Search Results (2,692)

Hepatitis B virus (HBV) infection is a global health concern with an estimated 254 million people with chronic HBV infection. The utilization of immunosuppressive therapies (ISTs) is increasing and expanding continuously with new agents being implemented across multiple medical disciplines. The occurrence of HBV reactivation (HBVr) during or after IST varies from 15% to 50% in HBsAg-positive individuals and can be higher than 75% after stem cell transplantation. HBVr is gaining increasing significance in contemporary clinical practice. The American Gastroenterological Association (AGA) in 2025, the European Association for the Study of the Liver (EASL) in 2025, and the Asian Pacific Association for the Study of the Liver (APASL) in 2021, published their most recent clinical guidelines as major societies in the area, which enables us to better predict and manage HBVr. This narrative review focuses on comparing these three current guidelines, highlighting key similarities and differences to provide valuable guidance for practitioners navigating the complex, sometimes conflicting recommendations, thereby aiding clinicians in their decision-making. The risk of HBVr during IST has been stratified into three categories in all three guidelines: high (>10%), moderate (1–10%), and low (<1%). The effectiveness of prophylaxis scales with baseline risk for HBV reactivation. Prophylaxis is clearly cost-effective for high-risk patients, potentially beneficial for those at moderate risk, and generally may not be justified for low-risk individuals. Entecavir (ETV), tenofovir disoproxil fumarate (TDF), and tenofovir alafenamide (TAF) are all highly effective in preventing HBV reactivation during immunosuppression and all are considered to be economically viable options for HBVr high risk patients. When selecting among these agents, safety considerations—particularly renal and bone toxicity—and insurance coverage remain the primary factors directing clinical decision-making. Full article

Background/Objectives: Hippocampal atrophy indicates Alzheimer’s disease (AD) progression and guides follow-up and trial enrichment. Identifying high-risk patients is crucial for optimizing care, but accessible, interpretable machine-learning models (ML) are limited. We developed an explainable ML model using clinical data and CSF erythrocyte load (CTRED) to classify adults with AD as high- or low-risk based on hippocampal volume decline. Methods: Included ADNI participants with ≥2 MRIs, baseline lumbar puncture, and vital signs within 6 months of MRI (n = 26). The outcome was the Annual Percentage Change (APC) in hippocampal volume, classified as low or high risk. Predictors were standardized; models included SVM, logistic regression, and Ridge Classifier, tuned and tested on a set (n = 6). Thresholds were based on out-of-fold predictions under a 10–90% positive rate. Explainability used PFI and SHAP for per-patient contributions. Results: All models gave identical classifications, but discrimination varied: Ridge AUC = 1.00, logistic = 0.889, and SVM = 0.667. PFI highlighted MAPres and sex as main signals; CTRED contributed, and age had a minor impact. Conclusions: The explainable ML model with clinical data and CTRED can stratify AD patients by hippocampal atrophy risk, aiding follow-up and vascular assessment planning rather than treatment decisions. Validation in larger cohorts is needed. This is the first ML study to use CSF erythrocyte load to predict hippocampal atrophy risk in AD. Full article

Background: Despite recent advances in new therapies for early-stage breast cancer (eBC), the impact of the current treatment landscape on patients’ quality of life remains poorly understood. This study explored the experiences and unmet needs of women with eBC, leading to the development of a patient-informed conceptual disease model (PI-CDM) that summarizes patient priorities. Methods: This qualitative study used a step-wise approach: (1) a targeted literature review; (2) draft CDM development; (3) interview guide development; (4) semi-structured interviews with women in the United States with a diagnosis of eBC; (5) thematic content analysis of interview transcripts; (6) patient steering committee insights; and (7) PI-CDM finalization. Results: Thirty-six women with eBC (stage I, n = 18; stage II, n = 11; stage III, n = 9) were interviewed between December 2023 and May 2024. Key health concepts included signs and symptoms leading to diagnosis and common treatment side effects. Emotional and psychological impacts were prominent, and 28 participants reported moderate to extremely severe anxiety or depression on the EQ-5D-5L. Other impacts included social life, body satisfaction, daily activities, physical functioning, sexual functioning, and finances. Needs for improved communication from healthcare providers about treatment options and better support were emphasized. These insights, combined with patient steering committee recommendations, resulted in a final PI-CDM. Conclusions: This study highlights the substantial burden women with eBC face and provides a framework for future patient-centric research. A CDM developed with patients summarizes the complexity of the eBC experience and can aid discussions between patients and physicians, facilitating shared decision-making to enhance care. Full article

