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Importance The effect of cornea donor diabetes on endothelial cell density (ECD) and morphometry after Descemet membrane endothelial keratoplasty (DMEK) is not known. Objective To determine whether endothelial cell loss (ECL) and morphometric changes 1 year after successful DMEK are related to cornea donor’s diabetes status. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2024 at 28 US clinical sites (46 surgeons) and 13 eye banks. Included in the trial were the eyes of recipients (some of whom received tissue from donors without diabetes and others who received tissue from donors with diabetes) who underwent successful DMEK, primarily for Fuchs endothelial corneal dystrophy, and had at least 1 analyzable postoperative endothelial image. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure to achieve an approximate 2:1 distribution. Main Outcomes and Measures ECD, ECL, coefficient of variation in cell area (CV), and percentage of hexagonal cells (HEX) at 1 year from eye bank and postoperative specular central endothelial images. Results A total 1274 eyes of 982 recipients (mean [SD] age, 70 [8] years; 569 female [57.9%]; 816 [64.1%] with tissue from donors without diabetes and 458 [35.9%] with tissue from donors with diabetes) were included in the study. Preoperatively, mean (SD) central ECD in tissue from donors without diabetes and with diabetes were 2676 (290) cells/mm 2 and 2671 (286) cells/mm 2 , respectively. At 1 year, mean (SD) ECL was 28.3% (16.1%) and 28.0% (17.0%), in the donor groups without and with diabetes, respectively (adjusted mean difference = −0.4%; 95% CI, −2.3% to 1.4%), resulting in a mean (SD) 1-year ECD of 1927 (498) cells/mm 2 and 1920 (496) cells/mm 2 , respectively (adjusted mean difference = 10 cells/mm 2 ; 95% CI, −36 to 56; P = .95). No difference in ECD at 1 year associated with diabetes severity was noted ( P = .97). Mean (SD) CV did not differ at 1 year between the 2 groups of eyes (31.5% [4.1%] vs 31.4% [4.1%]; adjusted mean difference = −0.4%; 95% CI, −0.9% to 0.1%; P = .51), and mean (SD) HEX did not differ at 1 year between the 2 groups of eyes (57.7% [5.8%] vs 57.2% [5.8%]; adjusted mean difference = 0.1%, 95% CI, −0.8% to 0.9%; P = .33). Conclusions and Relevance This randomized clinical trial found that ECL and morphometry 1 year after DMEK were not affected by cornea donor diabetes status. With comparable 1-year graft success with tissue from donors with and without diabetes demonstrated in this trial, these findings support the use of corneas from donors with diabetes for endothelial keratoplasty procedures. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Importance If the success of Descemet membrane endothelial keratoplasty (DMEK) is not affected by whether the donor has diabetes, then the donor pool should expand. Objective To determine whether the 1-year DMEK success rate is affected by the presence of diabetes in the donor. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2025 at 28 clinical sites (46 surgeons), with donor corneas provided by 13 eye banks in the US. Included in the study were individuals undergoing low to moderate risk DMEK (95% for Fuchs endothelial corneal dystrophy). Study data were analyzed from April to September 2025. