Citations

Abstract Background While type 2 diabetes (T2DM) is a known risk factor for aortic valve calcification (AVC) and aortic stenosis (AS), the specific mechanisms remain unclear. The aim of this study was to determine early factors associated with AVC in patients with T2DM. Methods This cross-sectional study is a pre-specified substudy of an ongoing multicenter randomized controlled trial, the STENO INTEN-CT trial (Screening and Intervention for Subclinical Coronary Artery Disease in Patients With Type 2 Diabetes). Eligibility criteria are patients with diagnosed T2DM without prior ischemic heart disease, and age between 55-69 years (male) and 60-74 years (female). All patients underwent a non-contrast cardiac computed tomography scan and AVC was measured in Agatston units. Results Of the 3635 patients included in this study, nearly half (n = 1694) demonstrated an AVC > 0 (n=1502 with 0 < AVC < 300 and n = 192 with AVC ≥ 300). With increasing AVC, patients were elder, predominantly males, and had significantly more comorbidities (obesity, hypertension, chronic obstructive pulmonary disease, and kidney dysfunction). Patients with higher AVC were more likely to be treated with statins (AVC = 0: n = 1509 (78%), 0 < AVC < 300: n = 1217 (81%), AVC ≥ 300: n = 163 (85%), p = 0.009). Higher body mass index (OR: 1.03 (1.02-1.04), p < 0.001), statin use (OR: 1.49 (1.24-1.80), p <0.001), low-density lipoprotein (OR: 1.24 (1.12 - 1.37), p < 0.001), and kidney dysfunction, measured as urine albumine-creatinine ratio (OR: 1.00 (1.00-1.00), p <0.001), were independently associated with increasing AVC (Figure 1). However, T2DM severity, measured as the duration of T2DM, HbA1c levels, and the number of antidiabetic drugs used, was not associated with increasing AVC severity (Figure 1+2). Conclusion AVC is highly prevalent, affecting nearly half of patients with T2DM. However, early factors driving AVC are not associated with T2DM severity, but rather to diabetes-related comorbidities and complications.

Abstract Hypoparathyroidism is an endocrine disease caused by insufficient levels of PTH, which acts directly on bone and kidney and indirectly on the intestine to regulate calcium and phosphate balance. In clinical trials, palopegteriparatide (TransCon PTH) treatment enabled independence from conventional therapy (no active vitamin D, ≤600 mg/d calcium) and maintained serum biochemistries within normal ranges. The current analyses describe patterns of change in BMD, serum bone turnover markers, and serum and urine calcium in adults with chronic hypoparathyroidism treated with palopegteriparatide through 3 years of the PaTH Forward trial. Baseline BMD Z-scores for the LS, TH, FN, and 1/3 distal radius were above zero, indicating bone mass exceeding age-adjusted normative values. BMD decreased from these elevated baseline levels with palopegteriparatide treatment, with larger reductions during the first 26 weeks and modest declines thereafter. Mean BMD Z-scores at week 162 remained above zero for all 4 sites. Participants with lower baseline BMD (Z-scores below −1 and T-scores below −2.5) generally exhibited lesser declines in BMD versus those with higher baseline BMD. Palopegteriparatide treatment was associated with early increases in bone resorption (serum C-terminal telopeptide of type I collagen, CTx) and bone formation (serum procollagen type 1 N-terminal propeptide, P1NP) that peaked at weeks 12 and 26, respectively, followed by declines to levels moderately higher than baseline at week 162. Mean CTx and P1NP in the overall population and the subgroup of postmenopausal women were below their upper limits of normal from weeks 58-162. At week 162, mean serum and median urine calcium remained within normal ranges and 91% of participants were independent from conventional therapy. These results suggest that long-term palopegteriparatide therapy in adults with chronic hypoparathyroidism gradually returns the skeleton toward its natural state thereby enhancing the skeleton’s contribution to calcium homeostasis.