Background: Incontinence is a widespread and socially taboo phenomenon worldwide. Incontinence, with its various manifestations, is one of the most common illnesses in outpatient medical care and represents a serious health problem for those affected of all ages. As part of the second update of the German expert standard published in 2024 on continence promotion, a systematic literature review was conducted to identify, appraise, and synthesize current evidence on nursing interventions to promote urinary and fecal continence. The expert standard does not provide a gradation of recommendations, but rather that the criteria depicted in the standard have the highest possible recommendation character in the sense of the best available knowledge. The aim of this article is to present the examination of available evidence within the context of the second update of the expert standard. Methodology: A systematic literature review was conducted between September and December 2022 with additional guideline research in December 2023 in Medline (via PubMed), CINAHL (via EBSCO), and the Cochrane Library, using predefined inclusion and exclusion criteria. Additional guideline databases and organizational websites were searched manually. The review process and reporting were guided by PRISMA 2020 reporting standards. Eligible studies included qualitative, quantitative, and guideline publications in English or German published since 2012. Study selection, data extraction, and critical appraisal were conducted independently by two reviewers. Results: Of 2850 initial records, 60 studies met the inclusion criteria and were included in the review. The majority were systematic reviews and evidence-based guidelines. The central literature-based results of the expert standard are presented based on the steps of the nursing process. The findings were thematically synthesized along the steps of the nursing process and informed key nursing interventions for continence promotion and compensation, including assessment, patient education, pelvic floor training, and selection of continence aids. Discussion: There are a variety of evidence-based interventions that can be used to deal with urinary and/or fecal incontinence and the tasks that professional nurses take on in promoting continence are complex. Patients and their relatives want information and advice on treatment options, reliable contacts and individual support offers to make informed decisions. Full article

This paper presents a comprehensive design framework for Enterprise Architecture aimed at facilitating decision-driven analytics in smart factories. The motivation behind this research lies in challenges faced by manufacturing companies, such as skilled labor shortages and increasing global competition, alongside the imperative for sustainable production. This journal provides a novel approach for designing and documenting prescriptive analytics use cases in manufacturing environments. The framework addresses the need for effective integration of advanced data analytics and prescriptive analytics solutions within existing production environments, thereby enhancing operational efficiency and decision-making processes. A Design Science Research approach is used to iteratively derive a framework based on stakeholder needs and activities along the prescriptive analytics use case development cycle. The resulting framework is demonstrated and evaluated in an IoT Factory setup in a research facility. From a practical perspective, the framework supports manufacturing companies in systematically designing prescriptive analytics use cases. From a research perspective, it contributes to the body of knowledge on Enterprise Architecture Management (EAM) by operationalizing the design of prescriptive analytics use cases in manufacturing contexts. The main contributions of this study include the development of a framework that supports the planning, design, and integration of prescriptive analytics use cases. This framework fosters interdisciplinary collaboration and aids in managing the complexity of data-driven projects. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

Climate change is increasing the frequency and intensity of Marine Heatwave (MHW) events, threatening Western Tropical Pacific Small Island Developing States (SIDSs). MHWs critically threaten the fisheries sector which vitally supports food and nutrition security in local communities and local livelihoods. Currently, MHW risk to fisheries in Western Tropical Pacific SIDSs remains underexplored. Vanuatu is a Western Tropical Pacific SIDS which requires expanded MHW risk knowledge to improve the adaptive capacity of fisheries. A fundamental method for expanding MHW risk knowledge is tailored risk assessment. This study conducts the initial steps in a tailored MHW risk assessment methodology, displaying how a tailored indicator selection and weighting process can inform effective MHW risk assessment for fisheries in Western Tropical Pacific SIDSs. Hazard, vulnerability, and exposure indicators were selected through a combined process utilising a literature review and participatory research survey. Survey results were also used to develop a user-informed indicator weighting scheme. Selected indicators included sea surface temperature (SST), coral bleaching/mortality, and chlorophyll-a concentration (hazard); terrestrial-based food and income generation, fishing skills and technology, fishery fish diversity/fishery flexibility, and primary production of commercial fisheries (vulnerability); seagrass population/C content, coral habitat health/crown-of-thorns prevalence, crab stock health, and fish mortality/fish stock health (exposure). These indicators and their assigned weights are recommended for use in a future MHW risk assessment for Vanuatu fisheries. A tailored, fisheries-specific MHW risk assessment could advise local decision-makers on where/when MHW risk is high and aid the implementation of more effective fisheries risk management. Full article

