Screening for Anti-tumor Activity of Fractions from Buddleja officinalis Maxim

[Objectives]The anti-tumor activity of fractions from Buddleja officinalis Maxim.by petroleum ether,ethyl acetate,n-butanol and water solvent was studied.[Methods]The ethanol extract from B.officinalis Maxim.was extracted and then concentrated with petroleum ether,ethyl acetate,n-butanol and water,respectively,and the extracts were obtained.The inhibitory effects of the four different fractions on the growth of three tumor cell lines in vitro were detected by CCK-8 method,and the median inhibitory concentration(IC 50 value)was calculated.[Results]The four fractions inhibited the growth of the three tumor cell lines in vitro,among which the n-butanol fraction had the best anti-tumor activity.The IC 50 values of the n-butanol fraction on human gastric cancer(SGC-7901),human breast cancer(MCF-7)and human liver cancer(BEL-7404)cell lines were 0.08,1.58 and 0.12 mg/mL,respectively.[Conclusions]Petroleum ether,ethyl acetate,n-butanol and water fractions from the ethanol extract of B.officinalis Maxim.had certain anti-tumor effects,and the n-butanol fraction had the best anti-tumor activity.

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

Herb pair of Huangqi-Danggui exerts anti-tumor immunity to breast cancer by upregulating PIK3R1

Background:According to traditional Chinese medicine(TCM),drugs supplementing the vital energy,Qi,can eliminate tumors by restoring host immunity.The objective of this study is to investigate the underlying immune mechanisms of anti-tumor activity associated with Qi-supplementing herbs,specifically the paired use of Huangqi and Danggui.Methods:Analysis of compatibility regularity was conducted to screen the combination of Qi-supplementing TCMs.Using the MTT assay and a transplanted tumor mice model,the anti-tumor effects of combination TCMs were investigated in vitro and in vivo.High content analysis and flow cytometry were then used to evaluate cellular immunity,followed by network pharmacology and molecular docking to dissect the significant active compounds and potential mechanisms.Finally,the anti-tumor activity and the mechanism of the active ingredients were verified by molecular experiments.Results:There is an optimal combination of Huangqi and Danggui that,administered as an aqueous extract,can activate immunity to suppress tumor and is more effective than each drug on its own in vitro and in vivo.Based on network pharmacology analysis,PIK3R1 is the core target for the anti-tumor immunity activity of combined Huangqi and Danggui.Molecular docking analysis shows 6 components of the combined Danggui and Huangqi extract(quercetin,jaranol,isorhamnetin,kaempferol,calycosin,and suchilactone)that bind to PIK3R1.Jaranol is the most important component against breast cancer.The suchilactone/jaranol combination and,especially,the suchilactone/kaempferol combination are key for immunity enhancement and the anti-tumor effects of the extract.Conclusions:The combination of Huangqi and Danggui can activate immunity to suppress breast cancer and is more effective than the individual drugs alone.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Unveiling health rights:A call to action for sex workers'HIV care in the Philippines

We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.

Spatiotemporal transformable nano-assembly for on-demand drug delivery to enhance anti-tumor immunotherapy

Induction of tumor cell senescence has become a promising strategy for anti-tumor immunotherapy,but fibrotic matrix severely blocks senescence inducers penetration and immune cells infiltration.Herein,we designed a cancer-associated fibroblasts(CAFs)triggered structure-transformable nano-assembly(HSD-P@V),which can directionally deliver valsartan(Val,CAFs regulator)and doxorubicin(DOX,senescence inducer)to the specific targets.In detail,DOX is conjugated with hyaluronic acid(HA)via diselenide bonds(Se-Se)to form HSD micelles,while CAFs-sensitive peptide is grafted onto the HSD to form a hydrophilic polymer,which is coated on Val nanocrystals(VNs)surface for improving the stability and achieving responsive release.Once arriving at tumor microenvironment and touching CAFs,HSD-P@V disintegrates into VNs and HSD micelles due to sensitive peptide detachment.VNs can degrade the extracellularmatrix,leading to the enhanced penetration of HSD.HSD targets tumor cells,releases DOX to induce senescence,and recruits effector immune cells.Furthermore,senescent cells are cleared by the recruited immune cells to finish the integrated anti-tumor therapy.In vitro and in vivo results show that the nanoassembly remarkably inhibits tumor growth as well as lungmetastasis,and extends tumorbearing mice survival.This work provides a promising paradigm of programmed delivering multi-site nanomedicine for cancer immunotherapy.

Impact of Action Observation Therapy along with Usual Physiotherapy Intervention of Individual with Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.

