In most developing countries, governments attempt to enforce the movement from analogue to digital for all their sectors, from public to private. These technological advancements have been noted to bring necessary and...In most developing countries, governments attempt to enforce the movement from analogue to digital for all their sectors, from public to private. These technological advancements have been noted to bring necessary and unavoidable changes to businesses and learning environments. Higher learning institutions have adopted various e-learning systems to support learning, research, and publication activities to stay competitive in global academic systems. However, most public higher learning institutions in Tanzania lag behind in the adoption of these systems. Thus, research shows a failure of these institutions in utilising the full benefit that today’s Information and Communication Technology (ICT) can offer in learning environments. Thus, this study examines factors affecting the adoption of such a system in developing countries like Tanzania, taking the Institute of Accountancy Arusha (IAA) as a case study. The study used a mixed methodology where thematic and descriptive analysis was used to analyse both qualitative and quantitative research data. The study population was 187 teaching staff, a sample size of 126 was obtained, and 157 study participants were involved in the study. The study found that factors affecting the adoption of e-learning systems in public higher learning institutions in Tanzania include lack of ICT infrastructure, lack of technical and managerial support and lack of computers and e-learning knowledge among facilitators. Thus, the study recommended investments in adequate and reliable ICT facilities, high intermate speed and bandwidth, and policies that support e-learning and training programs about e-learning knowledge and use. Also, this study recommends the use of the Multi-Factors Adoption Model (MFAM11) for the successful adoption of an e-learning system in public higher learning institutions in Tanzania.展开更多
All modern computer users need to be concerned about information system security (individuals and organisations). Many businesses established various security structures to protect information system security from har...All modern computer users need to be concerned about information system security (individuals and organisations). Many businesses established various security structures to protect information system security from harmful occurrences by implementing security procedures, processes, policies, and information system security organisational structures to ensure data security. Despite all the precautions, information security remains a disaster in Tanzania’s learning institutions. The fundamental issue appears to be a lack of awareness of crucial information security factors. Various companies have different security issues due to differences in ICT infrastructure, implementations, and usage. The study focuses on identifying information system security threats and vulnerabilities in public higher learning institutions in Tanzania, particularly the Institute of Accountancy Arusha (IAA). The study involved all employees of IAA, academics, and other supporting staff, which totalled 302, and the sample size was 170. The study utilised a descriptive research design, where the quantitative methodology was used through a five-point Likert scale questionnaire, and found that key factors that affect the security of information systems at IAA include human factors, policy-related issues, work environment and demographic factors. The study proposed regular awareness and training programs;an increase in women’s awareness of information system security;proper policy creation and reviews every 4 years;promote actions that lessen information system security threats and vulnerabilities, and the creation of information system security policy documents independently from ICT policy.展开更多
Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature....Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.展开更多
Natural products(NPs)derived from plants,microbes,and marine organisms have historically been a cornerstone of pharmacotherapy,accounting for approximately 35%of FDA-approved small-molecule drugs since 19811.These com...Natural products(NPs)derived from plants,microbes,and marine organisms have historically been a cornerstone of pharmacotherapy,accounting for approximately 35%of FDA-approved small-molecule drugs since 19811.These compounds have been instrumental in drug discovery,particularly for cancer and infectious diseases,as well as in other therapeutic areas such as cardiovascular diseases(e.g.,statins)and multiple sclerosis(e.g.,fingolimod).展开更多
The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss...The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss for some users.To combat this,the traffic load of base stations should be kept as balanced as possible.In this paper,we propose an efficient load balancing-aware handover algorithm for highly dynamic beyond 5G heterogeneous networks by assigning mobile users to base stations with lighter loads when a handover is performed.The proposed algorithm is evaluated in a scenario with users having different levels of mobility,such as pedestrians and vehicles,and is shown to outperform the conventional handover mechanism,as well as another algorithm from the literature.As a secondary benefit,the overall energy consumption in the network is shown to be reduced with the proposed algorithm.展开更多
Osteoarthritis(OA)is a widespread joint disorder that has emerged as a significant global healthcare challenge.Over the past decade,advancements in material science and medicine have transformed the development of fun...Osteoarthritis(OA)is a widespread joint disorder that has emerged as a significant global healthcare challenge.Over the past decade,advancements in material science and medicine have transformed the development of functional materials aimed at addressing the complex issues associated with the diagnosis and treatment of OA.This review synthesizes the latest advancements in various types of intelligent micro-structured materials and their design principles.By examining the exceptional structural characteristics of materials with unique properties such as tailored attributes,controllability,biocompatibility,and bioactivity,we emphasize the design of composite materials for precise and early intervention in OA.This is achieved through advanced imaging techniques and machine learning-based analysis,alongside the customization of micro-structured material properties to align with the biological and mechanical requirements of specific joint tissues.This review offers an in-depth analysis of the transformative potential of advanced technologies and artificial intelligence(AI)in the development of innovative solutions for OA diagnosis and therapy.It aims to inform future research and inspire the creation of next-generation smart materials with unprecedented performance,thereby enhancing our capabilities in the prevention and treatment of OA.展开更多
Bone marrow lesions(BML)are early signs of osteoarthritis(OA)and are strongly correlated with the deterioration of cartilage lesions.Single-cell RNA sequencing(scRNA-seq)analyses were performed on BM from non-BML and ...Bone marrow lesions(BML)are early signs of osteoarthritis(OA)and are strongly correlated with the deterioration of cartilage lesions.Single-cell RNA sequencing(scRNA-seq)analyses were performed on BM from non-BML and BML areas and articular cartilage from intact and damaged areas to explore BML landscape and BML-cartilage crosstalk.We revealed the immune landscape of BM in non-BML and BML,and the transition to pro-inflammatory states of clusters in BMLs,such as classical monocytes and nonclassical monocytes.Non-classical monocytes have high inflammation,OA gene signatures,and senescence scores,and are potential primary clusters promoting OA progression.Histological signs of OA related to the cellular landscape in damaged cartilage were identified,including PreFC exhaustion.The BM-cartilage crosstalk at the cell-cell interaction(CCIs)level and the TNF signal transmitted by non-classical monocytes are the critical CCIs in BML-induced cartilage damage,and PreFC is one of the primary receivers of the signal.We further validated the higher senescence level of non-classical monocyte and FC-2 in OA mice,compared with classical monocyte and PreFC,respectively.Transcription factor 7 like 2(TCF7L2)was identified as a shared transcription factor in the senescence of monocytes and chondrocytes,facilitating the development of the senescence-associated secretory phenotype(SASP).Therefore,senescent non-classical monocytes promote BMLs and inflammation and senescence of chondrocytes by modulating BML–cartilage crosstalk in OA,with TCF7L2 serving as a regulator.展开更多