The rapid rise of bio-hybrid robots and hybrid human–AI systems has triggered an explosion of terminology that inhibits clarity and progress. To investigate how terms are defined, we conduct a narrative scoping review and concept analysis. We extract 60 verbatim definitions spanning engineering, human–computer interaction, human factors, biomimetics, philosophy, and policy. Entries are coded on three axes: agency locus (human, shared, machine), integration depth (loose, moderate, high), and normative valence (negative, neutral, positive), and then clustered. Four categories emerged from the analysis: (i) machine-led, low-integration architectures such as neuro-symbolic or “Hybrid-AI” models; (ii) shared, moderately integrated systems like mixed-initiative cobots; (iii) human-led, medium-coupling decision aids; and (iv) human-centric, low-integration frameworks that focus on user agency. Most definitions adopt a generally positive valence, suggesting a gap with risk-heavy popular narratives. We show that, for researchers investigating where living meets machine, terminological precision is more than semantics and it can shape design, accountability, and public trust. This narrative review contributes a comparative taxonomy and a shared lexicon for reporting hybrid systems. Researchers are encouraged to clarify which sense of Hybrid-AI is intended (algorithmic fusion vs. human–AI ensemble), to specify agency locus and integration depth, and to adopt measures consistent with these conceptualizations. Such practices can reduce construct confusion, enhance cross-study comparability, and align design, safety, and regulatory expectations across domains. Full article

The prophylactic extraction of third molars is highly dependent on the surgeon’s experience as the common practices and guidelines contradict. The purpose of this study was to evaluate the eruption status of impacted third molars using deep learning-based artificial intelligence (AI) and to develop a model that predicts their final positions at an early stage to aid clinical decisions. In this retrospective study, 1102 panoramic radiographs (PANs) were annotated by three expert dentists to classify eruption status as either initial or definitive. A dataset was created and two deep learning architectures, InceptionV3 and ResNet50, were tested through a three-phase protocol: hyperparameter tuning, model evaluation, and assessment of preprocessing effects. Accuracy, recall, precision, and F1 score were used as performance metrics. Classical machine learning (ML) algorithms (SVM, KNN, and logistic regression) were also applied to features extracted from the deep models. ResNet50 with preprocessing achieved the best performance (F1 score: 0.829). Models performed better with definitive cases than with initial ones, where performance dropped (F1 score: 0.705). Clinically, the model predicted full eruption or impaction with 83% and 75% accuracy, respectively, but showed lower accuracy for partial impactions. These results suggest that AI can support early prediction of third molar eruption status and enhance clinical decision-making. Deep learning models (particularly ResNet50) demonstrated promising results in predicting third molar eruption outcomes. With larger datasets and improved optimization, AI tools may achieve greater accuracy and support routine clinical applications. Full article

Background/Objective: Esophageal cancer (EC) is a major global health issue due to its high mortality rate, as patients are often diagnosed at advanced stages. This research examines whether the Spectrum-Aided Vision Enhancer (SAVE), a hyperspectral imaging (HSI) technique, enhances endoscopic image categorization for superior diagnostic outcomes compared to traditional White Light Imaging (WLI) and Narrow Band Imaging (NBI). Methods: A dataset including 2400 photos categorized into eight disease types from National Taiwan University Hospital Yun-Lin Branch was utilized. Multiple machine learning and deep learning models were developed, including logistic regression, VGG16, YOLOv8, and MobileNetV2. SAVE was utilized to transform WLI photos into hyperspectral representations, and band selection was executed to enhance feature extraction and improve classification outcomes. The training and evaluation of the model incorporated precision, recall, F1-score, and accuracy metrics across WLI, NBI, and SAVE modalities. Results: The research findings indicated that SAVE surpassed both NBI and WLI by achieving superior precision, recall, and F1-scores. Logistic regression and VGG16 performed with a comparable reliability to SAVE and NBI, whereas MobileNetV2 and YOLOv8 demonstrated inconsistent yet enhanced results. Overall, SAVE exhibited exceptional categorization precision and recall, showcasing impeccable performance across many models. Conclusions: This research indicates that AI hyperspectral imaging facilitates early diagnosis of esophageal diseases, hence enhancing clinical decision-making and improving patient outcomes. The amalgamation of SAVE with machine learning and deep learning models enhances diagnostic capabilities, with SAVE and NBI surpassing WLI by offering superior tissue differentiation and diagnostic accuracy. Full article

