Search Results (65)

This study presents the first systematic evaluation of in-context learning for Tarifit machine translation, a low-resource Amazigh language spoken by 5 million people in Morocco and Europe. We assess three large language models (GPT-4, Claude-3.5, PaLM-2) across Tarifit–Arabic, Tarifit–French, and Tarifit–English translation using 1000 sentence pairs and 5-fold cross-validation. Results show that 8-shot similarity-based demonstration selection achieves optimal performance. GPT-4 achieved 20.2 BLEU for Tarifit–Arabic, 14.8 for Tarifit–French, and 10.9 for Tarifit–English. Linguistic proximity significantly impacts translation quality, with Tarifit–Arabic substantially outperforming other language pairs by 8.4 BLEU points due to shared vocabulary and morphological patterns. Error analysis reveals systematic issues with morphological complexity (42% of errors) and cultural terminology preservation (18% of errors). This work establishes baseline benchmarks for Tarifit translation and demonstrates the viability of in-context learning for morphologically complex low-resource languages, contributing to linguistic equity in AI systems. Full article

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens. Full article

This study provides a comprehensive philological analysis of ten Old Uyghur manuscript fragments of the Saddharmapuṇḍarīka-sūtra (Lotus Sutra) in the Berlin Turfan Collection, while systematically examining all extant Old Uyghur Lotus Sutra manuscripts to establish a complete corpus for comparative analysis. By collating this complete corpus with Kumārajīva’s Chinese translation, this research demonstrates a typology of Old Uyghur Lotus Sutra fragments. It identifies at least two distinct translation lineages: (1) early translations (pre-10th century) exhibiting lexical and structural divergences indicative of Sogdian mediation or hybrid source traditions, and (2) late translations (11th–14th centuries) directly derived from the Chinese version, characterized by syntactic fidelity and a standardized terminology. Through comparative textual analysis, orthographic scrutiny, and terminological cross-referencing, this paper aims to reconstruct the historical trajectory of the Lotus Sutra’s transmission. In addition, it discusses some facts indicating linguistic and cultural contact between the Sogdians and the progressive alignment of Uyghur Buddhist texts with Chinese Buddhist traditions. Full article

Background/Objectives: The global population is rapidly aging, raising interest in dietary practices for promoting the healthspan. This study aimed to comprehensively investigate the state of diet and longevity research over the past decade, addressing the lack of bibliometric synthesis within the field. Methods: A bibliometric analysis was performed using the keywords “diet” and “longevity” on English-language articles from the Web of Science database that were published from 2015 to 2024. Data were analyzed using Web of Science tools, InCites, and VOSviewer to identify trends in publication output, citation metrics, coauthorship networks, institutional contributions, and keyword co-occurrence patterns. Results: Overall, 2203 articles meeting the inclusion criteria were included in the analysis. Publication volume and citation counts gradually increased, peaking in 2021. Countries, including the United Kingdom, and organizations, such as the National Institutes of Health and Harvard University, had significant citation impact, and the United States and China led productivity. Molecular processes (oxidative stress and autophagy), dietary models (Mediterranean diet and calorie restriction), and public health issues (obesity and mortality) were the main thematic clusters. Model species, including Caenorhabditis elegans and Drosophila melanogaster, were frequently used. Regional disparities in research production and notable terminology variability were noted. Conclusions: This study emphasizes the development and diversity of nutrition and longevity research while highlighting novel molecular and translational topics. More international cooperation, uniform language, and multidisciplinary frameworks are warranted to promote equal scientific advancement worldwide and connect mechanistic discoveries with therapeutic outcomes. 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

