Hasil untuk "Medicine (General)"

J. Despres, C. Ewing

Bin Liu, Ihtisham Bukhari, Fazhan Li et al.

Rajat Vashistha, Arif Gulzar, Parveen Kundu et al.

India faces a significant cancer burden, with an incidence-to-mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mitigate these constraints. However, given the country's vast geography and high population density, it is imperative to explore alternative soft infrastructure solutions to complement existing frameworks. Artificial Intelligence agents are increasingly transforming problem-solving approaches across various domains, with their application in medicine proving particularly transformative. In this perspective, we examine the potential role of AI agents in advancing nuclear medicine for cancer research, diagnosis, and management in India. We begin with a brief overview of AI agents and their capabilities, followed by a proposed agent-based ecosystem that can address prevailing sustainability challenges in India nuclear medicine.

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.

Prabhdeep Kaur, Rupali Bharadwaj, Mogan Kaviprawin et al.

Background In India, to achieve a 25% relative reduction in the prevalence of raised blood pressure (BP) by 2025, approximately 4.5 crore additional people with hypertension will need to have their BP effectively treated. We conducted a Quality Improvement (QI) initiative to improve follow-up and reduce missed visits among individuals with hypertension registered under India Hypertension Control Initiative, District Hospital, Seoni, Madhya Pradesh, India, in 2022.Methods We conducted a quasiexperimental study from January to September 2022 in the District Hospital in Seoni, Madhya Pradesh. Following the Ishikawa diagram, the major root causes for missed visits were identified, and countermeasures were developed. The packages under Plan-Do-Study-Act (PDSA) included (i) training urban Accredited Social Health Activists to conduct house visits for individuals with missed visits and (ii) triangulating the follow-up records from various information systems. The review meetings for QI initiatives were conducted fortnightly to follow-up PDSAs. We calculated the proportion of individuals who were followed-up monthly, and the proportion of missed visits among those registered quarterly.Results Cumulatively, 2850 individuals were registered with hypertension till September 2022. Following the intervention, the monthly follow-up proportion increased from 21% in January to 37% in September 2022. Missed visit proportion decreased from 66% (228/345) in quarter four, 2021, to 22% (40/180) in quarter three, 2022. Of the 1438 individuals counselled by ASHA home visits, 74.9% returned for follow-up.Conclusion In our setting, QI initiatives suggested that missed visits decreased during the intervention period. However, the interventions must be implemented continuously for better monitoring and use in similar settings.

Oana-Maria Eșanu, Codruța Șoica, Marius Mioc

Skin and colorectal cancers are among the most pressing global health concerns, highlighting the urgent need for the development of novel therapeutic strategies [...]

Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, João Dallyson Sousa de Almeida et al.

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H&E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset’s versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.

Wasif Khan, Seowung Leem, Kyle B. See et al.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.

Ahmed Medhat Zayed, Arne Janssens, Pavlos Mamouris et al.

Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. Methods We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. Results lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm’s efficacy in handling large-scale data sets. Conclusions lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management. Graphical Abstract

Ya-Wen Peng, Ri Tang, Qiao-Yi Xu et al.

Background: Fibrosis is a heavy burden on the global healthcare system. Recently, an increasing number of studies have demonstrated that Extracellular vesicles play an important role in intercellular communication under both physiological and pathological conditions. This study aimed to explore the role of extracellular vesicles’ in fibrosis using bibliometric methods. Methods: Original articles and reviews related to extracellular vesicles and fibrosis were obtained from the Web of Science Core Collection database on November 9, 2022. VOSviewer was used to obtain general information, including co-institution, co-authorship, and co-occurrence visualization maps. The CiteSpace software was used to analyze citation bursts of keywords and references, a timeline view of the top clusters of keywords and cited articles, and the dual map. R package ''bibliometrix'' was used to analyze annual production, citation per year, collaboration network between countries/regions, thematic evolution map, and historiography network. Results: In total, 3376 articles related to extracellular vesicles and fibrosis published from 2013 to 2022 were included in this study, with China and the United States being the top contributors. Shanghai Jiao Tong University has the highest number of publications. The main collaborators were Giovanni Camussi, Stefania Bruno, Marta Tepparo, and Cristina Grange. Journals related to molecular, biology, genetics, health, immunology, and medicine tended to publish literature on extracellular vesicles and fibrosis. “Recovery,” “heterogeneity,” “degradation,” “inflammation,” and “mesenchymal stem cells” are the keywords in this research field. Literature on extracellular vesicles and fibrosis associated with several diseases, including “kidney disease,” “rheumatoid arthritis,” and “skin regeneration” may be the latest hot research field. Conclusions: This study provides a comprehensive perspective on extracellular vesicles and fibrosis through a bibliometric analysis of articles published between 2013 and 2022. We identified the most influential countries, institutions, authors, and journals. We provide information on recent research frontiers and trends for scholars interested in the field of extracellular vesicles and fibrosis. Their role in biological processes has great potential to initiate a new upsurge in future research.

