Citations

Abstract Aims Pronounced sex-differences are known in the incidence of atrial fibrillation (AF). In this study, we aimed to investigate the atrial electrophysiological properties that may underlie sex-differences in AF incidence in the younger population, focusing on IK1, a cardiac ion current important for action potential (AP) stability and triggered activity. Methods and results We assessed sex-differences in P-wave morphology in 12-lead ECG in healthy young New Zealand White rabbits. Males presented longer PWD and larger P-wave area compared to females. Patch-clamp experiments were performed in isolated rabbit atrial cardiomyocytes (CMs). Male atrial CMs presented higher delayed after depolarizations (DAD) incidence, amplitude, and area under the curve (AUC) than females, potentially facilitating the presence of atrial triggered activity in males. Male atrial CMs showed a less hyperpolarized resting membrane potential (RMP), a 50% smaller IK1, and a 26% reduction in Kir2.1 protein expression, a pore forming subunit of IK1, than females. Dihydrotestosterone (DHT) effects were investigated acutely and semi-chronically ex vivo. Experiments showed that the sex-difference in IK1 could be mimicked by DHT. In female atrial CMs, acute and semi-chronic (24 h) DHT administration reduced IK1. In the presence of a PKC-inhibitor, DHT-mediated IK1 reduction was not observed in atrial female CMs, suggesting it to be PKC-mediated. Chronic DHT-effects were investigated in vivo in female rabbits after hormone-releasing pellet implantation. After 2 weeks, animals showed a significantly prolonged and larger P-wave, a smaller atrial IK1 and a trend towards an increased DAD amplitude and AUC. Conclusion Sex impacts on atrial electrophysiology, leading to sex-differences in P-wave morphology, triggered activity, RMP, and IK1. These sex-differences can be mimicked by sex hormone-treatment, suggesting that sex hormones—particularly DHT—play a pivotal role in mediating sex-differences in atrial electrophysiology. Such sex-differences might impact on the propensity to develop AF, particularly in the younger population.

ABSTRACTIndividuals with primary progressive apraxia of speech (PPAOS) often develop parkinsonism and dysphagia. To evaluate the clinical correlates and impact of dysphagia in this population, we compared enrollment visit data between individuals with (n = 12) versus individuals without (n = 44) dysphagia symptoms. The group with dysphagia had more motor speech symptoms and parkinsonism. Longitudinal analysis revealed that almost everyone developed dysphagia before dying; the average time to death after developing dysphagia was 5.43 years and complications of dysphagia resulted in mortality for 35% of the individuals for whom data were available. These results emphasize the need for dysphagia management and provide useful prognostic estimates.

Abstract Aims To develop a deep learning model that: (i) utilizes transthoracic echocardiography (TTE) clips to detect left ventricular (LV) enlargement without being provided information regarding a patient’s sex and body size; and (ii) can be accurately applied to clips acquired using either standard comprehensive TTE or handheld cardiac ultrasound (HCU). Methods and results Using retrospective TTE data (training: 8722 patients; internal validation: 468 patients), we developed a deep learning model that estimates a patient’s end-diastolic LV volume (indexed to body surface area and normalized across the sexes), and then thresholds this estimate to perform the following classifications: (1) normally sized LV vs. ≥ mild LV enlargement; (2) normal/mildly enlarged LV vs. ≥ moderate LV enlargement. For retrospective datasets, the model showed strong performance in TTE across three geographically distinct locations (Minnesota and Wisconsin: 1082 patients, AUC = 0.925 and 0.953 for classifications 1 and 2, respectively; Arizona: 1475 patients, AUC = 0.935 and 0.969; and Florida: 1481 patients, AUC = 0.934 and 0.970). Additionally, performance was strong for both TTE and HCU clips collected from a prospective cohort of 410 patients who underwent HCU immediately following TTE (TTE: AUC = 0.925 and 0.971; HCU: AUC = 0.874 and 0.902, for classifications 1 and 2, respectively). Conclusion An automated deep learning model applied to TTE or HCU images accurately categorizes LV volumes. These results lay a foundation for future work aimed at optimizing clinical outcomes for heart failure patients by enabling early detection of LV enlargement across various point-of-care settings.

Abstract The National Institute on Aging-funded Geroscience Education and Training Network held the “Building an Interdisciplinary Workforce in Geroscience: Aligning Perspectives and Educational Goals” preconference workshop at the 2023 Gerontological Society of America’s Annual Meeting. Parties involved in geroscience education and interested stakeholders addressed the development of didactic educational materials, training, and career development opportunities to equip learners with the skills to meet the demands of the emerging field of geroscience, from research to clinic and industry. Recent successes were presented, existing opportunities, gaps, and challenges identified and discussed, and solutions and future directions described.

AbstractArtificial intelligence (AI) has been influencing healthcare and medical research for several years and will likely become indispensable in the near future. AI is intended to support healthcare professionals to make the healthcare system more efficient and ultimately improve patient outcomes. Despite the numerous benefits of AI systems, significant concerns remain. Errors in AI systems can pose serious risks to human health, underscoring the critical need for safety, as well as adherence to ethical and moral standards, before these technologies can be integrated into clinical practice. To address these challenges, the development, certification, and deployment of medical AI systems must adhere to strict and transparent regulations. The European Commission has already established a regulatory framework for AI systems by enacting the European Union Artificial Intelligence Act. This review article, part of an AI learning series, discusses key considerations for medical AI systems such as reliability, accuracy, trustworthiness, lawfulness and legal compliance, ethical and moral alignment, sustainability, and regulatory oversight.Level of EvidenceLevel V.

