Citations

Abstract Positron emission tomography (PET)-based molecular imaging is a clinically important tool for the evaluation of dementia and other neurodegenerative disorders, using radiotracers that target specific molecular and pathophysiological processes. Fluorodeoxyglucose brain PET has historically supported neurodegenerative disorder evaluation through detection of characteristic patterns of regional glucose hypometabolism. In recent years, the molecular imaging of protein aggregates has taken on an increasingly important role. This is evident with the widespread clinical adoption of amyloid PET and early use of tau PET to provide diagnostic and prognostic information for patients with suspected Alzheimer’s disease. Additional classes of radiotracers relevant to neurodegenerative diseases are under active investigation, including those targeting neuroinflammation, synaptic density, and α-synuclein aggregates. We review the major classes of PET radiotracers for neurodegenerative diseases, describing their molecular mechanisms, diagnostic performance, and their current and emerging clinical roles in dementia evaluation. Case examples for clinically used tracers illustrate imaging features relevant to diagnosis and characterization of prognosis and treatment effect. Finally, we discuss early developments and current challenges in the development of theranostic approaches and novel radiotracers for neurodegenerative diseases.

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a promising therapeutic option for women with polycystic ovary syndrome (PCOS). Clinical trials and data demonstrate significant improvements in body weight, insulin resistance, hyperandrogenism, and, in some studies, menstrual regularity and reproductive outcomes. However, the current evidence base remains disproportionately focused on short-term metabolic and anthropometric endpoints, while outcomes most relevant to patients—including treatment satisfaction, gastrointestinal tolerability, psychological well-being, body image, fertility planning, financial burden, and long-term adherence—remain inadequately characterized. Methods: We searched PubMed and Google Scholar for English-language literature published between 2021 and 2026, using structured keyword searches. This narrative review evaluates the literature on GLP-1RA use in PCOS beyond weight-focused outcomes, examining mechanisms of action, comparative tolerability across agents, and patient-centered domains including quality of life, eating behavior, and reproductive planning. Results: The published literature reveals a substantial gap between trial-reported completion rates and treatment success, and actual persistence and patient satisfaction. This discrepancy is particularly consequential in PCOS, where treatment decisions intersect with high baseline rates of depression, anxiety, and body image disturbance, and where ovulatory benefits must be weighed against mandatory discontinuation before conception. Conclusions: We identify a systematic absence of validated patient-reported outcome measures, standardized core outcome sets, and long-term follow-up data after treatment withdrawal. This review proposes a research agenda prioritizing patient-centered endpoints as co-primary outcomes, structured assessment of acceptability and persistence, qualitative and mixed-methods approaches, and a dedicated study of fertility planning and treatment interruption.

Background: Electrical and dielectric properties of the myocardium depend on its composition, fibrosis, edema, ischemia, and heat damage, and hence may play an important role in defining the electrophysiologic substrate. Local impedance and other impedance-based methods give information about these parameters that is available to clinicians, but not direct or substitutable for intrinsic conductivity and permittivity of the tissue. The use of artificial intelligence has been thoroughly studied for the ECGs, electrograms, images, and computational data analysis, but not for dielectric or impedance measurements. Objective: The study aims to review the scientific basis of dielectric properties of heart muscle critically in cardiology and electrophysiology, to differentiate between existing clinical and conceptual applications of myocardial dielectrics, and to estimate current and potential contributions of artificial intelligence to their interpretation. Data Sources and Study Selection: The current work represents a structured narrative review in PubMed, Scopus, and Google Scholar databases from inception until May 2026 based on various combinations of keywords that refer to myocardial impedance, dielectric properties, conductivity, permittivity, local impedance, dielectric imaging, cardiac electrophysiology, catheter ablation, machine learning, and artificial intelligence. This review included experimental, ex vivo, animal, computational, and human studies related to myocardial dielectric properties and their use in electrophysiology. Evidence was classified according to four categories: direct human clinical evidence; preclinical/translation evidence; indirect evidence from related techniques; and extrapolation concept. As a result of heterogeneity between studies, sensing technologies, and outcomes, the results have been analyzed narratively. Key Findings: Evidence regarding the usage of AI algorithms, which have been primarily trained on myocardial dielectric or catheter-based local impedance data, is extremely scarce and mostly consists of preclinical or ex vivo data. On the contrary, the majority of clinically used AI algorithms in the field of electrophysiology make use of ECG data, intracardiac electrogram data, image processing, or computational modeling without any inputs related to dielectrics or local impedance. Conclusions and Implications for Practice: Dielectric sensing is an attractive supplement to traditional methods of voltage mapping and imaging, although the clinical readiness of dielectric sensing greatly varies depending on the application. At the present time, the existing data provide a basis for utilizing the local impedance measurement in the ablation procedure and evaluation of the myocardium properties, while the combination of artificial intelligence and dielectric sensing is still experimental. Before being introduced into the clinical electrophysiology practice, signal acquisition standardization and prospective multicenter trials are needed.

