Hasil untuk "Internal medicine"

Kei Ito, Akira Endo, Hiromasa Hoshi et al.

ABSTRACT Background Although surgical treatment is the primary measure for patients with perforated peptic ulcer (PPU), nonoperative management (NOM) has become a common alternative. However, risk score models predicting the success of NOM based on the analysis of a large number of patients remain scarce. We developed a clinically applicable scoring system to predict the success of NOM in patients with PPU using data from a large cohort. Method We analyzed data of the Perforated Peptic ulcer Analyzing Project (PPAP), which was a retrospective survey of adult patients with PPU between January 2011 to December 2022. The successful NOM case was defined as patients who survived until hospital discharge without requiring surgery. Factors associated with NOM were identified using a multivariable logistic regression analysis, and a scoring system to predict NOM was developed by weighting these factors based on the regression coefficients. Result Of 702 potentially eligible patients, 584 were treated with NOM, of which 130 patients (22.2%) were treated successfully. Age, sex, body temperature, heart rate, the extent of peritoneal irritation signs, C reactive protein, spread of ascites, and sepsis were included in the final model. Using these variables, we developed the scoring system named PPAP score, which had favorable discriminating ability with the area under receiving operating characteristic curve of 0.799. When the cut‐off was set to 56, the sensitivity and the specificity were 0.738 and 0.722, respectively. Conclusion A predictive scoring model was developed. However, external validation of the model is required to confirm its clinical applicability.

Keshav Jha, Joseph Mayer

Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preoperative planning model has the capability to move in similar ways to the anatomy that is being represented by the model, or reveal additional issues that may arise during the use of a movement mechanism. The 3D-printed models are constructed in a similar manner to their static counterparts; however, in the digital post-processing phase, additional care is needed to ensure the dynamic functionality of the model. Here, we discuss the process of creating a dynamic 3D-printed model and its benefits and uses in modern medicine.

Heming Zhang, Di Huang, Wenyu Li et al.

In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets. Existing pipelines capture only part of these-numerical omics ignore topological context, text-centric LLMs lack quantitative grounded reasoning, and graph-only models underuse node semantics and the generalization of LLMs-limiting mechanistic interpretability. Although Process Reward Models (PRMs) aim to guide reasoning in LLMs, they remain limited by unreliable intermediate evaluation, and vulnerability to reward hacking with computational cost. These gaps motivate integrating quantitative multi-omic signals, topological structure with node annotations, and literature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context. Therefore, we propose GALAX (Graph Augmented LAnguage model with eXplainability), an innovative framework that integrates pretrained Graph Neural Networks (GNNs) into Large Language Models (LLMs) via reinforcement learning guided by a Graph Process Reward Model (GPRM), which generates disease-relevant subgraphs in a step-wise manner initiated by an LLM and iteratively evaluated by a pretrained GNN and schema-based rule check, enabling process-level supervision without explicit labels. As an application, we also introduced Target-QA, a benchmark combining CRISPR-identified targets, multi-omic profiles, and biomedical graph knowledge across diverse cancer cell lines, which enables GNN pretraining for supervising step-wise graph construction and supports long-context reasoning over text-numeric graphs (TNGs), providing a scalable and biologically grounded framework for explainable, reinforcement-guided subgraph reasoning toward reliable and interpretable target discovery in precision medicine.

