Search Results (47)

Cinnamic acid, an organic acid naturally occurring in plants of the Cinnamomum genus, has been highly valued for its medicinal properties in numerous ancient Chinese texts. This article reviews the chemical composition, pharmacological effects, and various applications of cinnamic acid and its derivatives reported in publications from 2016 to 2025, and anticipates their potential in medical and industrial fields. This review evaluates studies in major scientific databases, including Web of Science, PubMed, and ScienceDirect, to ensure a comprehensive analysis of the therapeutic potential of cinnamic acid. Through systematic integration of existing knowledge, it has been revealed that cinnamic acid has a wide range of pharmacological activities, including anti-tumor, antibacterial, anti-inflammatory, antidepressant and hypoglycemic effects. Additionally, it has been shown to be effective against a variety of pathogens such as Staphylococcus aureus, Pseudomonas aeruginosa, and foodborne Pseudomonas. Cinnamic acid acts by disrupting cell membranes, inhibiting ATPase activity, and preventing biofilm formation, thereby demonstrating its ability to act as a natural antimicrobial agent. Its anti-inflammatory properties are demonstrated by improving oxidative stress and reducing inflammatory cell infiltration. Furthermore, cinnamic acid enhances metabolic health by improving glucose uptake and insulin sensitivity, showing promising results in improving metabolic health in patients with diabetes and its complications. This systematic approach highlights the need for further investigation of the mechanisms and safety of cinnamic acid to substantiate its use as a basis for new drug development. Particularly in the context of increasing antibiotic resistance and the search for sustainable, effective medical treatments, the study of cinnamic acid is notably significant and innovative. Full article

The Yogācārabhūmi, compiled by Saṅgharakṣa, was first introduced to China by An Shigao’s abridged translation (T607, Daodi jing 道地經), later, in 284 CE, Dharmarakṣa produced a more comprehensive version (T606, Xiuxing daodi jing 修行道地經). Lacking extant Sanskrit or Pali parallels, the text is difficult to interpret literally, and the differences between T607 and T606 add to the analytical challenges. However, a substantial section in both translations describing omens of impending death in the sick exhibits systematic parallels with Indian Āyurvedic texts, such as the Caraka-saṃhitā and Suśruta-saṃhitā. These parallels help clarify the ambiguous passages through comparative analysis. This study explores the translation strategies of An Shigao and Dharmarakṣa in introducing Indian medical concepts to China. An Shigao adopted a localization strategy, replacing foreign terms with analogous Chinese concepts. His terminology, corroborated by usage in Eastern Han or earlier Chinese texts—particularly excavated manuscripts—supports claims in the Chu sanzang ji ji regarding his expertise in medicine and divination. By contrast, Dharmarakṣa’s Xiuxing daodi jing sought greater fidelity to the Indian source material, offering a more detailed and systematic presentation of Āyurvedic knowledge. However, Dharmarakṣa did not entirely abandon An Shigao’s localization approach. He adopted a balanced strategy that combined faithful representation with cultural adaptation, reflecting the broader capacity of his more diverse and sophisticated audience to engage with complex and extensive foreign knowledge. Full article

The intent classification of Chinese questions about respiratory diseases (CQRD) can not only promote the development of smart medical care, but also strengthen epidemic surveillance. The major core of the intent classification of CQRD is text representation. This paper studies how to utilize keywords to construct CQRD representation. Based on the characteristics of CQRD, we propose a keywords-based reinforcement learning model. In the reinforcement learning model based on keywords, we crafted a word frequency reward function to aid in generating the reward function and determining keyword categories. Simultaneously, to generate CQRD representations using keywords, we developed two models: keyword-driven LSTM (KD-LSTM) and keyword-driven GCN (KD-GCN). The KD-LSTM incorporates two methods: one based on word weights and the other based on category vectors. The KD-GCN employs keywords to construct a weight matrix for training. The method based on word weight achieves the best results on the CQRD_28000 dataset, which is 0.72% higher than the Bi-LSTM model. The method based on category vector outperforms the Bi-LSTM model in the CQRD_8000 dataset by 2.41%. The KD-GCN, although not attaining the optimal outcome, exhibited a superior performance of 3.12% compared to the GCN model. Both methods have significantly improved the classification results of minority classes. Full article

