Citations

Background/Objectives: Standard treatment of multiply relapsed Ewing sarcoma remains to be established. Recent studies evaluating tyrosine kinase inhibitors (TKIs) with anti-angiogenic properties have shown encouraging results. Therefore, we conducted a systematic review and meta-analysis to explore the efficacy and safety of TKIs in patients with Ewing sarcoma. Methods: We comprehensively searched PubMed, Embase, and Cochrane databases for clinical trials (CTs) and cohort studies assessing TKIs in the treatment of advanced Ewing sarcoma patients who received at least one prior line of therapy. The main outcome was objective response rate (ORR). All analyses were conducted using R software (v.4.2.2), employing random effects models with 95% confidence intervals (CIs). Results: We included 14 studies (seven phase II CT and seven retrospective cohorts), comprising 257 patients. The following TKIs were evaluated: cabozantinib, regorafenib, apatinib, anlotinib, sorafenib, lenvatinib, sunitinib, fruquintinib, and imatinib. In a pooled analysis of all Ewing sarcoma patients treated with TKIs, the ORR was 23% (95% CI, 11.2–37.1%) and the DCR was 61.1% (95% CI, 47.3–74.2%). Responses were numerically higher but statistically nonsignificant between clinical trials and real-world studies. The analysis including only single-agent TKIs showed better responses for anlotinib and apatinib, yet these drugs are not available in Western countries. Among the FDA-approved TKIs, superior outcomes were noted with single-agent cabozantinib. (ORR: 21.6%) and regorafenib (ORR: 11.3%). Several studies did not report toxicity data exclusively for Ewing sarcoma patients; thus, conclusions about toxicity are mostly based on the general population of studies and may not be fully representative of Ewing sarcoma patients. Conclusions: Anti-angiogenic TKIs have shown important anti-tumoral activity in patients with Ewing sarcoma. Efficacy was consistently seen in both clinical trials and real-world studies. Nonetheless, there are important differences in study design and population that may limit our interpretation of efficacy and toxicity findings.

Enhancing stability is a crucial factor in selecting bioactive compounds in most applications, while encapsulation techniques have proven to be a promising solution.

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

Abstract Computational analysis of speech enables precise, objective measurements of a behavioral signal closely linked to manic episodes. In a prospective cohort study conducted in Rochester, MN ( ClinicalTrials.gov Identifier: NCT05956340 ) , eighteen English-speaking adults voluntarily hospitalized for a manic episode of Bipolar I Disorder between August 2023 and September 2024 were enrolled. Participants completed semi-structured research interviews during both acute episodes and remission, with manic symptom severity assessed at each session using the Young Mania Rating Scale (YMRS). Across 57 recorded interviews, temporal prosodic features were extracted using automated computational algorithms. After adjusting for individual differences and moderating effects, participant articulation rate (β = 0.24, p = 0.0038), speech rate variability (β = 0.23, p = 0.0041), floor transfer offset (β = -0.31, p = 0.007), and speaking duration (β = 0.54, p < 0.001) were significantly associated with manic symptom severity. These temporal elements classified manic (CV AUC = 0.85) and remission (CV AUC = 0.83) status, highlighting their potential as monitoring and prognostic biomarkers for manic episodes.

