The acupoint name is a core term in traditional Chinese medicine and has its own mysterious and abstruse feature. Designated by the international organizations such as World Federation of Chinese Medicine Societies, World Health Organization, the phonetic translation method has been adopted for the standardization of acupuncture nomenclature. But this method neglects the cultural attributes of acupoint names. The liberal translation should be considered appropriately. English translation of acupoint name should be composed of two steps, intralingual translation (exegesis) and interlingual translation. During exegesis, the methods for discriminating phonetic loan character, selecting meanings and identifying character patterns should be sufficiently used. The interlingual translation is launched only after the fully understanding of acupoint names (based on intralingual translation).
Acupuncture Points ; Terminology as Topic ; Humans ; Translations ; Language ; Translating ; Medicine, Chinese Traditional

"Jingmai binghou" (meridian symptoms) is not the original term in ancient works, and it is proposed in modern teaching materials of acupuncture. It refers to "the diseases caused by the invasion of pathogenic factors into meridians", and "diseases of the affected meridians", recorded in jingmai (Meridian) of Lingshu (Miraculous Pivot). The proposal of this term is related to the academic tendency of textbook writers and the influence of TCM policy in China. Through collating and analyzing the records of meridian disorders in ancient works, it is found that besides the classic "meridian symptoms", many zangfu disorders, the disorders along the running course of meridian based on meridian differentiation, collateral disorders and the disorders of the exterior-interior relationship of meridians should be classified as meridian disorder. In order to accurately express the rich content of "Jingmai binghou", from the perspective of terminology normalization, it is believed that the expression as "meridian-collateral dominated disease" may reflect its connotation more comprehensively.
Meridians ; Humans ; China ; Terminology as Topic ; History, Ancient ; Acupuncture Therapy/history* ; Medicine, Chinese Traditional/history*

Medical cross-modal retrieval aims to achieve semantic similarity search between different modalities of medical cases, such as quickly locating relevant ultrasound images through ultrasound reports, or using ultrasound images to retrieve matching reports. However, existing medical cross-modal hash retrieval methods face significant challenges, including semantic and visual differences between modalities and the scalability issues of hash algorithms in handling large-scale data. To address these challenges, this paper proposes a Medical image Semantic Alignment Cross-modal Hashing based on Transformer (MSACH). The algorithm employed a segmented training strategy, combining modality feature extraction and hash function learning, effectively extracting low-dimensional features containing important semantic information. A Transformer encoder was used for cross-modal semantic learning. By introducing manifold similarity constraints, balance constraints, and a linear classification network constraint, the algorithm enhanced the discriminability of the hash codes. Experimental results demonstrated that the MSACH algorithm improved the mean average precision (MAP) by 11.8% and 12.8% on two datasets compared to traditional methods. The algorithm exhibits outstanding performance in enhancing retrieval accuracy and handling large-scale medical data, showing promising potential for practical applications.
Algorithms ; Semantics ; Humans ; Ultrasonography ; Information Storage and Retrieval/methods* ; Image Processing, Computer-Assisted/methods*

Automatic classification of medical questions is of great significance in improving the quality and efficiency of online medical services, and belongs to the task of intent recognition. Joint entity recognition and intent recognition perform better than single task models. Currently, most publicly available medical text intent recognition datasets lack entity annotation, and manual annotation of these entities requires a lot of time and manpower. To solve this problem, this paper proposes a medical text classification model, bidirectional encoder representation based on transformer-recurrent convolutional neural network-entity-label-semantics (BRELS), which integrates medical entity label semantics. This model firstly utilizes an adaptive fusion mechanism to absorb prior knowledge of medical entity labels, achieving local feature enhancement. Then in global feature extraction, a lightweight recurrent convolutional neural network (LRCNN) is used to suppress parameter growth while preserving the original semantics of the text. The ablation and comparison experiments are conducted on three public medical text intent recognition datasets to validate the performance of the model. The results show that F1 score reaches 87.34%, 81.71%, and 77.74% on each dataset, respectively. The results show that the BRELS model can effectively identify and understand medical terminology, thereby effectively identifying users' intentions, which can improve the quality and efficiency of online medical services.
Semantics ; Neural Networks, Computer ; Humans ; Natural Language Processing

