Hasil untuk "Medicine"

E. Levinson, R. H. Shukrun, N. Vigano et al.

A wide range of diagnostic information in medicine is currently obtained using radioactive tracers. While central to nuclear medicine, these methods are inherently constrained: radiation dose limits repeat examinations, short tracer half-lives and complex logistics restrict access and raise costs, and their relatively poor spatial resolution often necessitates complementary CT or MRI. Here we present a first proof-of-concept demonstration of a non-radioactive alternative based on x-ray fluorescence (XRF) computational ghost imaging (CGI) at the human-organ scale. Using a thyroid phantom filled with iodine solution as a model system, we show that structured illuminations combined with fluorescence detection reconstruct the iodine distribution with high fidelity. This approach eliminates the need for radioactive tracers while preserving image quality, and in principle can reach spatial resolution comparable to CT. Beyond this demonstration, XRF-CGI establishes a generalizable framework for non-radioactive tracer imaging, opening a route toward safer, repeatable, and more accessible diagnostics.

Soumick Chatterjee

The dependence on expert annotation has long constituted the primary rate-limiting step in the application of artificial intelligence to biomedicine. While supervised learning drove the initial wave of clinical algorithms, a paradigm shift towards unsupervised and self-supervised learning (SSL) is currently unlocking the latent potential of biobank-scale datasets. By learning directly from the intrinsic structure of data - whether pixels in a magnetic resonance image (MRI), voxels in a volumetric scan, or tokens in a genomic sequence - these methods facilitate the discovery of novel phenotypes, the linkage of morphology to genetics, and the detection of anomalies without human bias. This article synthesises seminal and recent advances in "learning without labels," highlighting how unsupervised frameworks can derive heritable cardiac traits, predict spatial gene expression in histology, and detect pathologies with performance that rivals or exceeds supervised counterparts.

Nikhil Kumar Rajput, Riya Bansal

Quantum computing holds transformative potential for medical applications, yet efficiently preparing quantum states from complex medical data remains a fundamental challenge. This survey provides a comprehensive examination of current approaches for encoding medical information into quantum systems, analyzing theoretical principles, algorithmic advancements, and practical limitations. It discusses tensor network decomposition, variational quantum algorithms, quantum machine learning techniques, and specialized error mitigation strategies for medical computing. The findings indicate that quantum advantages in medicine rely on leveraging inherent data structures such as spatial correlations in imaging, temporal patterns in physiological signals, and hierarchical biological organization. While current hardware restricts implementations to small-scale problems, emerging methods show potential for near-term use. The study provides a structured framework for assessing when quantum state preparation outperforms classical approaches in medicine, along with implementation guidelines and performance benchmarks.

Prateek Jaiswal, Esmaeil Keyvanshokooh, Junyu Cao

Randomized clinical trials often require large patient cohorts before drawing definitive conclusions, yet abundant observational data from parallel studies remains underutilized due to confounding and hidden biases. To bridge this gap, we propose Deconfounded Warm-Start Thompson Sampling (DWTS), a practical approach that leverages a Doubly Debiased LASSO (DDL) procedure to identify a sparse set of reliable measured covariates and combines them with key hidden covariates to form a reduced context. By initializing Thompson Sampling (LinTS) priors with DDL-estimated means and variances on these measured features -- while keeping uninformative priors on hidden features -- DWTS effectively harnesses confounded observational data to kick-start adaptive clinical trials. Evaluated on both a purely synthetic environment and a virtual environment created using real cardiovascular risk dataset, DWTS consistently achieves lower cumulative regret than standard LinTS, showing how offline causal insights from observational data can improve trial efficiency and support more personalized treatment decisions.

Matthew Cannon, Wesley Goar, In-Hee Lee et al.

