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Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in complex regional pain syndrome.

BackgroundCollaborative efforts of physicians and basic scientists are often necessary in the investigation of complex disorders. Difficulties can arise, however, when large amounts of information need to reviewed. Advanced information retrieval can be beneficial in combining and reviewing data obtained from the various scientific fields. In this paper, a team of investigators with varying backgrounds has applied advanced information retrieval methods, in the form of text mining and entity relationship tools, to review the current literature, with the intention to generate new insights into th

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… Bioinformatics applies informatics techniques to organize bio-molecular data on a large scale [ 1 ]. … INFOBIOMED is a Network of Excellence funded by the European Union that aims at enforcing European biomedical informatics as an integrative discipline [ 3 , 4 ]. …

Mirels Scores in Patients Undergoing Prophylactic Stabilization for Femoral Metastatic Bone Disease in the Veterans Administration Healthcare System.

IntroductionA retrospective review was performed for patients in the Veterans Administration Healthcare System who underwent prophylactic stabilization of the femur for metastatic disease. The goal was to evaluate indications for prophylactic stabilization through Mirels criteria.MethodsAll veterans who underwent inpatient prophylactic femoral stabilization between October 2010 and September 2015 were identified. Procedure and demographic variables were collected by using chart review. Provider notes and radiographs were reviewed to calculate Mirels scores.ResultsNinety-two patients underwent

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… Procedure date and location and demographic variables were collected from Veterans Affairs Informatics and Computing Infrastructure Corporate Data Warehouse using Structured Query Language. … While complex from an informatics perspective, the VA data may provide an avenue to further our understanding of these important topics in subsequent investigations. Verification and comparison with other healthcare delivery systems may be warranted. …

Chimeric antigen receptor T-cell therapy: Challenges and framework of outpatient administration.

Adoptive cellular therapy has made a landmark change within the treatment paradigm of several hematologic malignancies, and novel cellular therapy products, such as chimeric antigen receptor T-cell therapy (CART), have demonstrated impressive efficacy and produced durable responses. However, the CART treatment process is associated with significant toxicities, healthcare resource utilization, and financial burden. Most of these therapies have been administered in the inpatient setting due to their toxicity profile. Improved toxicity management strategies and a better understanding of cellular

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… Alliances to this central group include pharmacy informatics, emergency department (ED) staff and intensive care unit (ICU) staff, and hospitalists [ 11 ]. … Pharmacy informatics specialists could help build tocilizumab orders to be available for quicker dispensing and administration. …

Integrating digital and narrative medicine in modern healthcare: a systematic review.

The increasing integration of digital technologies in healthcare, such as electronic health records, telemedicine, and diagnostic algorithms, improved efficiency but raised concerns about the depersonalization of care. Narrative medicine has emerged as a pedagogical and clinical response to this shift, emphasizing the value of patient stories, socio-cultural contexts, and reflective practice. Understanding how digital tools support, rather than undermine, narrative competencies is critical for developing a more human-centered healthcare education. This review systematically examined empirical

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… Similarly [ 35 ], examine the use of digital tools and informatics to refine the narrative and linguistic aspects of nursing handoffs in varied-acuity hospital settings. … Their findings point to the viability of narrative-aware informatics models that align data integrity with humanistic practice. …

Development of a clinical trial knowledge management application for community oncology.

IntroductionParticipation in cancer clinical trials is low in community oncology settings, partly because institution-specific trial information is fragmented. We evaluated feasibility of embedding curated trial content in an AI-enabled knowledge management application.Materials and methodsAt a regional community oncology network, coordinators and disease teams compiled actively recruiting trials. Core elements (title, conditions, biomarkers, stage/line, and recruiting status) were structured for point-of-care display and uploaded. AI-assisted extraction generated protocol summaries and eligib

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… Content development was supported by existing clinical and research infrastructure, without the need for additional personnel or informatics resources. … This work adds to the growing literature on informatics tools supporting clinical trial operations. …

Translational Bioinformatics Applied to the Study of Complex Diseases.

Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment o…

Utilization of translational bioinformatics to identify novel biomarkers of bortezomib resistance in multiple myeloma.

