Hasil untuk "Specialties of internal medicine"

Min Jae Kang, Min Jae Kang, Roopa Kanakatti Shankar et al.

PurposeSkeletal abnormalities are common in Turner Syndrome (TS), yet data on objective radiographic markers are limited. We aimed to establish normative reference ranges for phalangeal length ratios and assess their utility in detecting skeletal abnormalities in TS.MethodsWe analyzed 4,082 female bone age X-rays (<18 years) from the Radiological Society of North America (RSNA) database after quality screening and outlier exclusion as a reference cohort. Phalangeal length ratios—4th to 3rd metacarpal (4:3 MC), 5th to 3rd metacarpal (5:3 MC), and 5th to 3rd middle phalanx (5:3 MP)—were measured and compared in 81 TS patients seen at a single center. Additional skeletal features such as SHOX deficiency-related signs and brachydactyly type A3 (BDA3) were assessed.ResultsIn reference subjects, 4:3 MC and 5:3 MC ratios remained stable across most age groups, while the 5:3 MP ratio increased with age. TS patients showed a significantly lower 4:3 MC and 5:3 MP ratios (P < 0.001, P = 0.002, respectively) compared to ones from reference subjects. A low 4:3 MC ratio (<–2 SD) was seen in 27.2% of TS patients. The 4:3 MC ratio correlated with height percentile (r = 0.27, P = 0.02). BDA3 was more prevalent in TS compared to reference subjects (13.6% vs. 2.1%, P < 0.001) and associated with low MC ratios.ConclusionNormative reference ranges for phalangeal length ratios were established and differences in 4:3 and 5:3 MP ratios in patients with TS were identified compared to the reference group. Further studies with larger TS cohorts are needed to confirm the clinical utility of these radiographic biomarkers.

Omid Seidizadeh, Luciano Baronciani, Flora Peyvandi

von Willebrand disease (VWD) is the most common inherited bleeding disorder characterized by defects in the quantity or function of the von Willebrand factor (VWF). The diagnosis of VWD is complex, requiring a battery of tests to evaluate the amount, functions, and multimeric structure of the VWF glycoprotein. The diagnosis can also be accomplished or confirmed by sequencing the VWF gene (VWF). Genetic testing of VWF has been around for 4 decades following the cloning of VWF, and nowadays, it has been integrated into the diagnostic panel of VWD. With the introduction of next-generation sequencing, genetic analysis of the VWF has become more practical than it was in the past, when Sanger sequencing was used. A number of laboratories have applied or started to use genetic testing with next-generation sequencing for VWD diagnosis. Considering the increasing application of genetic testing in VWD and the wide availability and decreasing cost of gene sequencing, we sought to discuss the challenges and considerations involved in applying genetic testing to VWD.

Jing Tian, Yan Dong, Zhongping Xu et al.

ObjectivesThe aim of this study was to analyze the association between TyG-BMI and 365-day mortality in critically ill patients with CHD.MethodsPatient data were extracted from the MIMIC-IV database. All patients were categorized into 3 groups based on TyG-BMI index: Low TyG-BMI index group, Medium TyG-BMI index group, and High TyG-BMI index group. Outcomes included primary and secondary outcomes, with the primary outcome being 365-day mortality and the secondary outcomes being hospital survival, intensive care unit (ICU) survival, and 28-day, 90-day, and 180-day mortality. The Kaplan-Meier survival curves were used to compare the outcomes of the three groups. The relationship between TyG-BMI index and 365-day mortality was assessed using multivariate Cox proportional risk regression models and restricted cubic spline curves (RCS).Results889 critically ill patients with CHD were analyzed. Among them, 600 (67.50%) were male patients with a mean age of 68.37 years and 289 (32.50%) were female patients with a mean age of 73.91 years. Patients with a medium TyG-BMI index had the best 365-day prognostic outcome and the highest survival rate compared with patients in the Low and High TyG-BMI index groups [201 (67.68%) vs. 166 (56.08%), 188 (63.51%); P=0.013]. After fully adjusted modeling analysis, the hazard ratio (HR) for 365-day mortality was found to be 0.71 (95% CI 0.54-0.93, P=0.012) for the Medium TyG-BMI index group. Meanwhile, RCS analysis showed an L-shaped relationship between TyG-BMI index and 365-day mortality.ConclusionsThe TyG-BMI index is significantly associated with 365-day mortality in patients with severe CHD.

