Hasil untuk "Arctic medicine. Tropical medicine"

Clare Huppatz, David N Durrheim

Silvia Otero-Rodríguez, Martín Casapía-Morales, C. de Mendoza et al.

HTLV-1/2 in people with HIV (PWH) has been little studied in the Peruvian Amazon, an endemic area for both viruses. We aimed to estimate its prevalence and describe the main clinical and epidemiological features of individuals with HTLV-HIV co-existence. We conducted a cross-sectional study (October–December 2023) at the Division of Infectious Diseases and Tropical Medicine at the Regional Hospital of Loreto in Iquitos. We performed a screening test (recombinant HTLV I+II ELISA) and confirmed the results with INNO-LIA. Among 293 PWH analyzed, 14 (4.8%) were HTLV-positive: 1/293 was HTLV-1-positive (0.3%; 95% CI 0.06–0.9), 11/293 were HTLV-2-positive (3.8%; 95% CI 2.1–6.8), and 2/293 were non-typeable (0.7%; 95% CI 0.1–2.7). Compared with HIV-monoinfected individuals, superinfected patients were older (55 vs. 39 years; p = 0.001). Low education was more frequent in the univariate analysis (35.7% vs. 15.4%; p = 0.05) but was not retained in the multivariable model. In conclusion, HIV–HTLV-2 co-existence is relatively common (~4%) in the Peruvian Amazon, particularly among older individuals, highlighting the need for targeted screening and prevention strategies. Integrating HTLV testing into routine HIV clinic workflows, along with brief and focused counseling for superinfected patients, may help optimize follow-up and care.

Nengyue Su, Liang Luo, Yu Gu et al.

The rise of autonomous driving technology has led to concerns about inactivity-induced fatigue. This paper explores Traditional Chinese Medicine (TCM) scents for mitigating. Two human-involved studies have been conducted in a high-fidelity driving simulator. Study 1 maps six prevalent TCM scents onto the arousal/valence circumplex to select proper candidates, i.e., argy wormwood (with the highest arousal) and tangerine peel (with the highest valence). Study 2 tests both scents in an auto-driving course. Statistics show both scents can improve driver alertness and reaction-time, but should be used in different ways: argy wormwood is suitable for short-term use due to its higher intensity but poor acceptance, while tangerine peel is ideal for long-term use due to its higher likeness. These findings provide insights for in-car fatigue mitigation to enhance driver safety and well-being. However, issues such as scent longevity as for aromatherapy and automatic fatigue prediction remain unresolved.

Zahra Akbari Khanaposhtani, Hossein Rajabi

Objective: Research on eye lens dosimetry for radiation workers has increased after the 2012 ICRP118 update on eye lens dose limits. However, corneal dosimetry remains underexplored due to historical focus and measurement challenges. This study uses a high-resolution digital eye phantom in Monte Carlo simulations to estimate corneal and lens doses for nuclear medicine staff, with and without lead glasses. Method: The Monte Carlo code GATE (version 9.0) based on GEANT4 (version 10.6) was used to estimate and compare doses in a digital eye phantom, accounting for primary and scattered radiation from common radionuclides (F18, I131, Tc99m) with varying lead glass shielding (0 to 0.75 mm). Results: Across all radionuclides, the dose to the cornea was consistently higher than the dose to the lens. Notably, the ratio of corneal to lens dose increased with thicker lead glasses, indicating a greater dose reduction to the lens compared to the cornea. Conclusion: The findings show that corneal doses from all studied radionuclides exceeded lens doses. Although increasing lead glass thickness reduced both, the reduction was more significant for the lens, raising the cornea-to-lens dose ratio. This trend suggests that while thicker lead glasses enhance lens protection, their practicality may be limited due to diminishing returns and potential discomfort. Keywords: Corneal Dosimetry, Lens Dosimetry, Monte Carlo, GATE, Nuclear Medicine, Simulation

Chris Brogly, Saif Rjaibi, Charlotte Liang et al.

Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean conditions on the phi-3 SLM scores. We found low-moderate significant correlations between the scores from the SLM and human evaluators for sports injury texts with low filtering (\r{ho} = 0.3413, p < 0.001) and medicine/health texts with high filtering (\r{ho} = 0.3854, p < 0.001), and low significant correlation for medicine/health texts with low filtering (\r{ho} = 0.2255, p < 0.001). There was negligible, insignificant correlation for sports injury-related texts with high filtering (\r{ho} = 0.0318, p = 0.4466).

Shufeng Kong, Xingru Yang, Yuanyuan Wei et al.

Traditional Chinese Medicine (TCM) is a holistic medical system with millennia of accumulated clinical experience, playing a vital role in global healthcare-particularly across East Asia. However, the implicit reasoning, diverse textual forms, and lack of standardization in TCM pose major challenges for computational modeling and evaluation. Large Language Models (LLMs) have demonstrated remarkable potential in processing natural language across diverse domains, including general medicine. Yet, their systematic evaluation in the TCM domain remains underdeveloped. Existing benchmarks either focus narrowly on factual question answering or lack domain-specific tasks and clinical realism. To fill this gap, we introduce MTCMB-a Multi-Task Benchmark for Evaluating LLMs on TCM Knowledge, Reasoning, and Safety. Developed in collaboration with certified TCM experts, MTCMB comprises 12 sub-datasets spanning five major categories: knowledge QA, language understanding, diagnostic reasoning, prescription generation, and safety evaluation. The benchmark integrates real-world case records, national licensing exams, and classical texts, providing an authentic and comprehensive testbed for TCM-capable models. Preliminary results indicate that current LLMs perform well on foundational knowledge but fall short in clinical reasoning, prescription planning, and safety compliance. These findings highlight the urgent need for domain-aligned benchmarks like MTCMB to guide the development of more competent and trustworthy medical AI systems. All datasets, code, and evaluation tools are publicly available at: https://github.com/Wayyuanyuan/MTCMB.

Anran Li, Lingfei Qian, Mengmeng Du et al.

Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety. However, a key open question remains: to what extent do LLMs memorize medical training data. Memorization can be beneficial when it enables LLMs to retain valuable medical knowledge during domain adaptation. Yet, it also raises concerns. LLMs may inadvertently reproduce sensitive clinical content (e.g., patient-specific details), and excessive memorization may reduce model generalizability, increasing risks of misdiagnosis and making unwarranted recommendations. These risks are further amplified by the generative nature of LLMs, which can not only surface memorized content but also produce overconfident, misleading outputs that may hinder clinical adoption. In this work, we present a study on memorization of LLMs in medicine, assessing its prevalence (how frequently it occurs), characteristics (what is memorized), volume (how much content is memorized), and potential downstream impacts (how memorization may affect medical applications). We systematically analyze common adaptation scenarios: (1) continued pretraining on medical corpora, (2) fine-tuning on standard medical benchmarks, and (3) fine-tuning on real-world clinical data, including over 13,000 unique inpatient records from Yale New Haven Health System. The results demonstrate that memorization is prevalent across all adaptation scenarios and significantly higher than that reported in the general domain. Moreover, memorization has distinct characteristics during continued pre-training and fine-tuning, and it is persistent: up to 87% of content memorized during continued pre-training remains after fine-tuning on new medical tasks.

Zheng Hui, Yijiang River Dong, Ehsan Shareghi et al.

As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based on the AMA Principles of Medical Ethics, the ABA Model Rules of Professional Conduct, and the CFA Institute Code of Ethics. Building on this foundation, we introduce Trident-Bench, a benchmark specifically targeting LLM safety in the legal, financial, and medical domains. We evaluated 19 general-purpose and domain-specialized models on Trident-Bench and show that it effectively reveals key safety gaps -- strong generalist models (e.g., GPT, Gemini) can meet basic expectations, whereas domain-specialized models often struggle with subtle ethical nuances. This highlights an urgent need for finer-grained domain-specific safety improvements. By introducing Trident-Bench, our work provides one of the first systematic resources for studying LLM safety in law and finance, and lays the groundwork for future research aimed at reducing the safety risks of deploying LLMs in professionally regulated fields. Code and benchmark will be released at: https://github.com/zackhuiiiii/TRIDENT

Chengfeng Dou, Ying Zhang, Zhi Jin et al.

Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making processes. Although it benefits from current retrieval-augmented generation~(RAG) technologies, it still faces two significant challenges: the collection of dispersed evidence and the efficient organization of this evidence to support the complex queries necessary for EBM. To tackle these issues, we propose using LLMs to gather scattered evidence from multiple sources and present a knowledge hypergraph-based evidence management model to integrate these evidence while capturing intricate relationships. Furthermore, to better support complex queries, we have developed an Importance-Driven Evidence Prioritization (IDEP) algorithm that utilizes the LLM to generate multiple evidence features, each with an associated importance score, which are then used to rank the evidence and produce the final retrieval results. Experimental results from six datasets demonstrate that our approach outperforms existing RAG techniques in application domains of interest to EBM, such as medical quizzing, hallucination detection, and decision support. Testsets and the constructed knowledge graph can be accessed at \href{https://drive.google.com/file/d/1WJ9QTokK3MdkjEmwuFQxwH96j_Byawj_/view?usp=drive_link}{https://drive.google.com/rag4ebm}.

Chun Yin Kong, Picasso Vasquez, Makan Farhoodimoghadam et al.

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.

Sibo Wei, Xueping Peng, Yi-Fei Wang et al.

The surge of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of specialized, high-quality corpora. To this end, in this paper we propose BianCang, a TCM-specific LLM, using a two-stage training process that first injects domain-specific knowledge and then aligns it through targeted stimulation to enhance diagnostic and differentiation capabilities. Specifically, we constructed pre-training corpora, instruction-aligned datasets based on real hospital records, and the ChP-TCM dataset derived from the Pharmacopoeia of the People's Republic of China. We compiled extensive TCM and medical corpora for continual pre-training and supervised fine-tuning, building a comprehensive dataset to refine the model's understanding of TCM. Evaluations across 11 test sets involving 31 models and 4 tasks demonstrate the effectiveness of BianCang, offering valuable insights for future research. Code, datasets, and models are available on https://github.com/QLU-NLP/BianCang.

Ahmed Abdeen Hamed, Tamer E. Fandy

The objective of this research is to introduce a network specialized in predicting drugs that can be repurposed by investigating real-world evidence sources, such as clinical trials and biomedical literature. Specifically, it aims to generate drug combination therapies for complex diseases (e.g., cancer, Alzheimer's). We present a multilayered network medicine approach, empowered by a highly configured ChatGPT prompt engineering system, which is constructed on the fly to extract drug mentions in clinical trials. Additionally, we introduce a novel algorithm that connects real-world evidence with disease-specific signaling pathways (e.g., KEGG database). This sheds light on the repurposability of drugs if they are found to bind with one or more protein constituents of a signaling pathway. To demonstrate, we instantiated the framework for breast cancer and found that, out of 46 breast cancer signaling pathways, the framework identified 38 pathways that were covered by at least two drugs. This evidence signals the potential for combining those drugs. Specifically, the most covered signaling pathway, ID hsa:2064, was covered by 108 drugs, some of which can be combined. Conversely, the signaling pathway ID hsa:1499 was covered by only two drugs, indicating a significant gap for further research. Our network medicine framework, empowered by GenAI, shows promise in identifying drug combinations with a high degree of specificity, knowing the exact signaling pathways and proteins that serve as targets. It is noteworthy that ChatGPT successfully accelerated the process of identifying drug mentions in clinical trials, though further investigations are required to determine the relationships among the drug mentions.

Kishi Kobe Yee Francisco, Andrane Estelle Carnicer Apuhin, Myles Joshua Toledo Tan et al.

Personalized medicine (PM) promises to transform healthcare by providing treatments tailored to individual genetic, environmental, and lifestyle factors. However, its high costs and infrastructure demands raise concerns about exacerbating health disparities, especially between high-income countries (HICs) and low- and middle-income countries (LMICs). While HICs benefit from advanced PM applications through AI and genomics, LMICs often lack the resources necessary to adopt these innovations, leading to a widening healthcare divide. This paper explores the financial and ethical challenges of PM implementation, with a focus on ensuring equitable access. It proposes strategies for global collaboration, infrastructure development, and ethical frameworks to support LMICs in adopting PM, aiming to prevent further disparities in healthcare accessibility and outcomes.

