Citations

ABSTRACT In 567 PH‐COPD patients from the PVRI GoDeep Meta‐Registry, ACE inhibitor use was associated with improved survival only in severe PH (PVR > 5 WU), supporting prospective trials targeting this high‐risk subgroup.

Objective Vacuoles, E1 enzyme, X‐linked, autoinflammatory, somatic syndrome (VEXAS) syndrome is characterized by a complex spectrum of inflammatory and hematologic manifestations. Clinical research to identify effective therapies is urgently needed but is hindered by the lack of validated outcome measures. A VEXAS‐specific disease activity index (DAI) is an essential tool for reliably capturing changes in disease activity over time, thereby supporting clinical research and informing patient care in this emerging disease. This study aimed to develop a comprehensive DAI to assess disease‐related inflammation across affected organ systems in patients with VEXAS syndrome. Methods Inflammatory manifestations of VEXAS were previously identified through a systematic literature review and expert input. A multinational expert advisory committee developed the VEXAS‐DAI using modified Delphi methodology until consensus (defined as ≥75% concurrence) was reached. Item grading was adapted from the Common Terminology Criteria for Adverse Events. Scoring criteria were developed based on 158 test cases using the Physician Global Assessment of Inflammation. Results Consensus on the VEXAS‐DAI was achieved after four Delphi rounds. The index assesses inflammatory activity at the time of evaluation across 12 domains: inflammatory rash, chondritis, periorbital, ophthalmologic, joint, pulmonary, cardiovascular, genitourinary, neurologic, oral and gastrointestinal, renal, and constitutional symptoms. The total score ranges from 0 to 40. Conclusion The VEXAS‐DAI is a novel instrument designed to quantify active inflammation over time in patients with VEXAS. Prospective validation within a clinical trial setting is ongoing. image

Abstract Anatomy is fundamental to clinical competence. However, reduced contact hours, limited cadaveric access and difficulties with technological integration raise concerns about students’ preparedness and development. The current study evaluated various practices, student perceptions, challenges, teaching quality and quantity of UK medicine anatomy education. A national cross-sectional survey was distributed to final-year medical students at 40 UK medical schools to assess teaching quality, student satisfaction, confidence and theimpact of emerging technologies and challenges associated with anatomy education. 628 students responded and 55.3% (n=347) preferred a curriculum integrating traditional teaching and emerging technologies,73.3% (n=460) were dissatisfied with teaching quantity and continuity, and 42.7% (n=268) were dissatisfied with teaching breadth and quality. Satisfaction was significantly higher for users of 3D imaging software (Users Mean=3.63 vs. Non-users=3.28;p<0.001,rrb=0.172) and 3D printed simulation models (3.59 vs. 3.23;p<0.001,rrb=0.181), despite familiarity among 32.3% (n=203) and 27.9% (n=175), respectively. 3D imaging software was most recognised (59.2%, n=372), followed by 3D printed simulation models (55.4%,n=348) and VR/AR (32.3%,n=203). Satisfaction was high for continuous teaching across ≥3 years (F(3,624)=24.91,p<0.001) Kruskal-Wallis H test showed significant variation in perceived importance of anatomy across specialties (H(5)=75.99,p<0.0001), with Surgery rating it highest (Median=5.0) and GP/Medicine lower (Median=4.0). Students are dissatisfied with the current curriculum content, structure and provision of anatomy teaching. Emerging technologies could enhance satisfaction, address limitations of cadaveric dissection and extend teaching beyond the classroom. Collaborative efforts among anatomists, clinicians and academics are essential in standardising clinically curricula and sound technological integration (VR/AR/3D models).

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti–volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting. Graphical Abstract

