Hasil untuk "Internal medicine"

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).

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.

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.

S. Manivannan, M.K. Mustafa, S. Ahmed et al.

Thushara Madathil, Praveen K. Neema

Alexandre D´Agostini Zottis, Júlia Luiz Agostinho, Eduardo Ricardo Santana et al.

Resumo: Introdução/Justificativa: O câncer engloba mais de 100 tipos de doenças malignas caracterizadas pelo crescimento descontrolado de células, que podem invadir tecidos adjacentes ou se espalhar para outras partes do corpo. Há décadas, as nanopartículas magnéticas (NPMs) de óxido de ferro vêm sendo estudadas por apresentarem grande potencial para aplicações biomédicas, especialmente na oncologia, no uso de agentes de contraste para imagem por ressonância magnética no realçamento de contraste negativo nos tecidos com a presença de tumores e não tumorais, em magneto hipertermia para destruição seletiva de células cancerosas e atuando no transporte vetorizado de fármacos quimioterápicos. Independente de suas aplicações biomédicas, para evitar a aglomeração das NPMs em células, tecidos e órgãos, que pode levar a embolismos, é essencial recobri-las com materiais biocompatíveis e não citotóxicos. Poliésteres derivados de lactonas e macrolactonas, como o copoliéster poli(globalide-co-ε-caprolactona) (PGlCL), têm sido explorados devido à sua biocompatibilidade, hidrofilicidade e biodegradabilidade. Objetivos: Este trabalho teve como objetivo a modificação e a funcionalização do copoliéster PGICL com cisteína, a fim de atingir três objetivos associados a funcionalização das NPMs, que garantirão sua aplicação em nanomedicina, tais como: a) melhorar sua hidrofilicidade (diminuindo sua cristalinidade) para que seja carreado com mais facilidade no meio intracelular; b) permitir que grupos amina e tiol sejam pontos de ancoragem para constituírem partes de ligantes com receptores de superfície celular, tais como o ácido fólico (AF) que só são expressos em células tumorais e c) possibilitar a ligação desses grupos químicos em sistemas de ''drug-delivery'' com o análogo do AF, o quimioterápico metotrexato (MTX) para o tratamento de câncer de mama. Neste estudo, o PGlCL foi modificado com cisteína (PGlCL-Cys) e utilizado para recobrir NPMs de óxido de ferro (Fe3O4 - magnetita), visando futuramente em um segundo passo, a funcionalização com AF e MTX em aplicações como vetorização ativas em sistemas como “drug-delivery” e a posteriori, em ensaios in vitro de radiosensibilização em células de câncer de mama. Materiais e Métodos: Soluções de Fe³⁺ e Fe²⁺ em HCl. Sob refluxo, adicionaram-se H₂O aquecida, NH₄OH (30mL, pH10, 90°C), PGICL em etanol. Agitou-se 45min, purificou-se com imã, lavou-se e armazenou as NPMs. Resultados: A caracterização físico-química das NPMs recobertas com PGlCL-Cys foi realizada por espectroscopia no infravermelho, confirmando a presença de bandas características da cisteína (ligações C-S-C em 715,21 cm⁻¹ e C-N em 1573,1 cm⁻¹) e do recobrimento das NPMs (bandas de deformação angular da ligação Fe- O em 635,63 cm⁻¹ e ∼590 cm⁻¹, correspondentes aos sítios octaédricos e tetraédricos da magnetita, respectivamente). A Microscopia Eletrônica de Transmissão (MET) revelou que as NPMs de Fe3O4@PGlCL-Cys possuem um diâmetro médio de 11,44 nm e exibem comportamento superparamagnético. Conclusão: Conclui-se que o método de coprecipitação e a síntese do copoliéster modificado com cisteína (PGlCL-Cys) foi eficaz, produzindo NPMs estáveis e monodispersas de modo que serão realizados futuramente outras caracterizações físico-químcias para avançar os estudos em ensaios biológicos in vitro para citotoxicidade e biocompatibilidade a fim de serem aplicadas no diagnóstico e tratamento de câncer de mama.

Marzena Zielińska, Alicja Bartkowska-Śniatkowska, Magdalena Mierzewska-Schmidt et al.

The anaesthesia of a young child under 3 years of age is a challenge for every anaesthetist. The peculiarities of this group of patients, particularly neonates and infants, resulting primarily from differences in both physiology, anatomy and the immaturity of individual organs which translate into different pharmacokinetics and pharmacodynamics of the drugs used in anaesthesiology, underlie the significantly more frequently recorded critical events during anaesthesia compared with the adult patient population. Concerned about the safety of children undergoing anaesthesia and aiming to ensure the highest possible quality and uniform standard of anaesthetic services, the Expert Panel of the Section of Paediatric Anaesthesiology and Intensive Care has prepared a Section position paper on anaesthesia in children under 3 years of age.

Anna Carolyna Gianlorenço, Paulo Eduardo Portes Teixeira, Valton Costa et al.