Coptis chinensis Franch.and Panax ginseng C.A.Mey.are traditional herbal medicines with millennia of documented use and broad therapeutic applications,including anti-diabetic properties.However,the synergistic effect ...Coptis chinensis Franch.and Panax ginseng C.A.Mey.are traditional herbal medicines with millennia of documented use and broad therapeutic applications,including anti-diabetic properties.However,the synergistic effect of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng on type 2 diabetes mellitus(T2DM)and its underlying mechanism remain unclear.The research demonstrated that the optimal ratio of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng was 4∶1,exhibiting maximal efficacy in improving insulin resistance and gluconeogenesis in primary mouse hepatocytes.This combination demonstrated significant synergistic effects in improving glucose tolerance,reducing fasting blood glucose(FBG),the weight ratio of epididymal white adipose tissue(eWAT),and the homeostasis model assessment of insulin resistance(HOMA-IR)in leptin receptor-deficient(db/db)mice.Subsequently,a T2DM liver-specific network was constructed based on RNA sequencing(RNA-seq)experiments and public databases by integrating transcriptional properties of disease-associated proteins and protein-protein interactions(PPIs).The network recovery index(NRI)score of the combined treatment group with a 4∶1 ratio exceeded that of groups treated with individual components.The research identified that activated adenosine 5'-monophosphate-activated protein kinase(AMPK)/acetyl-CoA carboxylase(ACC)signaling in the liver played a crucial role in the synergistic treatment of T2DM,as verified by western blot experiment in db/db mice.These findings demonstrate that the 4∶1 combination of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng significantly improves insulin resistance and glucose and lipid metabolism disorders in db/db mice,surpassing the efficacy of individual treatments.The synergistic mechanism correlates with enhanced AMPK/ACC signaling pathway activity.展开更多
In the evolving landscape of secure communication,steganography has become increasingly vital to secure the transmission of secret data through an insecure public network.Several steganographic algorithms have been pr...In the evolving landscape of secure communication,steganography has become increasingly vital to secure the transmission of secret data through an insecure public network.Several steganographic algorithms have been proposed using digital images with a common objective of balancing a trade-off between the payload size and the quality of the stego image.In the existing steganographic works,a remarkable distortion of the stego image persists when the payload size is increased,making several existing works impractical to the current world of vast data.This paper introduces FuzzyStego,a novel approach designed to enhance the stego image’s quality by minimizing the effect of the payload size on the stego image’s quality.In line with the limitations of traditional methods like Pixel Value Differencing(PVD),Transform Domain Techniques,and Least Significant Bit(LSB)insertion,such as image quality degradation,vulnerability to processing attacks,and restricted capacity,FuzzyStego utilizes fuzzy logic to categorize pixels into intensity levels:Low(L),Medium-Low(ML),Medium(M),Medium-High(MH),and High(H).This classification enables adaptive data embedding,minimizing detectability by adjusting the hidden bit count according to the intensity levels.Experimental results show that FuzzyStego achieves an average Peak Signal-to-Noise Ratio(PSNR)of 58.638 decibels(dB)and a Structural Similarity Index Measure(SSIM)of almost 1.00,demonstrating its promising capability to preserve image quality while embedding data effectively.展开更多
The NOD-like receptor family pyrin domain-containing protein 3(NLRP3)inflammasome is an intracellular protein complex containing a nucleotide-binding oligomerization domain,leucine-rich repeats,and a pyrin domain.It i...The NOD-like receptor family pyrin domain-containing protein 3(NLRP3)inflammasome is an intracellular protein complex containing a nucleotide-binding oligomerization domain,leucine-rich repeats,and a pyrin domain.It is a key regulator of inflammation in viral pneumonia(VP).Small-molecule inhibitors targeting various NLRP3 binding sites are advancing into early clinical trials,but their therapeutic utility is incompletely established.Xuanfei Baidu Formula(XF),clinically used for VP treatment,attenuates NLRP3 activation by hampering caspase-11 to impede polarization of pro-inflammatory macrophages in a model of lipopolysaccharide(LPS)-induced lung injury inmice.Herein,we demonstrate that XF attenuated influenza A virus(IAV)-induced lung inflammation as well as lung injury in immunocompetent(but not in macrophage-depleted)mice.RNA sequencing of sorted lung macrophages from IAV-infected mice revealed that XF inhibited activation of the NLRP3 inflammation and interleukin(IL)-1βproduction.Quantitative nuclear magnetic resonance of XF enabled us to develop XF-Comb1,a fixed-ratio combination of five bioactive compounds that recapitulated the bioactivity of XF in suppressing NLRP3 activation in macrophages in vitro and in vivo.Interestingly,XF-Comb1 inhibited assembly of the NLRP3 inflammasome through multi-site interactions with functional residues of NLRP3,apoptosis-associated speck-like protein containing caspase recruitment domain(ASC),and caspase-1.Taken together,this work advances the development of NLRP3 inhibitors by translating a complex herbal formula into defined bioactive compounds.展开更多
Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared...Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.展开更多
Falls are a leading cause of injury and morbidity among older adults,especially those with Alzheimer’s disease(AD),who face increased risks due to cognitive decline,gait instability,and impaired spatial awareness.Whi...Falls are a leading cause of injury and morbidity among older adults,especially those with Alzheimer’s disease(AD),who face increased risks due to cognitive decline,gait instability,and impaired spatial awareness.While wearable sensor-based fall detection systems offer promising solutions,their effectiveness is often hindered by domain shifts resulting from variations in sensor placement,sampling frequencies,and discrepancies in dataset distributions.To address these challenges,this paper proposes a novel unsupervised domain adaptation(UDA)framework specifically designed for cross-dataset fall detection in Alzheimer’s disease(AD)patients,utilizing advanced transfer learning to enhance generalizability.The proposed method incorporates a ResNet-Transformer Network(ResT)as a feature extractor,along with a novel DualAlign Loss formulation that aims to align feature distributions while maintaining class separability.Experiments on the preprocessed KFall and SisFall datasets demonstrate significant improvements in F1-score and recall,crucial metrics for reliable fall detection,outperforming existing UDA methods,including a convolutional neural network(CNN),DeepCORAL,DANN,and CDAN.By addressing domain shifts,the proposed approach enhances the practical viability of fall detection systems for AD patients,providing a scalable solution to minimize injury risks and improve caregiving outcomes in real-world environments.展开更多