Quan Wang, Yanmin Cao, Xinyu Liu et al.

<h4>Background</h4>Tuberculosis (TB) and COVID-19 co-infection poses a significant global health challenge with increased fatality rates and adverse outcomes. However, the existing evidence on the epidemiology and treatment of TB-COVID co-infection remains limited.<h4>Methods</h4>This updated systematic review aimed to investigate the prevalence, fatality rates, and treatment outcomes of TB-COVID co-infection. A comprehensive search across six electronic databases spanning November 1, 2019, to January 24, 2023, was conducted. The Joanna Briggs Institute Critical Appraisal Checklist assessed risk of bias of included studies, and meta-analysis estimated co-infection fatality rates and relative risk.<h4>Results</h4>From 5,095 studies screened, 17 were included. TB-COVID co-infection prevalence was reported in 38 countries or regions, spanning both high and low TB prevalence areas. Prevalence estimates were approximately 0.06% in West Cape Province, South Africa, and 0.02% in California, USA. Treatment approaches for TB-COVID co-infection displayed minimal evolution since 2021. Converging findings from diverse studies underscored increased hospitalization risks, extended recovery periods, and accelerated mortality compared to single COVID-19 cases. The pooled fatality rate among co-infected patients was 7.1% (95%CI: 4.0% ~ 10.8%), slightly lower than previous estimates. In-hospital co-infected patients faced a mean fatality rate of 11.4% (95%CI: 5.6% ~ 18.8%). The pooled relative risk of in-hospital fatality was 0.8 (95% CI, 0.18-3.68) for TB-COVID patients versus single COVID patients.<h4>Conclusion</h4>TB-COVID co-infection is increasingly prevalent worldwide, with fatality rates gradually declining but remaining higher than COVID-19 alone. This underscores the urgency of continued research to understand and address the challenges posed by TB-COVID co-infection.

Jie Lian, Xufang Luo, Caihua Shan et al.

Precision medicine tailored to individual patients has gained significant attention in recent times. Machine learning techniques are now employed to process personalized data from various sources, including images, genetics, and assessments. These techniques have demonstrated good outcomes in many clinical prediction tasks. Notably, the approach of constructing graphs by linking similar patients and then applying graph neural networks (GNNs) stands out, because related information from analogous patients are aggregated and considered for prediction. However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources. Previous studies rely on human expertise to select the edge feature, which is neither scalable nor efficient in pinpointing crucial edge features for complex diseases. In this paper, we propose a novel algorithm named \ours, which can automatically select important features to construct multiple patient similarity graphs, and train GNNs based on these graphs as weak learners in adaptive boosting. \ours{} is evaluated on two real-world medical scenarios and shows superiors performance.

Jie Xu, Lu Lu, Sen Yang et al.

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by five experts in medicine and engineering. Third, three clinical experts design a set of medical datasets to interact with LLMs. Finally, benchmarking experiments are conducted on the datasets. The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts. RESULTS: The obtained evaluation criteria cover medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with sixteen detailed indicators. The medical datasets include twenty-seven medical dialogues and seven case reports in Chinese. Three chatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and Doctor PuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimental results show that Dr. PJ outperforms ChatGPT and ERNIE Bot in both multiple-turn medical dialogue and case report scenarios.

Ngozi Ekeke, Francis S Iyama, Joseph N Chukwu et al.

<h4>Background</h4>Yaws is a disease caused by the bacteria Treponema pallidum subspecies pertenue, which is most commonly seen among children below 15 years. In the twentieth century yaws was endemic in Nigeria but eradication strategies markedly reduced the disease burden. Currently there is minimal data on the ongoing transmission of yaws in Nigeria, despite reports of confirmed yaws cases in neighbouring West African countries.<h4>Methods</h4>We conducted both community and school-based active yaws case search among school-aged children in southeast Nigeria. Children were screened by trained community volunteers. Suspected yaws cases were clinically reviewed and tested using rapid diagnostic serological tests.<h4>Results</h4>Between February and May 2021, up to 28 trained community volunteers screened a total of 105,015 school children for yaws. Overall, 7,706 children with various skin lesions were identified. Eight (8) suspected cases of yaws were reported, reviewed and screened, but none was confirmed using rapid diagnostic tests. The four most common skin conditions identified were scabies (39%), papular urticaria (29%), tinea corporis (14%) and tinea capitis (12%).<h4>Conclusions</h4>No case of yaws was confirmed in this large population of children in south-east Nigeria. Continuous community awareness and yaws case finding activities have been recommended across Nigeria.