<b>Background/Objectives:</b> Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. <b>Methods:</b> Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. <b>Results:</b> When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R² = 0.56, <i>p</i> = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R² = 0.56, <i>p</i> = 0.01). <b>Conclusions:</b> Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies.

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.

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.

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.

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.

Bethany McDonagh, Jin-Song von Storch, Emanuela Clementi et al.

The generation and propagation sites of internal tides in the Mediterranean Sea are mapped through a comprehensive high-resolution numerical study. Two ocean general circulation models were used for this: NEMO v3.6, and ICON-O, both hydrostatic ocean models based on primitive equations with Boussinesq approximation, where NEMO is a regional Mediterranean Sea model with an Atlantic box, and ICON a global model. Internal tides are widespread in the Mediterranean Sea. The primary generation sites: the Gibraltar Strait, Sicily Strait/Malta Bank, and Hellenic Arc, are mapped through analysis of the tidal barotropic to baroclinic energy conversion. Semidiurnal internal tides can propagate for hundreds of kilometres from these generation sites into the Algerian Sea, Tyrrhenian Sea, and Ionian Sea respectively. Diurnal internal tides remain trapped along the bathymetry, and are generated in the central Mediterranean Sea and southeastern coasts of the basin. The total energy used for internal tide generation in the Mediterranean Sea is 2.89 GW in NEMO and 1.36 GW in ICON. Wavelengths of the first baroclinic modes of the M2 tide are calculated in various regions of the Mediterranean Sea where internal tides are propagating, comparing model outputs to a theory-based calculation. The models are also intercompared to investigate the differences between them in their representation of internal tides.

Min Zhang, Ye Bai, Yutong Wang et al.

BackgroundGeneral obesity is a well-established risk factor for gallstone disease (GSD), but whether central obesity contributes additional independent risk remains controversial. We aimed to comprehensively clarify the effect of body fat distribution on GSD.MethodsWe first investigated the observational association of central adiposity, characterized by waist-to-hip ratio (WHR), with GSD risk using data from UK Biobank (N=472,050). We then explored the genetic relationship using summary statistics from the largest genome-wide association study of GSD (ncase=43,639, ncontrol=506,798) as well as WHR, with and without adjusting for body mass index (BMI) (WHR: n=697,734; WHRadjBMI: n=694,649).ResultsObservational analysis demonstrated an increased risk of GSD with one unit increase in WHR (HR=1.18, 95%CI=1.14-1.21). A positive WHR-GSD genetic correlation (rg =0.41, P=1.42×10-52) was observed, driven by yet independent of BMI (WHRadjBMI: rg =0.19, P=6.89×10-16). Cross-trait meta-analysis identified four novel pleiotropic loci underlying WHR and GSD with biological mechanisms outside of BMI. Mendelian randomization confirmed a robust WHR-GSD causal relationship (OR=1.50, 95%CI=1.35-1.65) which attenuated yet remained significant after adjusting for BMI (OR=1.17, 95%CI=1.09-1.26). Furthermore, observational analysis confirmed a positive association between general obesity and GSD, corroborated by a shared genetic basis (rg =0.40, P=2.16×10-43), multiple novel pleiotropic loci (N=11) and a causal relationship (OR=1.67, 95%CI=1.56-1.78).ConclusionBoth observational and genetic analyses consistently provide evidence on an association of central obesity with an increased risk of GSD, independent of general obesity. Our work highlights the need of considering both general and central obesity in the clinical management of GSD.

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.

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.

Deng-Ho Yang, Hsiang-Cheng Chen, James Cheng-Chung Wei

Abstract Background Gouty arthritis (GA) is a chronic systemic disease with recurrent acute monoarthritis. In a previous study, a higher incidence of acute flares was observed during the initial marked decrease in serum urate level. Our study evaluated the effect of early urate-lowering therapy in patients with acute GA flares. Methods This study included 40 patients with acute GA; of them, 20 received colchicine 0.5 mg colchicine twice daily, while 20 received probenecid 500 mg and colchicine 0.5 mg twice daily. We evaluated GA severity and laboratory data for 2 weeks after the initial therapy. Medians and interquartile ranges (IQRs) were calculated to evaluate clinical presentations between these two groups. Results Rapidly decreasing median serum uric acid levels was found in the patients treated with probenecid and colchicine compared with the patients treated with colchicine alone on day 8 (− 1.9 [IQR, − 3.7 to 0] vs 0.8 [IQR, − 0.1–2.2]; P < 0.001). However, the median decrease in visual analog scale score did not differ significantly between the two groups (− 5.5 [IQR, − 8.0 to − 3.0] vs − 3.5 [IQR, − 5.9 to − 2.0]; P = 0.080). Conclusion No significant increase was noted in acute gout flare severity or duration among GA patients treated with early aggressive control of hyperuricemia using probenecid plus colchicine.

M. R. Mohideen

No abstract available

Carl J. Lavie, Cemal Ozemek, Leonard A. Kaminsky