Abstract Background JAK inhibitors (JAKi) are small molecules that cross the placenta from early pregnancy.1 Evidence on the safety of in utero JAKi exposure remains limited, particularly regarding pregnancy outcomes, infant infections, vaccine responses, and early development.2, 3 Methods We conducted a multicenter retrospective study within the ECCO CONFER network, including pregnant women with inflammatory bowel disease (IBD) exposed to tofacitinib (TOFA), upadacitinib (UPA), or filgotinib (FILGO). Data were collected from 22 centers across 16 countries and included maternal characteristics, disease activity, medication patterns, and pregnancy and infant outcomes. Results Among 55 JAKi exposed pregnancies, 21 (38.2%) were unplanned and 30 (54.6%) had active disease. Nine women (16.4%) initiated JAKi during pregnancy. Overall, 38 (69.1%) received TOFA, 15 (27.3%) UPA, and 2 (3.6%) FILGO; 39 (70.9%) continued treatment throughout pregnancy. JAKi discontinuation in 1st-2nd trimester led to disease activity in 9/16 cases (56.3%). Maternal complication risk was low: no DVT or (pre) eclampsia occurred; three cases of premature rupture of membranes (GA 34–37) and four required hospitalizations due to infection or other complications. A total of 10 abortions occurred - five spontaneous (9.1%) and five induced (9.1%), including three due to concern about JAKi exposure. Among 45 live births, two infants (3.6%) were preterm, four (7.3%) small for gestational age, and two (3.6%) had Apgar <7 at 5 minutes. No congenital abnormalities were reported. Infant follow-up findings are summarized in Table 1. One-third of breastfeeding women continued JAKi postpartum. A high adherence rate to the national immunization schedule was seen. Eight (19.5%) received the live BCG-vaccine within the first 30 days of life. No adverse events followed live or inactivated vaccines, and no malignancies were observed. Conclusion In this global cohort of JAKi-exposed pregnancies in women with IBD, maternal complication rates were low, and no increased risk of adverse outcomes among live births was identified. While disease activity was common, particularly following discontinuation of JAKi, infant outcomes - including development, infection risk, and vaccine response - were reassuring. These findings provide important preliminary safety data to inform decision-making regarding JAKi use in pregnancy, though prospective studies are needed to confirm long-term safety. References: 1.Fein KC, Arral ML, Kim JS, et al. Placental drug transport and fetal exposure during pregnancy is determined by drug molecular size, chemistry, and conformation. J Control Release 2023;361:29–39. 2.Torres J, Chaparro M, Julsgaard M, et al. European Crohn’s and Colitis Guidelines on Sexuality, Fertility, Pregnancy, and Lactation. J Crohns Colitis 2023;17:1–27. 3.Mahadevan U, Seow CH, Barnes EL, et al. Global Consensus Statement on the Management of Pregnancy in Inflammatory Bowel Disease. J Crohns Colitis 2025;epub ahead of print. Conflict of interest: Julsgaard, Mette: No conflict of interest Baunwall, Simon MD: No conflict of interest Seow, Cynthia: No conflict of interest Duricova, Dana: No conflict of interest Kane, Sunanda: No conflict of interest Zacharias, Neha: No conflict of interest Leong, Rupert: No conflict of interest Cremer, Anneline: No conflict of interest Kok, Klaartje: No conflict of interest Kagramanova, Anna Valeryevna: No conflict of interest Pipek, Barbora: No conflict of interest Candido, Kristina: No conflict of interest Cooney, Rachel: No conflict of interest Filip, Rafal: No conflict of interest Kiudelis, Vytautas: No conflict of interest Konečný, Michal: No conflict of interest Paramsothy, Sudarshan: No conflict of interest Pugliese, Daniela: No conflict of interest Oliveira Santana Silva, Genoile: Honoraria - Abbvie, Johnson & Johnson, Ferring, Takeda. Research - Abivax, Takeda, Johnson & Johnson, Roche, Sanofi, Polpharma. Advisory board - Abbvie, Johnson & Johnson, MSD. Savarino, Edoardo Vincenzo: No conflict of interest Smolovic, Brigita: No conflict of interest Vieujean, Sophie: No conflict of interest Wei, Shu Chen: No conflict of interest Mahadevan-Velayos, Uma: No conflict of interest

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

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.