Multiple myeloma (MM) is an incurable malignant neoplasm hallmarked by a clonal expansion of plasma cells, the presence of a monoclonal protein in the serum and/or urine (M-spike), lytic bone lesions, and end organ damage. Clinical outcomes for patients with MM have improved greatly over the last decade as a result of the re-purposing of compounds such as thalidomide derivatives, as well as the development of novel chemotherapeutic agents including first and second generation proteasome inhibitors, bortezomib (Bz) and carfilzomib. Unfortunately, despite these improvements, the majority of pati

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… Utilizing translational informatics and applying genetic data to patient care has a variety of benefits such as improved treatment efficacy, increased patient safety, and reduced cost. … Translational informatics has the potential to improve treatment strategies for malignancies such as MM by evaluating large datasets of genetic findings that could provide insight into differing treatment effects between patients. …

Применение цифровой технологии в области управления качеством медицинских сестер: обзор аналитического исследования.

Справочная технология включает в себя такие информационно-коммуникационные средства, как электронные медицинские записи, износимые устройства и искусственный интеллект, которые стали важной движущей силой оптимизации управления качеством медицинских сестер. Вместе с тем существующие исследования по применению цифровых технологий в области управления качеством ухода за больными носят фрагментарный характер с нечеткими концептуальными границами и отсутствием систематического картирования фактических данных. Цель этого аналитического обзора состоит в том, чтобы систематически картировать прикладн

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… Shen ZY Zhong ZQ Ding SQ Research progress on nursing management informatization in China Chin J Nurs 2020 55 397 401 Shen ZY, Zhong ZQ, Ding SQ, et al. Research progress on nursing management informatization in China. Chin J Nurs. 2020;55:397–401. 4. … Using informatics framework in immunosuppressed or transplanted patients for a preferential care route at the emergency department. Stud Health Technol Inf. 2022;290:457–9. 10.3233/SHTI220116. 10.3233/SHTI220117 35673056 7. …

Seeking American Society of Clinical Oncology-Quality Oncology Practice Initiative (ASCO-QOPI) certification in a northern New England rural health system and cancer care network.

In 2006 following several years of preliminary study, the American Society of Clinical Oncology (ASCO) launched the Quality Oncology Practice Initiative (QOPI). This cancer-focused quality initiative evolved considerably over the next decade-and-a-half and is expanding globally. QOPI is undoubtedly the leading standard-bearer for quality cancer care and contemporary medical oncology practice. The program garners attention and respect among federal programs, private insurers, and medical oncology practices across the nation. The MaineHealth Cancer Care Network (MHCCN) has undergone expansive gr

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… The group is comprised of all MHCCN oncology providers, pharmacists, select Advance Practice Professionals (APPs), and practice managers, and our informatics team is devoted to the project. … Attention to identifying EMR searchable fields became readily apparent and the need for a strong informatics team to accommodate and build the changes that are required cannot be understated. …

Application and Improvement of YOLO11 for Carotid Plaque Detection in Handheld Ultrasound Images.

Early and accurate detection of carotid plaques is clinically significant for stroke risk assessment and primary care screening. To address challenges such as low resolution, small target size, and blurred boundaries in handheld ultrasound images of carotid plaques, this paper proposes an improved YOLO11n model named YOLO11-MGD. The model introduces the C2PSA_Mona module to suppress interference from normal anatomical structures and incorporates the C3k2_GCConv module to enhance global context modeling capabilities, thereby alleviating semantic fragmentation caused by heterogeneous plaques. Ad

Performance of Naïve Spectral Geometric Models in Histopathology AI.

Spectral geometric methods provide mathematically interpretable representations of tissue morphology, yet their standalone performance in histopathology artificial intelligence has not been systematically evaluated. This study benchmarks purely spectral pipelines across representative digital pathology and spatial omics tasks relative to convolutional baselines. Spectral features derived from graph Laplacians, heat kernel signatures, spectral wavelet energy, Slepian concentrations, fractal descriptors, and persistent homology were extracted from histopathology patches using fixed-grid graphs.

Effectiveness of a Stoma Self-Care Mobile App in Enhancing Patients' Ostomy Self-Care Ability: A Development and Usability Study.

Patients with colorectal cancer or severe abdominal trauma often require a stoma and must master the self-care necessary to prevent peristomal skin complications. In this study, we developed and evaluated a mobile health (mHealth) app, called The Stoma Self-Care App, to facilitate self-care knowledge and skill acquisition alongside clinical monitoring in patients with ostomy. The app was developed following the 6 stages of the System Development Life Cycle: planning, design, development, testing, deployment, and maintenance. To evaluate usability, the app was tested in 23 patients over 4 weeks

Customized CNN Architectures Outperform Pre-Trained Models in Differentiating Normal Brain Tissues, Glioma, Meningioma, and Pituitary Tumors.