Marta Rosado-Fernández, Elisenda Climent, Mercè Fernández-Miró et al.

Purpose: It has been previously described that some women with type 1 diabetes (T1D) may experience changes in glucose levels in relation to their menstrual cycle. However, whether an advanced hybrid closed-loop system (AHCL) can mitigate these cycle-dependent changes is yet to be determined. Methods: This study is a prospective analysis of a cohort of premenopausal women with T1D with spontaneous menstrual cycles who are users of an AHCL system 780G Medtronic^®. Three consecutive cycles were analyzed for each patient, and each cycle was divided into three phases (menstrual, luteal, and rest of cycle phase). Results: Fifteen subjects were included. Mean age was 38 ± 7.6 years, HbA1c was 7.12 ± 0.7%, and diabetes duration was 21 ± 13.7 years. Mean glucose was higher in the luteal phase compared to the menstrual period (<i>p</i> = 0.029 luteal vs. menstrual) and the rest of the cycle (<i>p</i> = 0.018 luteal vs. rest of cycle). The time in range (TIR) was lower in the luteal phase compared to the rest of cycle phase (<i>p</i> = 0.015 luteal vs. rest of cycle). The time below range (TBR) was significantly higher in the menstrual compared to the luteal phase (<i>p</i> = 0.007 luteal vs. menstrual). Daily insulin requirements were higher in luteal phase compared to rest of cycle (<i>p</i> = 0.017 luteal vs. rest of cycle). Conclusions: A higher mean glucose and lower TIR, despite a higher total insulin dose, was observed in the luteal phase. A higher TBR was observed in the menstrual phase. However, AHCL with 780G Medtronic^® achieves a TIR of almost 70% in all cycle phases.

Michael J. Domanski, MD, Colin O. Wu, PhD, Xin Tian, PhD et al.

Background: In prior studies of cumulative risk factor exposure, self-identified race was independently associated with incident cardiovascular disease (CVD). A recent study suggests clinical, demographic, and socioeconomic factors explain racial differences. We used propensity score matching to study race as an independent incident CVD risk factor. Objectives: The purpose of this study was to assess race as an independent risk factor for incident CVD. Methods: We analyzed CARDIA (Coronary Artery Risk Development in Young Adults) study data using propensity score matching of White and Black women, and, separately, White and Black men, with respect to known CVD risk factors. Results: Black men (n = 487), compared to White men (n = 487), had higher risk of CVD (HR: 2.30; 95% CI: 1.36-3.89; P = 0.0014), stroke (HR: 5.00; 95% CI: 1.45-17.3; P = 0.0047), and congestive heart failure (CHF) (HR: 3.60; 95% CI: 1.34-9.70; P = 0.0067). Black women (n = 640), compared to White women (n = 640), had higher CVD risk (HR: 2.36; 95% CI: 1.17-4.78; P = 0.014) and stroke risk (HR: 2.80; 95% CI: 1.01-7.77; P = 0.039) and borderline significantly higher CHF risk (HR: 3.50; 95% CI: 0.73-16.9; P = 0.096). Risk of coronary heart disease did not differ significantly by race in either sex. Multivariable analyses showed racial differences in the associations of multiple risk factors with incident CVD events independent of other known CVD risk factors. Conclusions: Propensity score matching analyses demonstrate that race is an independent risk factor for incident CVD and its components, CHF, and stroke. Multivariable analyses suggest racial differences in Black vs White risk factor impact as the possible cause. Reasons for these differences remain to be explored.

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.

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.

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.

