Hasil untuk "Medicine (General)"

Antonio Alcántara Montero

Jianmin Li, Ying Chang, Su-Kit Tang et al.

Background: Retrieval augmented generation (RAG) technology can empower large language models (LLMs) to generate more accurate, professional, and timely responses without fine tuning. However, due to the complex reasoning processes and substantial individual differences involved in traditional Chinese medicine (TCM) clinical diagnosis and treatment, traditional RAG methods often exhibit poor performance in this domain. Objective: To address the limitations of conventional RAG approaches in TCM applications, this study aims to develop an improved RAG framework tailored to the characteristics of TCM reasoning. Methods: We developed TCM-DiffRAG, an innovative RAG framework that integrates knowledge graphs (KG) with chains of thought (CoT). TCM-DiffRAG was evaluated on three distinctive TCM test datasets. Results: The experimental results demonstrated that TCM-DiffRAG achieved significant performance improvements over native LLMs. For example, the qwen-plus model achieved scores of 0.927, 0.361, and 0.038, which were significantly enhanced to 0.952, 0.788, and 0.356 with TCM-DiffRAG. The improvements were even more pronounced for non-Chinese LLMs. Additionally, TCM-DiffRAG outperformed directly supervised fine-tuned (SFT) LLMs and other benchmark RAG methods. Conclusions: TCM-DiffRAG shows that integrating structured TCM knowledge graphs with Chain of Thought based reasoning substantially improves performance in individualized diagnostic tasks. The joint use of universal and personalized knowledge graphs enables effective alignment between general knowledge and clinical reasoning. These results highlight the potential of reasoning-aware RAG frameworks for advancing LLM applications in traditional Chinese 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.

Ana Carolina Pismel Lobo, Gabriela Cristina Santin, Elen de Souza Tolentino

Acute herpetic gingivostomatitis (AHGS) is the oral manifestation of HVS-1 primary infection. Despite being a self-limiting infection, AHGS can progress to severe complications. Dentists should be prepared to correctly diagnose and treat the disease. Therefore, the purpose of this study is to assess knowledge, attitude, and practice (KAP) of dentists regarding acute herpetic gingivostomatitis (AHGS) among children. A cross-sectional and descriptive study was carried out through a KAP Survey of 416 Brazilian dentists. Descriptive analyzes with absolute and relative frequencies were performed and possible associations between socio-demographic variables with the KAP questions were investigated using Chi-square and Fisher's exact tests (significance level 5%). Results revealed high knowledge scores among 68% of the dentists. The worst knowledge scores were found for AHGS complications. High scores were only associated with degree of education (p<0.005). For the treatment of AHGS, the responses were variable and signaled possible overtreatment in practice. Therapeutic possibilities beyond acyclovir are still lacking. This study highlights the importance of providing continuous education and integrating the practice of oral pathology into the practice of dentistry. It can help to increase knowledge, avoid overtreatment, and stimulate decision-making by the dentist in cases of complications.

Fahimeh Pakravan, Ali Yousefian Najafabadi, Zohreh Meshkati et al.

Abstract Introduction Injuries sustained during physical activities are a common concern among athletes, with dental trauma representing a significant yet often under-addressed component. Timely and appropriate intervention is critical to successful outcomes, making the awareness and performance of first-line responders—particularly physical education graduates—an essential focus. This study evaluates their knowledge and practices regarding emergency management of dental trauma. Materials and methods This cross-sectional descriptive study assessed 206 physical education graduates in Isfahan between 2024 and 2025. Data were collected using a researcher-designed questionnaire comprising 12 targeted items, validated with a content validity ratio (CVR > 0.51) and confirmed reliability (ICC = 0.884). Statistical analysis was performed using SPSS (Version 26), including descriptive measures (mean, standard deviation, frequency) and inferential tests (independent t-tests, ANOVA). Results Participants demonstrated moderate proficiency in dental trauma awareness and self-reported practical knowledge, with an overall mean score of 9.74 ± 4.80 (scale: 0–20). Awareness and performance scores were closely aligned (9.08 ± 4.99 and 9.07 ± 5.39, respectively). Significant predictors of higher competency included academic achievement (P = 0.023), direct exposure to dental trauma (P = 0.001), engagement in high-contact sports such as martial arts (P = 0.016), and formal training in trauma management (P = 0.012). Conversely, gender, general athletic history, and school-level sports involvement were not statistically associated with performance outcomes. Conclusion Most PE graduates demonstrated limited preparedness for managing dental trauma. Academic progression, trauma exposure, and targeted training were associated with better awareness and applied knowledge. These findings support the integration of oral emergency response modules into sports education curricula and certification programs—promoting health literacy and alignment with WHO health promotion objectives.