Online hashing methods are receiving increasing attention in cross modal medical image retrieval research. However, existing online methods often lack the learning ability to maintain semantic correlation between new and existing data. To this end, we proposed online semantic similarity cross-modal hashing (OSCMH) learning framework to incrementally learn compact binary hash codes of medical stream data. Within it, a sparse representation of existing data based on online anchor datasets was designed to avoid semantic forgetting of the data and adaptively update hash codes, which effectively maintained semantic correlation between existing and arriving data and reduced information loss as well as improved training efficiency. Besides, an online discrete optimization method was proposed to solve the binary optimization problem of hash code by incrementally updating hash function and optimizing hash code on medical stream data. Compared with existing online or offline hashing methods, the proposed algorithm achieved average retrieval accuracy improvements of 12.5% and 14.3% on two datasets, respectively, effectively enhancing the retrieval efficiency in the field of medical images.
Semantics ; Humans ; Algorithms ; Information Storage and Retrieval/methods* ; Diagnostic Imaging ; Image Processing, Computer-Assisted/methods*

BACKGROUND AND OBJECTIVE: The development of modern palliative care in China began in the 1980s and is currently in an accelerating phase. However, inconsistencies in terminology and concepts have hindered policy-making, clinical practice, and academic research. The Terminology of Clinical Medicine (2023 edition) has determined huan-he-yi-liao () and an-ning-liao-hu () as the formal terms of "palliative care" and "hospice care", respectively. To align with these terms, this study aims to establish expert consensus definitions tailored to the Chinese context. METHODS: We systematically retrieved and collected domestic and international literature and policy documents related to the definition of palliative care, then deconstructed and analyzed the relevant conceptual elements of these definitions. Core expert panel built the initial recommended definition upon the conceptual elements and consensus definition of palliative care by the International Association for Hospice and Palliative Care (IAHPC) through two rounds of online discussions. After nomination and selection, 61 professionals in the field of palliative care in China were invited to participate in the consensus expert group. Two rounds of Delphi consultation were conducted among the consensus experts, who were asked to score their agreement using Likert scale to the items in the initial recommended definition and the definition statements of palliative care and hospice care. Agreement rate of over 80% was considered as reaching consensus for each items. The core expert panel revised the items and the statements of recommended definitions based on the results from Delphi surveys. The final recommended definitions were formulated after feedback from patient and public involvement (PPI) group members. RESULTS: The response rates for the first and second round of Delphi surveys were 83.6% and 100.0%, respectively. The agreement rates of the items and statements of the recommended definitions exceeded 90%. Accordingly, the definitions based on Chinese expert consensus are recommended. Palliative care is an active holistic approach aimed at patients of all ages suffering from life-threatening illness and their families and caregivers. It seeks to improve their quality of life by preventing, assessing, and relieving physical, psychological, social, and spiritual suffering. Hospice care is an integral part of palliative care, focusing on holistic care for patients at the end of life and their families and caregivers. Its goal is to help patients to maintain dignity and achieve a good death by alleviating physical, psychological, social, and spiritual distress without intentionally hastening or postponing death, meanwhile improve the quality of life for families and caregivers. CONCLUSIONS: This study has established the Chinese expert consensus definitions of palliative care and hospice care in China, as well as the relationship between the two. The definitions highlight the holistic nature of palliative care, providing a foundation for discipline development, clinical practice, and public communication.
Palliative Care ; Humans ; China ; Hospice Care ; Consensus ; Delphi Technique ; Terminology as Topic

OBJECTIVES: To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features. METHODS: A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation. RESULTS: Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively. CONCLUSIONS The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.
Semantics ; Humans ; Drug Interactions ; Neural Networks, Computer ; Algorithms

Objective:This study aims to explore the influence of vowels and sound intensity on formant, so as to provide reference for the selection of sound samples and vocal methods in acoustic detection. Methods:Thirty-eight healthy subjects, 19 male and 19 female, aged 19-24 years old were recruited. The formants of different vowels（/a/, //, /i/ and /u/） and different sound intensities（lowest sound, comfort sound, highest true sound and highest falsetto sound） were analyzed, and pairings were compared between groups with significant differences. Results:①The vowels /a/ and // in the first formant were larger than /i/ and /u/, and /i/ was the largest in the second formant. The minimum value of the first formant is the lowest sound of /i/ and the maximum is the highest sound of /a/. ②In the first formant, the chest sound area increases with the increase of sound intensity, while the second formant enters the highest falsetto and decreases significantly. Conclusion:Different vowels and sound intensity have different distribution of formant, that is, vowel and sound intensity have different degree of influence on formant. According to the extreme value of the first formant, the maximum normal range is determined initially, which is helpful to improve the acoustic detection.
Humans ; Male ; Female ; Young Adult ; Speech Acoustics ; Voice Quality ; Phonetics ; Voice/physiology* ; Adult

Child ; Humans ; Liver Diseases ; Terminology as Topic

In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted