Altered Iron-Mediated Metabolic Homeostasis Governs the Efficacy and Toxicity of Tripterygium Glycosides Tablets Against Rheumatoid Arthritis

Rheumatoid arthritis(RA),a globally increasing autoimmune disorder,is associated with increased disability rates due to the disruption of iron metabolism.Tripterygium glycoside tablets(TGTs),a Tripterygium wilfordii Hook.f.(TwHF)-based therapy,exhibit satisfactory clinical efficacy for RA treatment.However,drug-induced liver injury(DILI)remains a critical issue that hinders the clinical application of TGTs,and the molecular mechanisms underlying the efficacy and toxicity of TGTs in RA have not been fully elucidated.To address this problem,we integrated clinical multi-omics data associated with the anti-RA efficacy and DILI of TGTs with the chemical and target profiling of TGTs to perform a systematic network analysis.Subsequently,we identified effective and toxic targets following experimental validation in a collagen-induced arthritis(CIA)mouse model.Significantly different transcriptome–protein–metabolite profiles distinguishing patients with favorable TGTs responses from those with poor outcomes were identified.Intriguingly,the clinical efficacy and DILI of TGTs against RA were associated with metabolic homeostasis between iron and bone and between iron and lipids,respectively.Particularly,the signal transducer and activator of transcription 3(STAT3)–hepcidin(HAMP)/lipocalin 2(LCN2)–tartrate-resis tant acid phosphatase type 5(ACP5)and STAT3–HAMP–acyl-CoA synthetase long-chain family member 4(ACSL4)–lysophosphatidylcholine acyltransferase 3(LPCAT3)axes were identified as key drivers of the efficacy and toxicity of TGTs.TGTs play dual roles in ameliorating CIA-induced pathology and in inducing hepatic dysfunction,disruption of lipid metabolism,and hepatic lipid peroxidation.Notably,TGTs effectively reversed“iron–bone”disruptions in the inflamed joint tissues of CIA mice by inhibiting the STAT3–HAMP/LCN2–ACP5 axis,subsequently leading to“iron–lipid”disturbances in the liver tissues via modulation of the STAT3–HAMP–ACSL4–LPCAT3 axis.Additional bidirectional validation experiments were conducted using MH7A and AML12 cells to confirm the bidirectional regulatory effects of TGTs on key targets.Collectively,our data highlight the association between iron-mediated metabolic homeostasis and the clinical efficacy and toxicity of TGT in RA therapy,offering guidance for the rational clinical use of TwHF-based therapy with dual therapeutic and toxic potential.

Recent Advances on Deep Learning for Sign Language Recognition

Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification

(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society.Similar to the research of many biomedical image processing(such as automatic chest radiograph processing,diagnosis of chest radiological images,etc.),with the rapid development of artificial intelligence,especially deep learning technologies and algorithms,sign language image recognition ushered in the spring.This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.(Method)Three different combinations of blocks:Conv-BN-ReLU-Pooling,Conv-BN-ReLU,Conv-BN-ReLU-BN were employed,including some advanced technologies such as batch normalization,dropout,and Leaky ReLU.We proposed an optimized convolutional neural network to identify 1320 sign language images,which was called as CNN-CB method.Totally ten runs were implemented with the hold-out randomly set for each run.(Results)The results indicate that our CNN-CB method gained an overall accuracy of 94.88±0.99%.(Conclusion)Our CNN-CB method is superior to thirteen state-of-the-art methods:eight traditional machine learning approaches and five modern convolutional neural network approaches.