Ladi Alik Kumar, K Sunand, Jitendra Debata et al.

Cancer is a disorder characterized by the abnormal growth of cells that increases uncontrollably over an extended period of time. Treating cancerous brain tumors remains among the most challenging tasks for researchers, as brain tumors are among the hardest cancers to treat. Additionally, the condition often worsens because of the delayed diagnosis caused by the absence of early symptoms. The use of conventional treatment methods, such as radiation, chemotherapy, and surgery, continues to be highly limited. The low solubility, narrow therapeutic index, and limited ability to traverse the blood–brain barrier of most anticancer drugs result in limited therapeutic efficacy. In an attempt to overcome these predicaments, formulation scientists have been considering nanotechnology-based therapeutic solutions, particularly given the increasing rates of brain cancers that have low survivability and the drawbacks of the existing treatment methods. Different nanoplatforms, such as polymeric nanoparticles, nanoliposomes, dendrimers, carbon nanotubes, and magnetic nanoparticles, have been explored. Research has indicated that such nanocarriers can increase the delivery of drugs to cells in brain tumors with a minimal off-target distribution, resulting in minimal adverse effects and optimal treatment. This review presents a summary of nanocarrier-based drug delivery systems that have been reported in recent years for the treatment of brain tumors. In addition, it explains the existing difficulties with the clinical implementation of nanodrug carriers and the perspectives of this field.

Hisaaki Shinkai, Masao Takamoto, Hidetoshi Katori

Optical lattice clocks (OLCs) enable us to measure time and frequency with a fractional uncertainty at $10^{-18}$ level, which is 2 orders of magnitude better than Cs clocks. In this article, after briefly reviewing OLCs and the history of testing the fundamental principles of general relativity, we report our experiments of measuring the gravitational redshift between RIKEN and The University of Tokyo, and at Tokyo Skytree using transportable OLCs. We also discuss a couple of future applications of OLCs, such as detecting gravitational waves in space and relativistic geodesy. The possibility of testing second-order parametrized post-Newtonian potential around the Earth is also mentioned.

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.

Sutha Paramasivam, Sathishkumar Chidambaram, Palanisamy Karumalaiyan et al.

<b>Background:</b> Green synthesized nanoparticles (NPs) have gained increasing popularity in recent times due to their broad spectrum of antimicrobial properties. This study aimed to develop a phytofabrication approach for producing cuprous (Cu₂O) and cupric oxide (CuO) NPs using a simple, non-hazardous process and to examine their antimicrobial properties. <b>Methods:</b> The synthesis employed <i>Bidens pilosa</i> plant extract as a natural reducing and stabilizing agent, alongside copper chloride dihydrate as the precursor. The biosynthesized NPs were characterized through various techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared (FT-IR) spectroscopy, ultraviolet–visible (UV-Vis) spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). <b>Results:</b> XRD analysis confirmed that the synthesized CuO and Cu₂O NPs exhibited a high degree of crystallinity, with crystal structures corresponding to monoclinic and face-centered cubic systems. SEM images revealed that the NPs displayed distinct spherical and sponge-like morphologies. EDS analysis further validated the purity of the synthesized CuO NPs. The antimicrobial activity of the CuO and Cu₂O NPs was tested against various pathogenic bacterial strains, including <i>Staphylococcus aureus</i>, <i>Pseudomonas aeruginosa</i>, <i>Escherichia coli</i>, and <i>Bacillus cereus</i>, with the minimum inhibitory concentration (MIC) used to gauge their effectiveness. <b>Conclusions:</b> The results showed that the phytosynthesized NPs had promising antibacterial properties, particularly the Cu₂O NPs, which, with a larger crystal size of 68.19 nm, demonstrated significant inhibitory effects across all tested bacterial species. These findings suggest the potential of CuO and Cu₂O NPs as effective antimicrobial agents produced via green synthesis.

Paris Kantaras, Niki Mourouti, Theodora Mouratidou et al.

