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Abstract The outbreak of coronavirus disease 2019 (COVID‐19) stimulates a body of studies for better investigating the essential mechanisms and clinical characteristics of this disease. Notwithstanding, the underlying mechanisms of multiple organ failure (MOF) remain unclear. Understanding the mechanism of COVID‐19‐stimulated programmed cell death (RCD) may contribute to better strategies for these patients. Based on the exploration of COVID‐19‐related basic science reports and clinical research, we speculate that ferroptosis, a recently identified RCD, might be a substantial stimulator for MOF in COVID‐19, and it may act as a novel treatment target. However, the published investigations to determine how ferroptosis works in severe acute respiratory syndrome coronavirus 2 infection are still lacking. This review reveals new crucial missing links in our prevailing understanding of the mechanisms that ferroptosis‐triggered MOF in COVID‐19 patients. We also specifically summarize several literatures evidence about the functional food components as the nutritional supplement for COVID‐19 by its characteristics of ferroptosis inhibition. We hope this hypothesis may launch a new wave of studies to unveil the relationship between ferroptosis and COVID‐19 and explore more ferroptosis‐related functional food components in daily life for preventing the COVID‐19 and/or to improve the outcome during therapy.

Hydrogels formed by self-assembling peptides with low toxicity and high biocompatibility have been widely used in food and biomedical fields. Seafood contains rich protein resources and is also one of the important sources of natural bioactive peptides. The self-assembled peptides in seafood have good functional activity and are very beneficial to human health. In this review, the sequence of seafood self-assembly peptide was introduced, and the preparation, screening, identification and characterization. The rule of self-assembled peptides was elucidated from amino acid sequence composition, amino acid properties (hydrophilic, hydrophobic and electric), secondary structure, interaction and peptide properties (hydrophilic and hydrophobic). It was introduced that the application of hydrogels formed by self-assembled peptides, which lays a theoretical foundation for the development of seafood self-assembled peptides in functional foods and the application of biological materials.

IntroductionType 2 diabetes mellitus (T2DM) is a major cause of atherosclerosis (AS). However, definitive evidence regarding the common molecular mechanisms underlying these two diseases are lacking. This study aimed to investigate the mechanisms underlying the association between T2DM and AS.MethodsThe gene expression profiles of T2DM (GSE159984) and AS (GSE100927) were obtained from the Gene Expression Omnibus, after which overlapping differentially expressed gene identification, bioinformatics enrichment analyses, protein–protein interaction network construction, and core genes identification were performed. We confirmed the discriminatory capacity of core genes using receiver operating curve analysis. We further identified transcription factors using TRRUST database to build a transcription factor–mRNA regulatory network. Finally, the immune infiltration and the correlation between core genes and differential infiltrating immune cells were analyzed.ResultsA total of 27 overlapping differentially expressed genes were identified under the two-stress conditions. Functional analyses revealed that immune responses and transcriptional regulation may be involved in the potential pathogenesis. After protein–protein interaction network deconstruction, external datasets, and qRT-PCR experimental validation, four core genes (IL1B, C1QA, CCR5, and MSR1) were identified. ROC analysis further showed the reliable value of these core genes. Four common differential infiltrating immune cells (B cells, CD4+ T cells, regulatory T cells, and M2 macrophages) between T2DM and AS datasets were selected based on immune cell infiltration. A significant correlation between core genes and common differential immune cells. Additionally, five transcription factors (RELA, NFκB1, JUN, YY1, and SPI1) regulating the transcription of core genes were mined using upstream gene regulator analysis.DiscussionIn this study, common target genes and co-immune infiltration landscapes were identified between T2DM and AS. The relationship among five transcription factors, four core genes, and four immune cells profiles may be crucial to understanding T2DM complicated with AS pathogenesis and therapeutic direction.