ChaoBo Zhang, Long Tan

Artificial intelligence technology plays a crucial role in recommending prescriptions for traditional Chinese medicine (TCM). Previous studies have made significant progress by focusing on the symptom-herb relationship in prescriptions. However, several limitations hinder model performance: (i) Insufficient attention to patient-personalized information such as age, BMI, and medical history, which hampers accurate identification of syndrome and reduces efficacy. (ii) The typical long-tailed distribution of herb data introduces training biases and affects generalization ability. (iii) The oversight of the 'monarch, minister, assistant and envoy' compatibility among herbs increases the risk of toxicity or side effects, opposing the 'treatment based on syndrome differentiation' principle in clinical TCM. Therefore, we propose a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph diffusion guidance, namely TCM-HEDPR. Specifically, we pre-train symptom representations using patient-personalized prompt sequences and apply prompt-oriented contrastive learning for data augmentation. Furthermore, we employ a KG-guided homogeneous graph diffusion method integrated with a self-attention mechanism to globally capture the non-linear symptom-herb relationship. Lastly, we design a heterogeneous graph hierarchical network to integrate herbal dispensing relationships with implicit syndromes, guiding the prescription generation process at a fine-grained level and mitigating the long-tailed herb data distribution problem. Extensive experiments on two public datasets and one clinical dataset demonstrate the effectiveness of TCM-HEDPR. In addition, we incorporate insights from modern medicine and network pharmacology to evaluate the recommended prescriptions comprehensively. It can provide a new paradigm for the recommendation of modern TCM.

Nicole DeMarco, Shannon Carpenter, Brian Lee

Zhen Wang, Honglei Guo, Yanqi Song et al.

Lung cancer is the most common malignant tumor in the world. Presently, there are still problems, including a high recurrence rate, resistance, and serious toxic side effects, even if conventional treatments like chemotherapy, radiotherapy, and targeted therapy have somewhat improved patient survival. Even though immune checkpoint inhibitors that target programmed cell death-1/programmed cell death ligand 1 have fundamentally altered the therapeutic paradigm, the core mechanism is strongly linked to tumor immune escape, and some patients continue to have poor response rates or treatment resistance. The mechanisms of immune escape in the immunological microenvironment of lung cancer, involving metabolic reprogramming, overexpression of immune checkpoint molecules, and abnormalities in antigen presentation, are systematically summarized in this review. The article also sums up new therapeutic targets and promising clinical trials. The goal is to provide a solid theoretical foundation for further research into the immune escape mechanism, the creation of new immunotherapeutic targets, and personalized therapeutic strategies.

Konstantin M. Evdokimov, Ekaterina O. Ivanova, Amayak G. Brutyan et al.

Introduction. Functional tremor (FT) is the most common phenotype of functional movement disorders. Electrophysiological assessment is included in the diagnostic criteria for tremor; however, there is currently no consensus criteria for the differential diagnosis of FT. The objective of this study was to evaluate the utility of tremor frequency characteristics derived from accelerometry for the differential diagnosis between FT and organic tremor (OT). Materials and methods. Nineteen patients with FT, 20 patients with essential tremor, and 20 patients with Parkinson's disease were enrolled in the study and underwent electrophysiological examination with a two-channel accelerometer and subsequent data processing. Results. The study results revealed the differences in the frequency peak widths in patients with FT and OT, predominantly while performing a cognitive load task. This criterion showed a high sensitivity (100%) and a high specificity (97.5%) for the diagnosis of FT in the study population. Conclusion. Tremor characteristics recorded during accelerometry combined with cognitive load task can serve as an additional testing aid for differential diagnosis between functional and organic tremor.

Victor Fages, Gregory Baud, Marion Fericot et al.

Obesity is a major public health issue that affects a significant proportion of patients with end-stage renal disease (ESRD). In patients undergoing peritoneal dialysis (PD), obesity complicates treatment by increasing the risk of mechanical complications and infections and reducing the effectiveness of peritoneal exchanges. Furthermore, obesity limits access to kidney transplantation, making weight loss a crucial goal. Bariatric surgery is emerging as an effective strategy for improving metabolic condition and promoting placement on a transplant waiting list. Sleeve gastrectomy (SG) is now the preferred technique for helping obese patients on ESRD lose weight, particularly due to its favorable safety profile, reduced operating time, and absence of intestinal bypass, thus limiting the risk of deficiencies. The available data, although limited to case series and isolated reports, suggest that SG can be performed in PD patients either with early resumption of PD or after a temporary transition to hemodialysis depending on clinical status. Optimized protocols include a gradual resumption of PD at low volumes, minimizing the risk of leakage or infection. Bariatric surgery therefore appears feasible and generally safe in PD patients, provided that a rigorous multidisciplinary assessment and close nutritional monitoring are carried out to prevent malnutrition and sarcopenia. It is a relevant therapeutic option for improving access to kidney transplantation and optimizing the prognosis of obese patients with ESRD. This article was written following a presentation at the Société Francophone de Néphrologie, Dialyse et Transplantation 2025 on the feasibility of bariatric surgery in PD.