In total, 3274 adults (65.2% females) from six European countries were included in this cross-sectional analysis using data from the baseline assessment of the Feel4Diabetes study. Anthropometric, sociodemographic, dietary and behavioral data were assessed, and the existence of metabolic syndrome (MetS) was recorded. Beverage consumption patterns (BCPs) were derived via principal component analysis. Three BCPs were derived explaining 39.5% of the total variation. BCP1 was labeled as “Alcoholic beverage pattern”, which loaded heavily on high consumption of beer/cider, wine and other spirits; BCP2 was labeled as “High in sugars beverage pattern” that was mainly characterized by high consumption of soft drinks with sugar, juice containing sugar and low consumption of water; and BCP3 was labeled as “Healthy beverage pattern” that was mainly characterized by high consumption of water, tea, fruit juice freshly squeezed or prepacked without sugar and low consumption of soft drinks without sugar. After adjusting for various confounders, BCP2 was positively associated with elevated triglycerides (<i>p</i> = 0.001), elevated blood pressure (<i>p</i> = 0.001) elevated fasting glucose (<i>p</i> = 0.008) and the existence of MetS (<i>p</i> = 0.006), while BCP1 was inversely associated with reduced HDL-C (<i>p</i> = 0.005) and BCP3 was inversely associated with elevated blood pressure (<i>p</i> = 0.047). The establishment of policy actions as well as public health nutritional education can contribute to the promotion of a healthy beverage consumption.

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.

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.

Ahmed M Salih

Data pre-processing is a significant step in machine learning to improve the performance of the model and decreases the running time. This might include dealing with missing values, outliers detection and removing, data augmentation, dimensionality reduction, data normalization and handling the impact of confounding variables. Although it is found the steps improve the accuracy of the model, but they might hinder the explainability of the model if they are not carefully considered especially in medicine. They might block new findings when missing values and outliers removal are implemented inappropriately. In addition, they might make the model unfair against all the groups in the model when making the decision. Moreover, they turn the features into unitless and clinically meaningless and consequently not explainable. This paper discusses the common steps of the data preprocessing in machine learning and their impacts on the explainability and interpretability of the model. Finally, the paper discusses some possible solutions that improve the performance of the model while not decreasing its explainability.

Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff

Background: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support. Methods: We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care. Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration. Results: The diagnostic model achieves area under the receiver operating curve (AUROC) scores above 0.8 for 609 out of 1,428 conditions, covering both cardiac (e.g., myocardial infarction) and non-cardiac (e.g., renal disease, diabetes) diagnoses. The deterioration model attains AUROC scores above 0.8 for 14 out of 15 targets, accurately predicting critical events such as cardiac arrest, mechanical ventilation, ICU admission, and mortality. Conclusions: Our study highlights the positive impact of incorporating raw waveform data into decision support models, improving predictive performance. By introducing a unique, publicly available dataset and baseline models, we provide a foundation for measurable progress in AI-driven decision support for emergency care.

Masaki Fujisawa, Yoshihiro Haga, Masahiro Sota et al.

The International Commission on Radiological Protection has lowered the annual equivalent eye-lens dose to 20 mSv. Although occupational exposure can be high in nuclear medicine (NM) departments, few studies have been conducted regarding eye-lens exposure among NM staff. This study aimed to estimate the annual lens doses of staff in an NM department and identify factors contributing to lens exposure. Four nurses and six radiographers performing positron emission tomography (PET) examinations and four radiographers performing radioisotope (RI) examinations (excluding PET) were recruited for this study. A lens dosimeter was attached near the left eye to measure the 3-mm-dose equivalent; a personal dosimeter was attached to the left side of the neck to measure the 1-cm- and 70-µm-dose equivalents. Measurements were acquired over six months, and the cumulative lens dose was doubled to derive the annual dose. Correlations between the lens and personal-dosimeter doses, between the lens dose and the numbers of procedures, and between the lens dose and the amounts of PET drugs (radiopharmaceuticals) injected were examined. Wilcoxon’s signed-rank test was used to compare lens and personal-dosimeter doses. The estimated annual doses were 0.93 ± 0.13 mSv for PET nurses, 0.71 ± 0.41 mSv for PET radiographers, and 1.10 ± 0.53 mSv for RI radiographers. For PET nurses, but not for PET or RI radiographers, there was a positive correlation between the numbers of procedures and lens doses and between amounts injected and lens doses. There was a significant difference between the lens and personal-dosimeter doses of PET nurses. The use of protective measures, such as shielding, should prevent NM staff from receiving lens doses > 20 mSv/year. However, depending on the height of the protective shield, PET nurses may be unable to assess the lens dose accurately using personal dosimeters.

Rola Ajjawi, Margaret Bearman, Elizabeth Molloy et al.