Alcohol abuse has recently become a serious health concern worldwide, and the incidence of alcoholic liver disease (ALD) is rapidly increasing with high morbidity and mortality. Ferroptosis is a newly recognized form of regulated cell death caused by the iron-dependent accumulation of lipid peroxidation. Here we showed that the circadian clock protein BMAL1 in hepatocytes is both necessary and sufficient to protect against ALD by mitigating ferroptosis. Upon exposure to alcohol (5 % Lieber-DeCarli liquid alcohol diet for 10 days before binged alcohol with 5 g/kg body weight in vivo, 300 mmol/L for 12 h in vitro, respectively), the content of iron, reactive oxygen species (ROS) and malondialdehyde (MDA) was boosted significantly while glutathione (GSH) was decreased that mainly based on the downregulated protein expression of ferritin heavy chain (FTH), ferroportin (FPN), heme oxygenase1(HO-1) and anti-cystine/glutamate antiporter (SLC7A11), while these changes could be abolished by ferroptosis inhibitor Ferrostatin-1[Fer-1 (5 mg/kg body weight for 10 days in vivo, 10 μmol/L for 2 h in vitro, respectively)]. Further study indicated that the alcohol could activate the protein expression of brain and muscle arnt-like protein-1 (BMAL1) which exerts a protective effect against ferroptosis through promoting nuclear factor erythroid 2-related factor 2 (Nrf2) translocation into nuclear and subsequently stimulating its downstream proteins FTH, FPN, glutathione peroxidase 4 activity (GPX4), HO-1, SLC7A11, while knockdown of BMAL1 and Nrf2 by RNA interference further downregulated the expression of these protein and thus promoting ferroptosis in response to alcohol. Collectively, our results unveiled that the protective action of BMAL1 during alcohol challenge depends on its ability to activate Nrf2-ARE antiferroptosis pathway and targeting hepatic BMAL1 to dampen hepatic ferroptosis signaling may have therapeutic potential for ALD.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework’s reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

In this study, ultrafiltration fractions (3 k Da, HMH) and solid-phase extraction fractions (hydrophilic hydrolysate, HIH; hydrophobic hydrolysate, HOH) from trypsin hydrolysate purified from croceine croaker (Pseudosciaena crocea) isolate were obtained to investigate the cryoprotective effects of the different fractions, achieved by means of maceration of turbot fish meat after three freeze-thaw cycles. Alterations in the texture, color, moisture loss, myofibrillar protein oxidation stability and conformation, and microstructure of the fish were analyzed after freezing and thawing. The results demonstrate that HIH maximized the retention of fish texture, reduced moisture loss, minimized the oxidation and aggregation of myofibrillar proteins, and stabilized the secondary and tertiary structures of myofibrillar proteins compared to the control group. In conclusion, the HIH component in the trypsin hydrolysates of croceine croaker significantly contributes to minimizing freeze damage in fish meat and acts as an anti-freezing agent with high industrial application potential.

This paper systematically investigated the impact mechanisms of proton irradiation, atomic oxygen irradiation and space debris collision, both individually and in combination, on the laser damage threshold and damage evolution characteristics of HfO2/SiO2 triple-band high-reflection films and fused silica substrates using a simulated near-Earth space radiation experimental system. For the high-reflection film samples, the damage thresholds decreased by 15.38%, 13.12% and 46.80% after proton, atomic oxygen and simulated space debris (penetration) irradiation, respectively. The coupling irradiation of the first two factors resulted in a decrease of 26.93%, while the combined effect of all the three factors led to a reduction of 63.19%. Similarly, the fused silica substrates exhibited the same pattern of laser damage performance degradation. Notably, the study employed high-precision fixed-point in situ measurement techniques to track in detail the microstructural changes, surface roughness and optical-thermal absorption intensity before and after proton and atomic oxygen irradiation at the same location, thus providing a more accurate and comprehensive analysis of the damage mechanisms. In addition, simulations were conducted to quantitatively analyze the transmission trajectories and concentration distribution lines of protons and atomic oxygen incident at specific angles into the target material. The research findings contribute to elucidating the laser damage performance degradation mechanism of transmissive elements in near-Earth space environments and provide technical support for the development of high-damage-threshold optical components resistant to space radiation.

AbstractTo improve the anti-metastasis effects of honokiol (HNK) on breast cancer, we designed cationic liposomes (Lip) in which HNK was encapsulated into Lip, and its surface was modified with negatively charged polysialic acid (PSA-Lip-HNK) for efficient treatment of breast cancer. PSA-Lip-HNK possessed a homogeneous spherical shape and high encapsulation efficiency. In vitro 4T1 cell experiments indicated that PSA-Lip-HNK increased cellular uptake and cytotoxicity via the endocytosis pathway mediated by PSA and selectin receptors. Furthermore, the significant antitumor metastasis impact of PSA-Lip-HNK was confirmed by wound healing and cell migration and invasion. Enhanced in vivo tumor accumulation of the PSA-Lip-HNK was observed in 4T1 tumor-bearing mice by living fluorescence imaging. For in vivo antitumor experiments using 4T1 tumor-bearing mice, PSA-Lip-HNK exhibited a higher tumor growth and metastasis inhibition compared with unmodified liposomes. Therefore, we believe that PSA-Lip-HNK well combined biocompatible PSA nano-delivery and chemotherapy, providing a promising drug delivery approach for metastatic breast cancer therapy.

