Citations

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.

Objectives: Pulmonary function tests (PFTs) are essential for diagnosis, management, and follow-up of a wide range of respiratory conditions as they provide quantitative insights into various aspects of lung function. Precision in ordering and scheduling PFTs, like any test, is crucial as errors can lead to misdiagnoses, delayed treatments, unnecessary retesting, inflated healthcare costs, and potentially compromised patient care. Hence, the aim of this quality improvement initiative was to reduce the PFT ordering error rate to below 5% by redesigning the ordering workflow and implementing a standardized order set tailored to specific clinical scenarios. Methods: The quality improvement initiative was implemented from August 2023 to March 2024. Data was collected from the electronic medical record (EMR) using predefined operational definitions to identify incorrect PFT orders and scheduling errors. A redesigned orderset was created to align with specific clinical presentations, including symptoms, suspected diagnosis, confirmed diagnosis, and presurgical evaluation. Scheduling was also restructured to match the expected testing time for each category. Education sessions were conducted with ordering providers, and ongoing feedback loops were established. Results: Among 575 preintervention PFT orders, 130 (22.6%) contained errors. Following implementation, review of 607 postintervention orders showed a reduction in error rates to 4.1%, representing an ∼80% improvement ( P <0.01). There was no increase in unnecessary testing, supporting the effectiveness of the intervention without unintended negative consequences. Conclusions: Implementation of a knowledge-based CDSS with standardized order sets significantly reduced PFT ordering errors and variability, improving guideline adherence and care quality. Continued refinement and integration with evolving technologies are essential for broader applicability.

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.

ABSTRACT Background Pulsed field ablation (PFA) is an emerging non‐thermal ablation modality with proven safety in atrial arrhythmia ablation. However, data on its performance for ventricular arrhythmias (VA) remain limited. Objective To evaluate the feasibility, safety, and efficacy of PFA for complex VA in a multicenter real‐world US registry. Methods This retrospective study included consecutive patients undergoing PFA for ventricular tachycardia (VT) or premature ventricular contractions (PVCs) at three Mayo Clinic centers (April 2024 to July 2025). The primary feasibility outcome was the successful delivery of PFA lesions to the intended target substrate. Primary efficacy outcomes included acute procedural success; sustained VT recurrence requiring implantable cardioverter‐defibrillator (ICD) shocks or multiple antitachycardia pacing therapies; and ≥ 80% reduction in PVC burden. Safety outcomes included procedural complications, valvular function, thromboembolic events, renal function, and conduction disturbances. Results Twenty‐six patients were included (13 PVC, 13 VT). Acute success was achieved in all VT ablations (69.2% complete; 30.8% partial) and in 92.3% of PVC ablations. Complications occurred in 11.5% of patients (transient LBBB [ n = 1] and vascular complications [ n = 2]). No thromboembolic events, intracardiac thrombus, or acute kidney injury were observed. Lesions near the mitral valve apparatus did not worsen mitral regurgitation, including in patients with pre‐existing valve disease. After a median follow‐up of 365 days, one patient experienced VT recurrence requiring ICD shocks. Estimated recurrence‐free survival after VT ablation was 61.5% at 12 months; 58.3% of PVC patients achieved ≥ 80% burden reduction. Conclusion PFA for VA appears feasible and acutely effective, with a low rate of procedure‐related complications. While acute success was high, long‐term outcomes were acceptable for VT ablation and relatively modest for PVC ablation, highlighting the need for continued refinement of technology and energy‐delivery strategies. Larger prospective studies are warranted.