Ahmad Wisnu Mulyadi, Heung-Il Suk

Extensive adoption of electronic health records (EHRs) offers opportunities for their use in various downstream clinical analyses. To accomplish this purpose, enriching an EHR cohort with external knowledge (e.g., standardized medical ontology and wealthy semantics) could help us reveal more comprehensive insights via a spectrum of informative relations among medical codes. Nevertheless, harnessing those beneficial interconnections was scarcely exercised, especially in the medication recommendation task. This study proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed) to recommend medicines by inducing knowledge from myriad medical-related external sources upon the EHR cohort and rendering interconnected medical codes as medical knowledge graphs (KGs). On top of relation-aware graph representation learning to obtain an adequate embedding over such KGs, we leverage hierarchical sequence learning to discover and fuse temporal dynamics of clinical (i.e., diagnosis and procedures) and medicine streams across patients' historical admissions to foster personalized recommendations. Eventually, we employ attentive prescribing that accounts for three essential patient representations, i.e., a summary of joint historical medical records, clinical progression, and the current clinical state of patients. We validated the effectiveness of our KindMed on the augmented real-world EHR cohorts, achieving improved recommendation performances against a handful of graph-driven baselines.

Krystian Strzałka, Szymon Mazurek, Maciej Wielgosz et al.

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

Scott Mastromatteo, Angela Chen, Jiafen Gong et al.

Summary: Phasing of heterozygous alleles is critical for interpretation of cis-effects of disease-relevant variation. We sequenced 477 individuals with cystic fibrosis (CF) using linked-read sequencing, which display an average phase block N50 of 4.39 Mb. We use these samples to construct a graph representation of CFTR haplotypes, demonstrating its utility for understanding complex CF alleles. These are visualized in a Web app, CFTbaRcodes, that enables interactive exploration of CFTR haplotypes present in this cohort. We perform fine-mapping and phasing of the chr7q35 trypsinogen locus associated with CF meconium ileus, an intestinal obstruction at birth associated with more severe CF outcomes and pancreatic disease. A 20-kb deletion polymorphism and a PRSS2 missense variant p.Thr8Ile (rs62473563) are shown to independently contribute to meconium ileus risk (p = 0.0028, p = 0.011, respectively) and are PRSS2 pancreas eQTLs (p = 9.5 × 10−7 and p = 1.4 × 10−4, respectively), suggesting the mechanism by which these polymorphisms contribute to CF. The phase information from linked reads provides a putative causal explanation for variation at a CF-relevant locus, which also has implications for the genetic basis of non-CF pancreatitis, to which this locus has been reported to contribute.

Holger Engleitner, Ashwani Jha, Marta Suarez Pinilla et al.

The characteristics and determinants of health and disease are often organised in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Though a mature discipline, spatial analysis is comparatively rare in medicine, arguably a consequence of the complexity of the domain and the inclemency of the data regimes that govern it. Drawing on statistical parametric mapping, a framework for topological inference well-established in the realm of neuroimaging, we propose and validate a novel approach to the spatial analysis of diverse clinical data - GeoSPM - based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is transparently interpretable, can be implemented with ease by non-specialists, enables flexible modelling of complex spatial relations, exhibits robustness to noise and under-sampling, offers well-founded criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation of GeoSPM, and suggest that its adoption could catalyse the wider use of spatial analysis across the many aspects of medicine that urgently demand it.

Yan Luo, Elizabeth M. Mullin, Kathleen T. Mellano et al.