Danshen-Chuanxiongqin Injection(DCI)is a commonly used traditional Chinese medicine for the treatment of cerebral ischemic stroke in China.However,its underlying mechanisms remain completely understood.The current stu...Danshen-Chuanxiongqin Injection(DCI)is a commonly used traditional Chinese medicine for the treatment of cerebral ischemic stroke in China.However,its underlying mechanisms remain completely understood.The current study was designed to explore the protective mechanisms of DCI against cerebral ischemic stroke through integrating whole-transcriptome sequencing coupled with network pharmacology analysis.First,using a mouse model of cerebral ischemic stroke by transient middle cerebral artery occlusion(tMCAO),we found that DCI(4.10 mL·kg−1)significantly alleviated cerebral ischemic infarction,neurological deficits,and the pathological injury of hippocampal and cortical neurons in mice.Next,the whole-transcriptome sequencing was performed on brain tissues.The cerebral ischemia disease(CID)network was constructed by integrating transcriptome sequencing data and cerebrovascular disease-related genes.The results showed CID network was imbalanced due to tMCAO,but a recovery regulation was observed after DCI treatment.Pathway analysis of the key genes with recovery efficiency showed that the neuroinflammation signaling pathway was highly enriched,while the TLR2/TLR4-MyD88-NF-κB pathway was predicted to be affected.Consistently,the in vivo validation experiments confirmed that DCI exhibited protective effects against cerebral ischemic stroke by inhibiting neuroinflammation via the TLR2/TLR4-MyD88-NF-κB pathway.More interestingly,DCI markedly suppressed the neutrophils infiltrated into the brain parenchyma via the choroid plexus route and showed anti-neuroinflammation effects.In conclusion,our results provide dependable evidence that inhibiting neuroinflammation via the TLR2/TLR4-MyD88-NF-κB pathway is the main mechanism of DCI against cerebral ischemic stroke in mice.展开更多
A liquid chromatography coupled with diode array detector(DAD) and electrospray ionization time-of-flight mass spectrometry(ESI-TOF/MS) method was developed for the screening and identification of the multiple compone...A liquid chromatography coupled with diode array detector(DAD) and electrospray ionization time-of-flight mass spectrometry(ESI-TOF/MS) method was developed for the screening and identification of the multiple components in Tanreqing injection, a well-known Chinese medicine injection in China. By combining the DAD spectrum and the accurate mass measurement of ESI-TOF/MS, twelve components in Tanreqing injection were identified. This study contributes to clarifying the nature of Tanreqing injection, and provides an effective and reliable process for the comprehensive and systematic characterization of complex traditional Chinese medicine preparations.展开更多
Traditional Chinese medicine (TCM) is a medical system that has collected and summarized abundant clinical experience over its long history of more than 2000 years. However, the frequent occurrence of TCM-induced adve...Traditional Chinese medicine (TCM) is a medical system that has collected and summarized abundant clinical experience over its long history of more than 2000 years. However, the frequent occurrence of TCM-induced adverse reactions has hindered the modernization and internationalization of TCM, while attracting increasing attention from around the world. Unlike chemical drugs and biological agents, the difficulties involved in research on the toxicity and safety of TCM mainly include the complexity of its components and the unpredictability of drug–body interactions. Much of TCM, which has overall therapeutic effects, has the typical mechanisms of multiple components, multiple pathways, and multiple targets. While considering the gradualness and unpredictability of TCM toxicity, the ambiguity of toxicants and safe dosage, and individual differences during long-term TCM administration, we have systematically established key techniques for the toxicity assessment of TCM. These techniques mainly include TCM toxicity discovery in an early phase, based on a combination of drug toxicology genomics and metabolomics;methods to identify dose–toxicity relationships in TCM;and integrated techniques for the exploration of TCM interactions, such as fast-screening tests based on drug-metabolizing enzymes and receptor pathways. In particular, we have developed a new technical system for TCM safety evaluation using molecular toxicology, which has been validated well in research on TCM compatibility contraindication, quality control, and allergen discovery. The application of this key technical platform is introduced here in detail. This application includes model organisms, toxicant biomarkers, a magnetic suspension technique, and the application of network toxicology and computational toxicology in research on the toxicity of Fructus toosendan, Semen cassiae, Polygonum multiflorum, and Fructus psoraleae.展开更多
We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of m...We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of multiple samples, the genuine manufacturer of DEE J, e.g. Dong＇e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T2, distance to Model X （DModX）, and similarity match value （SMV） as dis- criminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEE J, storage time, the partial least squares-discriminant analysis （PLS-DA） method was applied with a satisfactory misjudgment ratio （15.6%） and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEE J, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.展开更多
Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its ...Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its underlying pharmacological mechanism remains unclear. Here, we combine a comprehensive research approach that includes network pharmacology, transcriptomics, and bioassays in multiple model systems to investigate the pharmacological mechanism of XFBD and its bioactive substances. High-resolution mass spectrometry was combined with molecular networking to profile the major active substances in XFBD. A total of 104 compounds were identified or tentatively characterized, including flavonoids, terpenes, carboxylic acids, and other types of constituents. Based on the chemical composition of XFBD, a network pharmacology-based analysis identified inflammation-related pathways as primary targets. Thus, we examined the anti-inflammation activity of XFBD in a lipopolysaccharide-induced acute inflammation mice model. XFBD significantly alleviated pulmonary inflammation and decreased the level of serum proinflammatory cytokines. Transcriptomic profiling suggested that genes related to macrophage function were differently expressed after XFBD treatment. Consequently, the effects of XFBD on macrophage activation and mobilization were investigated in a macrophage cell line and a zebrafish wounding model. XFBD exerts strong inhibitory effects on both macrophage activation and migration. Moreover, through multimodal screening, we further identified the major components and compounds from the different herbs of XFBD that mediate its anti-inflammation function. Active components from XFBD, including Polygoni cuspidati Rhizoma, Phragmitis Rhizoma, and Citri grandis Exocarpium rubrum, were then found to strongly downregulate macrophage activation, and polydatin, isoliquiritin, and acteoside were identified as active compounds. Components of Artemisiae annuae Herba and Ephedrae Herba were found to substantially inhibit endogenous macrophage migration, while the presence of ephedrine, atractylenolide I, and kaempferol was attributed to these effects. In summary, our study explores the pharmacological mechanism and effective components of XFBD in inflammation regulation via multimodal approaches, and thereby provides a biological illustration of the clinical efficacy of XFBD.展开更多
Evidence continues to grow on potential health risks associated with Ginkgo biloba and its constituents.While biflavonoid is a subclass of the flavonoid family in Ginkgo biloba with a plenty of pharmacological propert...Evidence continues to grow on potential health risks associated with Ginkgo biloba and its constituents.While biflavonoid is a subclass of the flavonoid family in Ginkgo biloba with a plenty of pharmacological properties,the potential toxicological effects of biflavonoids remains largely unknown.Thus,the aim of this study was to investigate the in vitro and in vivo toxicological effects of the biflavonoids from Ginkgo biloba(i.e.,amentoflavone,sciadopitysin,ginkgetin,isoginkgetin,and bilobetin).In the in vitro cytotoxicity test,the five biflavonoids all reduced cell viability in a dose-dependent manner in human renal tubular epithelial cells(HK-2)and human normal hepatocytes(L-02),indicating they might have potential liver and kidney toxicity.In the in vivo experiments,after intragastrical administration of these biflavonoids at 20 mg·kg^–1·d^–1 for 7 days,serum biochemical analysis and histopathological examinations were performed.The activity of alkaline phosphatase was significantly increased after all the bifl avonoid administrations and widespread hydropic degeneration of hepatocytes was observed in ginkgetin or b ilobetin-treated mice.Moreover,the five biflavonoids all induced acute kidney injury in treated mice and the main pathological lesions were confirmed to the tubule,glomeruli,and interstitium injuries.As the in vitro and in vivo results suggested that these biflavonoids may be more toxic to the kidney than the liver,we further detected the mechanism of biflavonoids-induced nephrotoxicity.The increased TUNEL-positive cells were detected in kidney tissues of biflavonoids-treated mice,accompanied by elevated expression of proapoptotic protein BAX and unchanged levels of antiapoptotic protein BCL-2,indicating apoptosis was involved in biflavonoids-induced nephrotoxicity.Taken together,our results suggested that the five biflavonoids from Ginkgo biloba may have potential hepatic and renal toxicity and more attentions should be paid to ensure Ginkgo biloba preparations safety.展开更多
