Hasil untuk "Arctic medicine. Tropical medicine"

Hua Li, Yingying Li, Xiaobin Feng et al.

The clinical burden of spleen-stomach disorders is substantial. While large language models (LLMs) offer new potential for medical applications, they face three major challenges in the context of integrative Chinese and Western medicine (ICWM): a lack of high-quality data, the absence of models capable of effectively integrating the reasoning logic of traditional Chinese medicine (TCM) syndrome differentiation with that of Western medical (WM) disease diagnosis, and the shortage of a standardized evaluation benchmark. To address these interrelated challenges, we propose DongYuan, an ICWM spleen-stomach diagnostic framework. Specifically, three ICWM datasets (SSDF-Syndrome, SSDF-Dialogue, and SSDF-PD) were curated to fill the gap in high-quality data for spleen-stomach disorders. We then developed SSDF-Core, a core diagnostic LLM that acquires robust ICWM reasoning capabilities through a two-stage training regimen of supervised fine-tuning. tuning (SFT) and direct preference optimization (DPO), and complemented it with SSDF-Navigator, a pluggable consultation navigation model designed to optimize clinical inquiry strategies. Additionally, we established SSDF-Bench, a comprehensive evaluation benchmark focused on ICWM diagnosis of spleen-stomach disorders. Experimental results demonstrate that SSDF-Core significantly outperforms 12 mainstream baselines on SSDF-Bench. DongYuan lays a solid methodological foundation and provides practical technical references for the future development of intelligent ICWM diagnostic systems.

Cyril Caminade, Diego Ayala, Thibaud de Chevigny et al.

Abstract In December 2024, L’Initiative-Expertise France organized a workshop in Musanze, Rwanda, for National Malaria Control and Elimination Programmes (NMC/EPs) representatives from 19 sub-Saharan African countries. The workshop focused on surveillance, modeling, climate forecasting, and innovative control methods to mitigate climate change impacts on malaria. Participants shared challenges, experiences and best practices. Key challenges highlighted include shifts in malaria transmission seasons, disease spread to mid-altitude regions, and infrastructure damage from extreme weather. Additional factors, such as drug and insecticide resistance, the spread of Anopheles stephensi, and changes in vector behaviour, are exacerbating malaria transmission in African cities. Participants stressed the need for collaborative efforts to tackle these evolving threats. This comment reflects the expertise and insights of 19 NMCPs actively managing malaria control and aims at raising awareness, inform policy discussions, and strengthen global partnerships to address the intersection of malaria and climate change.

Marin Caumartin, Sophie Nagle, Racha Eid et al.

Jiacheng Tang, Nankai Wu, Fan Gao et al.

Metaphorical expressions are abundant in Traditional Chinese Medicine (TCM), conveying complex disease mechanisms and holistic health concepts through culturally rich and often abstract terminology. Bridging these metaphors to anatomically driven Western medical (WM) concepts poses significant challenges for both automated language processing and real-world clinical practice. To address this gap, we propose a novel multi-agent and chain-of-thought (CoT) framework designed to interpret TCM metaphors accurately and map them to WM pathophysiology. Specifically, our approach combines domain-specialized agents (TCM Expert, WM Expert) with a Coordinator Agent, leveraging stepwise chain-of-thought prompts to ensure transparent reasoning and conflict resolution. We detail a methodology for building a metaphor-rich TCM dataset, discuss strategies for effectively integrating multi-agent collaboration and CoT reasoning, and articulate the theoretical underpinnings that guide metaphor interpretation across distinct medical paradigms. We present a comprehensive system design and highlight both the potential benefits and limitations of our approach, while leaving placeholders for future experimental validation. Our work aims to support clinical decision-making, cross-system educational initiatives, and integrated healthcare research, ultimately offering a robust scaffold for reconciling TCM's symbolic language with the mechanistic focus of Western medicine.

Rui Yang, Huitao Li, Matthew Yu Heng Wong et al.

Natural language processing (NLP) has been traditionally applied to medicine, and generative large language models (LLMs) have become prominent recently. However, the differences between them across different medical tasks remain underexplored. We analyzed 19,123 studies, finding that generative LLMs demonstrate advantages in open-ended tasks, while traditional NLP dominates in information extraction and analysis tasks. As these technologies advance, ethical use of them is essential to ensure their potential in medical applications.