Background: Pharmacogenetics (PGx) is the science of assessing how genetic variation affects drug efficacy, tolerability, and safety. While PGx is an emerging discipline which is becoming standard of care, many providers have misunderstandings about its utility. This is even more of a problem for patients, who may perceive that there is a single drug that is “right” for them. The primary objective of this study was to evaluate consumer comprehension of a newly developed patient-facing PGx report. Methods: In this study, we adapted a commercial pharmacogenetic test (Genomind Professional PGx) into a report intended to be more comprehensible to the consumer. The initial translation of the clinical terminology used in the PGx report, into lay terminology was conducted by PharmDs and PhDs who have collectively provided over 20,000 PGx consults to date. These reports were then evaluated with readability scoring software to ensure each translation’s complexity remained ≤8th-grade reading level. A total of 107 participants were recruited to conduct the initial analysis with a goal of achieving a 90% comprehension rate using the Genomind consumer comprehension survey. These participants were also given a modified Minnesota Assessment of Pharmacogenomic Literacy (MAPL™) both before and after the Genomind comprehension survey to assess overall PGx literacy. Results: Ninety-eight (98) out of 107 research participants scored one or zero questions incorrectly, translating to >90% comprehension score on the Genomind consumer comprehension survey. These participants also demonstrated a significant increase in overall pharmacogenetic literacy, as assessed by MAPL after viewing the consumer report and survey. Conclusions: This study found that translating pharmacogenetic test results into lay language may provide individuals with a greater understanding of how their DNA may impact prescribed medications. Full article

This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters. Full article

Background: The Music-Related Quality of Life (MuRQoL) was launched in 2017 as a valid psychometric measure of Cochlear Implant (CI) users’ music experience and its impact on Quality of Life (QoL). This scoping review aimed to explore the implementation and effectiveness of the instrument since its introduction. Methods: PubMed and Google Scholar databases were searched for publications written in English reporting a translation, validation or application of the MuRQoL. Results: Eleven publications were found, including three validated versions and eight studies that applied the MuRQoL in Italy, Turkey, Spain and the US in research and clinical settings for various purposes. The review showcases the reliability, validity and potential clinical utility of the tool underscoring its growing adoption and integration with other assessment tools. Based on the findings, we make recommendations and provide a roadmap for optimizing the use of MuRQoL globally, including naming and terminology conventions. Anecdotal evidence, such as forthcoming or non-English publications offer further insights into the tool’s future potential. Conclusions: The MuRQoL is currently available in four languages and applicable across diverse cultural contexts, populations and settings. It is a versatile, patient-centered tool providing a deep understanding of CI users’ music experiences. As research and validation efforts continue, the instrument has the potential to set a benchmark for assessing the broader impacts of CIs beyond speech perception, emphasizing the significance of music in enriching the lives of individuals with Hearing Loss (HL). Full article

Carbon neutrality is a critical global objective in the fight against climate change. As relevant knowledge and technologies advance rapidly, there is an escalating demand for sophisticated intelligent services. While large language models (LLMs) have demonstrated considerable promise in knowledge processing and generation, their application within the domain of carbon neutrality remains in the early stages of exploration. This study develops a carbon neutrality knowledge base (CN Knowledge Base) using the ChatGLM3 model aimed at enhancing question-answering capabilities in areas such as carbon emission monitoring, policy interpretation, and technical optimization. By refining domain-specific corpora and integrating a Retrieval-Augmented Generation (RAG) mechanism, the model’s ability to generate accurate and relevant responses is improved. To evaluate the performance of the proposed system, a comprehensive quantitative comparison is conducted using established evaluation metrics, including BLEU (Bilingual Evaluation Understudy), BERT (Bidirectional Encoder Representations from Transformers), and METEOR (Metric for Evaluation of Translation with Explicit Ordering). The CN Knowledge Base is benchmarked against leading models such as GPT-4, Gemini, and Bing. The results demonstrate that the CN Knowledge Base outperforms other models in METEOR (0.2697) and is comparable to GPT-4o in both BLEU (0.8755) and BERT (0.8260) Scores (GPT-4o: BLEU: 0.8784, BERT: 0.8404). These findings underscore the knowledge base’s strong adaptability and its ability to generate high-quality, domain-specific content. The study suggests that specialized models can overcome the limitations of general-purpose LLMs, particularly in precise terminology and accurate application of domain knowledge. With continued development, such models could significantly enhance digital and intelligent solutions for carbon neutrality and related fields. Full article