Background: Residual feed intake(RFI) describes an animal’s feed efficiency independent of growth performance.The objective of this study was to determine differences in growth performance, carcass traits, major bact...Background: Residual feed intake(RFI) describes an animal’s feed efficiency independent of growth performance.The objective of this study was to determine differences in growth performance, carcass traits, major bacteria attached to ruminal solids-fraction, and ruminal epithelium gene expression between the most-efficient and the least-efficient beef cattle. One-hundred and forty-nine Red Angus cattle were allocated to three contemporary groups according to sex and herd origin. Animals were fed a finishing diet in confinement for 70 d to determine the RFI category for each. Within each group, the two most-efficient(n = 6; RFI coefficient =-2.69 ± 0.58 kg dry matter intake(DMI)/d) and the two least-efficient animals(n = 6; RFI coefficient = 3.08 ± 0.55 kg DMI/d) were selected. Immediately after slaughter, ruminal solids-fraction and ruminal epithelium were collected for bacteria relative abundance and epithelial gene expression analyses, respectively, using real-time PCR.Results: The most-efficient animals consumed less feed(P = 0.01; 5.03 kg less DMI/d) compared with the leastefficient animals. No differences(P > 0.10) in initial body weight(BW), final BW, and average daily gain(ADG) were observed between the two RFI classes. There were no significant RFI × sex effects(P > 0.10) on growth performance.Compared with the least-efficient group, hot carcass weight(HCW), ribeye area(REA), and kidney, pelvic, and heart fat(KPH) were greater(P ≤ 0.05) in the most-efficient cattle. No RFI × sex effect(P > 0.10) for carcass traits was detected between RFI groups. Of the 10 bacterial species evaluated, the most-efficient compared with least efficient cattle had greater(P ≤ 0.05) relative abundance of Eubacterium ruminantium, Fibrobacter succinogenes, and Megasphaera elsdenii, and lower(P ≤ 0.05) Succinimonas amylolytica and total bacterial density. No RFI × sex effect on ruminal bacteria was detected between RFI groups. Of the 34 genes evaluated in ruminal epithelium, the mostefficient cattle had greater(P ≤ 0.05) abundance of genes involved in VFA absorption, metabolism, ketogenesis, and immune/inflammation-response. The RFI × sex interactions indicated that responses in gene expression between RFI groups were due to differences in sex. Steers in the most-efficient compared with least-efficient group had greater(P ≤ 0.05) expression of SLC9 A1, HIF1 A, and ACO2. The most-efficient compared with least-efficient heifers had greater(P ≤ 0.05) m RNA expression of BDH1 and lower expression(P ≤ 0.05) of SLC9 A2 and PDHA1.Conclusions: The present study revealed that greater feed efficiency in beef cattle is associated with differences in bacterial species and transcriptional adaptations in the ruminal epithelium that might enhance nutrient delivery and utilization by tissues. The lack of RFI × sex interaction for growth performance and carcass traits indicates that sex may not play a major role in improving these phenotypes in superior RFI beef cattle. However, it is important to note that this result should not be considered a solid biomarker of efficient beef cattle prior to further examination due to the limited number of heifers compared with steers used in the study.展开更多
Objective:Ganoderma lucidum spore(GLS)is gaining recognition as a medicinal part of G.lucidum and has been reported to possess various pharmacological properties,such as antitumor activity.In this work,wall-broken GLS...Objective:Ganoderma lucidum spore(GLS)is gaining recognition as a medicinal part of G.lucidum and has been reported to possess various pharmacological properties,such as antitumor activity.In this work,wall-broken GLS powder(BGLSP)and wall-removed GLS powder(RGLSP),two kinds of GLS powder with different manufacturing techniques,were compared in terms of contents of active constituents and in vivo and in vitro antitumor effects.Methods:The ultraviolet and visible spectrophotometry method was used to determine the contents of polysaccharides and total triterpenoids in BGLSP and RGLSP.Seventeen individual triterpenoids were further quantified using ultra-high-performance liquid chromatography and quantitative analysis of multicomponents by single marker.The antitumor effects of BGLSP and RGLSP were evaluated using in vitro cell viability assay against human gastric carcinoma SGC-7901,lung carcinoma A549 and lymphoma Ramos and further validated by in vivo zebrafish xenograft models with transplanted SGC-7901,A549 and Ramos,Results:The results showed that the contents of polysaccharides,total triterpenoids and individual triterpenoids of RGLSP were significantly higher than those of BGLSP.Although both BGLSP and RGLSP inhibited the three tumor cell lines in vitro in a dose-dependent manner,the inhibitory effects of RGLSP were much better than those of BGLSP.In the in vivo zebra fish assay,RGLSP exhibited more potent inhibitory activities against tumors transpla nted into the zebra fish compared with BGLSP,and the inhibition rates of RGLSP reached approximately 78%,31%and 83%on SGC-7901,A549 and Ramos,respectively.Conclusion:The results indicated that the antitumor effects of GLS were positively correlated with the contents of the polysaccha rides and triterpenoids and demonstrated that the wall-removing manufacturing technique could significantly improve the levels of active constituents,and thereby enhance the antitumor activity.展开更多
文摘In most developing countries, governments attempt to enforce the movement from analogue to digital for all their sectors, from public to private. These technological advancements have been noted to bring necessary and unavoidable changes to businesses and learning environments. Higher learning institutions have adopted various e-learning systems to support learning, research, and publication activities to stay competitive in global academic systems. However, most public higher learning institutions in Tanzania lag behind in the adoption of these systems. Thus, research shows a failure of these institutions in utilising the full benefit that today’s Information and Communication Technology (ICT) can offer in learning environments. Thus, this study examines factors affecting the adoption of such a system in developing countries like Tanzania, taking the Institute of Accountancy Arusha (IAA) as a case study. The study used a mixed methodology where thematic and descriptive analysis was used to analyse both qualitative and quantitative research data. The study population was 187 teaching staff, a sample size of 126 was obtained, and 157 study participants were involved in the study. The study found that factors affecting the adoption of e-learning systems in public higher learning institutions in Tanzania include lack of ICT infrastructure, lack of technical and managerial support and lack of computers and e-learning knowledge among facilitators. Thus, the study recommended investments in adequate and reliable ICT facilities, high intermate speed and bandwidth, and policies that support e-learning and training programs about e-learning knowledge and use. Also, this study recommends the use of the Multi-Factors Adoption Model (MFAM11) for the successful adoption of an e-learning system in public higher learning institutions in Tanzania.
