, we propose a general joint representation learning framework for visual reasoning, namely, knowledge-embedded mutual guidance. It realizes mutual guidance not only between visual data and natural language descriptions but also between knowledge graphs and reasoning models. In addition, it exploits the knowledge derived from the reasoning model to boost knowledge graphs when applying the visual relation detection task. The experimental results demonstrate that the proposed approach performs dramatically better than state-of-the-art methods on two benchmarks for visual reasoning.

Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting.

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://github.com/bridgeqiqi/DREAM.

SASAN: Shape-Adaptive Set Abstraction Network for Point-Voxel 3D Object Detection.

Point-voxel 3D object detectors have achieved impressive performance in complex traffic scenes. However, they utilize the 3D sparse convolution (spconv) layers with fixed receptive fields, such as voxel-based detectors, and inherit the fixed sphere radius from point-based methods for generating the features of keypoints, which make them weak in adaptively modeling various geometrical deformations and sizes of real objects. To tackle this issue, we propose a shape-adaptive set abstraction network (SASAN) for point-voxel 3D object detection. First, the proposal and offset generation module is adopted to learn the coordinates and confidences of 3D proposals and shape-adaptive offsets of the certain number of offset points for each voxel. Meanwhile, an extra offset supervision task is employed to guide the learning of shifting values of offset points, aiming at motivating the predicted offsets to preferably adapt to the various shapes of objects. Then, the shape-adaptive set abstraction module is proposed to extract multiscale keypoints features by grouping the neighboring offset points' features, as well as features learned from adjacent raw points and the 2-D bird-view map. Finally, the region of interest (RoI)-grid proposal refinement module is used to aggregate the keypoints features for further proposal refinement and confidence prediction. Extensive experiments on the competitive KITTI 3D detection benchmark demonstrate that the proposed SASAN gains superior performance as compared with state-of-the-art methods.

[Aqueous extract of Epimedium sagittatum mitigates pulmonary fibrosis in mice].

This study aims to investigate the intervention effect of the aqueous extract of Epimedium sagittatum Maxim on the mouse model of bleomycin(BLM)-induced pulmonary fibrosis, so as to provide data support for the clinical treatment of pulmonary fibrosis. Ninety male C57BL/6N mice were randomized into normal(n=10), model(BLM, n=20), pirfenidone(PFD, 270 mg·kg~(-1), n=15), and low-, medium-, and high-dose E. sagittatum extract(1.67 g·kg~(-1), n=15; 3.33 g·kg~(-1), n=15; 6.67 g·kg~(-1), n=15) groups. The model of pulmonary fibrosis was established by intratracheal instillation of BLM(5 mg·kg~(-1)) in the other five groups except the normal group, which was treated with an equal amount of normal saline. On the day following the modeling, each group was treated with the corresponding drug by gavage for 21 days. During this period, the survival rate of the mice was counted. After gavage, the lung index was calculated, and the morphology and collagen deposition of the lung tissue were observed by hematoxylin-eosin(HE) and Masson staining, respectively. The levels of reactive oxygen species(ROS) in lung cell suspensions were measured by flow cytometry. The levels of glutathione peroxidase(GSH-Px), total superoxide dismutase(T-SOD), and malondialdehyde(MDA) the in lung tissue were measured. Terminal-deoxynucleoitidyl transferase-mediated nick-end labeling(TUNEL) was employed to examine the apoptosis of lung tissue cells. The content of interleukin-6(IL-6), chemokine C-C motif ligand 2(CCL-2), matrix metalloproteinase-8(MMP-8), transforming growth factor-beta 1(TGF-ß1), alpha-smooth muscle actin(α-SMA), E-cadherin, collagen Ⅰ, and fibronectin in the lung tissue was measured by enzyme-linked immunosorbent assay(ELISA). The expression levels of F4/80, Ly-6G, TGF-ß1, and collagen Ⅰ in the lung tissue were determined by immunohistochemistry. The mRNA levels of CCL-2, IL-6, and MMP-7 in the lung tissue were determined by qRT-PCR. The content of hydroxyproline(HYP) in the lung tissue was determined by alkaline hydrolysation. The expression of α-SMA and E-cadherin was detected by immunofluorescence, and the protein levels of α-SMA, vimentin, E-cadherin in the lung tissue were determined by Western blot. The results showed the aqueous extract of E. sagittatum increased the survival rate, decreased the lung index, alleviated the pathological injury, collagen deposition, and oxidative stress in the lung tissue, and reduced the apoptotic cells. Furthermore, the aqueous extract of E. sagittatum down-regulated the protein levels of F4/80 and Ly-6G and the mRNA levels of CCL-2, IL-6, and MMP-7 in the lung tissue, reduced the content of IL-6, CCL-2, and MMP-8 in the alveolar lavage fluid. In addition, it lowered the levels of HYP, TGF-ß1, α-SMA, collagen Ⅰ, fibronectin, and vimentin, and elevated the levels of E-cadherin in the lung tissue. The aqueous extract of E. sagittatum can inhibit collagen deposition, alleviate oxidative stress, and reduce inflammatory response by regulating the expression of the molecules associated with epithelial-mesenchymal transition, thus alleviating the symptoms of bleomycin-induced pulmonary fibrosis in mice.

Toward parallel intelligence: An interdisciplinary solution for complex systems.

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in the artificial systems, computational experiments, and parallel execution (ACP) approach has been developed. The method cultivates a cycle termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.