Citations

ABSTRACT This international, multidisciplinary consensus report represents the first effort to systematically define and characterize fatty pancreas. A key outcome of this endeavor was the recommendation to adopt “fatty pancreas” as the standardized and inclusive term to describe all forms of fat accumulation in the pancreas. This terminological consensus provides a critical foundation for unified reporting and clinical communication. Another major contribution of the report is the consensus on diagnostic imaging findings, which was based on radiological and endoscopic modalities. The proposed criteria aim to enhance consistency in clinical assessment and support the development of standardized research protocols. In addition to establishing terminology and diagnostic frameworks, the report also synthesizes current knowledge across a wide range of relevant domains. These include the etiology and epidemiology of fatty pancreas, as well as its associations with alcohol consumption, smoking, acute and chronic pancreatitis, pancreatic exocrine insufficiency, type 2 diabetes mellitus, and surgical outcomes. The potential links between fatty pancreas and neoplastic conditions such as intraductal papillary mucinous neoplasms and pancreatic cancer are also addressed, alongside the current understanding of its metabolic implications (beta‐cell function and glucose homeostasis) and treatment strategies. Throughout the consensus process, a consistent theme emerged: the limited availability of high‐quality, prospective clinical data. Therefore, many of the recommendations in this report are based on expert consensus rather than strong empirical evidence. As such, the statements require rigorous prospective validation before they can be adopted into routine clinical practice. This underscores a critical need for further research, particularly studies aimed at clarifying causal relationships, validating diagnostic tools, and determining the clinical relevance of fatty pancreas across diverse patient populations. This report serves as both a summary of our current understanding and a roadmap for future investigations, aiming to close existing knowledge gaps and guide evidence‐based clinical practice in this emerging field.

Purpose: To determine current dacryocystorhinostomy (DCR) practice patterns of American Society of Ophthalmic Plastic and Reconstructive Surgery members and identify surgeon and case-related factors impacting preference for endoscopic versus external DCR and the use of preoperative imaging. Methods: A cross-sectional web-based anonymous 16-question survey was distributed to 277 American Society of Ophthalmic Plastic and Reconstructive Surgery members enrolled in survey-based research. The survey assessed surgeons’ demographics, practice settings, surgical preferences, and imaging practices. Responses were summarized and compared using statistical tests for frequency distribution. Results: A total of 111 surgeons (40.0%) responded to the survey. Utilization of endoscopic DCR (endoDCR) was highly variable, with 57% of junior surgeons (<5 years in practice) using it for >66% of their cases, compared with only 18.3% of surgeons with >15 years postfellowship experience. High utilization of endoDCR (>66% of cases) was more common in academic surgeons compared with private practice (48.4% vs. 19.6%, p < 0.01). The most cited reasons for endoDCR were scar avoidance and revision surgery. For external DCR, anatomical barriers and concern for mass/need for biopsy were common indications. Most surgeons only order imaging for certain indications, such as prior sinonasal surgery, facial trauma, suspected tumor, or atypical presentation. Conclusion: Utilization of endoDCR was correlated with years in practice, with younger surgeons preferring the endoscopic technique. A large majority felt that the relevance of imaging is low in most patients. Future research is needed to standardize case-based indications for endoscopic versus external approaches and preoperative imaging.

Abstract Categorical variants, or sets of genomic alterations constrained by shared properties, are pervasive across clinical, regulatory, and research domains in the biomedical ecosystem, yet their inconsistent and non-computable representation hinders data interoperability and clinical interpretation. We surveyed genomic knowledgebases spanning regulatory approvals and the biomedical literature and found that categorical variants underpin a substantial proportion of clinical genomics knowledge, but are largely described using incompatible bespoke models. To address this, we developed the GA4GH Categorical Variation Representation Specification (Cat-VRS), a constraint-based framework that provides a unified computable representation for both precise and intentionally broad categories across molecular and systemic variant domains. Cat-VRS enables harmonization of genomic knowledgebases, computable category-based search, and automated matching between assayed variants and categorical entities in clinical and research contexts. By providing a principled, extensible model for categorical variation, Cat-VRS enables computable reasoning over genomic variant categories and establishes a foundation for the standardized representation and exchange of genomic knowledge.