Abstract The apolipoprotein E ε4 ( APOE4 ) allele is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD), yet the mechanistic underpinnings by which it alters human microglial function remain poorly understood. Here, we utilize CRISPR/Cas9-engineered isogenic human induced pluripotent stem cell-derived microglia-like cells (iMGs) expressing APOE3 or APOE4 to systematically interrogate genotype-dependent effects on microglial proteomes and their secreted extracellular vesicles (EVs) under basal and fibrillar amyloid-β (fAβ)-challenged conditions. Quantitative proteomics revealed that APOE4 iMGs exhibit downregulated RNA processing, impaired phagocytosis, and exacerbated pro-inflammatory signaling, particularly upon fAβ exposure. Functionally, APOE4 iMGs demonstrated increased NF-κB activation, enhanced neuronal apoptosis and tau pathology in 2D and 3D co-culture systems. Interestingly, APOE4 iMGs released greater amount of EVs enriched in proteasomal components over APOE3 iMG. Integrated proteomic analyses further highlighted APOE4 -specific selective packaging of EV cargo linked to neuron-glia communication, inflammatory signaling, and proteostasis dysregulation, suggesting a potent, non-cell-autonomous mechanism driving neuronal damage. Consistently, these APOE4 iMGEVs were preferentially internalized by neurons, leading to disrupted neuronal proteostasis, impaired neuronal firing, and increased tau phosphorylation, particularly under pathological stress. Notably, pharmacologic inhibition of NF-κB in APOE4 iMGs reduced EV cargo-loading of proteasomal molecules and mitigated iMGEV-driven neuronal dysfunction and tau pathology. These findings establish a mechanistic link between APOE4 , dysregulated microglial homeostasis, and microglial EV-mediated propagation of proteostatic dysfunction and neurodegeneration, advancing our understanding of how APOE4 contributes to AD pathogenesis.

Abstract BRCA1 -deficient cancers experience persistent replication stress and structural genome instability yet retain the capacity for sustained proliferation, implying reliance on compensatory genome-maintenance mechanisms. Here, we establish BRCA1–FANCM synthetic lethality in human BRCA1 -deficient breast cancer and exploit temporally controlled FANCM depletion to capture genome evolution over successive cell divisions before declining cellular fitness becomes limiting. We show that FANCM restrains genome-wide structural variation in BRCA1 -deficient breast cancer cells under endogenous replication stress. FANCM loss amplifies the characteristic BRCA1 -associated short tandem duplication (TD) phenotype while permitting larger, including megabase-scale, TDs and diverse rearrangements to emerge. Newly emerged TDs preferentially associate with Pol II-occupied regions, and FANCM depletion increases proximity between the replication machinery and elongating RNAPII in BRCA1 -mutant breast cancer cells, linking FANCM-mediated genome protection to transcription–replication encounters. BRCA1 -altered human tumors with low FANCM expression recapitulate key features of this phenotype, while genome–transcriptome integration links newly emerged SVs to configuration-dependent local transcriptional changes. Together, these findings establish FANCM as a replication-stress safeguard coupling survival to restraint of structural genome evolution.

Abstract Purpose Bevacizumab is an effective treatment for radiation necrosis; dose dependent radiographic and edema response data are lacking. Methods Multicenter, retrospective cohort study of adult patients with primary central nervous system tumors or brain metastases who received bevacizumab for radiation necrosis between June 2021 and July 2024. Patients were grouped by bevacizumab dose (7.5 mg/kg vs 10 mg/kg administered every three weeks). Primary outcome was edema response, assessed by a blinded neuro-oncologist. Secondary outcomes included contrast enhancing disease response, clinical response, adverse events, treatment discontinuation, and re-dosing for recurrent radiation necrosis. Analyses were performed with and without adjustment for tumor type. Results 104 patients were included (7.5 mg/kg, n = 53; 10 mg/kg, n = 51). Edema response differed between dosing groups in unadjusted analyses (p = 0.032), favoring the 7.5 mg/kg cohort; however, this association was attenuated after adjustment for tumor type. Contrast enhancing disease response did not differ between groups in either unadjusted or adjusted analyses (p = 0.43). Clinical response was higher in the 7.5 mg/kg group (65.2% vs 37.8%, p = 0.013). Tumor type differed significantly between cohorts (p < 0.001), with more primary CNS tumors in the 10 mg/kg group. Rates of adverse events, treatment discontinuation, and re-dosing were similar. Conclusions While lower-dose bevacizumab was associated with greater edema reduction in unadjusted analyses, this effect was attenuated after accounting for tumor type. Importantly, 7.5 mg/kg dosing appeared sufficient for achieving edema control and contrast enhancing disease response. These findings support an individualized approach to bevacizumab dosing in radiation necrosis and highlight the need for prospective, tumor-stratified studies.