Hasil untuk "Specialties of internal medicine"

Adam P Wagner, Nicholas J Simmonds, Susan C Charman et al.

Introduction Yoga is an emerging exercise choice for people with cystic fibrosis (CF), but evidence of its effect in this population is scarce, with a recent systematic review advocating for further research. Yoga Outcomes Get Assessed in CF (YOGA-CF) is a real-world multicentre randomised controlled trial (RCT) investigating a bespoke CF-specific online 12-week yoga intervention, vers usual care, to determine effectiveness for adults with CF.Methods and analysis A multicentre RCT of adults with CF across the UK. Participants are randomised to usual care or a 12-week online bespoke yoga programme with an expectation of two classes completed weekly. Assessments of lung function, 1 min sit-to-stand, the Cystic Fibrosis Questionnaire-Revised (CFQ-R) and other trial questionnaires are completed preintervention and postintervention (0 and 12 weeks) and after 12 weeks of follow-up (week 24). The primary outcome is the difference in respiratory-related quality of life measured using the CFQ-R before and after yoga/control. Sample size was calculated based on detecting a minimally clinically important difference of 4 for the CFQ-R respiratory domain, with power of 80% and 5% significance level (total target, n=314).Ethics and dissemination Ethics approval gained from the South Yorkshire and Humber Research Ethics Committee (REC) (reference: 23/YH/0270, project ID 303898). Dissemination to involve direct participant feedback and lay webinar, scientific conference presentation and publication in a peer-reviewed journal.Trial registration number NCT06120465.

Sebastian Joseph, Lily Chen, Barry Wei et al.

Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public health and medicine has grown due to the high-stakes nature of medical decisions and challenges in critically appraising a vast and diverse medical literature. Evidence-based medicine connects to every individual, and yet the nature of it is highly technical, rendering the medical literacy of majority users inadequate to sufficiently navigate the domain. Such problems with medical communication ripens the ground for end-to-end fact-checking agents: check a claim against current medical literature and return with an evidence-backed verdict. And yet, such systems remain largely unused. In this position paper, developed with expert input, we present the first study examining how clinical experts verify real claims from social media by synthesizing medical evidence. In searching for this upper-bound, we reveal fundamental challenges in end-to-end fact-checking when applied to medicine: Difficulties connecting claims in the wild to scientific evidence in the form of clinical trials; ambiguities in underspecified claims mixed with mismatched intentions; and inherently subjective veracity labels. We argue that fact-checking should be approached and evaluated as an interactive communication problem, rather than an end-to-end process.

Elisha D. O. Roberson

The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.

YunHe Su, Zhengyang Lu, Junhui Liu et al.

This paper explores the advancements and applications of large-scale models in the medical field, with a particular focus on Medical Large Models (MedLMs). These models, encompassing Large Language Models (LLMs), Vision Models, 3D Large Models, and Multimodal Models, are revolutionizing healthcare by enhancing disease prediction, diagnostic assistance, personalized treatment planning, and drug discovery. The integration of graph neural networks in medical knowledge graphs and drug discovery highlights the potential of Large Graph Models (LGMs) in understanding complex biomedical relationships. The study also emphasizes the transformative role of Vision-Language Models (VLMs) and 3D Large Models in medical image analysis, anatomical modeling, and prosthetic design. Despite the challenges, these technologies are setting new benchmarks in medical innovation, improving diagnostic accuracy, and paving the way for personalized healthcare solutions. This paper aims to provide a comprehensive overview of the current state and future directions of large models in medicine, underscoring their significance in advancing global health.

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi et al.

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

Selene S Mak, Laura M Nally, Juanita Montoya et al.