ABSTRACTLung transplantation is indicated for selected patients with advanced pulmonary arterial hypertension (PAH). We used a modified Delphi process to develop recommendations on care of patients with PAH undergoing lung transplantation. This Delphi panel was recruited from the Pulmonary Vascular Research Institute's Innovative Drug Discovery Initiative ‐ Lung Transplantation Workstream, consisting of clinical and research experts in PAH and lung transplantation. In this process, 29 panelists were given open‐ended questions, querying topics related to lung transplantation in PAH. A steering group converted the responses into discrete statements. Panelists then rated agreement using a Likert scale in two further survey rounds: −5 (strongly disagree) to 5 (strongly agree). Consensus was defined as mean ≥ 2.5 or ≤ −2.5, with a standard deviation not crossing zero. Consensus was reached on 141 of 223 statements. Notable areas of consensus were for early discussions about transplantation, and agreement with previously published referral and listing criteria. There was agreement that lung transplantation could be offered in sick candidates, including those with concurrent renal or hepatic insufficiency. Bilateral lung transplantation was considered the procedure of choice for most patients, with rare indications for heart‐lung transplantation. Consensus on bridging strategies included use of veno‐arterial extracorporeal membrane oxygenation and preemptive awake cannulation in those with severe right ventricular dysfunction. Consensus was also achieved on intraoperative use of invasive hemodynamic monitoring, and prolonged postoperative circulatory support guided by hemodynamic response and echocardiography. Patients with PAH undergoing transplantation require specialized management, which differs somewhat from other candidates.

Sleep is critical to athlete health and sport performance. Purpose To evaluate the relationship between objective sleep data and next-day basketball game performance. Methods Collegiate men basketball athletes (n = 4) wore smart ring sleep trackers the night before games (n = 22). Sleep variables included hours of total sleep (TS), rapid eye movement sleep duration (RM), deep sleep duration (DS), and heart rate variability (HRV). Game performance was evaluated using field goal percentage (FG%) and player efficiency (EFF). Results TS, RM, DS, and HRV demonstrated very weak relationships with FG% ( p > 0.05) and EFF ( p > 0.05). Individual player analysis revealed one athlete exhibited strong to moderate correlations for TS-EFF (r = 0.67), RS-EFF (r = 0.41), TS-FG% (r = 0.51), and RM-FG% (r = 0.67) ( p < 0.05). Sleep metrics had no effect on FG% (R 2 = 0.025) and EFF (R 2 = 0.053) ( p > 0.05). Conclusions Results suggest a lack of group-level associations between smart ring-measured sleep variables and next-day game performance. This highlights the need for individualized sleep monitoring rather than relying solely on objective metrics.

Background Parkinsonism following hypoxic ischemic damage of the basal ganglia is an uncommon phenomenon that has been infrequently reported. However, only a few cases have noted improvement of symptoms with dopaminergic therapy. We report the clinical and imaging features of five patients with post-anoxic parkinsonism responsive to dopamine supplementation. Objective To describe a retrospective case series of five cases of dopamine-responsive post-anoxic parkinsonism. Methods We identified all the cases using the Mayo Clinic Data Management System utilizing advanced data explorer search engine for any patients evaluated for post anoxic parkinsonism and its associated acronyms from 2000–2024. Clinical features, neuroimaging, medication trials, and responses were obtained from chart review of identified patients. Results Five patients met the inclusion criteria. All patients underwent anoxic events followed by development of parkinsonism. Patients exhibited parkinsonism described as combinations of bradykinesia, rigidity, tremor, and postural instability. All patients underwent evaluation by a neurologist, MRI imaging, and treatment by dopaminergic agents. Of the five patients, four received carbidopa/levodopa whereas one received a dopamine agonist. All patients were clinically followed for a median of approximately 4 years and showed improvement in parkinsonism. Conclusions Parkinsonism following a hypoxic ischemic insult is a rare occurrence but response to dopaminergic therapy in those cases is even more scarcely described. Our cases series provides important implications for treatment options for patients with post anoxic parkinsonism.

ABSTRACT Anatomy education is a cornerstone of medical training and relies on cadaveric dissection and 2D illustrations. Technological advancements and integrated curricula have reduced the focus on detailed anatomy and challenged educators to engage Generation Z learners with interactive, tech‐driven methods. Advanced imaging and artificial intelligence (AI) offer a solution, providing virtual dissection simulations and personalized learning tools that mimic 3D anatomy and adapt to individual student needs. Machine learning, a subset of AI, enhances this process by enabling predictive analytics, adaptive feedback, and tailored learning pathways based on performance data, significantly improving anatomical comprehension. Despite its benefits, AI integration raises concerns about over‐reliance on technology, biases, and diminished human interaction in training. This review examines AI's transformative potential in anatomy education while emphasizing the need for balanced implementation and ethical oversight. A systematic review following PRISMA guidelines was conducted, utilizing PubMed and backward citation searches. The search yielded 56 studies, with 47 additional articles from citations, resulting in 61 included studies. These explored AI applications such as virtual dissection simulations, machine learning algorithms for adaptive feedback, and gamified learning experiences, which were shown to enhance engagement, personalize learning, and improve anatomical understanding. Concerns about over‐reliance on AI and the loss of human interaction were also raised. AI has the potential to enhance anatomy education, but careful consideration of ethical and practical implications is essential. A balanced approach combining traditional methods with AI and robust oversight is crucial for effective integration.