Ping Yu, Kaitao Song, Fengchen He et al.

The recently unprecedented advancements in Large Language Models (LLMs) have propelled the medical community by establishing advanced medical-domain models. However, due to the limited collection of medical datasets, there are only a few comprehensive benchmarks available to gauge progress in this area. In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. Specifically, our TCMD collects massive questions across diverse domains with their annotated medical subjects and thus supports us in comprehensively assessing the capability of LLMs in the TCM domain. Extensive evaluation of various general LLMs and medical-domain-specific LLMs is conducted. Moreover, we also analyze the robustness of current LLMs in solving TCM QA tasks by introducing randomness. The inconsistency of the experimental results also reveals the shortcomings of current LLMs in solving QA tasks. We also expect that our dataset can further facilitate the development of LLMs in the TCM area.

Anh Le, Amirreza Hashemi, Mark P. Ottensmeyer et al.

The design of nuclear imaging scanners is crucial for optimizing detection and imaging processes. While advancements have been made in simplistic, symmetrical modalities, current research is progressing towards more intricate structures, however, the widespread adoption of computer-aided design (CAD) tools for modeling and simulation is still limited. This paper introduces FreeCAD and the GDML Workbench as essential tools for designing and testing complex geometries in nuclear imaging modalities. FreeCAD is a parametric 3D CAD modeler, and GDML is an XML-based language for describing complex geometries in simulations. Their integration streamlines the design and simulation of nuclear medicine scanners, including PET and SPECT scanners. The paper demonstrates their application in creating calibration phantoms and conducting simulations with Geant4, showcasing their precision and versatility in generating sophisticated components for nuclear imaging. The integration of these tools is expected to streamline design processes, enhance efficiency, and facilitate widespread application in the nuclear imaging field.

David Wärn

A sheaf of modules on a site is said to be internally projective if sheaf hom with the module preserves epimorphism. In this note, we give an example showing that internally projective sheaves of abelian groups are not in general stable under base change to a slice. This shows that internal projectivity is weaker than projectivity in the internal logic of the topos, as expressed for example in terms of Shulman's stack semantics. The sheaf of groups that we use as a counterexample comes from recent work by Clausen and Scholze on light condensed sets.

Jaan Aru

Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creativity. This paper explores the neurobiological machinery that underlies these internal processes and describes the experiential component of creativity. It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different. The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the integration of knowledge, and the diversity of ideas.

Lingxiao Luo, Bingda Tang, Xuanzhong Chen et al.

Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks demand more versatile approaches. Additionally, while most VLMs process only 2D images, a large portion of medical images are 3D. The lack of medical data further compounds these obstacles. To address these challenges, we present VividMed, a vision language model with versatile visual grounding for medicine. Our model supports generating both semantic segmentation masks and instance-level bounding boxes, and accommodates various imaging modalities, including both 2D and 3D data. We design a three-stage training procedure and an automatic data synthesis pipeline based on open datasets and models. Besides visual grounding tasks, VividMed also excels in other common downstream tasks, including Visual Question Answering (VQA) and report generation. Ablation studies empirically show that the integration of visual grounding ability leads to improved performance on these tasks. Our code is publicly available at https://github.com/function2-llx/MMMM.

Jun-Young Oh, In-Gyu Lee, Tae-Eui Kam et al.