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP&gt;15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Background: Atrial arrhythmia recurrence after surgical ablation remains challenging to predict and integrating novel biomarkers may improve risk stratification. Objective: To evaluate whether combining preoperative transthoracic echocardiography (TTE) with artificial intelligence-enabled ECG (AI-ECG) scores enhances prediction of atrial fibrillation/flutter (AF/AFl) recurrence post surgical ablation. Methods: We retrospectively analyzed 1,696 patients undergoing Surgical AF/AFl Ablation between 2006 and 2025 with preoperative TTE and post-blanking ECGs. Clinical variables, TTE indices, and AI-ECG scores (AF probability, ECG-estimated age, HFpEF, LV dysfunction, and aortic stenosis scores) were assessed. The primary outcome was time to AF/AFl recurrence. Univariate/multivariable Cox models and a Random Survival Forest (RSF) model (80:20, training: testing) were developed to identify predictors. Results: Of 1,696 patients (mean age 67.3±10.2 years; 61.7% male) undergoing surgical AF/AFl ablation, 949 (56%) experienced AF/AFl recurrence during a median follow-up of 3.14 years. Patients with recurrence had larger left atria (mean LA area 30.4 vs 24.5 cm 2 , p < 0.001), more diastolic dysfunction (mitral inflow E-wave velocity 1.015 m/s vs 0.896 m/s , p < 0.001), and adverse AI-ECG biomarkers. In multivariable analysis, independent predictors of recurrence included a higher ECG-AF score (p < 1 x 10 -300 ), an older AI-ECG age (p = 0.0002), LA area (p = 0.046), body mass index (p = 0.036), and elevated diastolic blood pressure (HR 1.008 per mmHg, 95% CI 1.002 – 1.014; p = 0.010). The final Cox model achieved a C-index of ~0.67 and stratified patients into risk quartiles with 3-year freedom-from-arrhythmia rates of ~85% (lowest-risk) vs ~43% (highest-risk). An RSF model yielded a slightly higher test C-index (≈0.69), suggesting modest improvement with non-linear modeling. Conclusions: Preoperative AI-ECG biomarkers (AF probability, age discordance) and TTE markers of atrial remodeling independently predicted AF/AFl recurrence after surgical AF/AFl ablation and integration of these metrics improved risk stratification.

Background: Disease-causative variants in LMNA -encoded lamin A/C cause the laminopathies, a heterogeneous group of diseases variably resulting in arrhythmogenic/dilated cardiomyopathy (ACM/DCM), lipodystrophy, muscular dystrophy, and progeria. The artificial intelligence (AI)-derived electrocardiographic age gap (AI-EAG), determined by the discrepancy between a patient’s artificial AI-enabled electrocardiogram (ECG) predicted biological age versus their chronological age, is accelerated in laminopathy. Thus, we sought to determine if the AI-EAG serves as a prognostic marker in patients with cardiac laminopathy. Methods: Retrospective analysis of 1,049 genotype-positive patients with genetic ACM/DCM was used to identify those with pathogenic/likely pathogenic (P/LP) variants in LMNA . After the exclusion of those who lacked a 12-lead ECG while in sinus rhythm, a previously trained AI-ECG age algorithm was used to determine the AI-EAG by subtracting the patient’s chronological age from the AI-ECG derived biological age. The AI-EAG was then correlated with a combined outcome of major ventricular arrhythmia [sudden cardiac arrest, sustained ventricular tachycardia, and appropriate implantable cardioverter-defibrillator shocks], heart transplantation, and cardiovascular death. Results: Overall, 147/1,049 (14%) patients with genetically-mediated ACM/DCM had a P/LP variant in LMNA . Of these, 80/147 (54%) LMNA variant-positive patients (52% female, mean chronological age 36 ± 15 years) had ECGs suitable for AI-EAG analysis. Most (52/80; 65%) had an AI-EAG >10 years with a mean AI-EAG of 17 ± 13 years. Of note, the AI-EAG was greater in those with a clinical cardiac phenotype (20 ± 14 vs. 13 ± 10 years; p = 0.011). As a continuous variable, an increased AI-EAG was associated with increased risk of the composite outcome at a median follow-up of 20 months (HR 1.036; 95% CI 1.004–1.068; p = 0.026) and AI-EAG > 10 and > 20 years were both associated with elevated risk (HR 4.272; 95% CI 1.388–13.147; p = 0.011 and HR 3.732; 95% CI 1.505–9.255; p = 0.004, respectively). Conclusion: In cardiac laminopathy, an increased AI-EAG was common and correlated with adverse cardiovascular outcomes. Non-invasive ascertainment of electrocardiographic aging by AI may provide a novel prognostic marker in cardiac laminopathy. Future studies are needed to validate this finding and further define the role of the AI-EAG in the risk-stratification of patients with cardiac laminopathy.

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (>14), left atrial volume index (>34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (>2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.