BACKGROUND: The Ventral Rectopexy International Expert Panel recently published a consensus update on ventral rectopexy. The ability of large language models to synthesize the literature on ventral rectopexy without an explicit knowledge base was studied before the publication of the consensus. OBJECTIVE: To compare the responses and citations of different large language models on ventral rectopexy using the expert panel consensus as the reference standard. DESIGN: ChatGPT-4o, Gemini 1.5 Pro, and OpenEvidence were compared on content appropriateness (1 = inappropriate to 5 = appropriate), readability (Flesch reading ease), response length, citation fabrication, and citation quality per Oxford Levels of Evidence. The most content-appropriate chatbot response selected by the expert panel was de-identified and presented alongside the consensus text to 15 colorectal surgeons who attempted to identify the chatbot-generated text. SETTINGS: Questions were submitted from September 18 to 19, 2024. Analysis was performed before the online publication of the consensus on January 30, 2025. MAIN OUTCOME MEASURES: The main outcome measures included content appropriateness, fabricated citation rate, citation quality, and the accuracy of identifying human versus chatbot text by colorectal surgeons. RESULTS: OpenEvidence ranked highest for content appropriateness (mean 3.5/5), above Gemini (3.0/5) and ChatGPT (2.8/5; p < 0.001). ChatGPT was most verbose with the highest readability scores ( p = 0.021). ChatGPT fabricated 53% of citations; Gemini fabricated 12%; OpenEvidence fabricated 0% ( p < 0.001). All OpenEvidence citations were peer reviewed, with 40 of 117 (34%) citing level I to III studies compared with 46 of 94 (49%) of the references cited in the consensus ( p = 0.043). Chatbot-generated responses were identified with 28 of 51 (55%) accuracy. LIMITATIONS: Reproducibility may be affected due to the nature of large language models and the availability of the consensus after publication. CONCLUSIONS: OpenEvidence outperformed Gemini 1.5 Pro and ChatGPT-4o in content appropriateness and peer-reviewed citation quantity and quality. Chatbot-generated text was indistinguishable from expert-authored consensus to subject matter experts. Large language models, as an early-stage research tool, may be viable for future consensus working groups, provided transparent disclosure and rigorous oversight. See Video Abstract . ¿PUEDEN LOS MODELOS LINGÜÍSTICOS DE GRAN TAMAÑO SER UNA HERRAMIENTA VIABLE PARA LOS GRUPOS DE TRABAJO DE CONSENSO? LA EXPERIENCIA DEL GRUPO DE CONSENSO DE EXPERTOS EN RECTOPEXIA VENTRAL ANTECEDENTES: El Panel Internacional de Expertos en Rectopexia Ventral publicó recientemente una actualización consensuada sobre la rectopexia ventral. Antes de la publicación del consenso, se estudió la capacidad de los grandes modelos lingüísticos para sintetizar la bibliografía sobre la rectopexia ventral sin una base de conocimientos explícita. Objetivo: Comparar las respuestas y citas de diferentes grandes modelos lingüísticos sobre la rectopexia ventral utilizando el consenso del panel de expertos como estándar de referencia. DISEÑO: Se compararon ChatGPT-4o, Gemini 1.5 Pro y OpenEvidence en cuanto a la idoneidad del contenido (1 = inadecuado a 5 = adecuado), la legibilidad (índice de legibilidad de Flesch), la longitud de la respuesta, la fabricación de citas y la calidad de las citas según los niveles de evidencia de Oxford. La respuesta del chatbot más adecuada en cuanto al contenido seleccionada por el panel de expertos se anonimizó y se presentó junto con el texto del consenso a 15 cirujanos colorrectales que intentaron identificar el texto generado por el chatbot. ENTORNO: Las preguntas se enviaron los días 18 y 19 de septiembre de 2024. El análisis se realizó antes de la publicación del consenso en línea el 30 de enero de 2025. PRINCIPALES MEDIDAS DE RESULTADOS: Adecuación del contenido, tasa de citas inventadas, calidad de las citas, precisión en la identificación del texto humano frente al del chatbot por parte de los cirujanos colorrectales. RESULTADOS: OpenEvidence obtuvo la puntuación más alta en cuanto a la adecuación del contenido (media de 3,5/5), por encima de Gemini (3,0/5) y ChatGPT (2,8/5) (p<0,001). ChatGPT fue el más prolijo, con las puntuaciones más altas en legibilidad (p = 0,021). ChatGPT inventó el 53% de las citas; Gemini inventó el 12 %; OpenEvidence inventó el 0 % (p < 0,001). Todas las citas de OpenEvidence fueron revisadas por pares, con 40/117 (34 %) que citaban estudios de nivel I-III frente a 46/94 (49 %) de las referencias citadas en el consenso (p = 0,043). Las respuestas generadas por el chatbot se identificaron con una precisión del 28/51 (55 %). LIMITACIONES: Reproducibilidad debido a la naturaleza de los modelos de lenguaje grandes y a la disponibilidad del consenso tras la publicación. CONCLUSIONES: OpenEvidence superó a Gemini 1.5 Pro y ChatGPT-4o en cuanto a la idoneidad del contenido y la cantidad y calidad de las citas revisadas por pares. El texto generado por el chatbot era indistinguible del consenso redactado por expertos para los expertos en la materia. Los modelos de lenguaje grandes como herramienta de investigación en fase inicial pueden ser viables para futuros grupos de trabajo de consenso, siempre que se garantice una divulgación transparente y una supervisión rigurosa. ( AI-generated translation )

Understanding the evolution of clinical tools and models of care is fundamental to advancing the field of epilepsy. The 2025 American Epilepsy Society Annual Course examined the expanding spectrum of epilepsy care through the integration of multispecialty collaboration, innovative diagnostic modalities, and surgical and nonsurgical therapeutic interventions. The course underscored significant global advancements in epilepsy research and practice. This comprehensive review synthesizes key educational insights from the program, offering a critical perspective on the ongoing transformation of epilepsy care.

