Citations

Hypertension is the single most important modifiable risk factor for preventable disability and death worldwide and disproportionately affects socially disadvantaged populations. We face a paradox—blood pressure control is low and recent trends suggest it is even declining, despite the availability of inexpensive and effective therapies. A variety of barriers on the system, patient, and healthcare provider side hinder effective drug-based risk factor management. Clinical inertia represents a major barrier on the clinician side, as well as workload and limited education. Common barriers on the patient side include limited English proficiency, low health literacy, and nonadherence with misaligned incentives, limited resources, lack of structured clinical pathways, and reimbursement issues. New innovations in the field of RNA-targeted therapies and device-based interventions could prevent and potentially even cure diseases previously designated as chronic health conditions, such as hypertension. Such novel therapies could potentially overcome several major barriers to effective treatment, including nonadherence. Drug development of novel, long-acting treatments requires consideration of specific clinical trial design aspects, including safety collection, benefit: risk assessment, the development and assessment of novel, qualitative surrogate end points, such as time-in-therapeutic range, the use of representative trial settings as well as the definition of standard of care in placebo-controlled trials, which should be of reasonably high-quality allowing for credible evaluation of effectiveness. Here, we provide an overview on barriers to effective treatment and a framework for trials assessing novel treatments for cardiovascular disease risk factors, including early and broad implementation programs.

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps.

Thoracic aortic aneurysm and dissection (TAAD) encompasses a clinically heterogeneous group of diseases characterized by high morbidity and mortality. Genetic studies over the past two decades have significantly expanded our understanding of the molecular landscape underlying heritable TAAD, revealing contributions from multiple interconnected biological pathways. This review systematically summarizes more than 75 genes implicated in TAAD pathogenesis, categorizing them according to major mechanistic roles including TGF-β signaling, extracellular matrix remodeling, smooth muscle cell contractility and cytoskeletal regulation, cell–matrix and cell–cell adhesion, metabolic processes, ion transport, and transcriptional regulation. Special emphasis is placed on emerging genes with variable or overlapping clinical phenotypes, dual-mechanism candidates, and their implications for personalized clinical management, including surveillance and surgical intervention thresholds. The integration of molecular insights into clinical practice, along with cautious consideration of genes of uncertain significance, promises to enhance diagnostic precision and risk stratification in individuals and families affected by heritable aortic disease.

Background Birt–Hogg–Dubé syndrome (BHD) is a rare autosomal dominant disease characterised by the development of lung cysts, skin fibrofolliculomas and an increased risk for renal cancer. The progression of cystic lung disease with ageing in BHD has not been well characterised. Objectives To assess the severity of lung involvement in patients with BHD aged 50 years or older. Methods A retrospective computer-assisted search was performed to identify patients with BHD seen at Mayo Clinic in Rochester, Minnesota and the First Affiliated Hospital of University of Science and Technology of China from January 2000 to May 2023. Demographic, clinical, pulmonary function and radiological parameters were extracted and analysed. Results Among 82 patients with BHD aged 50 years or older, 49 (59%) were female and the median age was 57 years (range, 50–79) at baseline. Pneumothorax occurred in 46 patients (56%). Lung cysts were observed on CT in 77 patients (94%) and were bilateral in nearly all cases. The number of cysts was≥10 for most patients (84%) and the median size of the largest cyst was 31 mm (range, 4–160 mm). Median (range) % predicted values of forced vital capacity, forced expiratory volume in 1 s and diffusion capacity of the lung for carbon monoxide were 98 (62–147), 92 (56–145) and 85 (61–133), respectively, for those with data available. During follow-up (median, 41 months; range, 3–259), 35% of patients manifested worsening in pulmonary function or cystic lung disease. Conclusion Lung involvement in older patients with BHD manifests varying severity of cystic lung disease with relatively mild functional impairment, if any, and indolent progression.

Introduction and Objective: Kidney stone growth and new stone formation are common clinical trial endpoints and are associated with future symptomatic events. To date, a manual review of CT scans has been required to assess stone growth and new stone formation, which is laborious. We validated the performance of a software algorithm that automatically identified, registered, and measured stones over longitudinal CT studies. Methods: We validated the performance of a pretrained machine learning algorithm to classify stone outcomes on longitudinal CT scan images at baseline and at the end of the 2-year follow-up period for 62 participants aged >18 years in the Prevention of Urinary Stones with Hydration (PUSH) randomized controlled trial. Stones were defined as an area of voxels with a minimum linear dimension of 2 mm that was higher in density than the mean plus 4 standard deviations of all nonnegative HU values within the kidney. The four outcomes assessed were: (1) growth of at least one existing stone by ≥2 mm, (2) formation of at least one new ≥2 mm stone, (3) no stone growth or new stone formation, and (4) loss of at least one stone. The accuracy of the algorithm was determined by comparing its outcomes to the gold standard of independent review of the CT images by at least two expert clinicians. Results: The algorithm correctly classified outcomes for 61 paired scans (98.4%). One pair that the algorithm incorrectly classified as stone growth was a new renal artery calcification on end-of-study CT. Conclusions: An automated image analysis method validated for the prospective PUSH trial was highly accurate for determining clinical outcomes of new stone formation, stone growth, stable stone size, and stone loss on longitudinal CT images. This method has the potential to improve the accuracy and efficiency of clinical care and endpoint determination for future clinical trials.