Anbang Du, Michael Head, Markus Brede

Interdisciplinary research, a process of knowledge integration, is vital for scientific advancements. It remains unclear whether prestigious journals that are highly impactful lead in disseminating interdisciplinary knowledge. In this paper, by constructing topic-level correlation networks based on publications, we evaluated the interdisciplinarity of more and less prestigious journals in medicine. We found research from prestigious medical journals tends to be less interdisciplinary than research from other medical journals. We also established that cancer-related research is the main driver of interdisciplinarity in medical science. Our results indicate a weak tendency for differences in topic correlations between more and less prestigious journals to be co-located. Accordingly, we identified that interdisciplinarity in prestigious journals mainly differs from interdisciplinarity in other journals in areas such as infections, nervous system diseases and cancer. Overall, our results suggest that interdisciplinarity in science could benefit from prestigious journals easing rigid disciplinary boundaries.

Menghua Xia, Reimund Bayerlein, Yanis Chemli et al.

Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as image enhancement, motion correction, and attenuation correction. However, these advancements come with the risk of hallucinations, generating realistic yet factually incorrect content. Hallucinations can misrepresent anatomical and functional information, compromising diagnostic accuracy and clinical trust. This paper presents a comprehensive perspective of hallucination-related challenges in AIGC for NMI, introducing the DREAM report, which covers recommendations for definition, representative examples, detection and evaluation metrics, underlying causes, and mitigation strategies. This position statement paper aims to initiate a common understanding for discussions and future research toward enhancing AIGC applications in NMI, thereby supporting their safe and effective deployment in clinical practice.

Palang Chotsiri, Almahamoudou Mahamar, Halimatou Diawara et al.

Abstract Background Primaquine (PQ) is the prototype 8-aminoquinoline drug, a class which targets gametocytes and hypnozoites. The World Health Organization (WHO) recommends adding a single low dose of primaquine to the standard artemisinin-based combination therapy (ACT) in order to block malaria transmission in regions with low malaria transmission. However, the haemolytic toxicity is a major adverse outcome of primaquine in glucose-6-phosphate dehydrogenase (G6PD)-deficient subjects. This study aimed to characterize the pharmacokinetic properties of primaquine and its major metabolites in G6PD-deficient subjects. Methods A single low-dose of primaquine (0.4–0.5 mg/kg) was administered in twenty-eight African males. Venous and capillary plasma were sampled up to 24 h after the drug administration. Haemoglobin levels were observed up to 28 days after drug administration. Only PQ, carboxy-primaquine (CPQ), and primaquine carbamoyl-glucuronide (PQCG) were present in plasma samples and measured using liquid chromatography mass spectrometry. Drug and metabolites’ pharmacokinetic properties were investigated using nonlinear mixed-effects modelling. Results Population pharmacokinetic properties of PQ, CPQ, and PQCG can be described by one-compartment disposition kinetics with a transit-absorption model. Body weight was implemented as an allometric function on the clearance and volume parameters for all compounds. None of the covariates significantly affected the pharmacokinetic parameters. No significant correlations were detected between the exposures of the measured compounds and the change in haemoglobin or methaemoglobin levels. There was no significant haemoglobin drop in the G6PD-deficient patients after administration of a single low dose of PQ. Conclusions A single low-dose of PQ was haematologically safe in this population of G6PD-normal and G6PD-deficient African males without malaria. Trial registration NCT02535767

Sinya Yulibeth Núñez, Christian Ortiz, Ary Ávila et al.

En Honduras, los sistemas de salud se han visto en la obligación y necesidad de establecer mecanismos de vigilancia con el objetivo de conocer procesos de salud y enfermedad en la población. El objetivo del artículo es describir el proceso de análisis y las estrategias utilizadas durante el desarrollo de un sistema de información robusto para vigilar la seguridad de las vacunas, el cual también se puede replicar con otras vigilancias. Con este fin, se utilizan metodologías ágiles de desarrollo y herramientas de código abierto, se configura y desarrolla un sistema base de vigilancia, y se incorporan estándares reconocidos para la codificación, la validación y la verificación de la información. Se logró la implementación de un sistema de información confiable, estable y escalable que se utilizará para la vigilancia de eventos supuestamente atribuibles a la vacunación e inmunización (ESAVI) y eventos adversos de especial interés (EVADIE), el cual permitirá la captura oportuna y ágil de los datos de vigilancia y facilitará las tareas de análisis de datos.

Faithful Miebaka Daniel, Bonaventure Michael Ukoaka, Victoria Ezinne Emeruwa et al.

Abstract Hepatitis B virus (HBV) poses a significant public health threat, particularly in developing countries with high endemicity but poor vaccination among healthcare workers (HCWs). Needlestick injuries increase HCWs' risk, yet only about 42% of HCWs are fully vaccinated compared to 97% in high-income countries. Challenges to vaccine uptake include availability, demanding schedules with frequent unit rotations hindering access, high cost of acquiring shots, and stock shortages resulting in missed opportunities. Mandatory, cost-free HBV vaccinations for HCWs, supported by legislation, international aid, and digital reminders, could ensure self-protection and safety while contributing to the global objective of eradicating HBV by 2030.