Feng Zhang, Long Cheng

AimsThis research investigated menopausal women older than 50 years to find whether there were any independent relationships between the duration of sleep they got and their prevalence of depression.MethodsNational Health and Nutrition Examination Survey (NHANES) datasets from 2011-2020 were utilized in a cross-sectional study. Using multivariate linear regression models, the linear relationship between sleep duration and depression in menopausal women was investigated. Fitted smoothing curves and thresholds impact evaluation were used to investigate the nonlinear relationship. Then, subgroup analyses were performed according to smoking, drinking alcohol, diabetes, hypertension, heart disease, and moderate activities.ResultsThis population-based study included a total of 3,897 menopausal women (mean age 65.47 ± 9.06 years) aged≥50 years; 3,159 had a depression score &lt;10, and 738 had a depression score≥10. After controlling for all covariates, the prevalence of depression was 17% higher among participants with short sleep duration [OR=1.17, 95%CI=(0.65, 1.70), P&lt;0.0001] and 86% [OR=1.86, 95%CI=(1.05, 2.66), P&lt;0.0001] compared to participants with normal sleep duration. In subgroup analyses stratified by smoking and diabetes, the sleep duration and depression scores of non-smokers [β=-0.18, 95%CI= (-0.33, -0.02), P=0.0241] and diabetics were independently negatively correlated [β=-0.32, 95%CI= (-0.63, -0.01), P=0.0416]. Using a two-segment linear regression model, we discovered a U-shaped relationship between sleep duration and depression scores with an inflection point of 7.5 hours. Less than 7.5 hours of sleep was associated with an increased risk of developing depression [β=-0.81, 95%CI= (-1.05, -0.57), P&lt;0.001]. However, sleeping more than 7.5 hours per night increased the risk of depression considerably [β=0.80, 95%CI= (0.51, 1.08), P&lt;0.001].ConclusionsDepression is associated with sleep duration in menopausal women. Insufficient or excessive sleep may increase the risk of depression in menopausal women.

Paul Valensi

Abstract Prevalence of heart failure (HF) and diabetes are markedly increasing globally. In a population of HF patients, approximately 40% have diabetes which is associated with a more severe HF, poorer cardiovascular outcomes and higher hospitalization rates for HF than HF patients without diabetes. Similar trends were shown in HF patients with prediabetes. In addition, the association between HF and renal function decline was demonstrated in patients with or without diabetes. However, the exact prevalence of dysglycemia in HF patients requires further investigation aiming to clarify the most accurate test to detect dysglycemia in this population. The relationship between HF and diabetes is complex and probably bidirectional. In one way, patients with diabetes have a more than two-fold risk of developing incident HF with reduced or preserved ejection fraction than those without diabetes. In the other way, patients with HF, when compared with those without HF, show an increased risk for the onset of diabetes due to several mechanisms including insulin resistance (IR), which makes HF emerging as a precursor for diabetes development. This article provides epidemiological evidence of undetected dysglycemia (prediabetes or diabetes) in HF patients and reviews the pathophysiological mechanisms which favor the development of IR and the risks associated with these disorders in HF patients. This review also offers a discussion of various strategies for the prevention of diabetes in HF patients, based first on fasting plasma glucose and HbA1c measurement and if normal on an oral glucose tolerance test as diagnostic tools for prediabetes and unknown diabetes that should be performed more extensively in those patients. It discusses the implementation of diabetes prevention measures and well-structured management programs for HF patients who are generally overweight or obese, as well as current pharmacotherapeutic options for prediabetes, including sodium–glucose cotransporter 2 inhibitors which are among the pillars of HF treatment and which recently showed a benefit in the reduction of incident diabetes in HF patients. Thus, there is an urgent need of routine screening for dysglycemia in all HF patients, which should contribute to reduce the incidence of diabetes and to treat earlier diabetes when already present.

Carlo Perricone, Andrea Castellucci, Giacomo Cafaro et al.