文摘All modern computer users need to be concerned about information system security (individuals and organisations). Many businesses established various security structures to protect information system security from harmful occurrences by implementing security procedures, processes, policies, and information system security organisational structures to ensure data security. Despite all the precautions, information security remains a disaster in Tanzania’s learning institutions. The fundamental issue appears to be a lack of awareness of crucial information security factors. Various companies have different security issues due to differences in ICT infrastructure, implementations, and usage. The study focuses on identifying information system security threats and vulnerabilities in public higher learning institutions in Tanzania, particularly the Institute of Accountancy Arusha (IAA). The study involved all employees of IAA, academics, and other supporting staff, which totalled 302, and the sample size was 170. The study utilised a descriptive research design, where the quantitative methodology was used through a five-point Likert scale questionnaire, and found that key factors that affect the security of information systems at IAA include human factors, policy-related issues, work environment and demographic factors. The study proposed regular awareness and training programs;an increase in women’s awareness of information system security;proper policy creation and reviews every 4 years;promote actions that lessen information system security threats and vulnerabilities, and the creation of information system security policy documents independently from ICT policy.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2024C03106,X.F.)the National Natural Science Foundation of China(No.82474160,X.S.)+2 种基金the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBZ24H270001,X.P.)the Major Joint Projects Supported by the National Administration of TCM and Zhejiang Province(No.GZY-ZI-KJ-23037,X.P.)the Ningbo Top Medical and Health Research Program(No.2022030309,X.P.)。
文摘Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.
文摘Natural products(NPs)derived from plants,microbes,and marine organisms have historically been a cornerstone of pharmacotherapy,accounting for approximately 35%of FDA-approved small-molecule drugs since 19811.These compounds have been instrumental in drug discovery,particularly for cancer and infectious diseases,as well as in other therapeutic areas such as cardiovascular diseases(e.g.,statins)and multiple sclerosis(e.g.,fingolimod).
基金supported in part by the Istanbul Technical University Scientific Research Projects Coordination Unit under Grant FHD-2024-45764in part by TUBITAK 1515 Frontier R&D Laboratories Support Program for Turkcell 6GEN LAB under Grant 5229902Turkcell Technology R&D Center(Law no.5746)has partially supported this study。
文摘The increase in user mobility and density in modern cellular networks increases the risk of overloading certain base stations in popular locations such as shopping malls or stadiums,which can result in connection loss for some users.To combat this,the traffic load of base stations should be kept as balanced as possible.In this paper,we propose an efficient load balancing-aware handover algorithm for highly dynamic beyond 5G heterogeneous networks by assigning mobile users to base stations with lighter loads when a handover is performed.The proposed algorithm is evaluated in a scenario with users having different levels of mobility,such as pedestrians and vehicles,and is shown to outperform the conventional handover mechanism,as well as another algorithm from the literature.As a secondary benefit,the overall energy consumption in the network is shown to be reduced with the proposed algorithm.
基金supported by the National Key Research and Development Program of China(No.2023YFC2509200)the National Natural Science Foundation of China(Nos.82470998,82270995,81970956)+1 种基金Zhejiang Science Foundation for Distinguished Young Scholars(LR24H140001)The Science and Technology Department of the State Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine jointly established the Science and Technology Plan(GZY-ZJ-KJ-24086)。
文摘Osteoarthritis(OA)is a widespread joint disorder that has emerged as a significant global healthcare challenge.Over the past decade,advancements in material science and medicine have transformed the development of functional materials aimed at addressing the complex issues associated with the diagnosis and treatment of OA.This review synthesizes the latest advancements in various types of intelligent micro-structured materials and their design principles.By examining the exceptional structural characteristics of materials with unique properties such as tailored attributes,controllability,biocompatibility,and bioactivity,we emphasize the design of composite materials for precise and early intervention in OA.This is achieved through advanced imaging techniques and machine learning-based analysis,alongside the customization of micro-structured material properties to align with the biological and mechanical requirements of specific joint tissues.This review offers an in-depth analysis of the transformative potential of advanced technologies and artificial intelligence(AI)in the development of innovative solutions for OA diagnosis and therapy.It aims to inform future research and inspire the creation of next-generation smart materials with unprecedented performance,thereby enhancing our capabilities in the prevention and treatment of OA.
基金supported by the Nature Science Foundation of Zhejiang Province(grant no.LD22C060002)National Science Foundation of China(grant no.82274547,82474240)+2 种基金Zhejiang Provincial Medical and Health Science and Technology Fund(grant no.2024KY1223)Research Project of Zhejiang Chinese Medical University(grant no.2023JKZKTS34)Project of Chunyan Special Fund for Chinese Medicine Development of Zhejiang Chinese Medical University(grant no.CY202305)。
文摘Bone marrow lesions(BML)are early signs of osteoarthritis(OA)and are strongly correlated with the deterioration of cartilage lesions.Single-cell RNA sequencing(scRNA-seq)analyses were performed on BM from non-BML and BML areas and articular cartilage from intact and damaged areas to explore BML landscape and BML-cartilage crosstalk.We revealed the immune landscape of BM in non-BML and BML,and the transition to pro-inflammatory states of clusters in BMLs,such as classical monocytes and nonclassical monocytes.Non-classical monocytes have high inflammation,OA gene signatures,and senescence scores,and are potential primary clusters promoting OA progression.Histological signs of OA related to the cellular landscape in damaged cartilage were identified,including PreFC exhaustion.The BM-cartilage crosstalk at the cell-cell interaction(CCIs)level and the TNF signal transmitted by non-classical monocytes are the critical CCIs in BML-induced cartilage damage,and PreFC is one of the primary receivers of the signal.We further validated the higher senescence level of non-classical monocyte and FC-2 in OA mice,compared with classical monocyte and PreFC,respectively.Transcription factor 7 like 2(TCF7L2)was identified as a shared transcription factor in the senescence of monocytes and chondrocytes,facilitating the development of the senescence-associated secretory phenotype(SASP).Therefore,senescent non-classical monocytes promote BMLs and inflammation and senescence of chondrocytes by modulating BML–cartilage crosstalk in OA,with TCF7L2 serving as a regulator.
