Citations

Abstract Background The number of cancer survivors aged older than 65 years is rising rapidly. Current evidence‐based exercise guidelines lack specific guidance for older cancer survivors as a result of insufficient evidence. An expert panel was convened to develop consensus‐based recommendations for exercise in older cancer survivors. Methods The development of recommendations was guided by the Grading of Recommendations Assessment, Development, and Evaluation Evidence‐to‐Decision framework for good practice statements. The panel drew from the available literature, a Delphi survey of exercise and health professionals, other exercise guidelines, clinical and research expertise, and interest‐holder input provided by a community advisory board of older cancer survivors and caregivers ( n = 11). Recommendations had to be deemed accessible (i.e., no added barriers) to older cancer survivors and feasible to implement. The panel voted on the strength of the recommendation for or against each statement, with consensus set at 85% agreement. Results Consensus was reached on 11 recommendations covering the following areas: medical evaluation/clearance for exercise, pre‐exercise assessment, exercise prescription, exercise tolerance and safety, exercise delivery, and behavioral support. The recommendations aimed to promote engagement in and uptake of appropriately prescribed exercise programming by older cancer survivors, while keeping barriers and risks as low as possible. Conclusions Older cancer survivors can benefit from appropriately prescribed exercise, which should be an essential component of their cancer care. Exercise and health professionals need to consider the unique needs of older cancer survivors to ensure that exercise is safe and effective for this population, while also reducing barriers to reach as many people as possible.

Abstract Background The incidence of early onset breast cancers (BCs) has increased, paralleling rising trends in delayed childbearing. We hypothesize that a distinct postpartum BC (PPBC) subtype, identifiable by time since last birth (TSLB) and biomarker expression, contributes to this trend. Methods We applied GeoMx Digital Spatial Profiling (DSP) to measure associations between TSLB and 71 proteins in 640 BCs from women aged ≤40 years included in the Young Women’s Breast Cancer Study. We analyzed data using univariable linear regression and multivariable sliced inverse regression to account for higher order interactions among biomarkers. Results In keratin-rich segments, progesterone receptor (PR) (P = 1.00 × 10−4) and PTEN (P = 1.00 × 10−3) were associated with longer TSLB; multivariable analyses revealed positive associations for GZMB (P = 4.00 × 10−4), SMA (P = 1.00 × 10−4), and NF-1 (P = 1.00 × 10−3). In keratin-poor segments, univariable significant positive associations were found for PR (P = 2.00 × 10−4) and PTEN (P = 1.00 × 10−3), whereas CD20 (P = 3.00 × 10−4) and CTLA4 (P = 4.00 × 10−5) were negatively associated; multivariable significant associations were found for fibronectin (P = 3.00 × 10−5) and pan-Akt (P = 1.00 × 10−3). Associations persisted after adjustment for multiple comparisons and BC molecular subtypes. Associations including for PR, PTEN, and CD20 were strongest among women with the shortest TSLB. OPAL multiplex immunofluorescence assays for PR, PTEN, CD20, SMA, and CTLA4 replicated several DSP findings, particularly when stratified by subtype and with compartment matching. In TCGA, RNA species linked to proteins associated with TSLB correlated strongly with a T-cell exhaustion signature previously linked to poor prognosis among premenopausal women. Conclusion These data support PPBC as a biologically coherent phenotype defined by TSLB and biomarker profile, with potential implications for prevention and therapy.

Abstract Background Critical illness is a risk factor for carnitine deficiency. Carnitine deficiency may result in serious medical complications and poor clinical outcomes. This study aimed to identify the prevalence and potential predictors of carnitine deficiency. Methods This was a descriptive cross‐sectional study conducted in a convenience sample of 144 critically ill adults admitted to a surgical or cardiovascular intensive care unit at the University of Utah Hospital, with serum carnitine levels measured from January 1, 2022, to December 31, 2024. Binary and multivariable logistic regression models were constructed to explore potential predictors of carnitine deficiency, which was defined as a free serum carnitine level <36 µmol/L. Results The mean age of patients was 56.5 years; 41.7% of the sample had carnitine deficiency. For each unit decrease in body mass index, the odds of developing carnitine deficiency increased by 5% compared with maintaining normal free carnitine status (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.9–0.99). Those prescribed continuous renal replacement therapy were 2.43 times more likely to develop carnitine deficiency than those not on continuous renal replacement therapy (OR, 2.43; 95% CI, 1.12–5.25). Conclusion A high proportion of our study population had carnitine deficiency. Continuous renal replacement therapy and a lower body mass index were significant predictors in the multivariable model. Female sex and a history of chronic kidney disease may warrant further exploration based on significance in the univariable analysis.