Early and accurate detection of brain tumors is essential for improving treatment outcomes and patient survival. While pre-trained deep learning models such as ResNet, VGG, and MobileNet have achieved notable success in medical image classification, their generalized architectures often struggle to capture the intricate heterogeneity of brain tissues. This study introduces a customized Convolutional Neural Network (CNN) specifically designed for brain tumor classification, demonstrating superior performance over widely used pre-trained models. The primary objective of this research is to evalu

Radiomic Signatures of Smoking in Visually Normal Lung Parenchyma: Bilateral CT Analysis Using Machine Learning.

Smoking is associated with structural and functional alterations in the lungs, some of which may be reflected in quantitative imaging features. Radiomics enables the extraction of high-dimensional image-derived descriptors and may provide information beyond routinely available clinical variables. We investigated whether bilateral lung radiomics, both independently and integrated with clinical variables, could classify patient-level smoking status. An exploratory secondary analysis was also performed to assess whether radiomic and clinical feature representations could discriminate smoking expo

Federated Learning-Based Multi-Task Framework for Privacy-Preserving Diagnosis of Muscle Invasion in Bladder Cancer.

Accurate diagnosis of muscle-invasive bladder cancer (MIBC) is critical for effective treatment planning, yet remains challenging owing to the scarcity of large-scale multicenter datasets and stringent data privacy requirements. To address these issues, we propose a federated learning (FL)-based framework for privacy-preserving MIBC diagnosis. The framework enables multi-institutional collaboration by training local models on site-specific data and aggregating them into a global model through secure parameter exchange, without transferring raw data. Furthermore, we introduce a multi-task learn

Wavelet Scalogram-Based Nystagmus Classification for Automated Benign Paroxysmal Positional Vertigo Subtype Diagnosis.

Benign paroxysmal positional vertigo (BPPV) is the most common cause of vestibular vertigo, yet accurate diagnosis remains challenging without specialist expertise. This study aimed to develop and evaluate an artificial intelligence (AI)-based diagnostic algorithm for BPPV using video-nystagmography (VNG) recordings. A total of 1352 VNG recordings were collected from patients with posterior semicircular canal (PSCC) BPPV (n = 234), lateral semicircular canal (LSCC) BPPV (canalolithiasis, n = 188; cupulolithiasis, n = 58), and healthy controls (n = 224) at a single tertiary referral hospital, y

Cardiac Image Segmentation Under Respiratory Motion Artifacts Using Different Modified U-Net Models.

Precise segmentation of cardiac magnetic resonance images (CMR) is essential to quantitative measurements of the heart, yet it is often affected by respiratory motion misregistration's and this is particularly common in free-breathing patient environments. These artifacts reduce the quality of the image and present a big challenge to traditional deep learning models such as the baseline U-Net. This paper carries out a systematic evaluation of the capabilities of four alternate U-Net architectures, Attention U-Net, Dense U-Net, Residual U-Net and Sharp U-Net, on CMR data with synthetically adde

Comparison of an Artificial Intelligence Nutrition Platform With Dietitians in Providing Nutrition Support to Patients With Cancer.

PURPOSE: Patients with cancer experience nutrition challenges, but access to registered dietitians (RDs) is limited. The purpose of this study was to determine whether a specialized Artificial Intelligence (AI) Nutrition Platform trained on curated high-quality evidence is equivalent in providing nutrition information to patients with cancer compared with oncology-credentialed RDs. METHODS: Twenty nutrition queries were developed by an expert panel, encompassing common cancer types, symptoms, and food allergies/preferences. These queries were administered to the AI Platform and to four nation

Automated Multi-class Segmentation of Mandibular Odontogenic Cysts and the Mandibular Canal on CBCT Images Using a Hybrid Deep Learning Framework.

Mandibular odontogenic cysts can complicate surgical planning if located near the mandibular canal. Accurate delineation of both structures is therefore clinically important. This study presents a deep learning-based method for simultaneously segmenting mandibular odontogenic cysts and the mandibular canal in cone-beam computed tomography (CBCT) images. The developed method relies on a hybrid deep learning architecture integrating a ConvNeXt-Base encoder, a Dilated Context Block, a U-Net-based decoder, and a Deep Supervision mechanism. This framework simultaneously segments the mandibular cana

Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation.

Accurate preoperative differentiation of pediatric acute appendicitis subtypes is relevant to treatment planning. We developed and externally validated a machine-learning framework combining habitat-based computed tomography (CT) radiomics with clinical variables for three-class classification (simple, complex, and appendiceal abscess). This retrospective dual-center study included 317 children undergoing appendectomy at Institution A (training n = 222, internal validation n = 95) and 174 children at Institution B (external validation). K-means + + habitat segmentation (k = 3) of preoperative