IntroductionUnderperformance in clinical environments can be costly and emotional for all stakeholders. Feedback is an important pedagogical strategy for working with underperformance – both formal and informal strategies can make a difference. Feedback is a typical feature of remediation programs, and yet there is little consensus on how feedback should unfold in the context of underperformance.MethodsThis narrative review synthesises literature at the intersections of feedback and underperformance in clinical environments where service, learning and safety need to be considered. We do so with a critical eye towards generating insights for working with underperformance in the clinical environment.Synthesis and discussionThere are compounding and multi-level factors that contribute to underperformance and subsequent failure. This complexity overwrites simplistic notions of ‘earned’ failure through individual traits and deficit. Working with such complexity requires feedback that goes beyond educator input or ‘telling’. When we shift beyond feedback as input to process, we recognise that these processes are fundamentally relational, where trust and safety are necessary for trainees to share their weaknesses and doubts. Emotions are always present and they signal action. Feedback literacy might help us consider how to engage trainees with feedback so that they take an active (autonomous) role in developing their evaluative judgements. Finally, feedback cultures can be influential and take effort to shift if at all. A key mechanism running through all these considerations of feedback is enabling internal motivation, and creating conditions for trainees to feel relatedness, competence and autonomy. Broadening our perceptions of feedback, beyond telling, might help create environments for learning to flourish.

Anna Rebeka Kovács, Anita Sulina, Kincső Sára Kovács et al.

(1) The degree of cytoreduction achieved during primary debulking surgery (PDS) is an important prognostic factor for the survival of patients with epithelial ovarian cancer (EOC). Our aim was to investigate the prognostic value of preoperative laboratory parameters for the outcome of PDS. (2) We analyzed the preoperative laboratory parameters of 150 serous EOC patients who underwent PDS between 2006 and 2013. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values of the variables for predicting the PDS outcome. We used binary logistic regression to examine the independent predictive value of the factors for incomplete cytoreduction. (3) Among the parameters, we established optimal cut-off values for cancer antigen (Ca)-125, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) to predict the outcome of PDS. The results of binary logistic regression showed that stage (FIGO III-IV), MLR (>0.305), and Ca-125 (>169.15 kU/L) were independent significant predictors of the degree of tumor reduction achieved during PDS. (4) In the future, MLR, especially in combination with other parameters, may be useful in determining prognosis and selecting the best treatment option (PDS or neoadjuvant chemotherapy + interval debulking surgery) for ovarian cancer patients.

Lin Xie, Ye Cheng, Wen Du et al.

Abstract Numerous studies have proven the critical role of macrophages in the renal fibrosis process. Notably, G Protein-coupled Estrogen Receptor 1 (GPER1), a novel estrogen receptor, has been shown to play a ubiquitous role in regulating macrophage activities and proinflammatory pathways. However, the precise role of GPER1 in macrophage-mediated renal fibrosis is unknown. In this study, we aimed to investigate the function of macrophage GPER1 in the UUO-induced renal fibrosis model. Compared to vehicle-treated ovariectomized (OVX) female and male unilateral ureteral obstruction (UUO) models, we observed that G-1 (GPER1 agonist)-treated OVX female and male UUO mice had fewer renal fibrotic lesions and less M1 and M2 macrophage infiltration in the kidney tissues. Conversely, Gper1 deletion in male UUO mice accelerated renal fibrosis and increased inflammation. In vitro studies also revealed that GPER1 activation reduced M0 macrophage polarization towards M1 or M2 phenotypes. The RNA-sequencing analysis and immunoblotting indicated that GPER1 activation was primarily involved in downregulating immune pathways activation and inactivating MAPK pathways. Tubular epithelial cells co-cultured with G-1-pretreated M1 macrophages exhibited fewer injuries and immune activation. In addition, fibroblasts co-cultured with G-1-pretreated M2 macrophages showed downregulated extracellular matrix expression. Overall, this is the first study to demonstrate the effect of GPER1 on macrophage-mediated renal fibrosis via inhibition of M1 and M2 macrophage activation. These findings indicate that GPER1 may be a promising therapeutic target for treating renal fibrosis.

Evgeny S. Saveliev, Mihaela van der Schaar

TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports data in time series, static, and eventmodalities and provides an interface for prediction, causal inference, and time-to-event analysis, as well as common preprocessing utilities and model interpretability methods. The library aims to facilitate innovation in the medical ML space by offering a standardized temporal setting toolkit for model development, prototyping and benchmarking, bridging the gaps in the ML research, healthcare professional, medical/pharmacological industry, and data science communities. TemporAI is available on GitHub (https://github.com/vanderschaarlab/temporai) and we welcome community engagement through use, feedback, and code contributions.

Dimitrios Psaltis

The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin, and charge of the black hole, gives rise to a variety of phenomena in the motion of particles and photons outside the horizons that have no Newtonian counterparts. Moreover, the Kerr-Newman spacetime remains remarkably resilient to many attempts in modifying the underlying theory of gravity. The monitoring of stellar orbits around supermassive black holes, the detection of gravitational waves from the coalescence of stellar-mass black holes, and the observation of black-hole shadows in images with horizon-scale resolution, all of which have become possible during the last decade, are offering valuable tools in testing quantitatively the predictions of this remarkable solution to Einstein's equations.