Shannon L. Walston, Hiroshi Seki, Hirotaka Takita et al.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Aaron L. Sarvet, Mats J. Stensrud

In our original article (Sarvet & Stensrud, 2024), we examine twin definitions of "harm" in personalized medicine: one based on predictions of individuals' unmeasurable response types (counterfactual harm), and another based solely on the observations of experiments (interventionist harm). In their commentary, Mueller & Pearl (2024) (MP) read our review as an argument that "counterfactual logic should [...] be purged from consideration of harm and benefit" and "strongly object [...] that a rational decision maker may well apply the interventional perspective to the exclusion of counterfactual considerations." Here we show that this objection is misguided. We analyze MP's examples and derive a general result, showing that determinations of harm through interventionist and counterfactual analyses will always concur. Therefore, individuals who embrace counterfactual formulations and those who object to their use will make equivalent decisions in uncontroversial settings.

Li Yizhen, Huang Shaohan, Qi Jiaxing et al.

No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowledge with a rich history. To bridge this gap, we present a TCM question dataset named TCM-QA, which comprises three question types: single choice, multiple choice, and true or false, to examine the LLM's capacity for knowledge recall and comprehensive reasoning within the TCM domain. In our study, we evaluate two settings of the LLM, zero-shot and few-shot settings, while concurrently discussing the differences between English and Chinese prompts. Our results indicate that ChatGPT performs best in true or false questions, achieving the highest precision of 0.688 while scoring the lowest precision is 0.241 in multiple-choice questions. Furthermore, we observed that Chinese prompts outperformed English prompts in our evaluations. Additionally, we assess the quality of explanations generated by ChatGPT and their potential contribution to TCM knowledge comprehension. This paper offers valuable insights into the applicability of LLMs in specialized domains and paves the way for future research in leveraging these powerful models to advance TCM.

Arman Rahmim, Tyler J. Bradshaw, Guido Davidzon et al.

The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was: AI in Action. Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript.

SC Weber, J Pallas, G Olson et al.

STARR (STAnford Research Repository) is a clinical research support ecosystem that supports basic science research, population health research and translational research at Stanford University. STARR consists of raw and analysis ready multi-modal data, and tools for cohort analysis and self service data access. STARR data is accessible on secure shared computing systems for ad hoc analysis. Also present is a suite of services on top of STARR, that allow researchers access to complex purpose built data cuts, common data models and software solutions. This manuscript is a research resource description and describes the evolution of STARR Tools that are used to offer self-service access to detailed clinical data for research purposes to researchers at Stanford Medicine, along with a framework used to ensure that data acquired via the self-service tools is handled in compliance with all applicable regulations and rules.

Thomas F. Heston

"Prompt Engineering for Students of Medicine and Their Teachers" brings the principles of prompt engineering for large language models such as ChatGPT and Google Bard to medical education. This book contains a comprehensive guide to prompt engineering to help both teachers and students improve education in the medical field. Just as prompt engineering is critical in getting good information out of an AI, it is also critical to get students to think and understand more deeply. The principles of prompt engineering that we have learned from AI systems have the potential to simultaneously revolutionize learning in the healthcare field. The book analyzes from multiple angles the anatomy of a good prompt for both AI models and students. The different types of prompts are examined, showing how each style has unique characteristics and applications. The principles of prompt engineering, applied properly, are demonstrated to be effective in teaching across the diverse fields of anatomy, physiology, pathology, pharmacology, and clinical skills. Just like ChatGPT and similar large language AI models, students need clear and detailed prompting in order for them to fully understand a topic. Using identical principles, a prompt that gets good information from an AI will also cause a student to think more deeply and accurately. The process of prompt engineering facilitates this process. Because each chapter contains multiple examples and key takeaways, it is a practical guide for implementing prompt engineering in the learning process. It provides a hands-on approach to ensure readers can immediately apply the concepts they learn

Jingjin Liu, Hankz Hankui Zhuo, Kebing Jin et al.

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance.

Jonathan Steinhorst, L. Aglanu, S. Ravensbergen et al.