The treatment of patients with rheumatoid arthritis (RA) has dramatically changed in the past 30 years. Currently, numerous conventional, biologic, and targeted synthetic DMARDs have been licensed and used following recommendations provided by international and national scientific societies. However, the availability of biosimilars and the increasing necessity of savings impacted on the local/national prescription of these drugs. The information provided by data sheet of every single drug is a decisive factor on the choice of a certain treatment merged with the patient’s profile. Thus, our purpose was to construct a rational algorithm for the treatment strategy in RA according to costs and the product leaflet of the biologic and targeted-synthetic DMARDs currently licensed in Italy. We used the most recent available recommendations and then we performed a review of the literature considering all the factors that are known to influence drug safety/effectiveness. All these factors were considered in the context of the data sheets of currently available originators and biosimilars.

Nitchanant Kitcharanant, Prangmalee Leurcharusmee, Pichitchai Atthakomol et al.

Abstract Background Even with the great advancements in recent years in total knee arthroplasty (TKA), some patients continue to have persistent postsurgical pain (PPSP). The advantages of systemic corticosteroids in the perioperative context have been further supported by previously published trials. However, the impact of dexamethasone on the intensity of post-TKA PPSP is still unclear. We aimed to investigate its effect on the degree of PPSP and compare that with a placebo. Methods In this randomized, double-blind, placebo-controlled study, 48 patients undergoing unilateral TKA were given intravenous dexamethasone 10 mg or saline just before spinal anesthesia was induced, and they also received two additional doses of dexamethasone 10 mg or saline 24 and 48 h after surgery. A standardized, multimodal analgesic regimen was administered to each patient. The modified WOMAC pain scores at 12 weeks postoperative were the main outcome. The secondary outcomes included pain during a walk of five meters, pain during active knee flexion at 45 degrees, maximum pain at rest during the previous 24 h, nausea visual analogue scale values, and use of rescue opioid and antiemetic medications. Results There was no difference in modified WOMAC pain scores 12 weeks after surgery between patients who received and did not receive perioperative dexamethasone. At 24, 30, 48, 54, and 72 h following surgery, the dexamethasone group experienced considerably less pain during a five-meter walk and during 45 degrees active knee flexion (p < 0.01). At postoperative 0–24, 24–48, and 48–72 h, the dexamethasone group experienced less maximal pain at rest (p < 0.01). The dexamethasone group also had less visual analogue scale scores for nausea at 6, 24, 30, 48, and 54 h after surgery (p < 0.02). During the first 0–24 and 24–48 h, the dexamethasone group consumed fewer opioids and antiemetic medications (p < 0.01). All patients showed no signs of wound complications. Conclusions When compared to a placebo at 12 weeks after TKA, intravenous dexamethasone did not reduce PPSP. Nevertheless, early postoperative pain was relieved by perioperative intravenous dexamethasone, which also decreased the need for opioid and antiemetic medications and decreased postoperative nausea and vomiting. Trial registration NCT02760459.

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.

Michael Vollenweider, Manuel Schürch, Chiara Rohrer et al.

Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it faces significant challenges due to complex biases in clinical observational data and the high-dimensional nature of biological data. This study models various types of treatment assignment biases using mutual information and investigates their impact on ML models for counterfactual prediction and biomarker identification. Unlike traditional counterfactual benchmarks that rely on fixed treatment policies, our work focuses on modeling different characteristics of the underlying observational treatment policy in distinct clinical settings. We validate our approach through experiments on toy datasets, semi-synthetic tumor cancer genome atlas (TCGA) data, and real-world biological outcomes from drug and CRISPR screens. By incorporating empirical biological mechanisms, we create a more realistic benchmark that reflects the complexities of real-world data. Our analysis reveals that different biases lead to varying model performances, with some biases, especially those unrelated to outcome mechanisms, having minimal effect on prediction accuracy. This highlights the crucial need to account for specific biases in clinical observational data in counterfactual ML model development, ultimately enhancing the personalization of treatment decisions in precision medicine.

J. Li