Hasil untuk "Arctic medicine. Tropical medicine"

Tae-Young Choi, Tae-Ik Choi, Yu-Ri Lee et al.

Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases. With their see-through bodies, low maintenance costs and genetic similarity to humans, zebrafish provide a powerful animal model for studying mental disorders and metabolic diseases in the laboratory. Tae-Young Choi from Wonkwang University, Iksan, South Korea, and coworkers review the many physiological advantages and logistical benefits of rearing these small tropical fish for biomedical research. These include the ease of tissue imaging, the large number of offspring in each generation and the increasing number of genetic techniques available. The researchers highlight the various ways in which zebrafish have contributed to scientists’ understanding of mental disorders and the communication pathways between brain and other organs in the body. They also discuss the potential of zebrafish for tracking metabolism and how it can go awry in various disease settings.

Yishen Liu, Shengda Luo, Zishao Zhong et al.

Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheumatoid arthritis (RA). To address these issues, this paper introduces Hengqin-RA-v1, the first large language model specifically tailored for TCM with a focus on diagnosing and treating RA. We also present HQ-GCM-RA-C1, a comprehensive RA-specific dataset curated from ancient Chinese medical literature, classical texts, and modern clinical studies. This dataset empowers Hengqin-RA-v1 to deliver accurate and culturally informed responses, effectively bridging the gaps left by general-purpose models. Extensive experiments demonstrate that Hengqin-RA-v1 outperforms state-of-the-art models, even surpassing the diagnostic accuracy of TCM practitioners in certain cases.

Jiatong Han

Traditional Chinese Medicine (TCM) theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply them in clinical practice. This study adopted a human-in-the-loop (HITL) framework and selected four passages from the medical canon Huangdi Neijing that are fundamental in theory. Through prompt-based cognitive scaffolding, DeepSeek V3.1 was guided to identify metaphor and metonymy in the source text and convey the theory in translation. In the evaluation stage, ChatGPT 5 Pro and Gemini 2.5 Pro were instructed by prompts to simulate three types of real-world readers. Human translations, baseline model translations, and prompt-adjusted translations were scored by the simulated readers across five cognitive dimensions, followed by structured interviews and Interpretative Phenomenological Analysis (IPA). Results show that the prompt-adjusted LLM translations perform best across all five dimensions, with high cross-model and cross-role consistency. The interview themes reveal differences between human and machine translation, effective strategies for metaphor and metonymy transfer, and readers' cognitive preferences. This study provides a cognitive, efficient, and replicable HITL methodological pathway for the translation of ancient, concept-dense texts such as TCM.

Arya Kimiaghalam

The Arctic sea ice cover has significantly declined over the recent decades. The debate on whether this decline is caused by anthropogenic activity or internal cycles is still ongoing. However, despite this uncertainty, some physical factors reinforce this declining trend, one of which is sea ice thickness. The thinning of Arctic sea ice facilitates the melting of sea ice by reducing the heat capacity of the ice volume. The progression of this thinning can potentially accelerate sea ice loss. In this work, we attempt to understand the broad relationship of sea ice cover levels and average sea ice thickness in the Arctic. First, we attempt to understand whether the trend in the Arctic sea ice thickness is statistically significant over multi-year and inter-year seasonal scales, by using mostly non-parametric trend analysis tools. We subsequently study how sea ice thickness, as well as its momentum and fluctuations, are statistically correlated to those of sea ice cover in the Arctic. For this task, we use publicly available Arctic sea ice cover and thickness data from 1979 to 2021, provided by the Pan-Arctic Ice Ocean Modelling and Assimilation System (PIOMAS) and the National Snow and Ice Data Center (NSIDC).

Mengzhou Sun, Sendong Zhao, Jianyu Chen et al.