In the cutting-edge domain of medical artificial intelligence (AI), remarkable advances have been achieved in areas such as diagnosis, prediction, and therapeutic interventions. Despite these advances, the technology for image segmentation faces the significant barrier of having to produce extensively annotated datasets. To address this challenge, few-shot segmentation (FSS) has been recognized as one of the innovative solutions. Although most of the FSS research has focused on human health care, its application in veterinary medicine, particularly for pet care, remains largely limited. This study has focused on accurate segmentation of the heart and left atrial enlargement on canine chest radiographs using the proposed deep prototype alignment network (DPANet). The PANet architecture is adopted as the backbone model, and experiments are conducted using various encoders based on VGG-19, ResNet-18, and ResNet-50 to extract features. Experimental results demonstrate that the proposed DPANet achieves the highest performance. In the 2way-1shot scenario, it achieves the highest intersection over union (IoU) value of 0.6966, and in the 2way-5shot scenario, it achieves the highest IoU value of 0.797. The DPANet not only signifies a performance improvement, but also shows an improved training speed in the 2way-5shot scenario. These results highlight our model's exceptional capability as a trailblazing solution for segmenting the heart and left atrial enlargement in veterinary applications through FSS, setting a new benchmark in veterinary AI research, and demonstrating its superior potential to veterinary medicine advances.

G. Ortega Suero, M.J. Abenza Abildúa, C. Serrano Munuera et al.

Introduction: Ataxia and hereditary spastic paraplegia are rare neurodegenerative syndromes. We aimed to determine the prevalence of these disorders in Spain in 2019. Patients and methods: We conducted a cross-sectional, multicentre, retrospective, descriptive study of patients with ataxia and hereditary spastic paraplegia in Spain between March 2018 and December 2019. Results: We gathered data from a total of 1933 patients from 11 autonomous communities, provided by 47 neurologists or geneticists. Mean (SD) age in our sample was 53.64 (20.51) years; 938 patients were men (48.5%) and 995 were women (51.5%). The genetic defect was unidentified in 920 patients (47.6%). A total of 1371 patients (70.9%) had ataxia and 562 (29.1%) had hereditary spastic paraplegia. Prevalence rates for ataxia and hereditary spastic paraplegia were estimated at 5.48 and 2.24 cases per 100 000 population, respectively. The most frequent type of dominant ataxia in our sample was SCA3, and the most frequent recessive ataxia was Friedreich ataxia. The most frequent type of dominant hereditary spastic paraplegia in our sample was SPG4, and the most frequent recessive type was SPG7. Conclusions: In our sample, the estimated prevalence of ataxia and hereditary spastic paraplegia was 7.73 cases per 100 000 population. This rate is similar to those reported for other countries. Genetic diagnosis was not available in 47.6% of cases. Despite these limitations, our study provides useful data for estimating the necessary healthcare resources for these patients, raising awareness of these diseases, determining the most frequent causal mutations for local screening programmes, and promoting the development of clinical trials. Resumen: Introducción: Las ataxias (AT) y paraparesias espásticas hereditarias (PEH) son síndromes neurodegenerativos raros. Nos proponemos conocer la prevalencia de las AT y PEH (APEH) en España en 2019. Pacientes y métodos: Estudio transversal, multicéntrico, descriptivo y retrospectivo de los pacientes con AT y PEH, desde Marzo de 2018 a Diciembre de 2019 en toda España. Resultados: Se obtuvo información de 1.933 pacientes procedentes de 11 Comunidades Autónomas, de 47 neurólogos o genetistas. Edad media: 53,64 años ± 20,51 desviación estándar (DE); 938 varones (48,5%), 995 mujeres (51,1%). En 920 pacientes (47,6%) no se conoce el defecto genético. Por patologías, 1.371 pacientes (70,9%) diagnosticados de AT, 562 diagnosticados de PEH (29,1%). La prevalencia estimada de AT es 5,48/100.000 habitantes, y la de PEH es 2,24 casos/100.000 habitantes. La AT dominante más frecuente es la SCA3. La AT recesiva más frecuente es la ataxia de Friedreich (FRDA). La PEH dominante más frecuente es la SPG4, y la PEH recesiva más frecuente es la SPG7. Conclusiones: La prevalencia estimada de APEH en nuestra serie es de 7,73 casos/100.000 habitantes. Estas frecuencias son similares a las del resto del mundo. En el 47,6% no se ha conseguido un diagnóstico genético. A pesar de las limitaciones, este estudio puede contribuir a estimar los recursos, visibilizar estas enfermedades, detectar las mutaciones más frecuentes para hacer los screenings por comunidades, y favorecer los ensayos clínicos.