The Behavioral Regulation in Exercise Questionnaire (BREQ) was revised to its third iteration (BREQ-3) and has been widely used to measure different types of exercise motivation, including amotivation, external regulation, introjected regulation, identified regulation, integrated regulation, and intrinsic motivation. However, the Chinese version has not been similarly revised. The aim of this study was to develop and examine the psychometric properties of the Chinese BREQ-3 using alternative structural equation models. Specifically, this study aimed to translate the English BREQ-3 into Chinese to examine the best representation of the factor configuration of Chinese BREQ-3, measurement invariance for the best-fitted model, and the concurrent validity evidence and reliability for the Chinese BREQ-3. Undergraduate students (N = 825) from mainland China completed a battery of online questionnaires. After including two general motivation factors (controlled motivation and autonomous motivation), we discovered that the majority of items on the identified regulation, integrated regulation, and intrinsic motivation subscales no longer loaded on or had very low loadings on their specific factors, implying that these items essentially represent a unidimensional construct. Invariance testing supported the comparison between latent factor means across gender based on the bi-factor exploratory structural equation model (BESEM). Concurrent validity evidence was found for amotivation, controlled motivation, and autonomous motivation. The hierarchical omega, explained common variance (ECV), item explained common variance (I_ECV), and percentage of uncontaminated correlations (PUC) indicated that the external regulation and introjected regulation subscales had a multidimensional structure, while the identified regulation, integrated regulation, and intrinsic motivation subscales had a unidimensional structure (autonomous motivation). We advocate calculating amotivation, external regulation, introjected regulation, and a single autonomous motivation (excluding item 19) score when utilizing the Chinese BREQ-3.

Juan A. Méndez-Líter, Ana Pozo-Rodríguez, Enrique Madruga et al.

Glycoside hydrolases (GHs) are enzymes that hydrolyze glycosidic bonds, but some of them can also catalyze the synthesis of glycosides by transglycosylation. However, the yields of this reaction are generally low since the glycosides formed end up being hydrolyzed by these same enzymes. For this reason, mutagenic variants with null or drastically reduced hydrolytic activity have been developed, thus enhancing their synthetic ability. Two mutagenic variants, a glycosynthase engineered from a β-glucosidase (BGL-1-E521G) and a thioglycoligase from a β-xylosidase (BxTW1-E495A), both from the ascomycete <i>Talaromyces amestolkiae</i>, were used to synthesize three novel epigallocatechin gallate (EGCG) glycosides. EGCG is a phenolic compound from green tea known for its antioxidant effects and therapeutic benefits, whose glycosylation could increase its bioavailability and improve its bioactive properties. The glycosynthase BGL-1-E521G produced a β-glucoside and a β-sophoroside of EGCG, while the thioglycoligase BxTW1-E495A formed the β-xyloside of EGCG. Glycosylation occurred in the 5″ and 4″ positions of EGCG, respectively. In this work, the reaction conditions for glycosides’ production were optimized, achieving around 90% conversion of EGCG with BGL-1-E521G and 60% with BxTW1-E495A. The glycosylation of EGCG caused a slight loss of its antioxidant capacity but notably increased its solubility (between 23 and 44 times) and, in the case of glucoside, also improved its thermal stability. All three glycosides showed better antiproliferative properties on breast adenocarcinoma cell line MDA-MB-231 than EGCG, and the glucosylated and sophorylated derivatives induced higher neuroprotection, increasing the viability of SH-S5Y5 neurons exposed to okadaic acid.

ZUO Qiantao, CHENG Jingxia, PENG Weixue et al.

ObjectiveTo explore the urban-rural differences and influencing factors of the e⁃Health literacy level of community residents.MethodsA total of 452 rural community residents and 464 urban community' residents in Chengdu were enrolled in this study using the multi⁃stage sampling method as the research subjects.Use the general information questionnaire and E⁃Health Literacy Scale （eHEALS） to survey and analyze the influencing factors.ResultsThe eHEALS score of rural community residents was 32（24，40）points，the eHEALS score of urban community residents was 28（21，34） points，and the difference was statistically significant（<italic>Z</italic>=7.394，<italic>P</italic>&lt;0.001）.Multivariate Logistic regression analysis showed that gender，education level，marital status，and family's financial situation were the influencing factors of the e⁃Health literacy level of rural community residents；education level and family's financial situation were the influencing factors of the e⁃Health literacy level of urban community residents.ConclusionsThe urban⁃rural differences existed in the e⁃Health literacy level of community residents.Targeted intervention measures should be taken according to the influencing factors of e⁃Health literacy of urban and rural residents，and the application training of e⁃Health⁃related information of community residents should be strengthened.