Background: Arrhythmogenic and dilated cardiomyopathies (ACM/DCM) are associated with adverse cardiovascular outcomes, including sudden death. Artificial intelligence (AI)-enabled electrocardiographic aging algorithms estimate biological age from 12-lead tracings and predict cardiovascular outcomes in the general population. However, their utility in genetic ACM/DCM has not been studied. Methods: Retrospective review of 1,045 genotype-positive ACM/DCM patients was used to identify those with a pathogenic/likely pathogenic variant in ClinGen-classified definitive/strong evidence ACM/DCM-susceptibility genes. Following exclusion of patients without a baseline ECG for analysis (e.g. paced rhythms), the AI-ECG age gap (AI-EAG) was calculated by subtracting chronological from AI-ECG age. Genes were grouped into functional categories: desmosomal ( PKP2, DSP, DSG2, DSC2, JUP ), nuclear envelope ( LMNA ), sarcomeric ( TTN, MYH7, TNNT2 ), cytoskeleton/Z-disc ( FLNC, DES, DMD, BAG3 ), and regulatory/ion channel ( PLN, RBM20, SCN5A ). Cardiac MRI data included left ventricular structure, function, and late gadolinium enhancement (LGE) presence and pattern. Mann-Whitney U was used for group comparisons; results are shown as median (IQR). Results: Overall, 824 patients (52.8% male; median age of 40.7 years, IQR 26.5–53.4; and median AI-EAG of 7.49 years, IQR 0.43–15.26) were included. DES variant-positive patients had the highest (19.98 years, IQR 9.66–23.21) and BAG3 variant-positive patients the lowest (–0.95 years, IQR -6.05–7.61) AI-EAGs. Among gene groups, nuclear envelope had the highest (15.12 years, IQR 6.79–25.17) and sarcomeric the lowest (5.75 years, IQR -0.69-14.01) AI-EAGs. Cardiac MRI was available for 538/824 (65%) of which 233/538 (43.3%) had LGE. No significant difference in AI-EAG was observed between LGE-positive and -negative patients (6.99 vs. 7.94 years; p=0.831). However, several LGE patterns were associated with significantly higher AI-EAGs: subepicardial (9.88 vs. 6.84; p=0.004), transmural (13.83 vs. 7.25; p=0.012), apical (12.89 vs. 7.17; p=0.002), and septal (8.91 vs. 7.19; p=0.016). Conclusion: AI-EAG varies by gene and functional group, possibly reflecting gene-specific myocardial remodeling. While certain LGE patterns were linked to increased biological aging, overall LGE status was not. These findings suggest AI-EAG may capture disease activity that is distinct from LGE and could serve as a complementary, non-invasive biomarker in genetic ACM/DCM.

Background/Objectives: Chordoma, a rare slow-growing malignant bone tumor, remains therapeutically challenging due to its invasive nature. We examined institutional outcomes comparing imatinib and brachyury-directed vaccines. Methods: We used data from three sites of our quaternary care academic institution to analyze demographics, time to progression, overall survival, and adverse events in chordoma patients treated with imatinib or enrolled in a brachyury vaccine trial. Results: We included a total of 52 patients (8 in the brachyury vaccine cohort and 44 in the imatinib cohort) in the analysis. As expected, sacrococcygeal location was the most predominant in both cohorts: 62.5% in the brachyury cohort and 45.5% in the imatinib cohort. No demographic differences were present between the cohorts. ECOG was similar in both groups (p = 0.796). The primary outcome, time to progression (TTP), was shorter in the brachyury compared to the imatinib cohort (5.6 vs. 13 months, p = 0.0589), almost reaching statistical significance. Overall survival (OS) was comparable, 211 vs. 212 months for the brachyury and imatinib cohorts, respectively (p = 0.277). Although the brachyury vaccine cohort presented a higher incidence of adverse events than the imatinib cohort (75% vs. 31.8%, p = 0.021), the severity was milder. Conclusions: Imatinib showed longer disease control than the brachyury vaccine, though overall survival was similar. Future studies and deeper molecular insights are essential to develop better therapies and improve patient quality of life.