BackgroundNumerous barriers to moderate to vigorous physical activity exist for youths with type 1 diabetes (T1D). The virtual exercise games for youth with T1D (ExerT1D) intervention implement synchronous support of moderate to vigorous physical activity including T1D peers and role models. ObjectiveThis study aims to understand the acceptability of this intervention to participants. MethodsWe conducted postprogram, semistructured, televideo interviews with participating youths to elicit perspectives on the acceptability of the intervention and experience with the program. Two coders independently reviewed and analyzed each transcript using a coding scheme developed inductively by senior researchers. Discrepancies were resolved by team discussion, and multiple codes were grouped together to produce 4 main thematic areas. ResultsAll 15 participants provided interviews (aged 14-19 years; 2 nonbinary, 6 females; median hemoglobin A1c level of 7.8% (IQR 7.4%-11.2%), 5 with a hemoglobin A1c level of ≥10%). Qualitative data revealed four themes: (1) motivation to engage in physical activity (PA)—improving their physical capabilities and stabilizing glucose levels were cited as motivation for PA and challenges of living with T1D were cited as PA barriers; (2) experience with and motivation to manage diabetes while engaging in PA—participants provided details of accommodating the inherent uncertainty or limitations of PA with diabetes and sometimes preparing for PA involved psychological and motivational adjustments while some relayed feelings of avoidance; (3) peer support encouraged engagement with the intervention—participants appreciated the peer aspects of components of ExerT1D and participants’ reflections of the facilitated group experience highlight many benefits of a small-group virtual program; and (4) improvements in PA and diabetes self-management efficacy—all participants credited the program with improving or at least raising awareness of T1D management skills. ConclusionsOur virtual PA intervention using an active video game and discussion component provided adolescents with T1D the confidence and peer support to engage in PA, improved awareness of diabetes-specific tasks to prepare for exercise, and improved understanding of the effect of PA on glucose levels. Engaging youths with a virtual video game intervention is a viable approach to overcome barriers to PA for adolescents with T1D. Trial RegistrationClinicalTrials.gov NCT05163912; https://clinicaltrials.gov/ct2/show/NCT05163912

Shunsuke Miyamoto, Tomoya Hatayama, Hiroyuki Shikuma et al.

Objective: To assess the safety and effectiveness of urological tumor surgeries using the hinotori™ Surgical Robot System (hinotori) in a real-world clinical setting. Methods: All surgeries including robot-assisted radical prostatectomy (RARP), robot-assisted partial nephrectomy (RAPN), robot-assisted radical nephrectomy (RARN), robot-assisted nephroureterectomy (RANU), robot-assisted adrenalectomy (RAA), and robot-assisted radical cystectomy with intracorporeal urinary diversion (RARC+ICUD) for urological tumors with the hinotori and da Vinci surgical system (da Vinci) from January 2022 to September 2023 were enrolled. We evaluated the safety and effectiveness of surgeries using the hinotori compared with those using the da Vinci. Results: Robotic surgeries using the hinotori were performed in a total of 91 cases, comprising 42 cases of RARP, 18 cases of RAPN, six cases of RARN, 10 cases of RANU, 13 cases of RAA, and two cases of RARC+ICUD; no major intraoperative complications were observed in any of the cases using the hinotori; no major postoperative complications occurred in any of the cases; no case experienced an unrecoverable equipment error during surgery. Meanwhile, robotic surgeries using the da Vinci were performed in a total of 277 cases, comprising 126 cases of RARP, 94 cases of RAPN, 12 cases of RARN, 10 cases of RANU, 20 cases of RAA, and 15 cases of RARC+ICUD; major intraoperative complications occurred in two cases; major postoperative complications occurred in seven cases; seven cases required transfusion; one case underwent conversion to open surgery; during the study period, no case experienced an unrecoverable equipment error. Surgical outcomes for cases with the hinotori were comparable to those with the da Vinci. Conclusion: This study demonstrated that the hinotori is a safe and feasible tool for robotic surgeries in the field of urology.

Ke-rui Zhang, Yi Yang, Ya-qin Li et al.