Artificial intelligence (AI) applications have attracted widespread attention and have proven to be highly successful in understanding messages across various dimensions. These applications have the potential to assist astronomers in exploring the massive amounts of astronomical data. In fact, the integration of AI techniques with astronomy began some time ago, significantly advancing our understanding of the universe by aiding in exoplanet discovery, galaxy morphology classification, gravitational wave event analysis, and more. In particular, AI is now recognized as a crucial component in time-domain astronomy, particularly given the rapid evolution of targeting transients and the increasing number of candidates detected by powerful surveys. A notable success is SN 2023tyk, the first transient discovered and spectroscopically classified without human inspection, an achievement made even more remarkable given that it was identified by the Zwicky Transient Facility, which detects millions of alert sources every night. There is no doubt that AI will play a crucial role in future astronomical observations across various messenger channels, aiding in transient discovery and classification, and helping, or even replacing, observers in making real-time decisions. This review paper examines several cases where AI is transforming contemporary astronomy, especially time-domain astronomy. We discuss the AI algorithms and methodologies employed to date, highlight significant discoveries enabled by AI, and outline future research directions in this rapidly evolving field. Full article

Breast cancer is the second-leading cause of mortality among women worldwide. However, early detection and diagnosis significantly improve treatment outcomes. In recent years, Computer-Aided Diagnosis (CAD) systems, which leverage Artificial Intelligence (AI) techniques, have emerged as valuable tools for assisting radiologists in the accurate and efficient analysis of medical images. Following the PRISMA guidelines, this study presents the first meta-review that synthesizes evidence from 48 systematic reviews published between 2015 and January 2025. In contrast to previous reviews, which often focus on a single imaging modality or clinical task, our work provides a comprehensive overview of imaging techniques, publicly available datasets, AI methods, and clinical tasks employed in CAD systems for breast cancer diagnosis and treatment. Our analysis shows that mammography is the most frequently applied imaging modality, while DDSM, MIAS, and INBreast are the most commonly used datasets. Among clinical tasks, the detection and classification of breast lesions are the most extensively studied, with deep learning approaches being increasingly prevalent. However, current CAD systems face notable limitations, including the lack of large and diverse datasets, limited transparency and interpretability of AI-based decisions, and restricted clinical integration. By highlighting both the achievements and the limitations, this systematic review aims to support medical professionals and technical researchers in understanding the current state of CAD systems in breast cancer care and to provide guidance for future research directions. Full article

Background: Lymph node (LN) involvement is a negative prognostic factor for patients with cholangiocarcinoma (CCA). Preoperative assessment of the LN could potentially aid therapy decision making. Endoscopic ultrasound (EUS) can be used to sample suspicious LN. The aim of this study was to evaluate the clinical impact of EUS for suspicious LN in patients with presumed resectable CCA. Methods: In this single-center cohort study, patients with potentially resectable CCA who underwent preoperative linear EUS between 2019 and 2024 were retrospectively included. The primary aims were the percentage of malignant LN detected and the clinical impact of EUS, which was defined as the percentage of patients who were precluded from surgical exploration due to pathologically confirmed LN metastases found with EUS tissue acquisition (EUS-TA). The secondary aim was the complication rate of EUS-TA. Results: A total of 135 patients were included, of whom 12 (8.9%) had intrahepatic CCA (iCCA), 65 (48.1%) had perihilar CCA (pCCA), 13 had (9.6%) middle bile duct CCA (mCCA), and 45 (33.3%) had distal CCA (dCCA). Across 148 EUS procedures, 139 LNs were identified, and EUS-TA was performed on 63 LNs among 55 patients. LN metastases were detected by EUS-TA for iCCA, pCCA, mCCA, and dCCA, in 25%, 6.2%, 15.4%, and 4.4%, respectively. EUS and EUS-TA influenced surgical work-up for iCCA, pCCA, mCCA, and dCCA in 25%, 1.5%, 15.4%, and 0.0%, respectively. No complications associated with EUS were noted. Conclusions: Preoperative EUS for nodal staging had an important clinical impact in patients with presumed resectable iCCA and mCCA, but less for pCCA and dCCA. Further prospective studies should investigate whether systematic nodal staging with EUS could improve preoperative decision making even further. Full article

Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally. The advent of immune checkpoint inhibitors (ICIs) has significantly improved outcomes for a subset of patients; however, predictive biomarkers to identify responders are still lacking. Peripheral blood mononuclear cells (PBMCs) offer a minimally invasive means to assess systemic immune status and have emerged as a potential source of predictive biomarkers. Recent studies have highlighted the role of chemokines and their receptors in modulating immune responses against tumors. In particular, the expression levels of chemokine receptors such as CXCR4 on PBMCs have been associated with patient responses to ICIs. The differences in expression of these receptors correlates with enhanced T cell trafficking and infiltration into the tumor microenvironment, leading to improved antitumor activity. This review consolidates current evidence on the prognostic and predictive value of chemokine receptor expression in PBMCs, like T cells, for NSCLC patients treated with ICIs. Understanding these associations can aid in the development of non-invasive biomarkers to guide treatment decisions and improve patient stratification in immunotherapy. Full article