Hasil untuk "Internal 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.

Leonie Wagner, Jan Klocke, Philipp Enghard

Jiacheng Tang, Nankai Wu, Fan Gao et al.

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

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

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

Anbang Du, Michael Head, Markus Brede

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

Menghua Xia, Reimund Bayerlein, Yanis Chemli et al.

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

Mohamed Sedky, Sherif Abd El Aziz, Shaaban Abd Elmoneum et al.

Background: Coronavirus disease 2019 (COVID-19) is firstly reported in Wuhan, China. Then, it was quickly spread and becomes an epidemic. It is due to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is highly transmissible with a great risk of mortality. Patients with diabetes mellitus (DM) are more prone to infectious agents like SARS-COV-2. Aim: The aim of the study is to evaluate the relationship between DM and COVID-19 infection regarding to its severity, mortality, rate of admission, complications, and prognosis.Patients and methods: A cross sectional study was performed between April 2021 and September 2021 and included 75 patients divided into two groups: Group A (COVID-19 patients with diabetes: n= 25), Group B (COVID-19 patients who developed diabetes: n= 25) and Group C (COVID-19 patients without diabetes: n= 25). Demographics, clinical, laboratory, radiologic, management, complications, and clinical outcomes data were collected and compared between the groups.Results: Patients with diabetes had a higher complication rate, like respiratory failure, acute cardiac injury. The respiratory failure did not significantly different between groups (it was 20%, 28% and 12% in groups A, B and C, respectively, P = .368). However, acute cardiac injury had been significantly increased in groups A than group B and in A and B than group C. (It was 44%, 20% and 8%, in groups A, B and C, respectively, P= 0.01). The mortality rate was also significantly higher among the A and B than C group (56%, 40% vs 8%, P=0.001).Conclusion: Diabetes is an independent risk factor for the prognosis of COVID-19. Diabetic patients should be intensely monitored during treatment, especially those who require insulin therapy.Background: Coronavirus disease 2019 [COVID-19] was first reported in Wuhan, China. It then rapidly spread and became a global epidemic due to infection by severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]. COVID-19 is highly transmissible with a high risk of mortality. Patients with diabetes mellitus [DM] are more susceptible to infectious agents like SARS-CoV-2.Aim of the work: The aim of the study was to evaluate the relationship between DM and COVID-19 infection regarding severity, mortality, admission rate, complications, and prognosis.Patients and Methods: A cross-sectional study was performed between April 2021 and September 2021. It included 75 patients divided into three groups: Group A [COVID-19 patients with diabetes, n=25], Group B [COVID-19 patients who developed diabetes, n=25] and Group C [COVID-19 patients without diabetes, n=25]. Demographic, clinical, laboratory, radiologic, management, complication, and clinical outcome data were collected and compared between the groups.Results: Patients with diabetes had a higher rate of complications like respiratory failure and acute cardiac injury. Respiratory failure was not significantly different between groups [20%, 28% and 12% in groups A, B and C respectively, P=0.368]. However, acute cardiac injury was significantly higher in groups A than B and in A and B than C [[44%, 20% and 8% respectively, P=0.01]. The mortality rate was also significantly higher among groups A and B than C [56%, 40% vs 8%, P=0.001].Conclusion: Diabetes is an independent risk factor for COVID-19 prognosis. Diabetic patients should be closely monitored during treatment, especially those requiring insulin therapy.

Tanawin Sakarin, Tippawan Liabsuetrakul

Abstract Objective To assess the dispositions, management, and clinical outcomes of TIAMS patients in ED to improve the quality of management in ED. Material and Method A descriptive retrospective study was conducted in ED patients aged >18 years diagnosed with TIAMS in the ED from 1 January 2018, to 31 January 2019. Data regarding terms of clinical presentation, examination, management, disposition, and adverse events were collected. Results Three hundred and sixty‐three TIAMS patients were enrolled in the study. Majority of the patients aged <45 years were admitted or referred (15.4%). The highest proportion of patients whose onset times from the last normal were less than 4.5 h were admitted to the EDOU (55.6%), while all patients whose onset times from the last normal were more than 48 h were discharged. Patients with abnormal cerebellar signs or atrial fibrillation were less likely to be discharged from the hospital. Patients with lower National Institutes of Health Stroke Scale (NIHSS) and ABCD2 scores tended to be discharged. Conclusion Among TIAMS patients, age, symptom onset, presence of atrial fibrillation, positive cerebellar signs, and severity scores influenced the disposition. There was no difference in adverse events among disposition groups.

Md. Makshuder Rahman Zim, Nurnabi Ahmed, Mostak Ahmed et al.