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure (comparable with randomization) to achieve an approximate 2:1 distribution, respectively. Main Outcomes and Measures Graft success at 1 year Results A total of 1097 individuals (1421 study eyes; median [IQR] age, 71 [66-76] years; 631 female [57.5%]) were included in the study. The 1-year cumulative probability of graft success was 96.3% (95% CI, 95.0%-97.5%) among 912 study eyes (64.2%) receiving tissue from donors without diabetes and 97.1% (95% CI, 95.5%-98.4%) among 509 study eyes (35.8%) receiving tissue from donors with diabetes (difference between groups = 0.7 percentage points; 95% CI, −1.2 to 2.6; P = .63). The 1-year cumulative probability of graft success was 96.5% (95% CI, 93.6%-98.9%) in the mild donor diabetes severity subgroup (n = 173) and 97.3% (95% CI, 95.4%-98.8%) in the moderate to severe donor diabetes severity subgroup (n = 336), using a diabetes severity rating scale based on medical history. The rates of primary donor failure, early failure related to surgical complications, and subsequent failure were as follows: 2.5% (23 of 912), 0.7% (6 of 912), and 0.3% (3 of 912), respectively, in recipients of tissue from a donor without diabetes, and 2.6% (13 of 509), 0.4% (2 of 509), and 0%, respectively, among recipients of tissue from a donor with diabetes. There were no failures due to graft rejection. Conclusions and Relevance The 1-year success rate in eyes undergoing DMEK with successfully prepared tissue was very high regardless of donor diabetes status. These results, supported by the separately reported finding that endothelial cell loss and cornea morphometry after 1 year were not affected by donor diabetes status, provide strong support for having no restrictions on the use of tissue from donors with diabetes for DMEK. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Vascular surgery has evolved from durable yet invasive open reconstructions to less traumatic endovascular techniques. While endovascular repair reduces perioperative morbidity, it introduces durability challenges and the need for lifelong surveillance. Laparoscopic surgery bridged some gaps but was hindered by steep learning curves and technical limitations. Robotic-assisted surgery represents a “third revolution”, combining the durability of open repair with the recovery and ergonomic benefits of minimally invasive approaches through enhanced 3D visualization, wristed instrumentation, and tremor filtration. This review synthesizes current evidence on robotic applications in vascular surgery, including aortic, visceral, venous, and endovascular interventions. Feasibility of robotic vascular surgery has been demonstrated in over 1500 patients across aortic, visceral, venous, and decompression procedures. Reported outcomes include pooled conversion rates of ~5%, 30-day mortality of 1–3%, and long-term patency rates exceeding 90% in aortoiliac occlusive disease. Similarly favorable outcomes have been observed in AAA repair, visceral artery aneurysm repair, IVC reconstructions, renal vein transpositions, and minimally invasive decompression procedures such as median arcuate ligament and thoracic outlet syndromes. Endovascular robotics enhances catheter navigation precision and reduces operator radiation exposure by 85–95%, with multiple series demonstrating consistent benefit compared to manual techniques. Despite these advantages, adoption is limited by high costs, lack of dedicated vascular instruments, absent haptic feedback on most platforms, and the need for standardized training. Most available evidence is observational and from high-volume centers, highlighting the need for multicenter randomized trials. Future directions include AI-enabled planning and augmented-reality navigation, which are the most feasible near-term technologies since they rely largely on software integration with existing systems. Other advances such as microsurgical robotics, soft-robotic platforms, and telesurgery remain longer-term developments requiring new hardware and regulatory pathways. Overcoming barriers through collaborative innovation, structured training, and robust evidence generation is essential for robotics to become a new standard in vascular care.