Background Snakebite envenoming is a medical emergency which is common in many tropical lower- and middle-income countries. Traditional healers are frequently consulted as primary care-givers for snakebite victims in distress. Traditional healers therefore present a valuable source of information about how snakebite is perceived and handled at the community level, an understanding of which is critical to improve and extend snakebite-related healthcare. Method The study was approached from the interpretive paradigm with phenomenology as a methodology. Semi-structured interviews were conducted with 19 traditional healers who treat snakebite patients in two rural settings in Ghana. From the Ashanti and Upper West regions respectively, 11 and 8 healers were purposively sampled. Interview data was coded, collated and analysed thematically using ATLAS.ti 8 software. Demographic statistics were analysed using IBM SPSS Statistics version 26. Findings Snakebite was reportedly a frequent occurrence, perceived as dangerous and often deadly by healers. Healers felt optimistic in establishing a diagnosis of snakebite using a multitude of methods, ranging from herbal applications to spiritual consultations. They were equally confident about their therapies; encompassing the administration of plant and animal-based concoctions and manipulations of bite wounds. Traditional healers were consulted for both physical and spiritual manifestations of snakebite or after insufficient pain control and lack of antivenom at hospitals; referrals by healers to hospitals were primarily done to receive antivenom and care for wound complications. Most healers welcomed opportunities to engage more productively with hospitals and clinical staff. Conclusions The fact that traditional healers did sometimes refer victims to hospitals indicates that improvement of antivenom stocks, pain management and wound care can potentially improve health seeking at hospitals. Our results emphasize the need to explore future avenues for communication and collaboration with traditional healers to improve health seeking behaviour and the delivery of much-needed healthcare to snakebite victims.

U. Amazigo, S. Leak, Honorat G M Zoure et al.

The neglected tropical diseases (NTDs) affect hundreds of millions of people, predominantly in rural, often difficult-to-access areas, poorly served by national health services. Here, we review the contributions of 4.8 million community-directed distributors (CDDs) of medicines over 2 decades in 146,000 communities in 27 sub-Saharan African countries to control or eliminate onchocerciasis and lymphatic filariasis (LF). We examine their role in the control of other NTDs, malaria, HIV/AIDS interventions, immunisation campaigns, and support to overstretched health service personnel. We are of the opinion that CDDs as community selected, trained, and experienced “foot soldiers,” some of whom were involved in the Ebola outbreak responses at the community level in Liberia, if retrained, can assist community leaders and support health workers (HWs) in the ongoing Coronavirus Disease 2019 (COVID-19) crisis. The review highlights the improved treatment coverage where there are women CDDs, the benefits and lessons from the work of CDDs, their long-term engagement, and the challenges they face in healthcare delivery. It underscores the value of utilising the CDD model for strong community engagement and recommends the model, with some review, to hasten the achievement of the NTD 2030 goal and assist the health system cope with evolving epidemics and other challenges. We propose that, based on the unprecedented progress made in the control of NTDs directly linked to community engagement and contributions of CDDs “foot soldiers,” they deserve regional and global recognition. We also suggest that the World Health Organization (WHO) and other international stakeholders promote policy and guidance for countries to adapt this model for the elimination of NTDs and to strengthen national health services. This will enhance the accomplishment of some Sustainable Development Goals (SDGs) by 2030 in sub-Saharan Africa.

David S Lawrence, Agnes Ssali, Neo Moshashane et al.

<h4>Background</h4>The AMBITION-cm trial for HIV-associated cryptococcal meningitis demonstrated that a single, high-dose of liposomal amphotericin (AmBisome) plus 14-days of oral flucytosine and fluconazole was non-inferior in terms of all-cause mortality to 7-days of amphotericin B deoxycholate and flucytosine followed by 7-days of fluconazole (Control). The AmBisome regimen was associated with fewer adverse events. We explored the acceptability of the AmBisome regimen from the perspective of participants and providers.<h4>Methods</h4>We embedded a qualitative methods study within the AMBITION-cm sites in Botswana and Uganda. We conducted in-depth interviews with trial participants, surrogate decision makers, and researchers and combined these with direct observations. Interviews were transcribed, translated, and analysed thematically.<h4>Results</h4>We interviewed 38 trial participants, 20 surrogate decision makers, and 31 researchers. Participant understanding of the trial was limited; however, there was a preference for the AmBisome regimen due to the single intravenous dose and fewer side effects. More time was required to prepare the single AmBisome dose but this was felt to be acceptable given subsequent reductions in workload. The AmBisome regimen was reported to be associated with fewer episodes of rigors and thrombophlebitis and a reduction in the number of intravenous cannulae required. Less intensive monitoring and management was required for participants in the AmBisome arm.<h4>Conclusions</h4>The AmBisome regimen was highly acceptable, being simpler to administer despite the initial time investment required. The regimen was well tolerated and associated with less toxicity and resultant management. Widespread implementation would reduce the clinical workload of healthcare workers caring for patients with HIV-associated cryptococcal meningitis.