基金supported by the Pioneer and Leading Goose R&D Program of Zhejiang Province(No.2024C03106)the National Natural Science Foundation of China(No.U23A20513)+1 种基金Ningbo Top Medical and Health Research Program(No.2022030309)the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine(No.ZYYCXTD-D-202002).
文摘Coptis chinensis Franch.and Panax ginseng C.A.Mey.are traditional herbal medicines with millennia of documented use and broad therapeutic applications,including anti-diabetic properties.However,the synergistic effect of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng on type 2 diabetes mellitus(T2DM)and its underlying mechanism remain unclear.The research demonstrated that the optimal ratio of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng was 4∶1,exhibiting maximal efficacy in improving insulin resistance and gluconeogenesis in primary mouse hepatocytes.This combination demonstrated significant synergistic effects in improving glucose tolerance,reducing fasting blood glucose(FBG),the weight ratio of epididymal white adipose tissue(eWAT),and the homeostasis model assessment of insulin resistance(HOMA-IR)in leptin receptor-deficient(db/db)mice.Subsequently,a T2DM liver-specific network was constructed based on RNA sequencing(RNA-seq)experiments and public databases by integrating transcriptional properties of disease-associated proteins and protein-protein interactions(PPIs).The network recovery index(NRI)score of the combined treatment group with a 4∶1 ratio exceeded that of groups treated with individual components.The research identified that activated adenosine 5'-monophosphate-activated protein kinase(AMPK)/acetyl-CoA carboxylase(ACC)signaling in the liver played a crucial role in the synergistic treatment of T2DM,as verified by western blot experiment in db/db mice.These findings demonstrate that the 4∶1 combination of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng significantly improves insulin resistance and glucose and lipid metabolism disorders in db/db mice,surpassing the efficacy of individual treatments.The synergistic mechanism correlates with enhanced AMPK/ACC signaling pathway activity.
文摘In the evolving landscape of secure communication,steganography has become increasingly vital to secure the transmission of secret data through an insecure public network.Several steganographic algorithms have been proposed using digital images with a common objective of balancing a trade-off between the payload size and the quality of the stego image.In the existing steganographic works,a remarkable distortion of the stego image persists when the payload size is increased,making several existing works impractical to the current world of vast data.This paper introduces FuzzyStego,a novel approach designed to enhance the stego image’s quality by minimizing the effect of the payload size on the stego image’s quality.In line with the limitations of traditional methods like Pixel Value Differencing(PVD),Transform Domain Techniques,and Least Significant Bit(LSB)insertion,such as image quality degradation,vulnerability to processing attacks,and restricted capacity,FuzzyStego utilizes fuzzy logic to categorize pixels into intensity levels:Low(L),Medium-Low(ML),Medium(M),Medium-High(MH),and High(H).This classification enables adaptive data embedding,minimizing detectability by adjusting the hidden bit count according to the intensity levels.Experimental results show that FuzzyStego achieves an average Peak Signal-to-Noise Ratio(PSNR)of 58.638 decibels(dB)and a Structural Similarity Index Measure(SSIM)of almost 1.00,demonstrating its promising capability to preserve image quality while embedding data effectively.
基金supported by the National Natural Science Foundation of China(82141201,82405164,82204878,and 32170872)the Haihe Laboratory of Modern Chinese Medicine(Research and development of a universal treatment formula for respiratory viral infections)+3 种基金the National Key Research and Development Program of China(2021YFC1712905,2021YFC1712904,2020YFA0708004,2021YFE0200300,and 2023YFC2306202)the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-D-202002,ZYYCXTD-D-202001)the China Postdoctoral Science Foundation(2023M742626)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(GZC20231927).
文摘The NOD-like receptor family pyrin domain-containing protein 3(NLRP3)inflammasome is an intracellular protein complex containing a nucleotide-binding oligomerization domain,leucine-rich repeats,and a pyrin domain.It is a key regulator of inflammation in viral pneumonia(VP).Small-molecule inhibitors targeting various NLRP3 binding sites are advancing into early clinical trials,but their therapeutic utility is incompletely established.Xuanfei Baidu Formula(XF),clinically used for VP treatment,attenuates NLRP3 activation by hampering caspase-11 to impede polarization of pro-inflammatory macrophages in a model of lipopolysaccharide(LPS)-induced lung injury inmice.Herein,we demonstrate that XF attenuated influenza A virus(IAV)-induced lung inflammation as well as lung injury in immunocompetent(but not in macrophage-depleted)mice.RNA sequencing of sorted lung macrophages from IAV-infected mice revealed that XF inhibited activation of the NLRP3 inflammation and interleukin(IL)-1βproduction.Quantitative nuclear magnetic resonance of XF enabled us to develop XF-Comb1,a fixed-ratio combination of five bioactive compounds that recapitulated the bioactivity of XF in suppressing NLRP3 activation in macrophages in vitro and in vivo.Interestingly,XF-Comb1 inhibited assembly of the NLRP3 inflammasome through multi-site interactions with functional residues of NLRP3,apoptosis-associated speck-like protein containing caspase recruitment domain(ASC),and caspase-1.Taken together,this work advances the development of NLRP3 inhibitors by translating a complex herbal formula into defined bioactive compounds.
基金funded by Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(Grant No.L222103)the National Natural Science Foundation of China(Grant No.72174012)。
文摘Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.
基金funded by the King Salman Center for Disability Research through Research Group no.KSRG-2024-430.
文摘Falls are a leading cause of injury and morbidity among older adults,especially those with Alzheimer’s disease(AD),who face increased risks due to cognitive decline,gait instability,and impaired spatial awareness.While wearable sensor-based fall detection systems offer promising solutions,their effectiveness is often hindered by domain shifts resulting from variations in sensor placement,sampling frequencies,and discrepancies in dataset distributions.To address these challenges,this paper proposes a novel unsupervised domain adaptation(UDA)framework specifically designed for cross-dataset fall detection in Alzheimer’s disease(AD)patients,utilizing advanced transfer learning to enhance generalizability.The proposed method incorporates a ResNet-Transformer Network(ResT)as a feature extractor,along with a novel DualAlign Loss formulation that aims to align feature distributions while maintaining class separability.Experiments on the preprocessed KFall and SisFall datasets demonstrate significant improvements in F1-score and recall,crucial metrics for reliable fall detection,outperforming existing UDA methods,including a convolutional neural network(CNN),DeepCORAL,DANN,and CDAN.By addressing domain shifts,the proposed approach enhances the practical viability of fall detection systems for AD patients,providing a scalable solution to minimize injury risks and improve caregiving outcomes in real-world environments.