Andreea Avramescu, Richard Allmendinger, Manuel López-Ibáñez

With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received increased attention from the industry and medical research, driven by health and care services, and us, the patients. Notwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to treat progressive illnesses and rare genetic conditions, their delivery on large scale is still problematic. The biopharmaceutical companies are currently struggling to meet timely delivery and, given high prices of up to $2 million per patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs combined with the impossibility for replacements due to the nature of the treatment and the advanced stages of the patient's condition are some of the bottlenecks added to a generally critical supply chain. As a consequence, ATMPs are currently used in most cases only as a last resort. ATMPs are at the intersection of multiple healthcare logistic networks and, due to their novelty, research around their commercialisation is still in its infancy from an operations research perspective. To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation. The identified challenges and derived model will be contrasted with literature of related supply chains in terms of model formulations and suitable optimisation methods. Finally, needed technological advancements are discussed to pave the way to affordable commercialisation of PM.

R. García-Ramos, D. Santos-García, A. Alonso-Cánovas et al.

Introduction: Many diseases associated with hyperkinetic movement disorders manifest in women of childbearing age. It is important to understand the risks of these diseases during pregnancy, and the potential risks of treatment for the fetus. Objectives: This study aims to define the clinical characteristics and the factors affecting the lives of women of childbearing age with dystonia, chorea, Tourette syndrome, tremor, and restless legs syndrome, and to establish guidelines for management of pregnancy and breastfeeding in these patients. Results: This consensus document was developed through an exhaustive literature search and a discussion of the content by a group of movement disorder experts from the Spanish Society of Neurology. Conclusions: We must evaluate the risks and benefits of treatment in all women with hyperkinetic movement disorders, whether pre-existing or with onset during pregnancy, and aim to reduce effective doses as much as possible or to administer drugs only when necessary. In hereditary diseases, families should be offered genetic counselling. It is important to recognise movement disorders triggered during pregnancy, such as certain types of chorea and restless legs syndrome. Resumen: Introducción: Muchas enfermedades que cursan con trastornos del movimiento hipercinético debutan o afectan a mujeres en edad fértil. Es importante conocer los riesgos que tienen las mujeres con estas enfermedades durante el embarazo así como los posibles efectos de los tratamientos sobre el feto. Objetivos: Definir las características clínicas y los factores que condicionan la vida de la mujer en edad fértil con distonía, corea, síndrome de Tourette, temblor y síndrome de piernas inquietas. Definir una guía de actuación y manejo del embarazo y lactancia en las pacientes con esta enfermedad. Desarrollo: Este documento de consenso se ha realizado mediante una búsqueda bibliográfica exhaustiva y discusión de los contenidos llevadas a cabo por un grupo de expertos en trastornos del movimiento de la Sociedad Española de Neurología (SEN). Conclusiones: En todas las mujeres que padecen o debutan con trastornos del movimiento hipercinéticos se debe valorar el riesgo-beneficio de los tratamientos, reducir al máximo la dosis eficaz o administrarlo de forma puntual en los casos en que sea posible. En aquellas patologías de causa hereditaria es importante un consejo genético para las familias. Es importante reconocer los trastornos del movimiento desencadenados durante el embarazo como determinadas coreas y el síndrome de piernas inquietas.

Jiaming Qu, Yue Wang

The Precision Medicine Initiative states that treatments for a patient should take into account not only the patient's disease, but his/her specific genetic variation as well. The vast biomedical literature holds the potential for physicians to identify effective treatment options for a cancer patient. However, the complexity and ambiguity of medical terms can result in vocabulary mismatch between the physician's query and the literature. The physician's search intent (finding treatments instead of other types of studies) is difficult to explicitly formulate in a query. Therefore, simple ad hot retrieval approach will suffer from low recall and precision. In this paper, we propose a new retrieval system that helps physicians identify effective treatments in precision medicine. Given a cancer patient with a specific disease, genetic variation, and demographic information, the system aims to identify biomedical publications that report effective treatments. We approach this goal from two directions. First, we expand the original disease and gene terms using biomedical knowledge bases to improve recall of the initial retrieval. We then improve precision by promoting treatment-related publications to the top using a machine learning reranker trained on 2017 Text Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation results on 2018 PM track corpus show that the proposed approach effectively improves both recall and precision, achieving performance comparable to the top entries on the leaderboard of 2018 PM track.