Abstract: Melanoma exhibits a broad spectrum of histopathologic variations, including rare forms with neural differentiation. Neurotropism in melanoma encompasses both perineural invasion and neural-like transformation, but the prognostic implications of these features remain uncertain. Although (peri)neural invasion is well documented, true neural differentiation in melanomas is exceedingly rare. In this study, we describe an unusual case of primary cutaneous malignant melanoma exhibiting extensive Wagner–Meissner-like bodies, a feature more commonly associated with benign neural tumors and nevi. In this case, histopathology revealed a dual-component lesion with a neurotized and an epithelioid melanocytic component, both showing PRAME expression and p16 loss. Chromosomal microarray identified heterozygous loss of 9p22.1p13.1 (including CDKN2A / CDKN2B ), and loss of 10q22.2q26.3 (including PTEN ), supporting malignancy. These findings suggest that in this case, Wagner–Meissner-like bodies likely represent neurotization rather than a benign or collision lesion, highlighting the need for integrated histopathologic, immunohistochemical, and molecular analysis in challenging melanocytic neoplasms.

Abstract Diffuse hemispheric glioma, H3 G34‐mutant (DHG‐H3 G34) has been primarily molecularly characterized by methylation profiling and sequencing studies. We describe an integrated histomolecular evaluation including high‐resolution copy number profiling of a series of 60 DHG‐H3 G34 to further our understanding of the spectrum of genetic changes associated with this tumor type. Cases were clinically tested using an 187‐gene mutation and fusion targeted neuro‐oncology next‐generation sequencing panel ( n = 60) and Oncoscan chromosomal microarray ( n = 26) by a single laboratory (2018–2022). A subset of cases had immunohistochemical results for OLIG2 ( n = 42), p53 ( n = 48), and ATRX ( n = 46), and methylation array data ( n = 8). Median age at testing was 21 years (range, 12–50). No significant difference was noted in clinical, histopathological, and mutational profile between pediatric and adult patients. H3‐3A G34 mutations included G34R ( n = 56; 94%), G34V ( n = 3), and a non‐canonical G34E ( n = 1). Concurrent mutations most often involved TP53 ( n = 55; 92%), ATRX ( n = 50; 83%), and PDGFRA ( n = 34; 57%). A reportedly primary tumor was confirmed to be hypermutant and had a PMS2 mutation. A single case also showed an FGFR3::FAM184B fusion. All cases with available chromosomal microarray data had unbalanced genomes, which were often complex (14/26; 54%). The most frequent recurrent copy number abnormalities were losses involving 3q, 4q, 10q, 13q, and 18q, and 17p copy‐neutral loss of heterozygosity (cnLOH) encompassing TP53 . This copy number profile was reminiscent of that seen in Grade 4 IDH‐mutant astrocytomas. Collectively, a TP53 abnormality at copy number (12/26, all cnLOH), sequence (55/60) and protein expression (46/48) level was detected in all 60 cases. In conclusion, integrated high‐resolution copy number and histomolecular analysis expanded the spectrum of genetic changes associated with DHG‐H3 G34, including the presence of universal TP53 abnormalities with frequent cnLOH—a copy number abnormality that has been largely unrecognized—for this new 2021 World Health Organization central nervous system tumor type.

BACKGROUND AND OBJECTIVES: Myelomeningocele is defined by the presence of a fluid-filled sac that contains the neural placode and cerebrospinal fluid (CSF) at the level of the spinal defect. Alternatively, the term myeloschisis is used when the neural placode is not contained within a CSF-filled sac. While both are eligible for prenatal closure, few studies have characterized the impact of the type of lesion on postnatal outcomes. This study compares the outcomes of these 2 types of open spinal dysraphism using data from the Fetal Myelomeningocele Consortium registry sponsored by the North American Fetal Therapy Network. METHODS: The prospective observational Fetal Myelomeningocele Consortium registry was used to extract deidentified data corresponding to patients who underwent fetal closure of myelomeningocele vs myeloschisis. Demographics, operative characteristics, perioperative complications, and postnatal outcomes were collected. RESULTS: A total of 1327 patients were identified, including 991 (74.7%) with myelomeningocele and 336 (25.3%) with myeloschisis. Compared with myelomeningocele, myeloschisis was associated with lower rates of prenatal talipes (11% vs 24%, P < .001) and higher rates of favorable prenatal movement (94% vs 86%, P < .001). Postnatally, myeloschisis conferred higher rates of leg movement (97% vs 90%, P = .002) and an increased likelihood of ambulation in the household (10.4% vs 4.1%, P = .009) and outside (7.2% vs 1.9%, P = .004) at 1 year. However, myeloschisis was also linked to greater use of skin patches during closure (49% vs 27%, P < .001), higher CSF leak rates among those with wound dehiscence (26% vs 8%, P = .014), and more frequent CSF diversion and tethered cord surgeries at follow-up. CONCLUSION: Myeloschisis and myelomeningocele demonstrate distinct clinical profiles. Although patients with myeloschisis may have favorable motor outcomes, they are also characterized by higher rates of CSF leakage, CSF diversion, and tethered cord surgery. These findings highlight the need for lesion-specific prognostication and may inform surgical planning and parental counseling in the context of prenatal spina bifida closure.

