Immersive virtual reality (VR) may enhance long‑term pain management when used alongside usual care. VR interventions based on cognitive and behavioral principles have potential to improve patient engagement, stress regulation, and coping. Investigate feasibility, usability and exploratory clinical impact of immersive virtual reality (VR) as an adjunct to usual care for patients with long-term pain. Of the 30 patients approached 28 were enrolled. Patients used immersive VR software grounded in Cognitive Activation Theory of Stress (CATS), cognitive behavioral therapy (CBT) and Acceptance and Commitment Therapy (ACT), delivered through Head Mounted Displays (HMDs). Usability and feasibility were assessed with System usability Scale (SUS) and clinician interviews. Patient-reported outcome measures included health-related quality of life (EQ-5D-5 L, EQ-VAS), PROMIS − 29 and the Patient Global Impression of Change (PGIC). Patients completed a median of 10 VR sessions over 8 weeks. Usability was high (average SUS 82), and no serious adverse events occurred. All patients completed the intervention; five were lost to follow-up, leaving 23 for analysis. On the PGIC, 34
Obesity significantly alters drug disposition and contributes to large inter-individual variability in pharmacokinetics (PK). The virtual-twin concept is increasingly used to support model-informed precision dosing in specific populations. In this study, physiologically-based pharmacokinetic models linked with virtual twins (VT-PBPK) have been developed and applied to predict the PK of midazolam and digoxin in patients with obesity (n = 15) and severe obesity (n = 22). The first step of the individualization included basic demographic data with lean liver volume. In the second step, individual serum creatinine, albumin, and hepatic CYP3A4/5, UGT1A4 and P-gp abundance quantified from liver biopsies in the same individuals, were integrated within models. Substrate specific improvements were presented via the stepwise individualization. The final (Step 2) VT-PBPK models predicted midazolam AUC0-inf,iv within 2-fold for 86
Inflammatory bowel disease (IBD) is an incurable immune-mediated inflammatory disease, affecting the gut with a high rate of primary- and secondary- loss-of-response to therapy. By investigating the T cell receptor repertoire of individuals with IBD, novel therapeutic and preventive strategies can be identified, and a better understanding of IBD can be obtained. To identify and validate T cell clonotypes implicated in the pathogenesis of IBD, we profiled the T cell receptor alpha (TRA) repertoire of three cohorts containing treatment-naive, treated individuals, and individuals living with the disease for >20 years, resulting in an exhaustive dataset containing the TRA repertoire of 1,732 individuals. Using the generated datasets, we were able to replicate previous findings describing the expansion of Crohn’s-associated invariant T (CAIT) cells in individuals with Crohn’s disease (CD) in the three cohorts. Using a hypothesis-free statistical testing framework, we identified clonotypes that were associated with the disease at its different stages, e.g., at the time of diagnosis and decades post-diagnosis. By conducting a meta-analysis across the three cohorts, we were able to identify a set of clonotypes that were associated with the disease regardless of its stage. We validated our findings in a previously published independent test dataset from a German cohort, showing the robustness of the identified clonotypes. The identified clonotypes are novel therapeutic targets to treat IBD, for example, through targeted depletion. By identifying antigens recognized by these T cells, a better understanding of the etiopathology of IBD, particularly CD, can be obtained.
AIM:To describe the development of the initial version of the cerebral palsy (CP) directed acyclic graph (DAG). DAGs are visual representations of causal assumptions and powerful tools for representing and communicating complex causation. METHOD:An international working group of clinicians, researchers, and individuals with lived experience developed the CP-DAG, using methods adapted from the US National Aeronautics and Space Administration's risk modelling approach. The team developed a network of nodes and causal links spanning prenatal, perinatal, neonatal, and postneonatal pathways, along with a node dictionary ('DAGtionary'). Iterative drafts were created with feedback incorporated from presentations at international meetings and workshops. RESULTS:As of August 2025, the CP-DAG includes 106 nodes and 378 links, yielding a network density of 6.8%. It spans known and hypothesized causal pathways across biological, clinical, and social domains. The accompanying DAGtionary contains concise definitions and references for each node, totalling 210 citations. INTERPRETATION:The CP-DAG provides a visual, evidence-linked representation of causal reasoning in CP. It offers a foundation for identifying knowledge gaps, directing future research to under-researched areas, and, eventually, for probabilistic modelling. This initial version is intended as a starting point. We call on the global CP disability community to contribute to its ongoing refinement.
The classification of white blood cells (WBCs) from peripheral blood smears is critical for the diagnosis of leukemia. However, automated approaches still struggle due to challenges including class imbalance, domain shift, and morphological continuum confusion, where adjacent maturation stages exhibit subtle, overlapping features. We present a multi-stage fine-tuning methodology for 13-class WBC classification in the WBCBench 2026 Challenge (ISBI 2026). Our best-performing model is a fine-tuned DINOBloom-base, on which we train multiple classifier head families (linear, cosine, and multilayer perceptron (MLP)). The cosine head performed best on the mature granulocyte boundary (Band neutrophil (BNE) F1 = 0.470), the linear head on more immature granulocyte classes (Metamyelocyte (MMY) F1 = 0.585), and the MLP head on the most immature granulocyte (Promyelocyte (PMY) F1 = 0.733), revealing class-specific specialization. Based on this specialization, we construct a head-diverse ensemble, where the MLP head acts as the primary predictor, and its predictions within the four predefined confusion pairs are replaced only when two other head families agree. We further show that cases consistently misclassified by all models are substantially enriched for probable labeling errors or inherent morphological ambiguity.
