Search Results (43)

Background: Antinuclear antibodies (ANAs) serve as crucial biomarkers for diagnosing systemic autoimmune diseases; however, their interpretation can be complex and may not always correlate with clinical symptoms. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 1961 and 2025. Databases, including PubMed and Scopus, were searched using combinations of controlled vocabulary and free-text terms relating to antinuclear antibodies and their clinical significance. The objective was to gather and synthesize information regarding the diagnostic utility and interpretation of ANA testing in routine medical practice. Discussion: The indirect immunofluorescence assay (IIF) on HEp-2 cells is established as the gold standard for detecting ANAs, facilitating the classification of various fluorescent patterns. While a positive ANA test can suggest autoimmune disorders, the presence and titre must be interpreted alongside clinical findings, as low titres often lack diagnostic significance. Findings indicate that titres higher than 1:160 may provide greater specificity in differentiating true positives from false positives in healthy individuals. The study also emphasizes the relevance of fluorescence patterns, with specific patterns linked to particular diseases, although many do not have strong clinical correlations. Moreover, certain autoantibodies demonstrate high specificity for diseases like systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD). Ultimately, while ANA testing is invaluable for diagnosing connective tissue diseases, healthcare providers must consider its limitations to avoid misdiagnosis and unnecessary treatment. Conclusions: ANA testing is a valuable tool in the diagnosis of connective tissue diseases, but its interpretation must be approached with caution. Clinical context remains crucial when evaluating ANA results to avoid misdiagnosis and overtreatment. This review is about the diagnostic aspects and clinical consequences of ANA testing, as well as highlighting both the diagnostic benefits and the potential limitations of this procedure in everyday clinical practice. The review fills a gap in the literature by integrating the diagnostic and clinical aspects of ANA testing, with a focus on real-world interpretation challenges. Full article

Background: This study investigated whether a health literacy intervention program could improve the health literacy and quality of life among hospital volunteers. The study also explored the impact of health literacy on hospital volunteers’ health and psychological well-being. Methods: Overall, 35 hospital volunteers were recruited and divided into an experimental group (n = 22) and a control group (n = 13). The experimental group participated in an 8-week health literacy intervention program, which covered topics such as medication information, physiological and symptom-related vocabulary, and disease representation. The control group did not receive any intervention. A questionnaire survey was conducted to assess participants’ health literacy and quality of life before and after the intervention, and the comparison between two groups was statistically analyzed. Results: The experimental group showed significant improvements in multiple aspects of health literacy, particularly in medication information, physiology vocabulary, symptom vocabulary, and signs vocabulary (p < 0.05). In terms of quality of life, the experimental group demonstrated significant enhancements in psychological well-being and overall quality of life (p < 0.05). In contrast, the control group exhibited a downward trend in most health literacy dimensions with a significant decline in organ vocabulary (p < 0.05) and no significant changes in quality of life. Conclusions: The health literacy intervention program effectively improved hospital volunteers’ health literacy and quality of life with particularly notable effects on psychological well-being and the understanding of health-related professional terminology. By enhancing hospital volunteers’ health literacy and quality of life, healthcare organizations can foster more effective, sustainable, and satisfactory service quality. Full article

Medical laboratories are perhaps the largest measurement industry in the world. The metrology terminology is relevant for effective and efficient communication, particularly where metrology activities are carried out by operators with different metrology skills. The World Association of Societies of Pathology and Laboratory Medicine (WASPaLM) and SIPMeL have had some opportunities to propose changes to the documents in preparation for the Clinical and Laboratory Standards Institute (CLSI) and the ISO/TC 212 in order to harmonize the terminology with the Metrology Vocabulary (VIM) of the Joint Committee for Guides in Metrology (JCGM). Many proposals have been accepted. Here, we summarize some particularly critical points for metrological terms. The main terms discussed are the following: measuring, measuring range, examination, pre-examination, post-examination, manufacturer, measuring instrument, quantitative, qualitative, semi-quantitative, processing, measurement error, maximum permissible error of measurement, total error of measurement, monitoring, variability, performance, reliability, influence, interference, selectivity, sensitivity, detection limit, reliability, comparability, compatibility, control material. Despite all the efforts to coordinate terminologies, it is inevitable that overlapping and inconsistent terminologies will continue to be used because documents and policies are produced in different contexts. In some ISO/TC 212 and CLSI documents, the phenomenon of magnetic attraction toward common words (such as “analysis” and derivatives), without any consideration of the true metrological meaning, is noted. The ISO/TC 212 and CLSI working groups show, alongside moments of openness, phenomena of true self-referential conservatism. Full article