Metastatic meningioma exhibits diverse dissemination patterns across organ systems (lungs, bone, liver, soft tissue, etc), with molecular imaging—particularly gallium 68 DOTA Tyr3-octreotide PET/CT—playing a pivotal role in detection, characterization, and guiding management.

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.

BACKGROUND AND OBJECTIVES: The Management of Myelomeningocele Study established a role for maternal-fetal surgery for myelomeningocele (MMC) but was limited to patients with a maternal body mass index (BMI) less than 35 kg/m 2 . However, as the rate of obesity has increased globally, some centers have extended the Management of Myelomeningocele Study criteria to include patients with a BMI of 35 to 40 kg/m 2 . We used the Fetal MMC Consortium registry to analyze the complications associated with maternal-fetal surgery in patients with a maternal BMI < 35 kg/m 2 compared with 35 to 40 kg/m 2 . METHODS: The prospective observational Fetal MMC Consortium registry sponsored by the North American Fetal Therapy Network was used to extract deidentified “maternal complication” and “fetal/neonatal complication” data sets, corresponding to patients with complete entries related to maternal or fetal/neonatal complications, respectively. Demographics, clinical characteristics, and complications were recorded. Propensity score matching was conducted with a 2:1 algorithm, and outcomes were compared between patients with BMI < 35 kg/m 2 and those with BMI 35 to 40 kg/m 2 . RESULTS: Propensity score matching resulted in 156 patients with BMI < 35 kg/m 2 and 78 patients with BMI 35 to 40 kg/m 2 in the “maternal complication” data set, and 130 patients with BMI < 35 kg/m 2 and 65 patients with BMI 35 to 40 kg/m 2 in the “fetal/neonatal complication” data set. No significant differences in the median number of maternal (2.0 vs 2.0; P = .257) or median number of fetal/neonatal (1.0 vs 1.0; P = .409) complications were observed between the 2 cohorts. Furthermore, prematurity rates did not differ significantly between the BMI < 35 and BMI 35 to 40 cohorts. CONCLUSION: Maternal-fetal surgery for open neural tube defect closure is not associated with increased risks among patients with a maternal BMI of 35 to 40 kg/m 2 . Extending the initial inclusion criteria for prenatal MMC closure has the potential to expand access to patients with a higher risk for open neural tube defects.

Abstract Transcription factors and their cofactors are major and selective nononcogene dependencies in multiple myeloma cells. By performing a gain-of-function perturbation screen in human multiple myeloma cell lines, we identified the inhibitor of DNA binding (ID) genes as putative suppressors of multiple myeloma cell fitness. Among them, ID2 was found to be downregulated in multiple myeloma patient cells and acted as a tumor suppressor by directly binding and repressing the basic helix-loop-helix factor TCF3, also known as E2A. Lower ID2 expression in multiple myeloma cells conferred a proliferative advantage by increasing TCF3 activity, leading to a dependency on this transcription factor. In contrast, ID2 overexpression reduced TCF3 binding to DNA, which resulted in cell-cycle arrest and a halt in multiple myeloma cell proliferation. The myeloma bone marrow milieu supported this process by further decreasing the expression of ID2 and enhancing TCF3 activity, partly via IL6, revealing a mechanism by which the tumor microenvironment affects multiple myeloma cell behavior. Significance: Multiple myeloma cells exploit the oncogenic and proliferative potential of TCF3 by downregulating the transcriptional regulator ID2, a process facilitated by the bone marrow microenvironment.

Abstract Objectives Granulomatous myositis (GM) is defined by focal collections of activated macrophages that fuse to form multinucleated cells that aggregate into granulomas within skeletal muscle. This study aimed to elucidate the pathophysiology of GM by defining its specific transcriptomic profile. Methods Bulk RNA sequencing was performed on 722 muscle biopsies, including 38 from patients with GM, other myopathies and healthy comparators. Spatial transcriptomics and immunohistochemistry assays were used to identify the location of specific transcripts and proteins within the tissue. Results We conducted a systematic analysis of the inflammatory pathways implicated in GM. This analysis revealed that GM muscle biopsies are characterized by elevated levels of IFNγ, IFNγ-inducible genes and proinflammatory cytokine genes including IL1B, TNF and TGFB1. Additionally, we identified a specific gene signature in GM, comprising 1293 genes that were significantly upregulated and 256 significantly downregulated. These genes were strongly correlated with transcriptomic markers of disease activity. Using this gene signature, support vector machine models accurately identified GM, achieving an area under the curve of 99.6% (99.0–99.9%) and an accuracy of 98.6% (98.2–98.9%). Among the identified genes, CHIT1 emerged as the most significantly upregulated gene, with its expression strongly correlating with disease severity. Furthermore, CHIT1 was detected at both the RNA and protein levels in granulomas and giant cells. Conclusion GM is transcriptomically characterized by a strong IFNγ signature and overexpression of proinflammatory cytokines, including IL1B, TNF and TGFB1. Additionally, it exhibits a unique transcriptomic profile, including CHIT1, which correlates with disease activity.