Abstract Background Analyzing disease–disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear. Methods In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs. Results Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP–ICM, 5 pairs for both ICM–CHF and SAP–CHF. SAP–ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP–CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP–ICM–CHF, while SAP–ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K–Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP–ICM–CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP–ICM–CHF, and 53.85% SVs were defined as protein replication in SAP–ICM, while ICM–CHF genes were mainly affected by protein deletion. Conclusion The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.

BackgroundInfective endocarditis (IE), though uncommon, is a potentially lethal disease. Blood culture-negative endocarditis (BCNIE) accounts for 2.5%–31% of all cases of IE and can lead to life-threatening complications, including aortic root pseudoaneurysm. It is associated with considerable diagnostic and therapeutic dilemmas. TrueVue and TrueVue Glass include the latest two technologies applied in advanced three-dimensional echocardiography, which allow for novel photorealistic images of cardiac structures, and provide abundant previously unavailable diagnostic information. Herein, based on a series of novel three-dimensional echocardiographic methods, we report a case of BCNIE with aortic valve involvement, leading to aortic valve perforation and prolapse, and developing into a giant aortic root pseudoaneurysm.Case summaryIn this study, we presented a case of a 64-year-old man exhibiting symptoms of intermittent fever, asthenia, and dyspnea following light exertion. Physical examination, laboratory tests, and electrocardiograms were suspected of IE, though the results of blood cultures were exactly negative. Three-dimensional transthoracic echocardiography, as well as a series of novel advanced techniques, was adopted to clearly visualize the lesions of the aortic valve and aortic root. However, despite active medical treatment modalities, the patient eventually suffered from a sudden, unexpected death 5 days later.ConclusionBCNIE with aortic valve involvement and development into a giant aortic root pseudoaneurysm is a rare and serious clinical event. In addition, TrueVue and TrueVue Glass offer unprecedented photographic stereoscopic images, enhancing the diagnostic performance of such structural heart diseases.

Combination therapy with increased efficacy and reduced toxicity plays a crucial role in treating complex diseases, such as stroke, but it remains an insurmountable barrier to elucidate the mechanisms of synergistic effects. Here, we present a Driver-induced Modular Screening (DiMS) strategy integrated synergistic module and driver gene identification to elucidate the additive mechanisms of Baicalin (BA) and Jasminoidin (JA) on cerebral ischemia (CI) therapy. Based on anti-ischemia genomic networks BA, JA, and their combination (BJ), we obtained 4, 3, and 9 On-modules of BA, JA, and BJ by modular similarity analysis. Compared with the monotherapy groups, four additive modules (Add-module, BJ_Mod-4, 7, 9, and 13), 15 driver genes of BJ were identified by modular similarity and network control methods, and seven driver proteins (PAQR8, RhoA, EMC10, GGA2, VIPR1, FAM120A, and SEMA3F) were validated by animal experiments. The functional analysis found neuroprotective roles of the Add-modules and driver genes, such as the Neurotrophin signaling pathway and FoxO signaling pathway, which may reflect the additive mechanisms of BJ. Moreover, such a DiMS paradigm provides a new angle to explore the synergistic mechanisms of combination therapy and screen multi-targeted drugs for complex diseases.