With vocabulary playing an essential role in the learning of English for Specific Purposes, teachers face the challenge of organising and teaching lexis in a way that maximises opportunities for acquisition. Specialised word lists offer a solution, but a major obstacle is how to integrate these lists into learning materials containing items used in actual discourse. In this paper, we report on research involving the creation of a medical English word list (MEWL), integrated into a set of specially designed materials for students at a national university in Japan. These materials, developed through needs analysis at the university’s medical school, are primarily organised around body systems, with an additional focus on doctor–patient communication. The MEWL is complemented by a list of word parts, aiming to sensitise students to complex medical terms. We describe the delivery of the list, first through the courses and materials, and then via the development of a vocabulary learning tool, Hi-Lex, which analyses texts against any word lists it contains. Hi-Lex allows learners to create personalised word lists and understand word usage in context. The findings of a small trial study of Hi-Lex (N = 31) illustrate how the software provides insight into students’ selections of words in specialised texts. Full article

Although several studies have succeeded in creating medical word lists using corpus analysis methods, there is currently a shortage of comprehensive lists containing medical multi-word terms (MWTs). This study attempts to fill this gap by identifying medical MWTs using a large corpus of English language medical textbooks (28,384,681 running words). The term extraction function in Sketch Engine was used to extract high-frequency MWTs and to calculate keyness and dispersion data for each MWT. The validity of the resulting list and of specific subsets was tested using a different medical corpus and a general English corpus. The resulting list comprises 3307 MWTs with 63.83% (2111 MWTs) occurring at comparable frequencies in the different medical corpus and only 0.97% (32 MWTs) occurring at comparable frequencies in the general English corpus. The study also revealed clear differences in replicability between semantic subsets, with MWTs from the Anatomy and the Disorders semantic groups displaying high replicability, while MWTs from the Concepts and Ideas semantic group showed low to moderate replicability. The list may be used to develop evidence-based materials in English for Medical Purposes courses and to further explore how information is packaged in healthcare communication settings. Full article

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems. Full article

Research article abstracts, the second most-read part of research papers after titles, generally follow disciplinary conventions, which are often manifested in their language use. This study analyzed lexical bundles or multi-word sequences in move texts of a one-million-word corpus of English-language medical research article abstracts, with particular attention to vocabulary levels. The most frequent lexical bundles, such as “the primary end point was”, often occurred once per text and predominantly took part in realizing a move. The coverage of the first thousand New General Service List was 63.6% for the entire corpus but was around 80% for bundles in Move 3, describing principal results, and those in Move 4, evaluating the results. Many of the sequences were research-oriented bundles, used to express research contexts. The bundles were made up of relatively accessible word items, but the sequences occurred to realize highly specific research contexts. The findings suggest that becoming familiar with the bundle may need increasing awareness of disciplinary conventions such as guideline adherences and statistical procedures. This study may offer insights on the need for learners to familiarize themselves with these bundles. Full article

The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain. 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

Lexical substitution aims to generate a list of equivalent substitutions (i.e., synonyms) to a sentence’s target word or phrase while preserving the sentence’s meaning to improve writing, enhance language understanding, improve natural language processing models, and handle ambiguity. This task has recently attracted much attention in many languages. Despite the richness of Arabic vocabulary, limited research has been performed on the lexical substitution task due to the lack of annotated data. To bridge this gap, we present the first Arabic lexical substitution benchmark dataset AraLexSubD for benchmarking lexical substitution pipelines. AraLexSubD is manually built by eight native Arabic speakers and linguists (six linguist annotators, a doctor, and an economist) who annotate the 630 sentences. AraLexSubD covers three domains: general, finance, and medical. It encompasses 2476 substitution candidates ranked according to their semantic relatedness. We also present the first Arabic lexical substitution pipeline, AraLexSub, which uses the AraBERT pre-trained language model. The pipeline consists of several modules: substitute generation, substitute filtering, and candidate ranking. The filtering step shows its effectiveness by achieving an increase of 1.6 in the F1 score on the entire AraLexSubD dataset. Additionally, an error analysis of the experiment is reported. To our knowledge, this is the first study on Arabic lexical substitution. Full article

