Citations

Abstract Background Hidradenitis suppurativa (HS) is a chronic, relapsing, debilitating skin disease characterized by deep‐seated nodules, abscesses and tunnels. Objectives To investigate the efficacy of bimekizumab using International Hidradenitis Suppurativa Severity Score System (IHS4) outcomes in patients with moderate to severe HS in the pooled BE HEARD I and II Phase 3 studies. Methods Patients received (initial/maintenance) bimekizumab 320 mg every 2 weeks (Q2W)/Q2W, bimekizumab Q2W/every 4 weeks (Q4W), bimekizumab Q4W/Q4W or placebo/bimekizumab Q2W. Change from baseline (CfB) in IHS4, IHS4 severity stages and the dichotomous IHS4‐55/75/90/100 (an improvement of at least 55%/75%/90%/100% in IHS4 from baseline) are reported to Week 48. Lesion count data by lesion type are reported to Week48. Results In total, 1014 patients were enrolled across BE HEARD I and II. At baseline, mean (standard deviation [SD]) IHS4 for bimekizumab‐treated patients ranged from 33.4 (25.4) for bimekizumab Q2W/Q2W to 36.0 (34.0) for bimekizumab Q2W/Q4W versus 30.6 (21.8) for placebo. At Week16, patients receiving bimekizumab demonstrated greater reductions in mean (SD) IHS4 from baseline, ranging from −17.2 (24.8) for bimekizumab Q4W/Q4W to −17.8 (21.1) for bimekizumab Q2W/Q2W versus −6.1 (16.3) for placebo. Proportions of patients with severe HS at baseline ranged from 83.7% (bimekizumab Q2W/Q2W) to 88.4% (bimekizumab Q2W/Q4W) and decreased by ~50% to Week48 across all treatment arms, ranging from 28.6% (placebo/bimekizumab Q2W) to 34.3% (bimekizumab Q2W/Q2W). Proportions of patients with IHS4 = 0 at Week48 ranged from 23.7% (bimekizumab Q2W/Q4W) to 26.7% (placebo/bimekizumab Q2W). At Week16, IHS4‐55, IHS4‐75, IHS4‐90 and IHS4‐100 thresholds were achieved by greater proportions of patients treated with bimekizumab versus placebo, while comparable improvements were seen across treatment arms from Week16 to Week48. At Week16, lesion counts were lower for patients treated with bimekizumab versus placebo, decreasing to comparable numbers across all groups at Week48. Conclusions Bimekizumab‐treated patients showed improvements across IHS4 outcome measures.

OBJECTIVE Chordomas are rare, locally aggressive primary neoplasms. Resection with negative margins is the primary recommended therapeutic approach, while adjuvant radiotherapy and chemotherapy can also play a role in their treatment in certain situations, including lesions with positive margins or those that are poorly differentiated or dedifferentiated. Cervical spine chordomas pose significant surgical challenges given their proximity to critical anatomical structures and the mechanical constraints of the cervical spine. In the current case series, authors aimed to explore the clinical and patient-reported outcomes (PROs) of the surgical treatment of cervical chordomas in a large multicenter cohort. METHODS This multicenter case series analysis utilized data from the prospectively collected Primary Tumor Research and Outcomes Network (PTRON) registry, from its inception (May 16, 2016) to data extraction (February 29, 2024). The study population was restricted to patients with histologically confirmed cervical chordomas involving levels C0–7, who underwent surgical treatment at one of the participating centers, and for whom both the initially planned and postoperatively pathologically confirmed surgical margins were documented. Patient demographics, tumor characteristics, surgical and adjuvant treatments, local recurrence–free survival (LRFS), overall survival (OS), and perioperative adverse events were retrieved. PROs included the Spine Oncology Study Group Outcomes Questionnaire version 2.0 (SOSGOQ2.0), EQ-5D, and SF-36 version 2.0 (SF-36v2). RESULTS Thirty-eight patients were identified, 12 of whom underwent true en bloc resection (EBR), 18 of whom underwent deliberate intralesional resection, and 8 of whom underwent EBR after intralesional surgery or in whom EBR failed. True EBR led to better LRFS (92% vs 83% vs 63%, respectively) and OS (83% vs 39% vs 50%, respectively). Surgical adverse events within 1 year were more frequent with true EBR (100% vs 39% vs 75%, respectively). EQ-5D, SOSGOQ2.0, and SF-36v2 showed improvement with true EBR, whereas the trends for PROs from the other groups were more variable. CONCLUSIONS This multicenter case series analysis provides critical insights into the clinical outcomes and PROs in the largest cohort of surgically treated cervical spine chordomas described to date. It underscores the importance and challenges of wide resection for oncological control. It establishes the associated morbidity and provides an overview of PROs following surgery. These findings contribute valuable evidence to inform shared decision-making and optimize patient care.