Evidence-based medicine (EBM) holds a crucial role in clinical application. Given suitable medical articles, doctors effectively reduce the incidence of misdiagnoses. Researchers find it efficient to use large language models (LLMs) techniques like RAG for EBM tasks. However, the EBM maintains stringent requirements for evidence, and RAG applications in EBM struggle to efficiently distinguish high-quality evidence. Therefore, inspired by the meta-analysis used in EBM, we provide a new method to re-rank and filter the medical evidence. This method presents multiple principles to filter the best evidence for LLMs to diagnose. We employ a combination of several EBM methods to emulate the meta-analysis, which includes reliability analysis, heterogeneity analysis, and extrapolation analysis. These processes allow the users to retrieve the best medical evidence for the LLMs. Ultimately, we evaluate these high-quality articles and show an accuracy improvement of up to 11.4% in our experiments and results. Our method successfully enables RAG to extract higher-quality and more reliable evidence from the PubMed dataset. This work can reduce the infusion of incorrect knowledge into responses and help users receive more effective replies.

Jiacheng Xie, Shuai Zeng, Yang Yu et al.

Traditional Chinese Medicine (TCM) presents a rich and structurally unique knowledge system that challenges conventional applications of large language models (LLMs). Although previous TCM-specific LLMs have shown progress through supervised fine-tuning, they often face limitations in alignment, data quality, and evaluation consistency. In this study, we introduce Ladder-base, the first TCM-focused LLM trained with Group Relative Policy Optimization (GRPO), a reinforcement learning method that improves reasoning and factual consistency by optimizing response selection based on intra-group comparisons. Ladder-base is built upon the Qwen2.5-7B-Instruct foundation model and trained exclusively on the textual subset of the TCM-Ladder benchmark, using 80 percent of the data for training and the remaining 20 percent split evenly between validation and test sets. Through standardized evaluation, Ladder-base demonstrates superior performance across multiple reasoning metrics when compared to both state-of-the-art general-purpose LLMs such as GPT-4, Gemini 2.5, Claude 3, and Qwen3 and domain-specific TCM models including BenTsao, HuatuoGPT2, and Zhongjing. These findings suggest that GRPO provides an effective and efficient strategy for aligning LLMs with expert-level reasoning in traditional medical domains and supports the development of trustworthy and clinically grounded TCM artificial intelligence systems.

Arfan Ghani

Epileptic seizures arise from abnormally synchronised neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analogue electronics or high-power stimulation hardware. This study investigates a minimal digital custom-designed chip that generates a stable 6 Hz oscillation capable of entraining epileptic seizure activity. Using a publicly available EEG seizure dataset, we extracted and averaged analogue seizure waveforms, digitised them to emulate neural front-ends, and directly interfaced the digitised signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip pulse train was resampled and low-pass-reconstructed to produce an analogue 6 Hz waveform, allowing direct comparison between seizure morphology, its digitised representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable seizure-interruption devices for precision medicine and future healthcare devices.

Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin et al.

Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes.

Joschka Haltaufderheide, Robert Ranisch

With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare. Despite their potential benefits, researchers have underscored various ethical implications. While individual instances have drawn much attention, the debate lacks a systematic overview of practical applications currently researched and ethical issues connected to them. Against this background, this work aims to map the ethical landscape surrounding the current stage of deployment of LLMs in medicine and healthcare. Electronic databases and preprint servers were queried using a comprehensive search strategy. Studies were screened and extracted following a modified rapid review approach. Methodological quality was assessed using a hybrid approach. For 53 records, a meta-aggregative synthesis was performed. Four fields of applications emerged and testify to a vivid exploration phase. Advantages of using LLMs are attributed to their capacity in data analysis, personalized information provisioning, support in decision-making, mitigating information loss and enhancing information accessibility. However, we also identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful misinformation or convincingly but inaccurate content. A recurrent plea for ethical guidance and human oversight is evident. Given the variety of use cases, it is suggested that the ethical guidance debate be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering diverse settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. In addition, a critical inquiry is necessary to determine the extent to which the current experimental use of LLMs is necessary and justified.