Rapid advancements in technology have led to an increased use of artificial intelligence (AI) technologies in medicine and bioinformatics research. In anticipation of this, the National Institutes of Health (NIH) assembled the Bridge to Artificial Intelligence (Bridge2AI) consortium to coordinate development of AI-ready datasets that can be leveraged by AI models to address grand challenges in human health and disease. The widespread availability of genome sequencing technologies for biomedical research presents a key data type for informing AI models, necessitating that genomics data sets are AI-ready. To this end, the Genomic Information Standards Team (GIST) of the Bridge2AI Standards Working Group has documented a set of recommendations for maintaining AI-ready genomics datasets. In this report, we describe recommendations for the collection, storage, identification, and proper use of genomics datasets to enable them to be considered AI-ready and thus drive new insights in medicine through AI and machine learning applications.

Meiou Song, Colin C. Anderson, Nakul Sridhar et al.

Blood transfusion remains a cornerstone of modern medicine, saving countless lives daily. Yet the quality of transfused blood varies dramatically among donors-a critical factor often overlooked in clinical practice. Rapid, benchtop, and cost-effective methods for evaluating stored red blood cells (RBCs) at the site of transfusion are lacking, with concerns persisting about the association between metabolic signatures of stored RBC quality and transfusion outcomes. Recent studies utilizing metabolomics approaches to evaluate stored erythrocytes find that donor biology (e.g., genetics, age, lifestyle factors) underlies the heterogeneity associated with blood storage and transfusion. The appreciation of donor-intrinsic factors provides opportunities for precision transfusion medicine approaches for the evaluation of storage quality and prediction of transfusion efficacy. Here we propose a new platform, the Surface Acoustic Wave Hemolysis Assay (SAW-HA), for on-site evaluation of stored RBCs utilizing SAW Hemolysis Temperature (SAWHT) as a marker for RBC quality. We report SAWHT as a mechanism-dependent reproducible methodology for evaluating stored human RBCs up to 42 days. Our results define unique signatures for SAW hemolysis and metabolic profiles in RBCs from two of the six donors in which high body mass index (BMI) and RBC triglycerides associated with increased susceptibility to hemolysis. Metabolic age of the stored RBCs - a recently appreciated predictor of post-transfusion efficacy-reveal that RBCs from the two low SAWHT units were characterized by disrupted redox control, deficient tryptophan metabolism, and high BMI. Together, these findings indicate the potential of the SAW-HA as a point-of-care analysis for transfusion medicine.

Lizzy Farrugia, Lilian M. Azzopardi, Jeremy Debattista et al.

The role of pharmacists is evolving from medicine dispensing to delivering comprehensive pharmaceutical services within multidisciplinary healthcare teams. Central to this shift is access to accurate, up-to-date medicinal product information supported by robust data integration. Leveraging artificial intelligence and semantic technologies, Knowledge Graphs (KGs) uncover hidden relationships and enable data-driven decision-making. This paper presents medicX-KG, a pharmacist-oriented knowledge graph supporting clinical and regulatory decisions. It forms the semantic layer of the broader medicX platform, powering predictive and explainable pharmacy services. medicX-KG integrates data from three sources, including, the British National Formulary (BNF), DrugBank, and the Malta Medicines Authority (MMA) that addresses Malta's regulatory landscape and combines European Medicines Agency alignment with partial UK supply dependence. The KG tackles the absence of a unified national drug repository, reducing pharmacists' reliance on fragmented sources. Its design was informed by interviews with practicing pharmacists to ensure real-world applicability. We detail the KG's construction, including data extraction, ontology design, and semantic mapping. Evaluation demonstrates that medicX-KG effectively supports queries about drug availability, interactions, adverse reactions, and therapeutic classes. Limitations, including missing detailed dosage encoding and real-time updates, are discussed alongside directions for future enhancements.

Guoyang Rong, Ying Chen, Feicheng Ma et al.

This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981-2002), marked by sporadic and limited interdisciplinary activity; Period II (2003-2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017-present), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary collaborations.