Background: Anterior cervical discectomy and fusion (ACDF) is a widely performed procedure for treating degenerative cervical spine conditions. While it effectively addresses neural decompression and restores segmental alignment, the interplay of baseline alignment and implant-specific factors on postoperative segmental alignment remains underexplored. This study evaluates the influence of preoperative cervical alignment and interbody cage design on segmental alignment changes following 1- to 3-level ACDF. Methods: Following institutional review board approval, we identified 258 patients undergoing ACDF for degenerative pathology between 1 January 2010 and 31 December 2023. Preoperative and postoperative radiographs were analyzed for cervical alignment, disc height, and segmental lordosis. Cage dimensions, lordosis, and positioning relative to vertebral landmarks were recorded. Multivariable linear regression models evaluated predictors of postoperative disc height, segmental lordosis, and their respective changes. Results: Postoperative disc height was positively associated with greater cage height (β = 1.13 mm per mm, p < 0.001) and negatively associated with greater cage lordosis (β = −0.10 mm per °, p = 0.001). Segmental lordosis was positively influenced by cage height (β = 0.78° per mm, p = 0.002) and lordosis (β = 0.42° per °, p = 0.002) but was negatively correlated with the distance of the cage from the anterior edge of the cranial vertebra (β = −1.76° per mm, p = 0.004). Greater preoperative segmental kyphosis predicted more significant postoperative lordosis correction (β = −1.07° per °, p < 0.001). Conclusions: This study underscores the importance of preoperative alignment and interbody cage design in achieving optimal segmental correction following ACDF. While cage height primarily drives disc height restoration, surgical technique, particularly anterior placement of the cage, is pivotal for enhancing segmental lordosis. These findings support personalized surgical planning to optimize alignment and patient outcomes.

Higher-order chromatin structures (HOCS) are fundamental to genome organization, gene regulation, and cellular homeostasis. This review examines the epigenetic mechanisms shaping HOCS, including DNA methylation, histone modifications, chromatin remodeling, and RNA-based regulatory processes. We also discuss the role of architectural proteins in maintaining chromatin topology while allowing dynamic changes to chromatin structure, thereby influencing gene expression. Growing evidence indicates that disruptions in HOCS contribute to a diverse array of human diseases, including cancer, aging-related disorders, and congenital abnormalities, primarily through aberrant gene regulation. We further discuss the concept of distinct genomic areas, in which specific chromatin regions orchestrate three-dimensional (3D) genome dynamics, positioning them as potential biomarkers and therapeutic targets. By emphasizing chromatin architecture on a global scale rather than at the level of individual genes, this review underscores its emerging relevance to precision medicine. Finally, we synthesize current technical advances, outline future directions for leveraging chromatin topology in disease diagnosis and treatment, and highlight key biological insights to reshape our understanding of genome function.

Abstract Background Right ventricular (RV) systolic function is an independent predictor of outcome, but a robust automated method of quantitation is lacking. Purpose To develop and validate a deep learning model for quantifying RV systolic function from 2D echo clips and assess its prognostic value in various loading states and left-heart disease. Methods A deep learning convolutional neural network (CNN) was developed to quantitate RV systolic function using all available apical 4 chamber (A4C) views from 13,039 patients (61,187 A4C video clips) in a US institution. Videos were converted to frames of 256x256 grayscale pixels, with graphics, including the ECG, removed. The model used the S3D architecture, a 3D-CNN with many of the spatial and temporal convolutions separated. RV free wall longitudinal strain (RVFWLS) was used to define RV function: normal (≤-25%); mild (-20% to –24%); moderate (-15% to –19%) and severe RV dysfunction (0% to –14%). The AI-estimate was the average of multiple 32-frame segments for each clip identified as an A4C, including both standard and RV-focused variations. The model was validated in four distinct clinical cohorts from independent data sources: 1) 1365 patients referred to an external diagnostic echocardiography lab in a different US state; 2) 569 patients recruited prospectively with known or suspected pulmonary hypertension (PH); 3) 416 patients with moderate or greater tricuspid valve regurgitation (TR); 4) 872 patients imaged at an Eastern European University with left-sided cardiac diseases (valvular disease, cardiomyopathies, etc.), athletes, or healthy volunteers. Results Model output was strongly correlated with the presence of RV dysfunction by manual measurement [AUC 0.907 (95% CI: 0.888-0.926)] in the internal validation set. Results were similar in an external validation US cohort [N=1365; AUC 0.919 (95% CI: 0.899-0.939)]. When the model was applied to 569 patients with known or suspected PH, presence and severity of the AI measures of RV dysfunction were strongly associated with functional class, 6 min walk distance, NT-proBNP levels and right heart failure (p&lt;0.001). The presence and severity of RV dysfunction (as categorized by the AI model) was strongly associated with all-cause mortality in patients with PH (Figure 1A) and in patients with significant TR (Figure 1B). The European cohort was a geographically and institutionally distinct cohort with a low prevalence of &gt;mild TR (10.8%) or PH (6.4%). In this cohort the AI model predicted a reduced RVEF (&lt; 45%) with an AUC of 0.864 (95% CI: 0.831-0.898) and associated with both all-cause mortality (Figure 2A) and a composite endpoint of heart failure hospitalization or death (Figure 2B). Conclusions A fully automated, AI-based assessment of RV systolic function, using 2D apical 4 chamber echo views alone, is highly feasible and can accurately categorize disease severity and predict outcome across a wide spectrum of cardiovascular conditions.Figure 1 Figure 2