Abstract Background The left anterior descending (LAD) coronary artery is a critical organ‐at‐risk in thoracic radiotherapy. In thoracic stereotactic body radiation therapy (SBRT), sharp dose gradients and poor LAD contrast on 4DCT scans introduce uncertainty in LAD dose assessment. Methods We evaluated the performance of an AI‐generated LAD region structure as a surrogate for manual LAD contours in early‐stage NSCLC. We used deep inspiration breath hold (DIBH) scans from 49 patients with manually delineated LADs; planning risk volume contours were expanded by 3 mm. Then, 96 lung SBRT dose maps were registered by matching the left anterior portion of the heart contours. Dice similarity coefficient (DSC), inclusion ratios (IR), dose–volume histogram (DVH) metrics, linear regression, and linear mixed models were used to compare the structures. The correlation of the AI LAD region and LAD dose to mean heart dose (MHD) was assessed. Results The AI LAD region showed strong correlation with manual LAD contours for maximum dose (D1%, R 2 = 0.93) and volume receiving ≥10 Gy (V10Gy, R 2 = 0.81). The planning risk volume also demonstrated high correlation to the AI LAD region. MHD was a poor predictor of LAD dose ( R 2 ≈0.4). Additional dose metric correlations were assessed. Discussion To investigate dose volume effects to the LAD during radiotherapy for stage I NSCLC, a robust segmentation paradigm must be established. It must be easily implementable on typical imaging acquired during treatment, with an emphasis on clinical translatability. Our model has shown that the AI LAD region has a strong correlation with LAD dose under typical lung SBRT doses and supports its use for further investigation into LAD dose volume constraints during lung SBRT. Conclusion AI LAD region provides a clinically feasible and scalable alternative to manual LAD contouring for dose estimation in lung SBRT. Its strong correlation with key dose metrics validates its use in large‐scale retrospective and prospective studies.

A 64-year-old Asian woman diagnosed with synchronous breast ductal carcinoma in situ (DCIS) and stage IV EGFR-mutated non-small cell carcinoma of the lung (NSCLC). A decision was made to defer management of the DCIS and initiate Osimertinib for lung cancer treatment, since this was a life-limiting diagnosis. At 9 months, restaging FDG-PET CT showed an interval response in the NSCLC and complete loss of FDG avidity at the biopsy-proven DCIS site. Breast MRI confirmed complete imaging resolution of the DCIS. The clinical resolution of breast DCIS during third-generation EGFR inhibitor therapy has not been previously reported in humans and highlights a potential role for the EGFR/HER2 (ERBB) pathway in pre-invasive breast cancer.

Background: Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. Methods: Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. Results: In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). Conclusions: Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.

Ehrlichiosis is a rare tick-borne zoonosis caused by Ehrlichia chaffeensis , E. ewingii and related species. In solid organ transplant recipients, immunosuppression can result in atypical presentations, delayed diagnosis and poor outcomes. The disease remains under-recognised among lung transplant recipients despite its potentially fatal course. A man in his early 70s with a history of bilateral lung transplantation for chronic obstructive pulmonary disease presented with a high-grade fever and shortness of breath. Bronchoalveolar lavage (BAL) culture grew Pseudomonas aeruginosa and meropenem was initiated. He subsequently developed severe thrombocytopenia, acute kidney injury and elevated liver transaminases. Disseminated intravascular coagulation (DIC) and thrombotic thrombocytopenic purpura (TTP) were excluded, while haemophagocytic lymphohistiocytosis (HLH) was considered but not confirmed. His condition rapidly progressed to multiorgan failure requiring mechanical ventilation and haemodialysis. E. chaffeensis was identified postmortem by next-generation sequencing. This case highlights diagnostic challenges and the importance of early empiric doxycycline therapy in endemic areas.

Abstract Lung fibrosis, including idiopathic pulmonary fibrosis (IPF), represents a spectrum of progressive interstitial lung diseases characterized by disrupted epithelial repair, fibroblast activation, and excess extracellular matrix accumulation. A central feature of fibrotic progression is the loss of alveolar type 2 epithelial (AT2) cell identity and the emergence of aberrant transitional states that fail to support normal regeneration. Based on our re-analysis of multiple publicly available scRNA-seq datasets from the IPF Cell Atlas, S100A2 expression is tightly associated with basal-like reprogramming of AT2 cells and is a top upregulated gene at both mRNA and protein levels in IPF lungs. Furthermore, scRNA-seq data from human lung organoid models reveal that AT2 cells co-cultured with fibroblasts acquire a basal-like phenotype and express high levels of S100A2. Functionally, overexpression of S100A2 in human iPSC-derived lung alveolar epithelial type 2 (iAT2) cells leads to loss of AT2 cellular identity, increased generation of reactive oxygen species, and activation of RAGE signaling pathway. We demonstrate that pharmacologic inhibition of RAGE using Azeliragon preserves AT2 cell populations, associated with reduced oxidative stress in iAT2 cells, and significantly attenuates collagen deposition in bleomycin-induced lung fibrosis models. Collectively, our results indicate that S100A2 is a key driver of epithelial dysfunction in lung fibrosis, promoting loss of AT2 identity, aberrant basal fate acquisition, and persistent epithelial injury. Targeting the unrecognized S100A2–RAGE pathway may offer a new therapeutic strategy to restore epithelial homeostasis in lung fibrosis.