Tristan Cazenave

RNA design consists of discovering a nucleotide sequence that folds into a target secondary structure. It is useful for synthetic biology, medicine, and nanotechnology. We propose Montparnasse, a Multi Objective Generalized Nested Rollout Policy Adaptation with Limited Repetition (MOGNRPALR) RNA design algorithm. It solves the Eterna benchmark.

Dharmendra Kumar Gupta, Arunima Chaudhuri, Dip Gaine

Self-directed learning (SDL), which emphasizes the need for students to take ownership of their learning, has become a crucial part of medical education. With the increasing complexity of health care, SDL is seen as a crucial skill for fostering lifelong learning and adapting to new challenges. This systematic review examines the current landscape of SDL in undergraduate medical education, exploring its effectiveness, implementation strategies, and areas for future development. A methodical exploration was carried out within the PubMed database to locate pertinent research articles released between 2012 and 2024. Studies that reported results pertaining to academic achievement, clinical competence, or student perspectives and that concentrated on SDL in undergraduate medical education were included. Two reviewers independently extracted the data, evaluated its quality, and synthesized the results thematically. In all, twenty-three papers were covered in this study. The findings indicate SDL positively impacts students’ academic performance and clinical skills, with many students reporting increased engagement and motivation. Effective implementation strategies included integrating SDL into the curriculum, providing faculty support, and utilizing digital tools to enhance learning. However, the review also identified significant heterogeneity in the definition and assessment of SDL across studies, as well as challenges related to student self-regulation and faculty preparedness. SDL is a useful method in medical education for undergraduates since it helps students become self-reflective, independent practitioners. To fully comprehend its long-term effects, however, longitudinal research, faculty development initiatives, and standardized SDL frameworks are required.

Li Chen, Wei Liu, Renshan Cui

Abstract Background To investigate the hope level and identify its associated factors among widowed older adults residing in long-term care facilities. Methods A cross-sectional study was conducted using convenience sampling. 228 widowed older adults meeting inclusion criteria were recruited from several long-term care facilities in Liaoning Province for face-to-face questionnaire surveys. Results The hope level average score among widowed older adults in long-term care facilities was (31.73 ± 3.31). Multiple linear regression analysis revealed that nine factors were significantly associated with hope levels: educational level, duration of widowhood, frequency of children’s visits, pension income, number of chronic diseases, frequency of participation in recreational activities, medical payment method, evaluation of the long-term care facility, and total perceived social support score. These factors collectively explained 81.4% of the variance in hope levels (Adjusted R² = 0.814, F = 96.027, P < 0.001). Conclusion Hope levels among widowed older adults in long-term care facilities were at a moderate-low level. Nursing staff and facility administrators should pay attention to the hope levels of these residents and implement targeted interventions based on the identified associated factors to enhance hope levels and consequently improve their quality of life.

Natasa Przulj, Noel Malod-Dognin

Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, repurpose known and discover new drugs to personalize medical treatment. Existing methodologies are limited and a paradigm shift is needed to achieve quantitative and qualitative breakthroughs. In this perspective paper, we survey the literature and argue for the development of a comprehensive, general framework for embedding of multi-scale molecular network data that would enable their explainable exploitation in precision medicine in linear time. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. They have recently achieved unprecedented performance on hard problems of utilizing few omic data in various biomedical applications. However, research thus far has been limited to special variants of the problems and data, with the performance depending on the underlying topology-function network biology hypotheses, the biomedical applications and evaluation metrics. The availability of multi-omic data, modern graph embedding paradigms and compute power call for a creation and training of efficient, explainable and controllable models, having no potentially dangerous, unexpected behaviour, that make a qualitative breakthrough. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation, and to apply it to biomedical informatics. It will lead to a paradigm shift in computational and biomedical understanding of data and diseases that will open up ways to solving some of the major bottlenecks in precision medicine and other domains.

