Citations

Abstract Patients with rare genetic disorders often wait years for a correct diagnosis, highlighting the urgent need for early and efficient triage for those at risk. Electronic health records (EHRs) are rich in useful information, yet the extent to which this data is sufficient to generate prediagnostic signals is uncertain. We trained machine learning models on billing codes and phenotypes extracted from clinical notes and applied these across eight conditions in a longitudinal cohort of roughly 3 million patient records from the Mayo Clinic, seeking early identification of conditions. The number of patients identified early varied by condition, ranging from 10% to 89% at 99% specificity. Median lead times exceeded one year prior to the first diagnostic billing code for most conditions, including Fabry disease (1,408 days), hereditary angioedema (2,755 days), neurofibromatosis type 1 (1,067 days), and hereditary hemorrhagic telangiectasia (2,320 days). We show that ML models trained on EHR data can prioritize patients for clinician review and confirmatory genetic testing and that the integration of diverse phenotypic data sources provides superior predictive value.

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

ABSTRACT Glioblastoma (GBM) is universally lethal despite decades of research to find effective treatments. This highlights the need to identify druggable targets essential for sustaining the malignant phenotype but dispensable for normal tissue. We propose that the enzyme α-tubulin acetyl transferase ( ATAT1 ) meets these criteria. ATAT1 acetylates α-tubulin at lysine 40, which increases microtubule stability and promotes microtubule-based transport. While ATAT1 knockout mice have only a very mild phenotype, ATAT1 suppression in GBM has multiple therapeutic effects by reducing tumor invasion, proliferation, and therapeutic resistance. These translate not only into improved survival with ATAT1 targeting by itself, but also into synergy when ATAT1 deletion is combined with FDA approved therapies. This study strongly supports our conclusion that ATAT1 is a promising therapeutic target in GBM.

The International Academy for Clinical Hematology (IACH) is a global, non-profit educational organization dedicated to advancing excellence, equity, and innovation in clinical hematology. Since its creation, the IACH has built an international community spanning more than 130 countries, leveraging digital-first education, scientific collaboration, and professional networking to improve patient care worldwide. This document presents the IACH Strategic Vision for 2026–2030, outlining a structured roadmap for the Academy’s next phase of growth and international leadership. The strategy is built upon six interdependent pillars: scientific excellence and credibility; digital leadership and intelligent learning; academic alliances and global footprint; innovation, visibility, and advocacy; transparency and sustainability; and brand positioning with global impact. By reinforcing scientific rigor, embracing digital transformation, and promoting innovation, the IACH aims to become the most trusted global reference for clinical hematology education and collaboration. This strategic vision positions the IACH as a unifying platform to unite the world hematology community under one banner of knowledge, collaboration, and compassion, for the benefit of every patient, everywhere.

Abstract Asthma is a chronic inflammatory condition marked by episodic airway narrowing, with exercise-induced bronchoconstriction (EIB) a common trigger. Up to 70% of athletes experience EIB-related symptoms, yet current management relies on reactive treatment rather than early physiological monitoring. This study presents a proof-of-concept multimodal wearable platform integrating impedance pneumography (IPG), electrocardiography (ECG), and inertial measurement unit (IMU) sensing to detect early physiological signatures of EIB. Candidate biomarkers include elevated heart rate, increased respiratory rate, and reduced thoracic impedance amplitude. Real-time embedded algorithms perform on-device feature extraction and threshold-based decision logic without cloud dependence, while IMU data provide posture context for signal interpretation. The system was evaluated through subsystem validation and a resistive-breathing protocol simulating bronchoconstriction. Results demonstrate reliable detection of characteristic cardiorespiratory changes, establishing the feasibility of real-time wearable detection of early EIB markers and supporting future large-cohort validation and form-factor refinement.