Abstract Background Among potential hosts, the rejection of foreign eggs, which is a common and effective strategy to counter brood parasitism, depends on egg recognition. Multimodal and multicomponent recognition cues of brood parasitic eggs, which include both tactile (size, shape, and texture) and visual (size, shape, color, and maculation) cues, are potentially involved in the perception and discrimination of foreign eggs by hosts. An egg rejection experiment on the host with different types of model eggs can help to accurately assess the relative contribution of different components on egg recognition and constraints to rejection, in which videos can help identify the method of host rejection. Methods Here, we assessed egg recognition and rejection responses by Oriental reed warblers (Acrocephalus orientalis), one of the most common hosts of common cuckoos (Cuculus canorus) which breed in eastern China. We designed six groups of model eggs for rejection experiments in which sensory cues included three grades of size and two categories of visual mimicry. Results Our experiments confirmed that the multimodal traits, which included variation in size, were significant predictors of egg rejection: We detected significantly higher rejection rates of mimetic spotted model eggs than of nonmimetic blue eggs. However, large model eggs did not yield higher rejection rates and, instead, these were less likely to be rejected and more likely to be deserted compared with smaller eggs. Further video‐recording data showed that there was no significant effect of egg size on the egg recognition rate (percentage of nests with evidence of egg pecking). No evidence that the egg appearance had an effect on the method of egg rejection (ejection or nest desertion) was found. Conclusions Only visual signals, such as color and maculation, contributed to the recognition of foreign eggs by Oriental reed warblers as recognizable clues, but not the egg size. The egg size had an impact on the type of egg rejection. It was less feasible for the warblers to eject large eggs and that is why they opted more often for desertion as the mean of model egg rejection. The significantly lower egg rejection rate of large eggs suggested that although some of them were recognized as foreign eggs, hosts failed to reject these eggs and finally the eggs were assumed to being accepted by the commonly used nest‐checking methods.

The mechanism of the papaverine (PV) for the treatment of cerebral ischemia remains unclear. A total of 42 mice induced with focal cerebral ischemia were randomly divided into three groups: PV,baicalin (BA)and vehicle group. Both PV and BA could significantly reduce the ischemic infarct volume (P

Exosomes perform important functions for intercellular communication through extracellular signaling pathways, leading to the regulation of important biological processes, including cell proliferation, but also systemic dysfunctions such as preeclampsia (PE). However, the inhibitory effects of mesenchymal stem cell (MSCs)-derived exosomes in PE remain largely unknown. Thus, we assessed the possibility that exosomes could transport long non-coding RNA H19 and the correlation between H19 and the apoptosis of trophoblast cells. The expression of microRNA let-7b and forkhead box protein O1 (FOXO1) was characterized in placental tissues of PE patients. Gain- and loss-of-function experiments were performed to examine the roles of FOXO1 and let-7b in trophoblast cells. Interactions between let-7b and H19 as well as between let-7b and FOXO1 were confirmed by a dual-luciferase reporter assay, RNA pull-down, and RNA immunoprecipitation. HTR-8/SVneo cells were co-cultured with exosomes derived from MSCs overexpressing H19, followed by invasion, migration, and apoptosis assessments of trophoblast cells. We found that let-7b was highly expressed and FOXO1 was poorly expressed in placental tissues of PE patients. Furthermore, H19 acts as a competitive endogenous RNA against let-7b, and let-7b directly targeted FOXO1. Moreover, H19 could be transferred to trophoblast cells via MSC-secreted exosomes. MSC-derived exosomes overexpressing H19 decreased let-7b, increased FOXO1, and activated the protein kinase B (AKT) signaling pathway, thus increasing invasion and migration and inhibiting apoptosis of trophoblast cells. These results suggest that MSC-derived exosomes overexpressing H19 may be a novel direction for therapeutic strategies against PE. Keywords: preeclampsia, long non-coding RNA H19, let-7b, FOXO1, mesenchymal stem cells, exosomes, AKT signaling pathway

Hydrogel dressings provide a moist wound healing environment, absorb the exudates of the wound, and have better biocompatibility than traditional dressings. However, it is still difficult to meet the needs of modern medicine due to the defects in drug burst release, weak mechanical strength, and poor water retention. To solve these problems, we developed a double-layer (DL) hydrogel based on β-cyclodextrin polymer (β-CDP), poly(vinyl alcohol) (PVA), and carboxymethyl cellulose sodium (CMC) via a layer-by-layer method. Inspired by natural coconut, this hydrogel consisted of a drug release layer (DRL) and a mechanical support layer (MSL). In our design, the introduction of β-CDP into the DRL slowed the drug release rate of the DL hydrogel. Furthermore, the mechanical strength of the hydrogel was improved by immersing the MSL in a calcium chloride/boric acid solution. Combining these two layers, the tensile strength and elongation at break of the DL hydrogel reached 1504 kPa and 400%, respectively. More interestingly, the release mechanism of DL hydrogel conformed to the diffusion–relaxation–erosion model, which was different from traditional hydrogel dressings. Therefore, the as-prepared DL structure represents a feasible solution for fabricating high-performance mechanical hydrogel dressings with sustained drug release properties, and the DL hydrogel has potential to be used for medical dressings applied in daily life.