Cinnamon oil, an essential oil extracted from plants of the genus Cinnamomum, has been highly valued in ancient Chinese texts for its medicinal properties. This review summarizes the chemical composition, pharmacological actions, and various applications of cinnamon oil, highlighting its potential in medical and industrial fields. By systematically searching and evaluating studies from major scientific databases including Web of Science, PubMed, and ScienceDirect, we provide a comprehensive analysis of the therapeutic potential of cinnamon oil. Research indicates that cinnamon oil possesses a wide range of pharmacological activities, covering antibacterial, anti-inflammatory, anti-tumor, and hypoglycemic effects. It is currently an active ingredient in over 500 patented medicines. Cinnamon oil has demonstrated significant inhibitory effects against various pathogens comprising Staphylococcus aureus, Salmonella, and Escherichia coli. Its mechanisms of action include disrupting cell membranes, inhibiting ATPase activity, and preventing biofilm formation, suggesting its potential as a natural antimicrobial agent. Its anti-inflammatory properties are evidenced by its ability to suppress inflammatory markers like vascular cell adhesion molecules and macrophage colony-stimulating factors. Moreover, cinnamon oil has shown positive effects in lowering blood pressure and improving metabolism in diabetic patients by enhancing glucose uptake and increasing insulin sensitivity. The main active components of cinnamon oil include cinnamaldehyde, cinnamic acid, and eugenol, which play key roles in its pharmacological effects. Recently, the applications of cinnamon oil in industrial fields, including food preservation, cosmetics, and fragrances, have also become increasingly widespread. Despite the extensive research supporting its medicinal value, more clinical trials are needed to determine the optimal dosage, administration routes, and possible side effects of cinnamon oil. Additionally, exploring the interactions between cinnamon oil and other drugs, as well as its safety in different populations, is crucial. Considering the current increase in antibiotic resistance and the demand for sustainable and effective medical treatments, this review emphasizes the necessity for further research into the mechanisms and safety of cinnamon oil to confirm its feasibility as a basis for new drug development. In summary, as a versatile natural product, cinnamon oil holds broad application prospects and is expected to play a greater role in future medical research and clinical practice. Full article

Addressing the privacy protection and data sharing issues in Chinese medical texts, this paper introduces a federated learning approach named FLCMC for Chinese medical text classification. The paper first discusses the data heterogeneity issue in federated language modeling. Then, it proposes two perturbed federated learning algorithms, FedPA and FedPAP, based on the self-attention mechanism. In these algorithms, the self-attention mechanism is incorporated within the model aggregation module, while a perturbation term, which measures the differences between the client and the server, is added to the local update module along with a customized PAdam optimizer. Secondly, to enable a fair comparison of algorithms’ performance, existing federated algorithms are improved by integrating a customized Adam optimizer. Through experiments, this paper first conducts experimental analyses on hyperparameters, data heterogeneity, and validity on synthetic datasets, which proves that the proposed federated learning algorithm has significant advantages in classification performance and convergence stability when dealing with heterogeneous data. Then, the algorithm is applied to Chinese medical text datasets to verify its effectiveness on real datasets. The comparative analysis of algorithm performance and communication efficiency shows that the algorithm exhibits strong generalization ability on deep learning models for Chinese medical texts. As for the synthetic dataset, upon comparing with comparison algorithms FedAvg, FedProx, FedAtt, and their improved versions, the experimental results show that for data with general heterogeneity, both FedPA and FedPAP show significantly more accurate and stable convergence behavior. On the real Chinese medical dataset of doctor–patient conversations, IMCS-V2, with logistic regression and long short-term memory network as training models, the experiment results show that in comparison to the above three comparison algorithms and their improved versions, FedPA and FedPAP both possess the best accuracy performance and display significantly more stable and accurate convergence behavior, proving that the method in this paper has better classification effects for Chinese medical texts. Full article