Background Burnout arises from prolonged, unresolved work-related stress and adversely affects professional performance and overall well-being. Resident doctors and medical students are particularly vulnerable because of the demands of balancing clinical, academic and related responsibilities. Aims This study aimed to assess self-reported burnout and related stressors among UK resident doctors and medical students, and estimate the economic impact of burnout-related absenteeism among resident doctors. Method Self-report questionnaires assessing burnout and related stressors were distributed to UK resident doctors and medical students. A total of 2793 responses were received: 1439 (51.5%) from resident doctors and 1354 (48.5%) from medical students. Economic losses from absenteeism were calculated with a human capital approach. Results Burnout was reported by 70% ( n = 1008) of resident doctors and 65.7% ( n = 889) of medical students. Notably, 56.8% ( n = 573) and 79.3% ( n = 705) of resident doctors and medical students affected by burnout, respectively, were unaware of available support services. Relocation stress (85.1% of residents and 62.6% of students), financial stress (53.9% of residents and 57.8% of students) and poor work–life balance (32.1% of residents and 20.7% of students) were commonly reported. The annual economic burden of absenteeism related to burnout among resident doctors was estimated to reach £251.5 million. Conclusions Burnout among UK resident doctors and medical students may reflect a combination of underlying factors, including financial strain, relocation stress and difficulties with work–life balance. These findings underscore the need for targeted interventions that support well-being and address financial and relocation challenges, which may, in turn, help prevent burnout and improve retention within the medical workforce. Reducing burnout and associated stressors may also help lessen its economic impact, allowing resources to be redirected towards patient care.

Abstract Human genomic studies link reduced CUB domain-containing protein 1 (CDCP1) expression with myocardial recovery in heart failure. While CDCP1 regulates cardiac fibroblast proliferation in vitro, it’s in vivo role in cardiac fibrosis remains unclear. Using a Cdcp1 -knockout (KO) angiotensin II/phenylephrine mouse model, we show that Cdcp1 deletion reduces echocardiographic left ventricular mass, histologic cardiac fibrosis, and pro-fibrotic gene expression, along with decreased fibroblast activation and inflammatory markers. Spatial transcriptomics identified a pressure overload–expanded fibroblast subpopulation enriched for growth factor and TGF-β signaling (FB5), which was markedly attenuated in Cdcp1 -KO hearts, alongside reduction of a pro-inflammatory cardiomyocyte subtype (CM4). Complementary studies in human ventricular fibroblasts demonstrate that CDCP1 knockdown reduced extracellular matrix gene expression and collagen I deposition. These findings establish CDCP1 as a regulator of cardiac fibrotic remodeling in vivo and open avenues for its further investigation as a potential therapeutic target.

Abstract Pathogenic variants in cardiomyopathy genes confer substantial disease risk, yet clinical penetrance varies widely. The population-level prevalence, age-specific penetrance, and the extent to which polygenic susceptibility modifies disease risk across diverse ancestries remain poorly defined. This population-based cohort study leveraged genome sequencing and linked electronic health record data from the All of Us Research Program (n=414,830). This study found that P/LP variants in cardiomyopathy-associated genes were present in 0.91% of participants (95% CI, 0.88%–0.94%), with similar prevalence across ancestry groups. Compared with non-carriers, variant carriers had significantly higher odds of all-cause heart failure (ACHF, OR, 2.58; 95% CI, 2.31–2.89), non-ischemic heart failure (NIHF, OR, 2.74; 95% CI, 2.42–3.10), and non-ischemic cardiomyopathy (NICM, OR, 4.99; 95% CI, 4.44–5.63). Among carriers, by age 50 years, 24.5% (95% CI, 14.5%–33.4%) developed ACHF, 17.2% (95% CI, 8.8%–24.8%) developed NIHF, and 30.1% (95% CI, 19.5%–40.2%) developed NICM. Higher polygenic risk for heart failure was independently associated with increased risk of ACHF (OR, 1.81; 95% CI, 1.74–1.89), NIHF (OR, 1.66; 95% CI, 1.58–1.75), and NICM (OR, 1.69; 95% CI, 1.59–1.80). Among carriers with elevated polygenic risk, odds of disease were substantially higher than among non-carriers at average polygenic risk (ACHF: OR, 4.71; 95% CI, 4.17–5.32; NIHF: OR, 4.56; 95% CI, 3.99–5.22; NICM: OR, 8.51; 95% CI, 7.44–9.73). Results showed that P/LP variants in cardiomyopathy genes were relatively common in this large, ancestrally diverse cohort, and conferred increased risk of cardiomyopathy and HF, with incomplete but clinically meaningful penetrance. Polygenic risk for HF independently and additively increased disease risk across ancestries. Integrating both may improve risk stratification and enable more precise, equitable care.