Background The Impella 5.5 is employed to provide left ventricular support in cardiogenic shock. Mechanistic understanding of biventricular relationships is essential in device optimization. Using a porcine model of acute myocardial infarction in tandem with pressure–volume loops, this study aimed to understand the impact of Impella 5.5 on biventricular hemodynamics. Methods Using Domestic Yorkshire pigs, an Impella 5.5 was inserted percutaneously under fluoroscopic guidance via carotid/aortic access and an acute myocardial infarction was created. Biventricular pressure–volume loops and derived parameters were generated for each level of support before (n=6) and after myocardial infarction (n=6). Results With increasing Impella 5.5 support, left ventricular pressure–volume loops shifted left, and right ventricular (RV) loops shifted right. In a healthy porcine heart, the RV adequately compensated for increased preload from the device, demonstrated by increased RV performance at higher P levels. After myocardial infarction, pigs were unable to tolerate speeds beyond P7 from diminished RV stroke volume reserve compared to healthy hearts. Median RV stroke volume percentage change from P1 to P9 before myocardial infarction was from 5.7% (interquartile range, −15.1% to 9.7%) to 50.6% (interquartile range, −11.6% to 60.2; P <0.05), and following acute myocardial infarction from P1 to P7 was from 3.9% (interquartile range, −2.7 to 78.4) to 13.4% (interquartile range, −8.9% to 147.2; P =0.94). Conclusions Impella 5.5 speed modulation highlights significant interventricular interactions with opposing biventricular trends. While the heart can mount an improvement in RV stroke volume with increasing Impella 5.5 support, these data suggest an attenuated response following acute myocardial infarction, which may relate to inadequate response to increased preload needed by the device after infarction affecting the interventricular septum.

Abstract Introduction Remote cognitive assessments offer an accessible, scalable way to support clinical characterization and longitudinal monitoring of cognition. This study evaluates the feasibility of implementing Mayo Test Drive (MTD), a remote, self-administered digital cognitive assessment, within a specialized clinic focused on eligibility assessments for anti-amyloid therapies for Alzheimer’s disease (AD). Method Two-hundred and nine ( N =209) patients (M age =71.2, 53% female) were evaluated through the Mayo Clinic Rochester Alzheimer’s Disease Treatment Clinic (ADTC) and had the opportunity to complete MTD, either as a remote, patient-initiated session through a patient-facing healthcare portal or during an in-clinic neuropsychological evaluation. Results Clinical diagnoses included 55.2% mild cognitive impairment (MCI), 31.0% mild dementia, and 11.6% moderate dementia, in addition to 2.2% cognitively unimpaired (CU), as determined by neurology consensus at a multidisciplinary case conference. Among those who initiated MTD ( n =197/209), 97.5% completed a session (70.6% remotely, 26.9% in clinic). Completion rates were high across diagnostic groups and did not significantly differ by clinical severity ( p =.12): CU (100%), MCI (99.1%), mild dementia (97.0%), and moderate dementia (89.5%). Remote completion rates overall were high (97.9%). Among completers, administration setting (remote vs. in-clinic) did not significantly differ by clinical stage, χ²(3)=2.37, p =.49. Reasons for non-initiation included not receiving or opening portal messages and other/unknown factors. Discussion Remote digital cognitive assessment is feasible within the context of clinic eligibility evaluations for AD therapies. The high completion rates of MTD across clinical impairment levels in this setting support development of paths for broader use in cognitive characterization in AD care.

Background Menopause-related symptoms are common but undertreated. Digital tools that support symptom assessment and patient education may improve engagement. Objective To assess whether a previsit digital menopause symptom assessment and education tool increased treatment initiation and patient engagement compared to an educational pamphlet. Design Women aged 45–55 years with upcoming primary care appointments were invited to participate. Those with a total Menopause Rating Scale (MRS) score ≥5 indicating bothersome menopause symptoms were included. Participants were randomized to a digital menopause symptom assessment tool or a digital educational pamphlet and completed a post-visit survey (NCT06919887). Participants Of the 14,726 women invited to participate between May 12, 2025, and December 5, 2025, there were 653 (4.4%) enrolled that met eligibility criteria. Interventions Randomized to an mHealth app called emmii or a standard digital menopause educational pamphlet. Main measures Menopause treatment initiation within 30 days of the clinical visit was assessed by electronic health data. Baseline and post-visit surveys were collected assessing patient experience, engagement, and perceived usefulness. Rates of menopause hormone therapy (HT) prescribing were compared with a reference population of nonparticipants aged 45–55 who had a primary care appointment at Mayo Clinic during the same period and received no study education materials. Key results Of 653 participants enrolled and randomized, 383 completed both the baseline and post-surveys (171 intervention and 212 control). HT initiation among study participants was significantly higher than in the reference population of women (9.3% vs. 4.4%, p &lt; 0.001). There was no difference in HT initiation between the intervention (9.1%) and control groups (9.6%) ( p = 0.83). Initiation rates were 3.8% and 4.5%, respectively ( p = 0.64) for nonhormone therapies. Conclusions Among women with bothersome menopause symptoms, those who underwent previsit symptom screening and received educational materials had higher rates of HT initiation than women who did not participate in the study. These findings suggest that structured menopause education may improve recognition and management of menopause symptoms in routine clinical practice.

Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.