Abstract Background Astroblastomas are rare brain tumors predominantly affecting children and young adults, for which molecular subtypes and clinical management remain undefined. Methods We analyzed tumor samples, molecular profiles, and clinical data from 200 patients, classified as “Astroblastoma, MN1-altered” under WHO criteria, using DNA methylation profiling, DNA/RNA profiling/sequencing, and survival analyses. Results DNA methylation analyses identified 3 groups: Group A (n = 143, characterized by MN1::BEND2 fusions, predominantly supratentorial location, with striking female predominance and favorable survival); Group B (n = 37, epigenetically and transcriptionally closely related to Group A, but characterized by EWSR1::BEND2 fusions, with spinal and infratentorial locations and poor prognosis); and Group C (n = 20, epigenetically and transcriptionally distinct, characterized by MN1::CXXC5 fusions, exclusively supratentorially located, with favorable survival). Progression-free and overall survival were significantly shorter in Group B (5-year PFS 14%; 10-year OS 54%) compared to A (5-year PFS 47%; 10-year OS 89%) and C (5-year PFS 75%; 10-year OS 89%). Radiotherapy improved PFS in Group B (hazard ratio 0.25), while no clear benefit was identified for Groups A and C. Conclusions Astroblastoma, MN1-altered, comprises 3 molecularly and clinically distinct groups, characterized by different fusion genes, including those without MN1. These new insights, including the identification of potential predictive biomarkers like 14q/16q loss, provide a framework for the development of risk-stratified therapeutic approaches. Importantly, we identified a molecularly defined high-risk group that benefits from radiation therapy. Our findings redefine Astroblastoma as a molecularly diverse tumor type, propose a refined classification, support the development of risk-adapted therapeutic strategies and provide a rational standard of care.

Background The hemodynamic profile and remodeling patterns of paradoxical low‐flow, low‐gradient aortic stenosis are well understood. However, some patients with severe aortic stenosis exhibit eccentric remodeling with enlarged left ventricular cavities, enabling a normal flow, low ejection fraction ( NF ‐ LEF ) phenotype. We assessed the prevalence, hemodynamic characteristics, and post‐transcatheter aortic valve implantation outcomes of NF ‐ LEF . Methods In this substudy of the COMPARE‐TAVI 1 trial, 979 patients undergoing transfemoral transcatheter aortic valve implantation were stratified by left ventricular ejection fraction (LVEF) (< or ≥50%) and stroke volume index (< or ≥35 mL/m 2 ) into 4 groups: normal flow, normal LVEF, NF‐LEF, low flow, normal ejection fraction, and low flow, low ejection fraction. Results NF‐LEF was observed in 10.7% of patients. Reduced LVEF was associated with previous myocardial infarction, pacemakers, New York Heart Association class >2, atrial fibrillation, moderate or worse mitral regurgitation, and larger left ventricular cavities ( P <0.001) with eccentric hypertrophy ( P =0.004). Compared with low‐flow, low‐ejection fraction, patients with NF‐LEF more frequently had moderate or worse aortic regurgitation ( P =0.007), whereas low flow, low ejection fraction showed poorer diastolic indices, suggesting diastolic dysfunction with high left ventricular filling pressures. Patients with low flow, normal ejection fraction were the most symptomatic and showed less improvement in 6‐minute walk distance at 1 year compared with normal flow, normal ejection fraction. During follow‐up, both low‐flow groups demonstrated significant increases in stroke volume index ( P <0.001), whereas LVEF also improved among patients with reduced LVEF ( P <0.001). Conclusions NF‐LEF is a prevalent aortic stenosis phenotype characterized by preserved flow despite reduced LVEF, likely driven by eccentric remodeling with moderate or worse aortic regurgitation and more favorable diastolic properties, with similar survival and time‐to‐event outcomes across phenotypes after transcatheter aortic valve implantation at long‐term follow‐up. Despite higher comorbidity burdens, patients with NF‐LEF showed functional and clinical improvements comparable with normal flow, normal ejection fraction, indicating a severe aortic stenosis phenotype.