Sociodemographic characteristics, cultural biases, and environmental attitudes: An empirical application of grid-group cultural theory in Northwestern China

Natural resource-management studies have become increasingly attentive to the influences of human factors. Among these,cultural biases shape people’s responses to changes in natural resource systems. Several studies have applied grid-group cultural theory to assess the effects of multiple value biases among stakeholders on natural resource management. We developed and administered a questionnaire in the Heihe River Basin(n = 364) in northwestern China to investigate the appropriateness of applying this theory in the Chinese context of natural resource management. The results revealed various cultural biases among the respondents. In descending order of prevalence, these biases were hierarchism(46.98%), individualism(26.65%), egalitarianism(18.96%), and fatalism(2.78%), with the remaining respondents(4.67%) evidencing no obvious bias. Our empirical study revealed respondents’ worldviews and the influence of sociodemographic characteristics on cultural biases, as theoretically posited. Among the variables examined, age had a positive and significant effect across all biases except individualism. The correlation of income to all cultural biases was consistently negative. Only education had a negative and significant effect across all biases. Women were found to adhere to egalitarianism, whereas men adhered to individualism and hierarchism. Thus, grid-group cultural theory was found to be appropriate in the Chinese context, with gender, age, education, and income evidently accounting for cultural biases. Relationships between environmental attitudes and cultural biases conformed with the hypothesis advanced by grid-group cultural theory. This finding may be of value in explaining individuals’ environmental attitudes and facilitating the development and implementation of natural resource-management policies.

ETCM v2.0:An update with comprehensive resource and rich annotations for traditional Chinese medicine

Existing traditional Chinese medicine(TCM)-related databases are still insufficient in data standardization,integrity and precision,and need to be updated urgently.Herein,an Encyclopedia of Traditional Chinese Medicine version 2.0(ETCM v2.0,http://www.tcmip.cn/ETCM2/front/#/)was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books,9872 Chinese patent drugs,2079 Chinese medicinal materials and 38,298 ingredients.To facilitate the mechanistic research and new drug discovery,we improved the target identification method based on a two-dimensional ligand similarity search module,which provides the confirmed and/or potential targets of each ingredient,as well as their binding activities.Importantly,five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0,which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy,to summarize the rules of prescription use,and to find alternative drugs for endangered Chinese medicinal materials.Moreover,ETCM v2.0 provides an enhanced Java Script-based network visualization tool for creating,modifying and exploring multi-scale biological networks.ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs,the TCM-derived drug discovery and repurposing,and the pharmacological mechanism investigation of TCMs against various human diseases.

Metformin escape in prostate cancer by activating the PTGR1 transcriptional program through a novel super-enhancer

The therapeutic efficacy of metformin in prostate cancer(PCa)appears uncertain based on various clinical trials.Metformin treatment failure may be attributed to the high frequency of transcriptional dysregulation,which leads to drug resistance.However,the underlying mechanism is still unclear.In this study,we found evidences that metformin resistance in PCa cells may be linked to cell cycle reactivation.Super-enhancers(SEs),crucial regulatory elements,have been shown to be associated with drug resistance in various cancers.Our analysis of SEs in metformin-resistant(MetR)PCa cells revealed a correlation with Prostaglandin Reductase 1(PTGR1)expression,which was identified as significantly increased in a cluster of cells with metformin resistance through single-cell transcriptome sequencing.Our functional experiments showed that PTGR1 overexpression accelerated cell cycle progression by promoting progression from the G0/G1 to the S and G2/M phases,resulting in reduced sensitivity to metformin.Additionally,we identified key transcription factors that significantly increase PTGR1 expression,such as SRF and RUNX3,providing potential new targets to address metformin resistance in PCa.In conclusion,our study sheds new light on the cellular mechanism underlying metformin resistance and the regulation of the SE-TFs-PTGR1 axis,offering potential avenues to enhance metformin’s therapeutic efficacy in PCa.

Distinct Types of Cell Death and Implications in Liver Diseases:An Overview of Mechanisms and Application

Cell death is associated with a variety of liver diseases,and hepatocyte death is a core factor in the occurrence and progression of liver diseases.In recent years,new cell death modes have been identified,and certain biomarkers have been detected in the circulation during various cell death modes that mediate liver injury.In this review,cell death modes associated with liver diseases are summarized,including some cell death modes that have emerged in recent years.We described the mechanisms associated with liver diseases and summarized recent applications of targeting cell death in liver diseases.It provides new ideas for the diagnosis and treatment of liver diseases.In addition,multiple cell death modes can contribute to the same liver disease.Different cell death modes are not isolated,and they interact with each other in liver diseases.Future studies may focus on exploring the regulation between various cell death response pathways in liver diseases.

