A case report of a cat infected with European bat lyssavirus type 1, the Netherlands, October 2024.

In October 2024, an infection of European bat lyssavirus type 1 was confirmed in a domestic cat in the Netherlands. Several weeks before, the owners had found a dead bat considered to be caught by the cat. Nine persons exposed to the cat received post-exposure prophylaxis and four domestic animals from the same household were quarantined.

Sexual assault experience, depression, and heavy substance use among German adults: an exploratory mediation analysis.

Background: The experience of sexual assault may be associated with numerous adverse outcomes, including depressive disorders and heavy substance use. We aimed to examine the relationship between heavy substance use and depression in victims of sexual assault.

Methods: We used nationally representative data from the German Health and Sexuality Survey (GeSiD) with N = 4,955 women and men aged 18-75 years.

Emergency Clinician Buprenorphine Initiation, Subsequent Prescriptions, and Continuous Prescriptions.

Importance: Rates of opioid use disorder (OUD) and associated mortality in the US remain high. Treatment of OUD with buprenorphine reduces morbidity and mortality. There have been national efforts to expand buprenorphine initiation to the emergency department (ED), where many patients with low treatment access seek medical care.

Identifying pathways to the prevention of dementia: the Netherlands consortium of dementia cohorts.

Background: Aggregation of cohort data increases precision for studying neurodegenerative disease pathways, but efforts to combine data and expertise are often hampered by infrastructural, ethical and legal considerations. We aimed to unite various cohort studies in the Netherlands to enhance research infrastructure and facilitate research on dementia etiology and its public health implications.

Methods: The Netherlands Consortium of Dementia Cohorts (NCDC) includes participants with initially no established cognitive impairment from 9 Dutch cohorts: the Amsterdam Dementia Cohort (ADC), Doetinchem Cohort Study (DCS), European Medical Information Framework for Alzheimer's Disease (EMIF-AD), Longitudinal Aging Study Amsterdam (LASA), the Leiden Longevity Study (LLS), The Maastricht Study, the Memolife substudy of the Lifelines cohort, Rotterdam Study and Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study.

Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based Code Selection.

Background: The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings.

Objective: The potential of large language models for assisting medical coders in the ICD-10 coding was explored through the development of a computer-assisted coding system. This study aimed to augment human coding by initially identifying lead terms and using retrieval-augmented generation (RAG)-based methods for computer-assisted coding enhancement.

Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study.

Background: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data.

Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy.

Background: Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.

A scoping review of the Trauma Recovery Center model for underserved victims of violent crime.

Victimization in the United States is common and has long lasting negative impacts for individuals, often disproportionately impacting those of color and from low socioeconomic communities. The Trauma Recovery Center (TRC) model aims to provide comprehensive mental health and wrap-around case management services for underserved victims of crime. Following PRISMA-ScR guidelines, we sought to further our knowledge about the impact of the TRC model.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.

Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored.

The anatomical basis for surgical intervention in myositis ossificans-A systematic review.

Myositis ossificans (MO) is a benign condition characterized by heterotrophic bone formation, most commonly within muscle tissue. Multiple types have been described, the most predominant being myositis ossificans circumscripta, which occurs in response to trauma. Myositis ossificans cases reported in the literature were reviewed systematically.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Background: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.

Assessing Patient Understanding and Satisfaction in Orthopedic Trauma and Elective Surgery Admissions.

Background When seeking healthcare, patients often struggle to understand the information provided by healthcare professionals regarding their condition and treatment plan. Additionally, patient satisfaction with their experience can vary widely. Improved patient understanding and satisfaction are linked to better outcomes.

Contextual success and pitfalls of mHealth service for maternal and child health in Africa: An Intervention, Context, Actors, Mechanism, and Outcome (ICAMO) framework guided systematic review of qualitative evidence.

Introduction: Mobile health (mHealth) interventions have shown potential to improve maternal and child health outcomes in Africa, but their effectiveness depends on specific interventions, context, and implementation quality. Challenges such as limited infrastructure, low digital literacy, and sustainability need to be addressed. Further evaluation studies are essential to summarize the impact of mHealth interventions.

Effect of the iTransHealth e-health intervention on psychological distress among transgender and gender diverse adults from remote areas in Germany: a randomised controlled trial.

Background: Transgender and gender diverse (TGD) people in remote areas face challenges accessing health-care services, including mental health care and gender-affirming medical treatment, which can be associated with psychological distress. In this study, we aimed to evaluate the effectiveness of a 4-month TGD-informed e-health intervention to improve psychological distress among TGD people from remote areas in northern Germany.

Methods: In a randomised controlled trial done at a single centre in Germany, adults (aged ≥18 years) who met criteria for gender incongruence or gender dysphoria and who lived at least 50 km outside of Hamburg in one of the northern German federal states were recruited and randomly assigned (1:1) to iTransHealth intervention or a wait list control group.

Outcomes for centenarian patients admitted with orthopaedic trauma.

Introduction: The United Kingdom (UK) and world's population is aging with patients living longer, often with many co-morbidities. It is expected that patients of extreme old age would have poor outcomes following trauma; however, this assumption is not clearly evidenced. This study aims to present the outcomes of patients aged 100 or older admitted to a single hospital trust following admission for orthopaedic trauma.

Rise and Decay of Photoluminescence in Upconverting Lanthanide-Doped Nanocrystals.

Nanocrystals (NCs) doped with lanthanides are capable of efficient photon upconversion, i.e., absorbing long-wavelength light and emitting shorter-wavelength light.

Prediction of new-onset atrial fibrillation in patients with non-small cell lung cancer treated with curative-intent conventional radiotherapy.

Background: Atrial fibrillation (AF) is an important side effect of thoracic Radiotherapy (RT), which may impair quality of life and survival. This study aimed to develop a prediction model for new-onset AF in patients with Non-Small Cell Lung Cancer (NSCLC) receiving RT alone or as a part of their multi-modal treatment.

Patients And Methods: Patients with stage I-IV NSCLC treated with curative-intent conventional photon RT were included.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.

Objectives: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

Objectives: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

Methods: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023.

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

Background: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status.

Purpose: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer.

The value of PROMs for predicting erectile dysfunction in prostate cancer patients with Bayesian network.

Purpose: This study aims to develop and externally validate a clinically plausible Bayesian network structure to predict one-year erectile dysfunction in prostate cancer patients by combining expert knowledge with evidence from data using clinical and Patient-reported outcome measures (PROMs) data. In addition, compare and contrast structures that stem from PROM information and routine clinical data.

Summary Of Background: For men with localized prostate cancer, choosing the optimal treatment can be challenging since each option comes with different side effects, such as erectile dysfunction, which negatively impacts their quality of life.