Manjiang Yu, Xue Li

In time-critical decisions, human decision-makers can interact with AI-enabled situation-aware software to evaluate many imminent and possible scenarios, retrieve billions of facts, and estimate different outcomes based on trillions of parameters in a fraction of a second. In high-order reasoning, "what-if" questions can be used to challenge the assumptions or pre-conditions of the reasoning, "why-not" questions can be used to challenge on the method applied in the reasoning, "so-what" questions can be used to challenge the purpose of the decision, and "how-about" questions can be used to challenge the applicability of the method. When above high-order reasoning questions are applied to assist human decision-making, it can help humans to make time-critical decisions and avoid false-negative or false-positive types of errors. In this paper, we present a model of high-order reasoning to offer recommendations in evidence-based medicine in a time-critical fashion for the applications in ICU. The Large Language Model (LLM) is used in our system. The experiments demonstrated the LLM exhibited optimal performance in the "What-if" scenario, achieving a similarity of 88.52% with the treatment plans of human doctors. In the "Why-not" scenario, the best-performing model tended to opt for alternative treatment plans in 70% of cases for patients who died after being discharged from the ICU. In the "So-what" scenario, the optimal model provided a detailed analysis of the motivation and significance of treatment plans for ICU patients, with its reasoning achieving a similarity of 55.6% with actual diagnostic information. In the "How-about" scenario, the top-performing LLM demonstrated a content similarity of 66.5% in designing treatment plans transferring for similar diseases. Meanwhile, LLMs managed to predict the life status of patients after their discharge from the ICU with an accuracy of 70%.

Lingxia Qiao, Ali Khalilimeybodi, Nathaniel J Linden-Santangeli et al.

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms.

Guangzhi Xiong, Qiao Jin, Xiao Wang et al.

The emergent abilities of large language models (LLMs) have demonstrated great potential in solving medical questions. They can possess considerable medical knowledge, but may still hallucinate and are inflexible in the knowledge updates. While Retrieval-Augmented Generation (RAG) has been proposed to enhance the medical question-answering capabilities of LLMs with external knowledge bases, it may still fail in complex cases where multiple rounds of information-seeking are required. To address such an issue, we propose iterative RAG for medicine (i-MedRAG), where LLMs can iteratively ask follow-up queries based on previous information-seeking attempts. In each iteration of i-MedRAG, the follow-up queries will be answered by a conventional RAG system and they will be further used to guide the query generation in the next iteration. Our experiments show the improved performance of various LLMs brought by i-MedRAG compared with conventional RAG on complex questions from clinical vignettes in the United States Medical Licensing Examination (USMLE), as well as various knowledge tests in the Massive Multitask Language Understanding (MMLU) dataset. Notably, our zero-shot i-MedRAG outperforms all existing prompt engineering and fine-tuning methods on GPT-3.5, achieving an accuracy of 69.68% on the MedQA dataset. In addition, we characterize the scaling properties of i-MedRAG with different iterations of follow-up queries and different numbers of queries per iteration. Our case studies show that i-MedRAG can flexibly ask follow-up queries to form reasoning chains, providing an in-depth analysis of medical questions. To the best of our knowledge, this is the first-of-its-kind study on incorporating follow-up queries into medical RAG. The implementation of i-MedRAG is available at https://github.com/Teddy-XiongGZ/MedRAG.

Yuanyuan Wei, Xianxian Liu, Changran Xu et al.