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks. Full article

Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical literature retrieval, and intelligent medical question–answering systems, enabling doctors and researchers to obtain the required medical information more quickly and thereby helping to improve the accuracy of diagnosis and treatment decisions. The current methods have certain limitations in dealing with contextual dependencies and entity memory and fail to fully consider the contextual relevance and interactivity between entities. To address these issues, this paper proposes a Chinese medical named entity recognition model that combines contextual dependency perception and a new memory unit. The model combines the BERT pre-trained model with a new memory unit (GLMU) and a recall network (RMN). The GLMU can efficiently capture long-distance dependencies, while the RMN enhances multi-level semantic information processing. The model also incorporates fully connected layers (FC) and conditional random fields (CRF) to further optimize the performance of entity classification and sequence labeling. The experimental results show that the model achieved F1 values of 91.53% and 64.92% on the Chinese medical datasets MCSCSet and CMeEE, respectively, surpassing other related models and demonstrating significant advantages in the field of medical entity recognition. Full article

Medical texts are rich in specialized knowledge and medical information. As the medical and healthcare sectors are becoming more digitized, many medical texts must be effectively harnessed to derive insights and patterns. Thus, great attention is directed to this emerging research area. Generally, natural language processing (NLP) algorithms are employed to extract comprehensive information from unstructured medical texts, aiming to construct a graphical database for medical knowledge. One of the needs is to optimize model sizes while maintaining the precision of the BART algorithm. A novel carefully designed algorithm, called ALBERT+Bi-LSTM+CRF, is introduced. In this way, both enhanced efficiency and scalability are attained. When entities are extracted, the constructed algorithm achieves 91.8%, 92.5%, and 94.3% for the F-score, precision, and recall, respectively. The proposed algorithm also achieves remarkable outcomes in extracting relations, with 88.3%, 88.1%, and 88.4% for the F-score, precision, and recall, respectively. This further underscores its practicality in the graphical construction of medical knowledge. Full article

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot’s judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system’s complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value. Full article

Medication errors, which could often be detected in advance, are a significant cause of patient deaths each year, highlighting the critical importance of medication safety. The rapid advancement of data analysis technologies has made intelligent medication assistance applications possible, and these applications rely heavily on medical knowledge graphs. However, current knowledge graph construction techniques are predominantly focused on general domains, leaving a gap in specialized fields, particularly in the medical domain for medication assistance. The specialized nature of medical knowledge and the distinct distribution of vocabulary between general and biomedical texts pose challenges. Applying general natural language processing techniques directly to the medical domain often results in lower accuracy due to the inadequate utilization of contextual semantics and entity information. To address these issues and enhance knowledge graph production, this paper proposes an optimized model for named entity recognition and relationship extraction in the Chinese medical domain. Key innovations include utilizing Medical Bidirectional Encoder Representations from Transformers (MCBERT) for character-level embeddings pre-trained on Chinese biomedical corpora, employing Bi-directional Gated Recurrent Unit (BiGRU) networks for extracting enriched contextual features, integrating a Conditional Random Field (CRF) layer for optimal label sequence output, using the Piecewise Convolutional Neural Network (PCNN) to capture comprehensive semantic information and fusing it with entity features for better classification accuracy, and implementing a microservices architecture for the medication assistance review system. These enhancements significantly improve the accuracy of entity relationship classification in Chinese medical texts. The model achieved good performance in recognizing most entity types, with an accuracy of 88.3%, a recall rate of 85.8%, and an F1 score of 87.0%. In the relationship extraction stage, the accuracy reached 85.7%, the recall rate 82.5%, and the F1 score 84.0%. Full article

Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such as symptoms, tests, and drugs. The framework integrates seamlessly with real-world healthcare systems, offering scalable and efficient solutions for processing large volumes of clinical data. This paper provides an in-depth discussion on the architecture and implementation of MedNER, featuring the concept of Deep Learning as a Service (DLaaS). A prototype has encapsulated BiLSTM-CRF and BERT-BiLSTM-CRF models into the core service, demonstrating its flexibility, usability, and effectiveness in addressing the unique challenges of Chinese medical text processing. Full article

Unstructured Chinese medical texts are rich sources of entity and relational information. The extraction of entity relationships from medical texts is pivotal for the construction of medical knowledge graphs and aiding healthcare professionals in making swift and informed decisions. However, the extraction of entity relationships from these texts presents a formidable challenge, notably due to the issue of overlapping entity relationships. This study introduces a novel extraction model that leverages RoFormer’s rotational position encoding (RoPE) technique for an efficient implementation of relative position encoding. This approach not only optimizes positional information utilization but also captures syntactic dependency information by constructing a weighted adjacency matrix. During the feature fusion phase, the model employs an entity attention mechanism for a deeper integration of features, effectively addressing the challenge of overlapping entity relationships. Experimental outcomes demonstrate that our model achieves an F1 score of 83.42 on datasets featuring overlapping entity relations, significantly outperforming other baseline models. Full article

The Sanyuan Canzan Yanshou Shu 三元參贊延壽書, compiled by Li Pengfei during the Yuan dynasty, is a comprehensive collection of the essence of earlier health preservation literature. Recently, the Jianwen first-year (1399) re-engraved edition by Liu Yuanran 劉淵然 (1351–1432) has emerged, which is currently housed in the Imperial Household Agency Library in Japan. It has challenged the prevailing consensus in China that the edition (1445) in the Daozang 道藏 is the earliest version. This discovery not only enriches our understanding of the text’s historical dissemination but also highlights the international appreciation and preservation of Chinese traditional medical and health knowledge. Upon meticulous examination, the various editions of this text can be systematically classified into two distinct lineages: Yanshou Canzan 延壽參贊 and Canzan Yanshou 參贊延壽. The latter lineage is notably more comprehensive, with the Wanli 萬曆 edition serving as a prime exemplar of this expanded scope. Li Pengfei primarily drew upon the Yangsheng Leizuan 養生類纂 as the foundational text for his work, skillfully integrating a wealth of Daoism and medical scriptures. He adeptly restructured the content by employing the conceptual framework of three primes (sanyuan 三元), incorporating the health preservation philosophies of Confucianism and Buddhism, thereby transforming it into a more systematic and diverse Daoism scripture dedicated to health preservation. The book eloquently advocates for health-preserving philosophies centered around the principle of not diminishing (busun 不損) primordial pneuma (yuanqi 元氣), extending life through three primes, and prolonging life through the virtue of yin (yinde 陰德). These ideas emphasize a human-centered approach, focusing on preserving the primordial pneuma as the foundation and employing both loss prevention and supplementation as dual pathways. It aims to achieve a state of health preservation where there is unity of man with heaven (tianren heyi 天人合一) and a harmonious balance of yin and yang energies (yinyang qihe 陰陽氣和). Full article

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies. Full article

This article discusses how and why Christian medical missionaries established their foothold in Chinese society through the medical career of Benjamin Hobson, who was active in China from the late 1830s to the 1850s. Apart from his evangelical work among the Chinese, one of his key contributions was the new medical vocabularies he created to communicate medical knowledge. In addition to literary considerations, Hobson had his strategies for sharing modern medical knowledge. Moreover, he was prepared to debate with the Chinese over the validity of the pulse theory. The debate did not happen, however. His intention to establish the case for the superior position of Western medicine was not contested. His medical texts, at best, became the necessary underpinning for introducing modern Western medicine to China. When Western medical college projects took place in China at the turn of the century, biomedicine took over as the key paradigm, with Hobson’s medical texts being of limited use. Full article