Lay Summary This multicenter study evaluated clinical outcomes in pregnancy for women with ulcerative colitis and an ileal pouch-anal anastomosis. Close monitoring is needed with a multidisciplinary team for management of inflammatory complications of the pouch such as pouchitis in pregnancy and postpartum.

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.

Background/Objectives: Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. Methods: Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. Results: When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R2 = 0.56, p = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R2 = 0.56, p = 0.01). Conclusions: Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies.

Artificial intelligence (AI) is revolutionizing interventional pulmonology (IP) by enhancing diagnostics, procedural precision, and patient outcomes. AI-powered tools improve lung nodule detection, radiomics-based risk stratification, and bronchoscopic navigation. Machine learning (ML) algorithms aid in lung cancer screening by analyzing imaging data, reducing false positives, and improving early diagnosis. AI-driven robotic-assisted bronchoscopy enhances navigation and biopsy accuracy, particularly for peripheral lung lesions. Endobronchial ultrasound (EBUS) and cytopathology benefit from AI’s ability to assess lymph node malignancy and optimize rapid on-site evaluation (ROSE). AI applications extend to phenotyping chronic obstructive pulmonary disease (COPD) and identifying candidates for bronchoscopic lung volume reduction (BLVR). Deep learning (DL) models analyze computed tomography (CT) imaging and spirometry data to optimize patient selection. AI-driven algorithms are also advancing pleural effusion detection, differentiation, and classification, supporting clinical decision-making. Education and research in IP are also transforming with AI-driven simulation, virtual reality, and automated assessment tools that enhance procedural training and competency evaluation. The integration of AI into clinical work and procedural training accelerates advancements while presenting challenges in ethical AI implementation, data security, and bias mitigation. As AI continues to evolve, its role in IP will expand, improving procedural efficiency, personalizing treatment plans, and optimizing patient selection for interventions. Future developments will focus on refining AI-driven predictive analytics, enhancing robotic-assisted procedures, and integrating AI seamlessly into clinical workflows. The responsible implementation of AI in IP holds the potential to transform patient care, reduce complications, and advance precision medicine.

The Concise Guide to Pharmacology 2025/26 marks the seventh edition in this series of biennial publications in the British Journal of Pharmacology. Presented in landscape format, the guide provides a comparative overview of the pharmacology of drug target families. The concise nature of the Concise Guide refers to the style of presentation, being clear, accessible, and well‐structured, rather than the scope of the content, which spans approximately 500 pages. The Concise Guide summarises the key pharmacological properties of around 1900 human drug targets, and nearly 7000 interactions, involving around 4400 ligands. While the content is a substantially condensed version of the more detailed information and links available at the www.guidetopharmacology.org website, the printed guide serves as a permanent, citable, point‐in‐time record, that remains stable despite ongoing updates to the online database. The full contents of this publication can be found at https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bph.70233 . The Concise Guides provide expert‐curated recommendations of ‘Gold Standard’ selective pharmacological tools, available either commercially or as donations, which enable the identification of individual drug targets or families of drug targets. While the Concise Guide offers a more streamlined overview, more comprehensive information, including detailed pharmacological profiles and links to multiple online databases, is available through the Guide to Pharmacology website. The 2025/26 edition of the Concise Guide is based on material current as of mid‐2025, and supersedes all previous editions, including the 2023/24 Guide, and earlier Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC‐IUPHAR), and as such provides official IUPHAR classification and nomenclature for human drug targets, where applicable. Catalytic receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein‐coupled receptors, ion channels, nuclear hormone receptors, enzymes and transporters. Each section includes nomenclature guidance, concise summaries, information of the best available pharmacological tools, key references, and suggestions for further reading.

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.