Bovine anaplasmosis is an infectious, tick-borne disease caused by Anaplasma species, which is accountable for huge economic loss in dairy industry. This study was aimed to determine the seroprevalence of bovine anaplasmosis on randomly selected 61 commercial dairy farms in 3 intensive regions of Bangladesh. A total of 1472 sera were analysed using VMRD Anaplasma Antibody Test Kit cELISA v2 for the presence of Anaplasma-specific antibodies. The highest regional seroprevalence of Anaplasma was 45.93% in individual level and 74.4% in herd level recorded in the southeast region, whereas it was 48.8% in individual level and 83.3% in herd level in Khagrachari and Sherpur districts, indicating an emerging state of the disease. The herd size and type in herd level and regions, districts, sex, age and breed in individual level were significantly (P ≤ 0.05) associated with anaplasmosis. Multivariate logistic regression analysis showed that cattle aged >1 year had 1.86 times higher odds compared to cattle younger than 1 year. Dairy cows had the highest odds (2.25) of anaplasmosis, followed by dairy heifers (1.68), compared to bulls. Compared to herd sizes of <4, the odds of Anaplasma infection were 11.3 and 7.45 times greater in herd sizes of >28 and 4–28. Crossbred cattle had 2.4 times higher odds of anaplasmosis compared to indigenous cattle. This first seroprevalence study signifies the widespread presence and underscores the importance of monitoring and managing anaplasmosis to safeguard cattle health in Bangladesh. Study on the molecular epidemiology and genetic diversity of Anaplasma among cattle populations should be prioritized.

Chenlong Yang, Xiaohui Lou, Xiaohui Lou et al.

ObjectiveThis study aimed to develop an arbitrary-dimensional nerve root reconstruction magnetic resonance imaging (ANRR-MRI) technique for identifying the leakage orificium of sacral meningeal cysts (SMCs) without spinal nerve root fibres (SNRFs).MethodsThis prospective study enrolled 40 consecutive patients with SMCs without SNRFs between March 2021 and March 2022. Magnetic resonance neural reconstruction sequences were performed for preoperative evaluation. The cyst and the cyst-dura intersection planes were initially identified based on the original thin-slice axial T2-weighted images. Sagittal and coronal images were then reconstructed by setting each intersecting plane as the centre. Then, three-dimensional reconstruction was performed, focusing on the suspected leakage point of the cyst. Based on the identified leakage location and size of the SMC, individual surgical plans were formulated.ResultsThis cohort included 30 females and 10 males, with an average age of 42.6 ± 12.2 years (range, 17–66 years). The leakage orificium was located at the rostral pole of the cyst in 23 patients, at the body region of the cyst in 12 patients, and at the caudal pole in 5 patients. The maximum diameter of the cysts ranged from 2 cm to 11 cm (average, 5.2 ± 1.9 cm). The leakage orificium was clearly identified in all patients and was ligated microscopically through a 4 cm minimally invasive incision. Postoperative imaging showed that the cysts had disappeared.ConclusionANRR-MRI is an accurate and efficient approach for identifying leakage orificium, facilitating the precise diagnosis and surgical treatment of SMCs without SNRFs.

Vadim K. Weinstein, Tamara Alshammari, Kalle G. Timperi et al.

When designing a robot's internal system, one often makes assumptions about the structure of the intended environment of the robot. One may even assign meaning to various internal components of the robot in terms of expected environmental correlates. In this paper we want to make the distinction between robot's internal and external worlds clear-cut. Can the robot learn about its environment, relying only on internally available information, including the sensor data? Are there mathematical conditions on the internal robot system which can be internally verified and make the robot's internal system mirror the structure of the environment? We prove that sufficiency is such a mathematical principle, and mathematically describe the emergence of the robot's internal structure isomorphic or bisimulation equivalent to that of the environment. A connection to the free-energy principle is established, when sufficiency is interpreted as a limit case of surprise minimization. As such, we show that surprise minimization leads to having an internal model isomorphic to the environment. This also parallels the Good Regulator Principle which states that controlling a system sufficiently well means having a model of it. Unlike the mentioned theories, ours is discrete, and non-probabilistic.

Aaron L. Sarvet, Mats J. Stensrud

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

Demi Brownlie, Andreas von Kries, Giampiero Valenzano et al.