基金supported by the National S&T Major Project(No.2018ZX09201011)the National Youth Topnotch Talent Support Program(No.W02070098).
文摘Danshen-Chuanxiongqin Injection(DCI)is a commonly used traditional Chinese medicine for the treatment of cerebral ischemic stroke in China.However,its underlying mechanisms remain completely understood.The current study was designed to explore the protective mechanisms of DCI against cerebral ischemic stroke through integrating whole-transcriptome sequencing coupled with network pharmacology analysis.First,using a mouse model of cerebral ischemic stroke by transient middle cerebral artery occlusion(tMCAO),we found that DCI(4.10 mL·kg−1)significantly alleviated cerebral ischemic infarction,neurological deficits,and the pathological injury of hippocampal and cortical neurons in mice.Next,the whole-transcriptome sequencing was performed on brain tissues.The cerebral ischemia disease(CID)network was constructed by integrating transcriptome sequencing data and cerebrovascular disease-related genes.The results showed CID network was imbalanced due to tMCAO,but a recovery regulation was observed after DCI treatment.Pathway analysis of the key genes with recovery efficiency showed that the neuroinflammation signaling pathway was highly enriched,while the TLR2/TLR4-MyD88-NF-κB pathway was predicted to be affected.Consistently,the in vivo validation experiments confirmed that DCI exhibited protective effects against cerebral ischemic stroke by inhibiting neuroinflammation via the TLR2/TLR4-MyD88-NF-κB pathway.More interestingly,DCI markedly suppressed the neutrophils infiltrated into the brain parenchyma via the choroid plexus route and showed anti-neuroinflammation effects.In conclusion,our results provide dependable evidence that inhibiting neuroinflammation via the TLR2/TLR4-MyD88-NF-κB pathway is the main mechanism of DCI against cerebral ischemic stroke in mice.
基金supported by the Program for New Century Excellent Talents in University(No.NCET-06-0515)China Postdoctoral Science Foundation(No.2012M511380)
文摘A liquid chromatography coupled with diode array detector(DAD) and electrospray ionization time-of-flight mass spectrometry(ESI-TOF/MS) method was developed for the screening and identification of the multiple components in Tanreqing injection, a well-known Chinese medicine injection in China. By combining the DAD spectrum and the accurate mass measurement of ESI-TOF/MS, twelve components in Tanreqing injection were identified. This study contributes to clarifying the nature of Tanreqing injection, and provides an effective and reliable process for the comprehensive and systematic characterization of complex traditional Chinese medicine preparations.
文摘Traditional Chinese medicine (TCM) is a medical system that has collected and summarized abundant clinical experience over its long history of more than 2000 years. However, the frequent occurrence of TCM-induced adverse reactions has hindered the modernization and internationalization of TCM, while attracting increasing attention from around the world. Unlike chemical drugs and biological agents, the difficulties involved in research on the toxicity and safety of TCM mainly include the complexity of its components and the unpredictability of drug–body interactions. Much of TCM, which has overall therapeutic effects, has the typical mechanisms of multiple components, multiple pathways, and multiple targets. While considering the gradualness and unpredictability of TCM toxicity, the ambiguity of toxicants and safe dosage, and individual differences during long-term TCM administration, we have systematically established key techniques for the toxicity assessment of TCM. These techniques mainly include TCM toxicity discovery in an early phase, based on a combination of drug toxicology genomics and metabolomics;methods to identify dose–toxicity relationships in TCM;and integrated techniques for the exploration of TCM interactions, such as fast-screening tests based on drug-metabolizing enzymes and receptor pathways. In particular, we have developed a new technical system for TCM safety evaluation using molecular toxicology, which has been validated well in research on TCM compatibility contraindication, quality control, and allergen discovery. The application of this key technical platform is introduced here in detail. This application includes model organisms, toxicant biomarkers, a magnetic suspension technique, and the application of network toxicology and computational toxicology in research on the toxicity of Fructus toosendan, Semen cassiae, Polygonum multiflorum, and Fructus psoraleae.
基金Project supported by the National Science and Technology Major Project(No.2011ZX09201-201-10),China
文摘We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine （TCM）, Dong＇e Ejiao （DEE J）. Based on near infrared （NIR） spectra of multiple samples, the genuine manufacturer of DEE J, e.g. Dong＇e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T2, distance to Model X （DModX）, and similarity match value （SMV） as dis- criminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEE J, storage time, the partial least squares-discriminant analysis （PLS-DA） method was applied with a satisfactory misjudgment ratio （15.6%） and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEE J, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.
基金supported by the National Key Research and Development Program of China(2020YFA0708004)the National Natural Science Foundation of China(81822047 and 31971088)+1 种基金the Foundation of State Key Laboratory of Component-based Chinese Medicine(CBCM2020104)Yi Wang was supported by the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-D-202002).
文摘Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its underlying pharmacological mechanism remains unclear. Here, we combine a comprehensive research approach that includes network pharmacology, transcriptomics, and bioassays in multiple model systems to investigate the pharmacological mechanism of XFBD and its bioactive substances. High-resolution mass spectrometry was combined with molecular networking to profile the major active substances in XFBD. A total of 104 compounds were identified or tentatively characterized, including flavonoids, terpenes, carboxylic acids, and other types of constituents. Based on the chemical composition of XFBD, a network pharmacology-based analysis identified inflammation-related pathways as primary targets. Thus, we examined the anti-inflammation activity of XFBD in a lipopolysaccharide-induced acute inflammation mice model. XFBD significantly alleviated pulmonary inflammation and decreased the level of serum proinflammatory cytokines. Transcriptomic profiling suggested that genes related to macrophage function were differently expressed after XFBD treatment. Consequently, the effects of XFBD on macrophage activation and mobilization were investigated in a macrophage cell line and a zebrafish wounding model. XFBD exerts strong inhibitory effects on both macrophage activation and migration. Moreover, through multimodal screening, we further identified the major components and compounds from the different herbs of XFBD that mediate its anti-inflammation function. Active components from XFBD, including Polygoni cuspidati Rhizoma, Phragmitis Rhizoma, and Citri grandis Exocarpium rubrum, were then found to strongly downregulate macrophage activation, and polydatin, isoliquiritin, and acteoside were identified as active compounds. Components of Artemisiae annuae Herba and Ephedrae Herba were found to substantially inhibit endogenous macrophage migration, while the presence of ephedrine, atractylenolide I, and kaempferol was attributed to these effects. In summary, our study explores the pharmacological mechanism and effective components of XFBD in inflammation regulation via multimodal approaches, and thereby provides a biological illustration of the clinical efficacy of XFBD.