Background Artificial intelligence (AI)–enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance of AI‐enabled electrocardiography–derived diastolic dysfunction (DD) in patients undergoing transcatheter aortic valve replacement had not been assessed. Methods We analyzed 3197 patients undergoing transcatheter aortic valve replacement for severe aortic stenosis between 2010 and 2023 with baseline 12‐lead ECGs processed by a validated AI model to classify diastolic function (grades 0–3). Multivariable Cox models and nested model comparisons assessed associations with all‐cause mortality, including a prespecified analysis in patients with indeterminate echocardiographic grading. Trajectories were defined by change in AI‐enabled electrocardiography DD grade (<2 versus ≥2) from baseline to 30‐day or 1‐year follow‐up. Results At baseline, 21% had grade 3, 57% had grade 2, 8% had grade 1, and 14% had grade 0 AI‐enabled electrocardiography DD. Higher grades were associated with adverse cardiac remodeling and comorbidities. Over a median follow‐up of 3.4 years, grade 3 AI‐enabled electrocardiography DD independently predicted mortality (hazard ratio [HR], 1.80 [95% CI, 1.47–2.20]; P <0.001). AI‐enabled electrocardiography DD improved prognostic discrimination beyond clinical and echocardiographic measures (ΔHarrell concordance statistic, 0.016; Δ χ 2 =57; P <0.001). Among 1259 patients with indeterminate echocardiographic grading, AI‐enabled electrocardiography added prognostic value (ΔHarrell concordance statistic, 0.02; Δ χ 2 =13; P =0.006). Worsening or persistently high‐risk trajectories were associated with increased mortality (HRs, 1.45–1.80; all P <0.05). Conclusions AI‐enabled electrocardiography–derived DD independently predicts mortality after transcatheter aortic valve replacement, adds value beyond echocardiographic grading, and enables dynamic risk stratification through longitudinal tracking.

Abstract Background Intracranial mesenchymal tumors, FET::CREB fusion-positive (ICMT), show fusions involving FET RNA-binding protein family genes (EWSR1 or FUS) and CREB family of transcription factors (ATF1, CREB1, or CREM). The methylation signature(s), gene expression characteristics, and clinical behavior of this important tumor type require further characterization. Methods We study the methylation profiles of 81 ICMT cases (61 newly profiled cases and 20 cases from publicly available sources). Clinicopathologic and genomic data were recorded for each case when available. Results ICMT showed a relatively distinct methylation signature compared to related tumors. Among the 65 cases where fusion types were documented, the identified fusions included EWSR1::ATF1 (25 cases), EWSR1::CREB1 (12 cases), EWSR1::CREM (21 cases), FUS::CREM (3 cases), and SMARCA2::CREM (4 cases). We confirmed the prior description of 2 distinct subgroups of ICMT (subclasses A and B). The majority of the cases belonged to subclass A (n = 69; 85%), which showed a higher median age compared to subclass B patients (26 years vs. 15 years). Subclass B cases (n = 12; 15%) showed shorter progression-free survival (P &lt; 0.01). Gene expression analysis of ICMT showed key overexpressed markers in ICMT, with significant CREM overexpression regardless of fusion type, when compared to either meningioma alone or a larger group of CNS tumors. Conclusions This work provides further characterization of ICMT as an important CNS mesenchymal neoplasm that is prone to tumor recurrence, showing 2 prognostically relevant methylation subclasses and warranting diagnostic distinction from other epigenetically and histologically related tumors. ICMTs show substantial overexpression of the CREM gene, independent of fusion type.

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.