Anti-Tumor and Anti-Diabetic Effects of Sarsasapogenin

In this paper,the pharmacological effects and molecular mechanisms of sarsasapogenin,such as anti-oxidant,anti-inflammatory and anti-diabetic effects,are reviewed in order to provide a theoretical basis for the subsequent development and clinical application of sarsasapogenin.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

Mechanism of Anti-tumor Effects of Viola Medicinal Materials Based on Network Pharmacology and Molecular Docking

[Objectives]To explore the relationship between anti-tumor components,targets,and pathways involved in Viola medicinal materials,study its main active components,and evaluate its inhibitory activity.[Methods]Through network pharmacological analysis,molecular docking simulation experiments and in vitro experiments,the main components and corresponding targets of Viola were screened out,and their anti-tumor signaling pathways were confirmed.MTT colorimetric assay was used to investigate the inhibitory effect of different extraction layers of Viola on the growth of tumor cells.[Results]18 anti-tumor active components such as 2α,19α-Dihydroxyursolic acid,Corlumine,Madolin U,Trifolirhizin and etc.,and 52 action targets such as PTGS2,PTGS1,P2RX7,MMP1,and GABRB3,and anti-tumor signaling pathways were confirmed.The results of molecular docking showed that all the selected Viola compounds had good binding activity.The results of MTT colorimetric assay showed that the petroleum ether layer and n-butanol layer had a good inhibitory effect on the growth of tumor cell lines.[Conclusions]Viola medicinal materials have the potential of anti-tumor,triterpenoids and flavonoids may be the main active components,and the petroleum ether layer and n-butanol layer have better inhibitory effect on the growth of tumor cells.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Advances in Research of Anti-tumor Effect of Aconiti Radix

In this paper,the anti-tumor effects of Aconiti Radix were reviewed and summarized,and the clinical feasibility of Aconiti Radix as a potential anti-tumor drug was analyzed,in order to provide a useful reference for the future research and development of new anti-cancer drugs of Aconiti Radix.

Treatment of primary nasal tuberculosis with anti-tumor necrosis factor immunotherapy:A case report

BACKGROUND Primary nasal tuberculosis(TB)is a rare form of extrapulmonary TB,particularly in patients receiving anti-tumor necrosis factor(TNF)immunotherapy.As a result,its diagnosis remains challenging.CASE SUMMARY A 58-year-old male patient presented to the ear,nose,and throat department with right-sided nasal obstruction and bloody discharge for 1 month.He was diagnosed with psoriatic arthritis and received anti-TNF immunotherapy for 3 years prior to presentation.Biopsy findings revealed chronic granulomatous inammation and a few acid-fast bacilli,suggestive of primary nasal TB.He was referred to our TB management department for treatment with oral anti-TB agents.After 9 months,the nasal lesions had disappeared.No recurrence was noted during follow-up.CONCLUSION The diagnosis of primary nasal TB should be considered in patients receiving TNF antagonists who exhibit thickening and crusting of the nasal septum mucosa or inferior turbinate,particularly when pathological findings suggest granulomatous inflammation.

Research Progress on Purification Process, Content Determination and Pharmacological Action of Atractylodin

Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rheumatic arthralgia.Atractylodin is the main active component extracted and isolated from Atractylodis Rhizoma.A large number of studies have found that atractylodin has excellent drug activity in improving gastrointestinal emptying,anti-inflammation,inhibiting malignant tumor and reducing blood lipid.In this paper,the purification process and pharmacological activity of Atractylodin were summarized to provide a theoretical basis for basic research,clinical application and further development and utilization of atractylodin.

Study on the Mechanism of Action of Glyasperin A in the Treatment of Atherosclerosis Based on Network Pharmacology and Molecular Docking

[Objectives] This study was conducted to investigate the mechanism of action of glyasperin A in the treatment of atherosclerosis using a network pharmacology approach. [Methods] Targets related to atherosclerosis were searched in GeneCards database. An active ingredient-disease-target network was constructed by Cytoscape 3.7.1. A target protein interaction network was constructed by String database. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the DAVID database. [Results] Glyasperin A acted on 36 atherosclerosis-related targets, and the biofunctional and pathway enrichment analyses showed that it was mainly involved in response to xenobiotic stimulus, drug transport across blood-brain barrier, lipid oxidation, barrier, and lipid oxidation, etc. The results showed that glyasperin A acted on 36 atherosclerosis-related targets. The biofunctional and pathway enrichment analyses showed that it was mainly involved in response to xenobiotic stimulus, drug transport across blood-brain barrier, lipid oxidation, positive regulation of protein localization to nucleus, and hepoxilin biosynthetic process, and it played an anti-fatigue role through signal pathways such as serotonergic synapse, efferocytosis, arachidonic acid metabolism, chemical carcinogenesis-receptor activation and platelet activation. [Conclusions] Glyasperin A has multi-target and multi-pathway effects in the treatment of atherosclerosis. This study provides reference for further research on glyasperin A in the treatment of atherosclerosis.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

Tangled web: Animal-human interaction and the rise of antibiotic resistance

Introduction Egypt is a Low-Middle Income Country(LMIC)with a population of 106,472,382 people in August 2022(Worldometers&Department of Economic and Social Affairs,2022).Available evidence suggests that there are misuse/overuse of antibiotic in Egypt[1].On recognizing the global threat of antibiotic resistance(AMR),Egypt launched the National Action Plan(NAP)in 2018.This comprehensive plan,inspired by the Global Action Plan(GAP),united diverse stakeholders like ministries,universities,and international organizations.The NAP tackled AMR through four key strategies:boosting public understanding of antibiotics,optimizing their use across humans and animals,embracing a“One Health”approach,and implementing effective infection prevention practices[2].

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.