Shaorong Zhang, Shaorong Zhang, Qihui Wang et al.

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.

Sutirna Sutirna, Safuri Musa

The objective study is to know students' level of ability, understanding, skills, and attitudes in practice service guidance and counseling in schools. The approach research used is a study survey of guidance and counseling teachers who become tutors in accompaniment student practice guidance and counseling. Instruments in questionnaires closed as many as 25 items with indicator understanding, skills and attitudes students in implementation activity practice guidance and counseling. While processing techniques results survey uses percentages from many answer respondents compared amount whole respondents multiplied by 100%, the results percentage categorized as very good, good, well enough, less well, and very less. Research results conclude that students' level of ability in understanding, skills, and attitudes in implementation service guidance and counseling. The research results are concluded (1) the level of ability to understand guidance and counseling for students who carry out practices in schools is included in the sufficient category (very good 29.17% and good 56.25% ), (2) the level of students' skills in providing guidance and counseling services to students in the aspects of attending, responding, personalizing, and initiating is included in the sufficient category (very good 33.16% and good 56.88%), and (3) the level of ability of students' attitudes in carrying out guidance and counseling services in schools is categorized as sufficient (for very good 51.49% and good 41.96%).  

Álvaro Ozório de Almeida

Comunicação feita à Sociedade Brasileira de Cancerologia e perante ela verbalmente  desenvolvida e comentada pelo autor. Trata dos intervalos de tempo entre as aplicações de raios x nas seções de radioterapia.

Jianzhong Du, Siyang Gao, Chun-Hung Chen

Problem definition: Personalized medicine (PM) seeks the best treatment for each patient among a set of available treatment methods. Since a specific treatment does not work well on all patients, traditionally, the best treatment was selected based on the doctor's personal experience and expertise, which is subject to human errors. In the meantime, stochastic models have been well developed in the literature for a lot of major diseases. This gives rise to a simulation-based solution for PM, which uses the simulation tool to evaluate the performance for pairs of treatment and patient biometric characteristics, and based on that, selects the best treatment for each patient characteristics. Methodology/results: In this research, we extend the ranking and selection (R&S) model in simulation-based decision making to solving PM. The biometric characteristics of a patient is treated as a context for R&S, and we call it contextual ranking and selection (CR&S). We consider two formulations of CR&S with small and large context spaces respectively and develop new techniques for solving them and identifying the rate-optimal budget allocation rules. Based on them, two selection algorithms are proposed, which can be shown to be numerically superior via a set of tests on abstract and real-world examples. Managerial implications: This research provides a systematic way of conducting simulation-based decision-making for PM. To improve the overall decision quality for the possible contexts, more simulation efforts should be devoted to contexts in which it is difficult to distinguish between the best treatment and non-best treatments, and our results quantify the optimal tradeoff of the simulation efforts between the pairs of contexts and treatments.

Louis Martini, Sebastian Wolf

The goal of this article is to develop the theory of presentable categories and topoi internal to an arbitrary $\infty$-topos $\mathcal{B}$. Our main results are internal analogues of Lurie's and Lurie-Simpson's characterisations of presentable $\infty$-categories and $\infty$-topoi. In the process, we introduce a theory of internal filteredness and accessible internal categories and establish a number of structural results about presentable $\mathcal{B}$-categories such as adjoint functor theorems and the existence of an internal analogue of the Lurie tensor product. We also compare these internal notions with external variants. We show that $\mathcal{B}$-modules embed fully faithfully into presentable $\mathcal{B}$-categories and prove that there is an equivalence between topoi internal to $\mathcal{B}$ and $\infty$-topoi over $\mathcal{B}$. We also include a number of applications of our results, such as a general version of Diaconescu's theorem for $\infty$-topoi and a characterisation of locally contractible geometric morphisms in terms of smoothness.