Md Jamir Anwar, Sattam Khulaif Alenezi, Faizul Azam et al.

Objective: To investigate the effect of Nigella sativa oil on cardiomyopathy and neurobehavioral changes induced by doxorubicin in mice. Methods: Swiss strain of albino female mice were divided into 6 groups of 5 animals in each: Group I (control group), group II (doxorubicin, 10 mg/kg, i.v.), group III, IV, and V (Nigella sativa oil; 1.5, 3, and 6 mL/kg, respectively), group Ή (Nigella sativa oil per se; 6 mL/kg, p.o.). The duration of treatment was 15 d (10 days’ pre-treatment and 5 days’ post-treatment) and doxorubicin was administered on day 11th of the treatment schedule. Following Nigella sativa oil treatment, neurobehavioral tests, cardiac hypertrophy tests, and biochemical tests in serum and tissues were performed. Neurological tests included assessment of anxiety-like behavior in the elevated plus maze, spontaneous alternation behavior in the cross maze, and depression-like behavior in modified forced swim tests. Biochemical tests included serum lactate dehydrogenase and creatinine kinase-MB, malondialdehyde and reduced glutathione in tissues. Lastly, molecular docking was used to estimate the affinity of the phytoconstituents of Nigella sativa oil with histone deacetylases. Results: Nigella sativa oil treatment significantly (P<0.001) restored doxorubicin-induced neurobehavioral changes, decreased lactate dehydrogenase and creatinine kinase-MB in the plasma, malondialdehyde contents in tissues, and increased reduced glutathione level. Besides, no significant alteration was observed in Nigella sativa oil per se group as compared to the control. Molecular docking showed that Nigella sativa oil components had appreciable binding affinitiy with the protein cavities of HDAC1 and HDAC6. Conclusions: The result shows that Nigella sativa oil exerts anxiolytic, antidepressant, and memory-enhancing effects in addition to cardioprotective effect against doxorubicin-induced cardiomyopathy in mice. The modulatory effect of Nigella sativa oil on oxidative stress could contribute to the cardioprotective effect and associated neurobehavioral changes in mice.

Célia Landmann Szwarcwald, Paulo Roberto Borges de Souza Júnior, Ana Roberta Pati Pascom et al.

Abstract INTRODUCTION HIV incidence estimates are essential to monitor the progress of prevention and control interventions. METHODS Data collected by Brazilian surveillance systems were used to derive HIV incidence estimates by age group (15-24; 25+) and sex from 1986 to 2018. This study used a back-calculation method based on the first CD4 count among treatment-naïve cases. Incidence estimates for the population aged 15 years or over were compared to Global Burden of Disease Study (GBD) estimates from 2000 to 2018. RESULTS Among young men (15-24 years), HIV incidence increased from 6,400 (95% CI: 4,900-8,400), in 2000, to 12,800 (95% CI: 10,800-15,900), in 2015, reaching incidence rates higher than 70/100,000 inhabitants and an annual growth rate of 3.7%. Among young women, HIV incidence decreased from 5,000 (95% CI: 4,200-6,100) to 3,200 (95% CI: 3,000-3,700). Men aged ≥25 years and both female groups showed significant annual decreases in incidence rates from 2000 to 2018. In 2018, the estimated number of new infections was 48,500 (95% CI: 45300-57500), 34,800 (95% CI: 32800-41500) men, 13,600 (95% CI: 12,500-16,000) women. Improvements in the time from infection to diagnosis and in the proportion of cases receiving antiretroviral therapy immediately after diagnosis were found for all groups. Comparison with GBD estimates shows similar rates for men with overlapping confidence intervals. Among women, differences are higher mainly in more recent years. CONCLUSIONS The results indicate that efforts to control the HIV epidemic are having an impact. However, there is an urgent need to address the vulnerability of young men.