Background: The World Health Organization (WHO) recently developed the Global Scales for Early Development (GSED) tool to address the lack of a population-level metric for early childhood development globally. This paper describes learning from the first experience with the translation, adaptation, and implementation of the GSED tool in Ethiopia. Methods: WHO guidelines were followed to translate and adapt the GSED tool to Amharic. Two Ethiopian child health experts were trained as GSED Master Trainers. The tool was forward translated by two independent translators with previous experience in child development assessment, consensus was obtained, and the back translation was reviewed/approved by the WHO. The GSED app was programmed in Amharic and piloted for 40 children aged 6–36 months, followed by its implementation by trained study nurses in a child development study in Bahir Dar. Results: Minor adaptations were made to terminologies and certain items were rephrased in the short and long forms. Modifications were made to physical objects used in the long form to make the items culturally appropriate and familiar. Local examples were used where necessary. The tool was administered to 364 children aged 6–36 months with an average administration time of 50–60 min. Quality control assessments by master trainers showed high agreement with assessments by trained study nurses throughout the study (average agreement: 91%). This study demonstrated the high acceptability and feasibility of the GSED tools. Conclusions: Local adaptations were required to contextualize the GSED tool for the Ethiopian setting. The preliminary experience with the GSED tool in Ethiopia is positive, with high-competency trained staff and ease of administration. Full article

There is a need to improve communication for patients and relatives who belong to cultural minority communities in intensive care units (ICUs). As a matter of fact, language barriers negatively impact patient safety and family participation in the care of critically ill patients, as well as recruitment to clinical trials. Recent studies indicate that Google Translate and ChatGPT are not accurate enough for advanced medical terminology. Therefore, developing and implementing an ad hoc machine translation tool is essential for bridging language barriers. This tool would enable language minority communities to access advanced healthcare facilities and innovative research in a timely and effective manner, ensuring they receive the comprehensive care and information they need. Method: Key factors that facilitate access to advanced health services, in particular ICUs, for language minority communities are reviewed. Results: The existing digital communication tools in emergency departments and ICUs are reviewed. To the best of our knowledge, no AI English/French translation app has been developed for deployment in ICUs. Patient privacy and data confidentiality are other important issues that should be addressed. Conclusions: Developing an artificial intelligence-driven translation tool for intensive care units (AITIC) which uses language models trained with medical/ICU terminology datasets could offer fast and accurate real-time translation. An AITIC could support communication, and consolidate and expand original research involving language minority communities. Full article

This study conducts a comprehensive evaluation of large language models (LLMs), including ChatGPT 4, ERNIE Bot 4, and Gemini Advanced, in the context of translating Buddhist texts from classical Chinese to modern English. Focusing on three distinct Buddhist texts encompassing various literary forms and complexities, the analysis examines the models’ capabilities in handling specialized Buddhist terminology, classical Chinese grammar, and the translation of complex, lengthy sentences. The study employs a methodology where selected excerpts from these texts are translated by the LLMs, followed by an in-depth analysis comparing these machine-generated translations to human translations. The evaluation criteria include word translation accuracy, the ability to recognize and correctly interpret specific meanings within both classical and modern contexts, and the completeness of phrases without omitting or unnecessarily adding words. The findings reveal significant variations in the performance of these LLMs, with detailed observations on their strengths and weaknesses in translating specialized terms, managing grammatical structures unique to classical Chinese, and maintaining the integrity of the original texts’ meanings. This paper aims to shed light on the potential and limitations of using LLMs for translating complex literary works from ancient to modern languages, contributing valuable insights into the field of computational linguistics and the ongoing development of translation technologies. Full article