Abstract Background Anterior cervical corpectomy and fusion (ACCF) with Traditional Titanium Mesh Cages (TTMCs) can lead to complications such as cage subsidence, dysphagia, and implant-related issues. These complications suggest that the biomechanical stability of ACCF with TTMC may be insufficient. This study aims to evaluate whether a New Assembled Titanium Mesh Cage (NTMC) can improve the biomechanical performance after ACCF. Methods ACCF procedures using both TTMC and NTMC models were constructed and compared. The range of motion (ROM) of the surgical segments and stress peaks in various regions including the endplate, bone-screw interface, facet joints, and adjacent intervertebral discs were analyzed. Results The use of NTMC significantly reduced the postoperative ROM of the surgical segments by 80.7%-82.0% compared to ACCF with TTMC. Additionally, stress peaks at the endplate, bone-screw interface, and facet contact force (FCF) were higher in ACCF with TTMC compared to NTMC. TTMC also induced higher stress peaks in the C3/4 and C6/7 intervertebral discs (ranging from 0.2009–6.961 MPa and 0.2477–4.735 MPa, respectively), followed by the NTMC (ranging from 0.1322–3.820 MPa and 0.2227–4.104 MPa, respectively). Conclusions The utilization of NTMC, which includes enlarged spacers and emulates endplate geometries, effectively reduces the risks of cage subsidence and instrument-related complications in ACCF. Furthermore, ACCF with NTMC also decreases the risks of dysphagia, facet joint degeneration, and adjacent disc degeneration during the follow-up period by altering the fixing method while maintaining construct stability.

Jingqi Zeng, Xiaobin Jia

Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which are challenging to quantify. To address this challenge, we applied graph artificial intelligence to develop a TCM multi-dimensional knowledge graph that bridges traditional TCM theory and modern biomedical science (https://zenodo.org/records/13763953 ). Using feature engineering and embedding, we processed key TCM terminology and Chinese herbal pieces (CHP), introducing medicinal properties as virtual nodes and employing graph neural networks with attention mechanisms to model and analyze 6,080 Chinese herbal formulas (CHF). Our method quantitatively assessed the roles of CHP within CHF and was validated using 215 CHF designed for COVID-19 management. With interpretable models, open-source data, and code (https://github.com/ZENGJingqi/GraphAI-for-TCM ), this study provides robust tools for advancing TCM theory and drug discovery.

Shannon L. Walston, Hiroshi Seki, Hirotaka Takita et al.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Sunan He, Yuxiang Nie, Hongmei Wang et al.

Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of medicine, while GFMs exhibit superior generalizability based on their extensive intrinsic knowledge as well as proficiency in instruction following and in-context learning, specialist models excel in precision due to their domain knowledge. In this work, for the first time, we explore the synergy between the GFM and specialist models, to enable precise medical image analysis on a broader scope. Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks. In the construction stage, we develop MedDr, the largest open-source GFM tailored for medicine, showcasing exceptional instruction-following and in-context learning capabilities. Meanwhile, a series of lightweight specialists are crafted for downstream tasks with low computational cost. In the collaborative inference stage, we introduce two cooperative mechanisms, Mixture-of-Expert Diagnosis and Retrieval-Augmented Diagnosis, to harvest the generalist's in-context learning abilities alongside the specialists' domain expertise. For a comprehensive evaluation, we curate a large-scale benchmark featuring 28 datasets and about 250,000 images. Extensive results demonstrate that MedDr consistently outperforms state-of-the-art GFMs on downstream datasets. Furthermore, GSCo exceeds both GFMs and specialists across all out-of-domain disease diagnosis datasets. These findings indicate a significant paradigm shift in the application of GFMs, transitioning from separate models for specific tasks to a collaborative approach between GFMs and specialists, thereby advancing the frontiers of generalizable AI in medicine.

Ehsan Abadi, Aldo Badano, Predrag Bakic et al.

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.

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.

José Nunes de Alencar

Jin-Chuan Liu, Jin-Chuan Liu, Qunxiong Zeng et al.