Lung cancer is a leading cause of cancer-related death worldwide. Despite recent advances in tissue immunology, little is known about the spatial distribution of tissue-resident lymphocyte subsets in lung tumors. Using high-parameter flow cytometry, we identified an accumulation of tissue-resident lymphocytes including tissue-resident NK (trNK) cells and CD8+ tissue-resident memory T (TRM) cells toward the center of human non-small cell lung carcinomas (NSCLC). Chemokine receptor expression patterns indicated different modes of tumor-infiltration and/or residency between trNK cells and CD8+ TRM cells. In contrast to CD8+ TRM cells, trNK cells and ILCs generally expressed low levels of immune checkpoint receptors independent of location in the tumor. Additionally, granzyme expression in trNK cells and CD8+ TRM cells was highest in the tumor center, and intratumoral CD49a+CD16− NK cells were functional and responded stronger to target cell stimulation than their CD49a− counterparts, indicating functional relevance of trNK cells in lung tumors.In summary, the present spatial mapping of lymphocyte subsets in human NSCLC provides novel insights into the composition and functionality of tissue-resident immune cells, suggesting a role for trNK cells and CD8+ TRM cells in lung tumors and their potential relevance for future therapeutic approaches.

Marcin Nowakowski

In both quantum mechanics and relativity theory, the concept of the observer plays a critical role. However, there is no consensus on the definition of observer in these theories. Following Einstein's thought experiments, one could ask: What would it look like to sit inside a photon or to be a photon? And what type of observer could represent this more global perspective of the photon's interior? To address these questions, we introduce the concepts of internal and external observers with a focus on their relationship in quantum theory and relativity theory. The internal observer, associated with the internal observables super-algebra, glues the external interactions. Drawing inspiration from the advancements in abstract algebraic topology, we propose mathematical representation of the internal observer. We also outline principles for ensuring the consistency of observers in terms of information theory. It becomes evident, through the analysis of the introduced hierarchy of observers, that entanglement is a primitive of space-time causal relationships. While external observers must abide by the relativistic causality linked with the no-signaling principle in quantum mechanics, the internal observer is inherently non-local and may be acausal. However, its consistency is maintained through the formulation of the self-consistency principle. One of the goals of this paper is to construct the representation of the internal observer from the local external algebra of observables, which can be associated with external observers. Additionally, we demonstrate how the concepts of internal and external observers can be applied in the fields of quantum information theory, algebraic quantum field theory, and loop quantum gravity. The concept of internal observer seems to be also fundamental for further development of quantum gravity.

Thomas F. Heston

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

Martina Stojić

We introduce a natural generalization of the definition of a symmetric Hopf algebroid, internal to any symmetric monoidal category with coequalizers that commute with the monoidal product. Motivation for this is the study of Heisenberg doubles of countably dimensional Hopf algebras $A$ as internal Hopf algebroids over a (noncommutative) base $A$ in the category $\mathrm{indproVect}$ of filtered cofiltered vector spaces introduced by the author. One example of such Heisenberg double is internal Hopf algebroid $U(\mathfrak{g}) \sharp U(\mathfrak{g})^*$ over universal enveloping algebra $U(\mathfrak{g})$ of a finite-dimesional Lie algebra $\mathfrak{g}$ that is a properly internalized version of a completed Hopf algebroid previously studied as a Lie algebra type noncommutative phase space.

Omayma O Abdelaleem, Olfat G. Shaker, Mohamed M. Mohamed et al.

Background: Ischemic stroke is one of the serious complications of diabetes. Non-coding RNAs are established as promising biomarkers for diabetes and its complications. The present research investigated the expression profiles of serum TUG1, LINC00657, miR-9, and miR-106a in diabetic patients with and without stroke.Methods: A total of 75 diabetic patients without stroke, 77 patients with stroke, and 71 healthy controls were recruited in the current study. The serum expression levels of TUG1, LINC00657, miR-9, and miR-106a were assessed using quantitative real-time polymerase chain reaction assays.Results: We observed significant high expression levels of LINC00657 and miR-9 in the serum of diabetic patients without stroke compared to control participants. At the same time, we found marked increases of serum TUG1, LINC00657, and miR-9 and a marked decrease of serum miR-106a in diabetic patients who had stroke relative to those without stroke. Also, we revealed positive correlations between each of TUG1, LINC00657, and miR-9 and the National Institutes of Health Stroke Scale (NIHSS). However, there was a negative correlation between miR-106a and NIHSS. Finally, we demonstrated a negative correlation between LINC00657 and miR-106a in diabetic patients with stroke.Conclusion: Serum non-coding RNAs, TUG1, LINC00657, miR-9, and miR-106a displayed potential as novel molecular biomarkers for diabetes complicated with stroke, suggesting that they might be new therapeutic targets for the treatment of diabetic patients with stroke.

Arman Rahmim, Tyler J. Bradshaw, Irène Buvat et al.

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

M. Z. Naser

This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted approaches to causality: Humes regularity, counterfactual, manipulation, and mechanisms. This short review is motivated by presenting simplified views and definitions and then supplements them with examples from various fields, including economics, education, medicine, politics, physics, and engineering. It is the hope that this short review comes in handy for new and interested readers with little knowledge of causality and causal inference.