Trigeminal neuralgia (TN) is chronic pain caused by damage to the somatosensorial system on the trigeminal nerve or its branches, which involves peripheral and central dysfunction pain pathways. Trigeminal pain triggers disruptive pain in regions of the face, including within and around the mouth. Besides clinical experiences, translating the language of suffering into scientific terminology presents substantial challenges. Due to the complex and multifactorial pathophysiology underlying trigeminal pain, elucidating its social impact presents significant difficulties. Carbamazepine and oxcarbazepine are first-line treatments for TN, achieving approximately 50% pain reduction in 60–70% of treated patients. However, their efficacy is often limited by common side effects, such as dizziness, vertigo, nausea, seizures, and cognitive symptoms. In some cases, patients experience severe side effects, including myelosuppression, hyponatremia, hormonal imbalances, liver toxicity, suicidal ideation, teratogenicity, and other adverse reactions. Given these clinical limitations, the search for new painkiller candidates continues. Hence, we focused this review on salvinorin A (SalA), a natural agonist of κ-opioid receptors (KORs), which demonstrated anti-nociceptive, anti-inflammatory, and anti-neuropathic properties in various experimental models of the spinal sensory system. Furthermore, preclinical evidence indicates that SalA does not induce dependence and demonstrates a favorable toxicological and safety profile in comparison with currently marketed opioid drugs. We propose Salvinorin A as a promising candidate for treating trigeminal neuralgia, offering the potential for reduced adverse effects. Full article

This conceptual paper critically examines the use of traditional medicalized terminology in speech-language therapy, with a particular focus on the Quebec context. It highlights how current language practices, rooted in a medical model of disability, often marginalize individuals with communication differences such as stuttering, autism, and aphasia by pathologizing these variations. Drawing on contemporary frameworks such as the social model of disability, neurodiversity, and “diversité capacitaire” (a French term that translates to “capacity diversity” or “ability diversity”, emphasizing the richness of diverse abilities and communication styles), the article advocates for more inclusive and empowering language that respects and reflects communicative diversity. The authors emphasize the importance of participatory approaches, including consultation with the communities directly involved and the establishment of terminological committees, to develop respectful and affirming language. Ultimately, this paper calls for a shift in speech-language therapy practices to promote a more inclusive understanding of communication, enabling individuals with communication differences to fully participate in society. Full article

Pratītyasamutpāda, the doctrine of dependent origination, has a long history in Old Uyghur Buddhism. It was first articulated in the Early Old Uyghur Buddhist texts and is evident in the terminology of Maitrisimit and the Daśakarmapathāvadāna-mālā. The dependent origination is systematically illustrated in at least three Pratītyasamutpāda texts, one text with Brāhmī elements, and the other two in Dunhuang and Turfan prints. The latter two are discussed in detail in this paper. The Dunhuang print provides the most comprehensive demonstration of the Old Uyghur understanding of dependent origination. The structure of the text is largely consistent with the corresponding passages in the Abhidharmamahāvibhāṣaśāstra and other Abhidharma texts. The text offers a more comprehensive account than the Chinese text. The Turfan prints, which consist of four fragments, are derived from two distinct prints. Print U 4170 is an Abhidharma text, and it has parallels in the Abhidharmakośabhāṣya. It seems plausible to suggest that the print bearing the abbreviated titles Pratyitasamutpad in Old Uyghur and Buladi 布剌帝 in Chinese may have been translated from a Chinese text sharing the same or a similar Chinese name. However, as with the Dunhuang print, the Turfan prints may have been produced by the Old Uyghurs from some Abhidharma texts. The Dunhuang print and the Tufan prints are unique within the corpus of known Old Uyghur prints. These texts represent the first known printed examples of the Abhidharma tradition. Moreover, the illustration employed in the Dunhuang print is not known in other printed texts discovered in Dunhuang and Turfan, representing the first instance of such an illustration in printed form. Full article