B cells constitute a diverse and adaptable immune cell population with functions that can vary according to the environment and circumstances. The involvement of B cells in pregnancy, as well as the associated molecular pathways, has yet to be investigated. This review consolidates current knowledge on B cell activities and regulation during pregnancy, with a particular focus on the roles of various B cell subsets and the effects of B cell-derived factors on pregnancy outcomes. Moreover, the review examines the significance of B cell-associated autoantibodies, cytokines, and signaling pathways in relation to pregnancy complications such as pregnancy loss, preeclampsia, and preterm birth.

Shishir Rajendran, Prathic Sundararajan, Ashi Awasthi et al.

Nanorobotics offers an emerging frontier in biomedicine, holding the potential to revolutionize diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, resulting in the identification and analysis of a total of 414 papers. The studies were filtered to include only those that addressed both nanorobotics and direct medical applications. Our analysis traces the technology's evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications over time. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of the state of nanorobotics in medicine, drawing attention to current challenges and opportunities, and providing directions for future research in this rapidly advancing field.

Hua Pu, Jiacong Mi, Shan Lu et al.

Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results.

Kavita Dharamraj

Aim: To determine the association between the frequency of physical activity and blood pressure (BP) levels among persons with type 2 diabetes at a public health center in Southwest Trinidad. Settings and Design: In 2011, the Penal Health Center, Diabetes Patient Self-Care Study enrolled 523 persons with type 2 diabetes in routine care in Southwest Trinidad aiming to obtain information on health status including diabetes and cardiovascular disease. The study was cross-sectional and included both males and females aged 25–87 years, having the exposure – physical activity and the outcome – BP levels. Subjects and Methods: Adults with type 2 diabetes aged 25–87 years with available information on physical activity and BP (n = 469). The main outcomes measures were systolic and diastolic BP (DBP) levels. Linear regression models examined the association between the frequency of physical activity (infrequent: <3x/week or frequent: ≥3x/week) and systolic BP (SBP)/DBP adjusting for potential confounders. Episodes of physical activity were defined as continuous physical activity, averaging ≥ 20 min/episode/week. Results: BP among hypertensive participants who exercise ≥ 3x/week was 5.3 mmHg lower than those who exercise <3x/week (Unadjusted β = −5.3, [95% confidence interval (CI) −10.0, −0.6], P = 0.026). DBP among hypertensive participants who exercise ≥3x/week was 0.4 mmHg lower than those who exercise <3x/week (Model 2: Adjusted β = −0.4, [95% CI – −3.5, 2.8], P = 0.818). Conclusion: Our findings may suggest an association between the frequency of physical inactivity and SBP levels in persons with type 2 diabetes.

Blanca Ines García-Gomez, Tamara Alejandrina Sánchez, Sayra Natalia Cano et al.

Bacillus thuringiensis (Bt) produces different insecticidal proteins effective for pest control. Among them, Cry insecticidal proteins have been used in transgenic plants for the control of insect pests. However, evolution of resistance by insects endangers this technology. Previous work showed that the lepidopteran insect Plutella xylostella PxHsp90 chaperone enhanced the toxicity of Bt Cry1A protoxins by protecting them from degradation by the larval gut proteases and by enhancing binding of the protoxin to its receptors present in larval midgut cells. In this work, we show that PxHsp70 chaperone also protects Cry1Ab protoxin from gut proteases degradation, enhancing Cry1Ab toxicity. We also show that both PxHsp70 and PxHsp90 chaperones act cooperatively, increasing toxicity and the binding of Cry1Ab439D mutant, affected in binding to midgut receptors, to cadherin receptor. Also, insect chaperones recovered toxicity of Cry1Ac protein to a Cry1Ac-highly resistant P. xylostella population, NO-QAGE, that has a disruptive mutation in an ABCC2 transporter linked to Cry1Ac resistance. These data show that Bt hijacked an important cellular function for enhancing its infection capability, making use of insect cellular chaperones for enhancing Cry toxicity and for lowering the evolution of insect resistance to these toxins.

Takehiro Yamashita, MD, PhD, Naoki Iwakiri, MD, Eigo Kurebayashi, MD et al.