Kai Nishikawa, Akiyoshi Murakami

The existence of an open access (OA) citation advantage, that is, whether OA increases citations, has been a topic of interest for many years. Although numerous previous studies have focused on whether OA increases citations, expectations for OA go beyond that. One such expectation is the promotion of knowledge transfer across various fields. This study aimed to clarify whether OA, especially gold OA, increases interdisciplinary citations in various natural science fields. Specifically, we measured the effect of OA on interdisciplinary and within-discipline citation counts by decomposing an existing metric of the OA citation advantage. The results revealed that OA increases both interdisciplinary and within-discipline citations in many fields and increases only interdisciplinary citations in chemistry, computer science, and clinical medicine. Among these fields, clinical medicine tends to obtain more interdisciplinary citations without being influenced by specific journals or papers. The findings indicate that OA fosters knowledge transfer to different fields, which extends our understanding of its effects.

Zeki Sabah MUSIHB, Hasan Saud Abdul HUSSEIN, Alaa Mahdi Abd ALI

Objectives: This study aims to assess sleep disorders among secondary school adolescents and explore the relationship between sociodemographic factors (age, gender, household income, and sleep duration) and the occurrence of these disorders. Methods: A quantitative, descriptive, cross-sectional study, was conducted from November 20th, 2022, to May 25th, 2023, involving 200 secondary school students selected through convenience sampling. Data collection utilized a structured questionnaire divided into sociodemographic and sleep disorder sections. Validity was ensured by a panel of ten experts, and reliability was confirmed using Cronbach’s Alpha (0.77). Statistical analysis employed SPSS version 26. Results: Findings revealed that a majority of participants (70.5%) had low-level sleep disorders, followed by moderate disorders represented (29%). Significant associations were found between sleep disorders and gender (P = 0.000), economic status for family (P = 0.020), and nightly sleep duration (P = 0.016). However, no significant relationship was observed between sleep disorders and family structure or age (P > 0.05). Conclusions: The study highlights that most secondary school students experience mild sleep disorders, followed by moderate disorders. Notably, gender, income, and sleep duration showed significant correlations with sleep disorders.

Konstantin Hess, Valentyn Melnychuk, Dennis Frauen et al.

Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in medical applications. To fill this gap, we propose a novel Bayesian neural controlled differential equation (BNCDE) for treatment effect estimation in continuous time. In our BNCDE, the time dimension is modeled through a coupled system of neural controlled differential equations and neural stochastic differential equations, where the neural stochastic differential equations allow for tractable variational Bayesian inference. Thereby, for an assigned sequence of treatments, our BNCDE provides meaningful posterior predictive distributions of the potential outcomes. To the best of our knowledge, ours is the first tailored neural method to provide uncertainty estimates of treatment effects in continuous time. As such, our method is of direct practical value for promoting reliable decision-making in medicine.

Eun Kyoung Kim, Hong-Mi Choi, Jong-Hwan Lee et al.

BackgroundDue to increased needs to reduce non-fatal as well as fatal cardiac events, preoperative echocardiography remains part of routine clinical practice in many hospitals. Data on the role of preoperative echocardiography in low-risk non-cardiac surgery (NCS) other than ambulatory surgeries do not exist. We aimed to investigate the role of preoperative echocardiography in predicting postoperative adverse cardiovascular events (CVEs) in asymptomatic patients undergoing low-risk NCS.MethodsThe study population was derived from a retrospective cohort of 1,264 patients who underwent elective low-risk surgery at three tertiary hospitals from June 1, 2021, to June 30, 2021. Breast, distal bone, thyroid, and transurethral surgeries were included. Preoperative examination data including electrocardiography, chest radiography, and echocardiography were collected. The primary outcome was a composite of postoperative adverse CVEs including all-cause death, myocardial infarction, cerebrovascular events, newly diagnosed or acutely decompensated heart failure (HF), lethal arrhythmia such as sustained ventricular tachycardia/fibrillation, and new-onset atrial fibrillation within 30 days after the index surgery.ResultsPreoperative echocardiography was performed in 503 patients (39.8%), most frequently in patients with breast surgery (73.5%), followed by transurethral (37.7%), distal bone (21.6%), and thyroid surgeries (11.9%). Abnormal findings were observed in 5.0% of patients with preoperative echocardiography. Postoperative adverse CVEs occurred in 10 (0.79%) patients. Although a history of previous HF was an independent predictor of postoperative CVE occurrence (adjusted odds ratio, aOR: 17.98; 95% confidence interval, CI: 1.21–266.71, P = 0.036), preoperative echocardiography did not significantly predict CVE in multivariate analysis (P = 0.097). However, in patients who underwent preoperative echocardiography, the presence of abnormal echocardiographic findings was independently associated with development of CVE after NCS (aOR: 23.93; 95% CI: 1.2.28–250.76, P = 0.008). In particular, the presence of wall motion abnormality was a strong predictor of postoperative adverse CVE.ConclusionIn real-world clinical practice, preoperative echocardiography was performed in substantial number of patients with potential cardiac risk even in low-risk NCS, and abnormal findings were independently associated with postoperative CVE. Future studies should identify patients undergoing low-risk NCS for whom preoperative echocardiography would be helpful to predict adverse CVE.