Neurodevelopmental outcomes for preschool-age children in the United States with in utero Zika virus (ZIKV) exposure have not yet been reported. We performed a case-control study to assess whether children exposed in utero to ZIKV have abnormal neurodevelopment at age 4–5 years compared to unexposed controls. Thirteen ZIKV-exposed cases that did not have microcephaly or other specific features of congenital Zika syndrome and 12 controls were evaluated between ages 4–5 years. Child neurodevelopment was assessed using the Pediatric Evaluation of Disability Inventory, Behavior Rating Inventory of Executive Function, Peabody Picture Vocabulary Test, Bracken School Readiness Assessment (BSRA), and Movement Assessment Battery for Children (MABC). Caregivers answered questions on the child’s medical history and family demographics. Cases and controls were evaluated at mean (SD) ages 4.9 (0.3) and 4.8 (0.4) years, respectively. Caregivers reported more behavior and mood problems in cases than controls. MABC scores showed more gross and fine motor coordination difficulties among cases than controls. Controls trended towards higher performance on concepts underlying school readiness on BSRA. Three cases had a diagnosis of autism spectrum disorder or global developmental delay. Continued follow-up through school age for children with prenatal ZIKV exposure is needed to understand the impact of in utero ZIKV exposure on motor coordination, cognition, executive function, and academic achievement. 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

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials. Full article

Background: Head and neck cancers (H&NCs) constitute a significant part of all cancer cases. H&NC patients experience unintentional weight loss, poor nutritional status, or speech disorders. Medical interventions affect appearance and interfere with patients’ self-perception of their bodies. Psychological consultations are not affordable due to limited time. Methods: We used NLP to analyze the basic emotion intensity, sentiment about one’s body, characteristic vocabulary, and potential areas of difficulty in free notes. The emotion intensity research uses the extended NAWL dictionary developed using word embedding. The sentiment analysis used a hybrid approach: a sentiment dictionary and a deep recursive network. The part-of-speech tagging and domain rules defined by a psycho-oncologist determine the distinct language traits. Potential areas of difficulty were analyzed using the dictionaries method with word polarity to define a given area and the presentation of a note using bag-of-words. Here, we applied the LSA method using SVD to reduce dimensionality. A total of 50 cancer patients requiring enteral nutrition participated in the study. Results: The results confirmed the complexity of emotions in patients with H&NC in relation to their body image. A negative attitude towards body image was detected in most of the patients. The method presented in the study appeared to be effective in assessing body image perception disturbances, but it cannot be used as the sole indicator of body image perception issues. Limitations: The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary. Conclusions: The combination of the attributes of a patient’s condition, possible to determine using the method for a specific patient, can indicate the direction of support for the patient, relatives, direct medical personnel, and psycho-oncologists. Full article

In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) operates as the legally required national standard for oncological reporting. Unfortunately, the usage of various documentation software solutions leads to semantic and technical heterogeneity of the data, complicating the establishment of research networks and collective data analysis. Within this feasibility study, we evaluated the transferability of all oBDS characteristics to the standardized vocabularies, a metadata repository of the observational medical outcomes partnership (OMOP) common data model (CDM). A total of 17,844 oBDS expressions were mapped automatically or manually to standardized concepts of the OMOP CDM. In a second step, we converted real patient data retrieved from the Hamburg Cancer Registry to the new terminologies. Given our pipeline, we transformed 1773.373 cancer-related data elements to the OMOP CDM. The mapping of the oBDS to the standardized vocabularies of the OMOP CDM promotes the semantic interoperability of oncological data in Germany. Moreover, it allows the participation in network studies of the observational health data sciences and informatics under the usage of federated analysis beyond the level of individual countries. Full article