A comprehensive review of integrative pharmacology-based investigation:A paradigm shift in traditional Chinese medicine

Over the past decade,traditional Chinese medicine(TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization.Thus,integrative pharmacology-based traditional Chinese medicine(TCMIP) was proposed as a paradigm shift in TCM.This review focuses on the presentation of this novel concept and the main research contents,methodologies and applications of TCMIP.First,TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics(PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo.Then,the main research contents of TCMIP are introduced as follows:chemical and ADME/PK profiles of TCM formulas;confirming the three forms of active substances and the three action modes;establishing the qualitative PK-PD correlation;and building the quantitative PK-PD correlations,etc.After that,we summarize the existing data resources,computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods.Finally,we further discuss the applications of TCMIP for the improvement of TCM quality control,clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs,especially TCM-related combination drug disco very.

SoFDA:an integrated web platform from syndrome ontology to network-based evaluation of disease–syndrome–formula associations for precision medicine

Clinical manifestations of symptoms play a crucial role in the diagnosis and appropriate treatment of diseases and are considered one of the main clinical features for contemporary disease taxonomy(i.e.,international classification of diseases,ICD)[1].Deep investigation on molecular connections among symptoms is one of the key tasks for developing a disease-specific knowledge network and thus promoting the refinement of disease taxonomy toward precision medicine[2].

Biomolecular network-based synergistic drug combination discovery:a combination of paeoniflorin and liquiritin alleviates neuropathic pain by inhibiting neuroinflammation via suppressing the chemokine signaling pathway

Dear Editor,Neuropathic pain(NP)resulting from injuries or diseases affecting the somatosensory nervous system is highly prevalent in various pathological conditions.1 Since NP is debilitating and impacts health and the quality of life,there is an urgent need for effective non-addictive new therapies.Traditional Chinese medicine(TCM)has been increasingly used for its benefits in relieving pain in clinics.Wu-Tou Decoction(WTD),which was first described by the famous TCM classic“Jin Gui Yao Lue”,is one of the most effective TCM herbal prescriptions for pain management.A growing body of clinical evidence shows that WTD markedly alleviates different types of NP,such as trigeminal neuralgia,inflammatory pain,and cancer-induced pain,with a total effectiveness of~80%.2 Our previous data demonstrated the analgesic effects of WTD via suppression of glial cell activation and neuroinflammation and further revealed that WTD attenuated NP partially by inhibiting spinal astrocytic IL-1R1/TRAF6/JNK signaling and regulating the glutamatergic system in CA3 in spinal nerve ligation(SNL)-induced NP in vivo.3,4 However,the bioactive compounds(BACs)of WTD that have an effect on NP and the underlying mechanisms remain unclear.

Potential game design for a class of distributed optimization problems

The state-based potential game is discussed and a game-based approach is proposed for distributed optimization problem in this paper.A continuous-time model is employed to design the state dynamics and learning algorithms of the state-based potential game with Lagrangian multipliers as the states.It is shown that the stationary state Nash equilibrium of the designed game contains the optimal solution of the optimization problem.Moreover,the convergence and stability of the learning algorithms are obtained for both undirected and directed communication graph.Additionally,the application to plug-in electric vehicle management is also discussed.

Nash equilibrium seeking over directed graphs

In this paper,we aim to develop distributed continuous-time algorithms over directed graphs to seek the Nash equilibrium in a noncooperative game.Motivated by the recent consensus-based designs,we present a distributed algorithm with a proportional gain for weight-balanced directed graphs.By further embedding a distributed estimator of the left eigenvector associated with zero eigenvalue of the graph Laplacian,we extend it to the case with arbitrary strongly connected directed graphs having possible unbalanced weights.In both cases,the Nash equilibrium is proven to be exactly reached with an exponential convergence rate.An example is given to illustrate the validity of the theoretical results.