Hao Zhang, Xupo Ding, Xupo Ding et al.

IntroductionAgarwood, the dark-brown resin produced by Aquilaria trees, has been widely used as incense, spice, perfume or traditional medicine and 2-(2-phenethyl) chromones (PECs) are the key markers responsible for agarwood formation. But the biosynthesis and regulatory mechanism of PECs were still not illuminated. The transcription factor of basic leucine zipper (bZIP) presented the pivotal regulatory roles in various secondary metabolites biosynthesis in plants, which might also contribute to regulate PECs biosynthesis. However, molecular evolution and function of bZIP are rarely reported in Malvales plants, especially in Aquilaria trees.Methods and resultsHere, 1,150 bZIPs were comprehensively identified from twelve Malvales and model species genomes and the evolutionary process were subsequently analyzed. Duplication types and collinearity indicated that bZIP is an ancient or conserved TF family and recent whole genome duplication drove its evolution. Interesting is that fewer bZIPs in A. sinensis than that species also experienced two genome duplication events in Malvales. 62 AsbZIPs were divided into 13 subfamilies and gene structures, conservative domains, motifs, cis-elements, and nearby genes of AsbZIPs were further characterized. Seven AsbZIPs in subfamily D were significantly regulated by ethylene and agarwood inducer. As the typical representation of subfamily D, AsbZIP14 and AsbZIP41 were localized in nuclear and potentially regulated PECs biosynthesis by activating or suppressing type III polyketide synthases (PKSs) genes expression via interaction with the AsPKS promoters.DiscussionOur results provide a basis for molecular evolution of bZIP gene family in Malvales and facilitate the understanding the potential functions of AsbZIP in regulating 2-(2-phenethyl) chromone biosynthesis and agarwood formation.

Satheesh Kumar, Guei-Sheung Liu

Manuel Alberto M. Ferreira, José António Filipe

There is already a significant time, but it gives the sensation of extremely short,nanotechnology has become one of the most promising scientific hopes in innumerable human domains. Now the hope become reality. Countless scientific studies in several areas of knowledge have been made since the nanoscale emergence, carrying their contribution to the nanoscience development. The recent research in this field allowed the union of interests among several areas, such as physical sciences, molecular engineering, biology, biotechnology and medicine for example, contributing to the investigation of biosystems at a nanoscale. In this work begin discussing nanotechnology in a general way. Then nanotechnology and the applications in industry, in electronics and in medicine are presented and some discussion is proposed in order to define the boundaries for the advances on those areas. In the end, nanotechnology is discussed in terms of ethics and in terms of the borders that nanotechnology applications must satisfyand concluding notes are presented, highlighting the results of the analysis. Important considerations are made about the close connection between ethics and the nanotechnology and the effects over the society and values. Some future directions for the research are suggested.