The precise quantification of nucleic acids is pivotal in molecular biology, underscored by the rising prominence of nucleic acid amplification tests (NAAT) in diagnosing infectious diseases and conducting genomic studies. This review examines recent advancements in digital Polymerase Chain Reaction (dPCR) and digital Loop-mediated Isothermal Amplification (dLAMP), which surpass the limitations of traditional NAAT by offering absolute quantification and enhanced sensitivity. In this review, we summarize the compelling advancements of dNNAT in addressing pressing public health issues, especially during the COVID-19 pandemic. Further, we explore the transformative role of artificial intelligence (AI) in enhancing dNAAT image analysis, which not only improves efficiency and accuracy but also addresses traditional constraints related to cost, complexity, and data interpretation. In encompassing the state-of-the-art (SOTA) development and potential of both software and hardware, the all-encompassing Point-of-Care Testing (POCT) systems cast new light on benefits including higher throughput, label-free detection, and expanded multiplex analyses. While acknowledging the enhancement of AI-enhanced dNAAT technology, this review aims to both fill critical gaps in the existing technologies through comparative assessments and offer a balanced perspective on the current trajectory, including attendant challenges and future directions. Leveraging AI, next-generation dPCR and dLAMP technologies promises integration into clinical practice, improving personalized medicine, real-time epidemic surveillance, and global diagnostic accessibility.

Xiaoyu Wang, Haoyong Ouyang, Balu Bhasuran et al.

Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring conditional factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using retrieval-augmented generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 122 lab tests: 40 with conditional factors and 82 without. For tests with factors, normal ranges depend on patient-specific information. Our results show GPT-4-turbo with RAG achieved a 0.948 F1 score for factor retrieval and 0.995 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 33.5% in factor retrieval and showed 132% and 100% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.

Nixache Vázquez-Cabrera, Araceli Espinosa-Márquez, María Lilia Cedillo-Ramírez

Objetivo. Mostrar la evolución de los lineamientos sobre políticas públicas en salud enfocadas en farmacorresistencia microbiana o resistencia a los antimicrobianos (RAM) que la Organización Mundial de la Salud (OMS) ha emitido desde 1948 hasta 2022. Además, se mencionan otras acciones gubernamentales relacionadas. Métodos. Se llevó a cabo una revisión detallada de los archivos de la Asamblea Mundial de la Salud y el Consejo Ejecutivo de la OMS. Se realizó un análisis textual de resoluciones sobre la RAM, que dan pauta al diseño de políticas y acciones gubernamentales para los Estados Miembros de la OMS. También se realizó una búsqueda sistemática en SCOPUS, Pubmed y literatura gris con categoría de análisis: políticas públicas en salud sobre la RAM. Resultados. La RAM se ha convertido en la mayor amenaza para la salud pública, y compromete el cumplimiento de los objetivos de desarrollo sostenible. Presentamos resoluciones de la OMS como evidencia de lineamientos para combatir la RAM. En consonancia, se menciona el enfoque “Una salud”, estrategias, iniciativas, planes y programas relacionados. Se identificó una brecha en la investigación y el desarrollo de antimicrobianos nuevos, que requiere un análisis más profundo. Conclusiones. La OMS ha realizado esfuerzos para combatir la RAM. Esto ha generado un desarrollo integral de políticas públicas en salud, para que los Estados Miembros las apliquen según la soberanía de sus gobiernos.

Buğra Kerget, Ferhan Kerget, Mehmet Eren Tuna

Emma Chen, Aman Kansal, Julie Chen et al.

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data.