基金supported by the National S&T Major Project(2018ZX09201011)the Key Program from Sci-Tech Plan of Zhejiang Province(2018C03075)
文摘Evidence continues to grow on potential health risks associated with Ginkgo biloba and its constituents.While biflavonoid is a subclass of the flavonoid family in Ginkgo biloba with a plenty of pharmacological properties,the potential toxicological effects of biflavonoids remains largely unknown.Thus,the aim of this study was to investigate the in vitro and in vivo toxicological effects of the biflavonoids from Ginkgo biloba(i.e.,amentoflavone,sciadopitysin,ginkgetin,isoginkgetin,and bilobetin).In the in vitro cytotoxicity test,the five biflavonoids all reduced cell viability in a dose-dependent manner in human renal tubular epithelial cells(HK-2)and human normal hepatocytes(L-02),indicating they might have potential liver and kidney toxicity.In the in vivo experiments,after intragastrical administration of these biflavonoids at 20 mg·kg^–1·d^–1 for 7 days,serum biochemical analysis and histopathological examinations were performed.The activity of alkaline phosphatase was significantly increased after all the bifl avonoid administrations and widespread hydropic degeneration of hepatocytes was observed in ginkgetin or b ilobetin-treated mice.Moreover,the five biflavonoids all induced acute kidney injury in treated mice and the main pathological lesions were confirmed to the tubule,glomeruli,and interstitium injuries.As the in vitro and in vivo results suggested that these biflavonoids may be more toxic to the kidney than the liver,we further detected the mechanism of biflavonoids-induced nephrotoxicity.The increased TUNEL-positive cells were detected in kidney tissues of biflavonoids-treated mice,accompanied by elevated expression of proapoptotic protein BAX and unchanged levels of antiapoptotic protein BCL-2,indicating apoptosis was involved in biflavonoids-induced nephrotoxicity.Taken together,our results suggested that the five biflavonoids from Ginkgo biloba may have potential hepatic and renal toxicity and more attentions should be paid to ensure Ginkgo biloba preparations safety.
文摘Background: Residual feed intake(RFI) describes an animal’s feed efficiency independent of growth performance.The objective of this study was to determine differences in growth performance, carcass traits, major bacteria attached to ruminal solids-fraction, and ruminal epithelium gene expression between the most-efficient and the least-efficient beef cattle. One-hundred and forty-nine Red Angus cattle were allocated to three contemporary groups according to sex and herd origin. Animals were fed a finishing diet in confinement for 70 d to determine the RFI category for each. Within each group, the two most-efficient(n = 6; RFI coefficient =-2.69 ± 0.58 kg dry matter intake(DMI)/d) and the two least-efficient animals(n = 6; RFI coefficient = 3.08 ± 0.55 kg DMI/d) were selected. Immediately after slaughter, ruminal solids-fraction and ruminal epithelium were collected for bacteria relative abundance and epithelial gene expression analyses, respectively, using real-time PCR.Results: The most-efficient animals consumed less feed(P = 0.01; 5.03 kg less DMI/d) compared with the leastefficient animals. No differences(P > 0.10) in initial body weight(BW), final BW, and average daily gain(ADG) were observed between the two RFI classes. There were no significant RFI × sex effects(P > 0.10) on growth performance.Compared with the least-efficient group, hot carcass weight(HCW), ribeye area(REA), and kidney, pelvic, and heart fat(KPH) were greater(P ≤ 0.05) in the most-efficient cattle. No RFI × sex effect(P > 0.10) for carcass traits was detected between RFI groups. Of the 10 bacterial species evaluated, the most-efficient compared with least efficient cattle had greater(P ≤ 0.05) relative abundance of Eubacterium ruminantium, Fibrobacter succinogenes, and Megasphaera elsdenii, and lower(P ≤ 0.05) Succinimonas amylolytica and total bacterial density. No RFI × sex effect on ruminal bacteria was detected between RFI groups. Of the 34 genes evaluated in ruminal epithelium, the mostefficient cattle had greater(P ≤ 0.05) abundance of genes involved in VFA absorption, metabolism, ketogenesis, and immune/inflammation-response. The RFI × sex interactions indicated that responses in gene expression between RFI groups were due to differences in sex. Steers in the most-efficient compared with least-efficient group had greater(P ≤ 0.05) expression of SLC9 A1, HIF1 A, and ACO2. The most-efficient compared with least-efficient heifers had greater(P ≤ 0.05) m RNA expression of BDH1 and lower expression(P ≤ 0.05) of SLC9 A2 and PDHA1.Conclusions: The present study revealed that greater feed efficiency in beef cattle is associated with differences in bacterial species and transcriptional adaptations in the ruminal epithelium that might enhance nutrient delivery and utilization by tissues. The lack of RFI × sex interaction for growth performance and carcass traits indicates that sex may not play a major role in improving these phenotypes in superior RFI beef cattle. However, it is important to note that this result should not be considered a solid biomarker of efficient beef cattle prior to further examination due to the limited number of heifers compared with steers used in the study.
基金supported by Key R&D Foundation of Zhejiang Province(No.2017C02011 and No.2019C02100)the Zhejiang Key Agricultural Enterprise Institute Project(No.2017Y20001)。
文摘Objective:Ganoderma lucidum spore(GLS)is gaining recognition as a medicinal part of G.lucidum and has been reported to possess various pharmacological properties,such as antitumor activity.In this work,wall-broken GLS powder(BGLSP)and wall-removed GLS powder(RGLSP),two kinds of GLS powder with different manufacturing techniques,were compared in terms of contents of active constituents and in vivo and in vitro antitumor effects.Methods:The ultraviolet and visible spectrophotometry method was used to determine the contents of polysaccharides and total triterpenoids in BGLSP and RGLSP.Seventeen individual triterpenoids were further quantified using ultra-high-performance liquid chromatography and quantitative analysis of multicomponents by single marker.The antitumor effects of BGLSP and RGLSP were evaluated using in vitro cell viability assay against human gastric carcinoma SGC-7901,lung carcinoma A549 and lymphoma Ramos and further validated by in vivo zebrafish xenograft models with transplanted SGC-7901,A549 and Ramos,Results:The results showed that the contents of polysaccharides,total triterpenoids and individual triterpenoids of RGLSP were significantly higher than those of BGLSP.Although both BGLSP and RGLSP inhibited the three tumor cell lines in vitro in a dose-dependent manner,the inhibitory effects of RGLSP were much better than those of BGLSP.In the in vivo zebra fish assay,RGLSP exhibited more potent inhibitory activities against tumors transpla nted into the zebra fish compared with BGLSP,and the inhibition rates of RGLSP reached approximately 78%,31%and 83%on SGC-7901,A549 and Ramos,respectively.Conclusion:The results indicated that the antitumor effects of GLS were positively correlated with the contents of the polysaccha rides and triterpenoids and demonstrated that the wall-removing manufacturing technique could significantly improve the levels of active constituents,and thereby enhance the antitumor activity.