Traditional time series forecasting models mainly assume a clear and definite functional relationship between the historical values and the current/future values of a dataset. In this paper, we extended the current model by generating multi-attribute forecasting rules based on the consideration of combining multiple related variables. In this model, neutrosophic soft sets (NSSs) are employed to represent historical statues of several closely related attributes in stock market, such as volumes, stock market index, and daily amplitudes. Specifically speaking, the status of up, equal, and down in historical stock index can be represented by truth, indeterminacy, and false, respectively, by neutrosophic sets (NSs) and NSSs can build mappings of different related attributes to NSs. The advantages of the proposed model are: 1) using NSSs to enclose different historical characteristics in time series to preserve inherent complexity of a dataset with mapping adequate features and 2) with the existing researches of NSSs, it is efficient with using Euclidean distance to find the optimal rules, and thus, the model can avoid incomplete of rules due to the limited sample dataset. To evaluate the performance of the model, we explored the closing prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major parameter that we forecast and the stock amplitudes and volumes as the other factors to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast some other influential indexes as well.

By reducing innovation costs, innovation subsidies can help private enterprises convert their production modes to green production. Based on method of computational experiment in social science, we construct a dynamic model for environmental innovation behaviors of private enterprises to simulate their evolution process in different market mechanisms, product competitions, and innovation subsidies and explore the impact of different subsidy modes on environmental technological innovation behaviors. The experimental results show that, under actions of multiagents, the combination of market subsidy and technology transformation subsidy can achieve the highest utilization efficiency of subsidy funds. However, when level of innovation technology is low, the innovation process should be subsidized at the same time to improve the competitiveness of innovative products. Besides, according to the level of innovation technology, flexible innovation subsidy combinations can be adopted to optimize subsidy in the different stages. The experimental results are of great significance for increasing efficiency of innovation subsidy funds and promoting green sustainable development of private enterprises.

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

7-O-Methylnaringenin, extracted from Rhododendron speciferum, belongs to the flavanone class of polyphenols. In the present study, we investigated the anti-inflammatory effects of 7-O-methylnaringenin on cytokine production by lipopoly-saccharide (LPS)-stimulated RAW 264.7 macrophages in vitro. The results showed that pretreatment with 10, 20 or 40 μg/mL of 7-O-methylnaringenin could downregulate tumour necrosis factor (TNF-α), interleukin (IL-6) and interleukin (IL-1β) in a dose-dependent manner. Furthermore, we investigated the signal transduction mechanisms to determine how 7-O-methylnaringenin affects RAW 264.7 macrophages. The activation of mitogen-activated protein kinases (MAPK) and IκBα were measured by Western blotting. The data showed that 7-O-methylnaringenin could downregulate LPS-induced levels of phosphorylation of ERK1/2, JNK and IκBα. These observations indicated that 7-O-methylnaringenin modulated inflammatory cytokine responses by blocking NF-қB, ERK1/2 and JNK/MAPKs activation.

Ginkgolide B is an anti-inflammatory extract of Ginkgo biloba and has been used therapeutically. It is a known inhibitor of platelet activating factor (PAF), which is important in the pathogenesis of asthma. Here, a non-infectious mouse model of asthma is used to evaluate the anti-inflammatory capacity of ginkgolide B (GKB) and characterize the interaction of GKB with the mitogen activated protein kinase (MAPK) pathway. BALB/c mice that were sensitized and challenged to ovalbumin (OVA) were treated with GKB (40 mg/kg) one hour before they were challenged with OVA. Our study demonstrated that GKB may effectively inhibit the increase of T-helper 2 cytokines, such as interleukin (IL)-5 and IL-13 in bronchoalveolar lavage fluid (BALF). Furthermore, the eosinophil count in BALF significantly decreased after treatment of GKB when compared with the OVA-challenged group. Histological studies demonstrated that GKB substantially inhibited OVA-induced eosinophilia in lung tissue and mucus hyper-secretion by goblet cells in the airway. These results suggest that ginkgolide B may be useful for the treatment of asthma and its efficacy is related to suppression of extracellular regulating kinase/MAPK pathway.