J. Siengsanan-Lamont, W. Theppangna, Phouvong Phommachanh et al.

A national animal disease surveillance network initiated by the Lao PDR government is adopted and reinforced by a joint research project between the National Animal Health Laboratory (NAHL), the Department of Livestock and Fisheries (DLF), and the Mahidol Oxford Tropical Medicine Research Unit (MORU). The network is strengthened by staff training and practical exercises and is utilised to provide zoonotic or high-impact disease information on a national scale. Between January and December 2020, large ruminant samples are collected monthly from 18 abattoirs, one in each province, by provincial and district agriculture and forestry officers. The surveillance network collected a total of 4247 serum samples (1316 buffaloes and 2931 cattle) over this period. Samples are tested for antibodies against Brucella spp., Coxiella burnetii (Q fever) and Foot-and-Mouth Disease Non-Structural Protein (FMD NSP) using commercial ELISA kits and the Rose Bengal test. Seroprevalences of Q fever and brucellosis in large ruminants are low at 1.7% (95% CI: 1.3, 2.1) and 0.7% (95% CI: 0.5, 1.0) respectively, while for FMD NSP it is 50.5% (95% CI: 49.0, 52.0). Univariate analyses show differences in seroprevalences of Q fever between destination (abattoir) province (p-value = 0.005), province of origin (p-value = 0.005), animal type (buffalo or cattle) (p-value = 0.0008), and collection month (p-value = 3.4 × 10−6). Similar to Q fever, seroprevalences of brucellosis were significantly different for destination province (p-value < 0.00001), province of origin (p-value < 0.00001), animal type (p-value = 9.9 × 10−5) and collection month (p-value < 0.00001), plus body condition score (p-value = 0.003), and age (p-value = 0.007). Additionally, risk factors of the FMD NSP dataset include the destination province (p-value < 0.00001), province of origin (p-value < 0.00001), sex (p-value = 7.97 × 10−8), age (p-value = 0.009), collection date (p-value < 0.00001), and collection month (p-value < 0.00001). Spatial analyses revealed that there is no spatial correlation of FMD NSP seropositive animals. High-risk areas for Q fever and brucellosis are identified by spatial analyses. Further investigation of the higher risk areas would provide a better epidemiological understanding of both diseases in Lao PDR. In conclusion, the abattoir serological survey provides useful information about disease exposure and potential risk factors. The network is a good base for field and laboratory staff training in practical technical skills. However, the sustainability of such a surveillance activity is relatively low without an external source of funding, given the operational costs and insufficient government budget. The cost-effectiveness of the abattoir survey could be increased by targeting hotspot areas, reducing fixed costs, and extending the focus to cover more diseases.

G. Ferns

Andreas Woschke, M. Faber, K. Stark et al.

Background Giardia duodenalis is a leading cause of gastroenteritis worldwide. Humans are mainly infected by two different subtypes, i.e., assemblage A and B. Genotyping is hampered by allelic sequence heterozygosity (ASH) mainly in assemblage B, and by occurrence of mixed infections. Here we assessed the suitability of current genotyping protocols of G. duodenalis for epidemiological applications such as molecular tracing of transmission chains. Methodology/Principal findings Two G. duodenalis isolate collections, from an outpatient tropical medicine clinic and from several primary care laboratories, were characterized by assemblage-specific qPCR (TIF, CATH gene loci) and a common multi locus sequence typing (MLST; TPI, BG, GDH gene loci). Assemblage A isolates were further typed at additional loci (HCMP22547, CID1, RHP26, HCMP6372, DIS3, NEK15411). Of 175/202 (86.6%) patients the G. duodenalis assemblage could be identified: Assemblages A 25/175 (14.3%), B 115/175 (65.7%) and A+B mixed 35/175 (20.0%). By incorporating allelic sequence heterozygosity in the analysis, the three marker MLST correctly identified 6/9 (66,7%) and 4/5 (80.0%) consecutive samples from chronic assemblage B infections in the two collections, respectively, and identified a cluster of five independent patients carrying assemblage B parasites of identical MLST type. Extended MLST for assemblage A altogether identified 5/6 (83,3%) consecutive samples from chronic assemblage A infections and 15 novel genotypes. Based on the observed A+B mixed infections it is estimated that only 75% and 50% of assemblage A or B only cases represent single strain infections, respectively. We demonstrate that typing results are consistent with this prediction. Conclusions/Significance Typing of assemblage A and B isolates with resolution for epidemiological applications is possible but requires separate genotyping protocols. The high frequency of multiple infections and their impact on typing results are findings with immediate consequences for result interpretation in this field.