Salidroside, a major component of Rhodiola rosea L., has shown various pharmacological functions, including antioxidant effects, but the signal transduction pathway of its antioxidant effects is not very clear. In this study, we found that salidroside could attenuate hydrogen peroxide (H2O2)-induced HL-7702 cell damage, inhibit H2O2-induced cytosolic free Ca2+ ([Ca2+]i) elevation, scavenge reactive oxygen species (ROS) and increase 3’-5’-cyclic adenosine monophosphate (cAMP) level in a dose-dependent manner, but it couldn’t influence 3’-5’-cyclic guanosine monophosphate (cGMP) levels. Therefore, these results indicated that the antioxidant effects of salidroside were associated with down-regulation of [Ca2+]i, ROS occur via a cAMP-dependent pathway.

8-Gingerol is one of the principal components of ginger, which is widely used in China and elsewhere as a food, spice and herb. It shows immunosuppressive activity on the immune responses to ovalbumin (OVA) in mice. In the present study, we found that 8-gingerol suppressed lipopolysaccharide (LPS) and concanavalin A (ConA)-stimulated splenocyte proliferation in vitro. In vivo, 8-gingerol not only significantly suppressed Con A-, LPS- and OVA-induced splenocyte proliferation (P < 0.05) but also decreased the percentage of CD19+ B cells and CD3+ T cell (P < 0.05) at high doses (50, 100 mg/kg). Moreover, OVA-specific IgG, IgG1 and IgG2b levels in OVA-immunized mice were reduced by 8-gingerol at doses of 50, 100 mg/kg. These results suggest that 8-gingerol could suppress humoral and cellular immune responses in mice. The mechanism might be related to direct inhibition of sensitized T and B lymphocytes.

Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.

Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.

Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS) according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs) for a two-factor first-order autoregressive (AR(1)) model and forecasting the training data with the AR(1) model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m)) model. Lastly, we forecasted test data with the ARMA(1,m) model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) from 2001 to 2015 and the international gold price from 2000 to 2010.

The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

Most existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertain. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each piece of data with the data of theprevious day in a historical training time series to generate a new fluctuation trend time series (FTTS). Then, we fuzzify the FTTS into a fuzzy-fluctuation time series (FFTS) according to the up, equal, or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of the future is nonlinear, a particle swarm optimization (PSO) algorithm is employed to estimate the proportions for the lagged variables of the fuzzy AR (n) model. Finally, we use the acquired parameters to forecast future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or similarkinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) and DAX30 index to verify its effectiveness and universality.

Daphnetin, a plant-derived dihydroxylated derivative of coumarin, is an effective compound extracted from a plant called Daphne Korean Nakai. Coumarin derivates were known for their antithrombotic, anti-inflammatory, and antioxidant activities. The present study was aimed to determine the immunosuppressive effects and the underlying mechanisms of daphnetin on concanavalin A (ConA) induced T lymphocytes in mice. We showed that, in vitro, daphnetin suppressed ConA-induced splenocyte proliferation, influenced production of the cytokines and inhibited cell cycle progression through the G0/G1 transition. The data also revealed that daphnetin could down-regulate activation of ConA induced NF-κB and NFAT signal transduction pathways in mouse T lymphocyte. In vivo, daphnetin treatment significantly inhibited the 2, 4- dinitrofluorobenzene (DNFB) -induced delayed type hypersensitivity (DTH) reactions in mice. Collectively, daphnetin had strong immunosuppressive activity both in vitro and in vivo, suggesting a potential role for daphnetin as an immunosuppressive agent, and established the groundwork for further research on daphnetin.

In the title compound, C14H12BrN, the fused-ring system is slightly buckled as its two benzene rings are inclined to one another by 3.41 (14)°.

In the title molecule, C19H25NO2S6, the butyl chains are each disordered over two conformations in a 0.689 (10):0.311 (10) ratio. In the crystal, pairs of N—H...O hydrogen bonds link molecules into centrosymmetric dimers. Short S...S contacts of 3.553 (4) Å are observed.

In the title compound, C16H13N3S, the pyrazoline ring forms dihedral angles of 6.89 (14) and 4.96 (11)° with the benzene ring and the benzothiazole group, respectively. In the crystal, weak C—H...N interactions link the molecules into chains extending along the b-axis direction.

In the title compound, C12H11NO2, the indole and methyl acrylate mean planes are inclined at an angle of 10.6 (1)°. In the crystal, N—H...π interactions link molecules into chains along [010] and weak intermolecular C—H...O hydrogen bonds further consolidate the crystal packing.