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] &lt;=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF &lt;=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Resistant hypertension (RH) is a high-risk phenotype characterized by blood pressure readings ≥130/80 mmHg despite maximally tolerated therapy with three antihypertensive agents, including a diuretic, or controlled blood pressure requiring four or more medications. The diagnosis of RH requires a structured evaluation that confirms accurate blood pressure measurement, excludes pseudoresistance—particularly nonadherence and white coat hypertension—and identifies secondary causes such as obstructive sleep apnea, primary aldosteronism, renovascular disease, pheochromocytoma, and Cushing syndrome. RH arises from overlapping mechanisms, including activation of the renin–angiotensin–aldosterone system (RAAS), sympathetic overactivity, arterial stiffness, volume expansion, and immune-mediated pathways. Management begins with lifestyle modification and optimized triple therapy, followed by mineralocorticoid receptor antagonists as the preferred fourth-line treatment. Emerging pharmacological options, such as sodium–glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, endothelin receptor antagonists, aldosterone synthase inhibitors, and angiotensin receptor and neprilysin inhibitors (ARNIs), offer additional therapeutic potential; meanwhile, device-based interventions, including renal denervation and baroreflex activation therapy, have shown sustained blood pressure reductions in selected patients. Future directions highlight precision medicine, digital health technologies, and artificial intelligence as methods to improve diagnosis, guide individualized therapy, and enhance long-term blood pressure control.

Abstract Objective Despite being safe and effective, surgery for pediatric epilepsy is underutilized. Social determinants of health (SDoH) are important to consider when examining this treatment gap. This study examined the potential systemic inequities at three different stages in the journey toward epilepsy surgery across the Pediatric Epilepsy Research Consortium (PERC). Methods Any youth in the PERC prospective surgery database (≤18 years) was eligible and included if sociodemographic factors were also available. We examined if there were differences in disparity factors in: (1) referral to Phase I evaluation, (2) offering of surgery, and (3) completion of surgery. Results Of 1309 patients, the majority were male (53%, p = .04), White (82.2%, p < .001), not Hispanic or Latino (84.3%, p < .001), and privately insured (56.3%, p < .001). Of the youth who were offered surgical treatment ( n = 1103, 84.3%), there were no significant differences in race/ethnicity or sex. Those with a structural lesion were more likely to be offered surgery, and historically marginalized youth were more likely to have a lesion. Of patients who completed surgery ( n = 927, 84.0%), White patients were 1.88 times (95% confidence interval [CI]: 1.14–3.04) more likely than Black patients to complete surgery ( p = .01). Significance Youth who began surgery evaluation are not representative of the demographics of those with epilepsy in the United States, with greater representation of White, non‐Hispanic male individuals. In addition, youth who began surgery evaluation disproportionally have private insurance. At the next stage of offering surgery, there were no differences based on SDoH. However, there is evidence that underrepresented youth that were magnetic resonance negative were less likely to be referred for surgical workup and that Black youth were less likely to undergo surgery. Therefore, efforts to provide equitable opportunity for all youth with refractory epilepsy should be concentrated at the referral for surgery evaluation point of care; however, additional efforts are also needed at later stages.

Abstract Objective To develop a comprehensive risk stratification model to identify patients at high risk of cesarean surgical site infection (cSSI) based on information available at the time of hospital discharge. Methods This is a retrospective cohort study based on datasets from two independent level IV academic medical centers to improve generalizability. The first study includes 2336 cesarean deliveries for analysis with 109 instances of cSSI (4.7%). The second study includes 2936 cesarean deliveries with 72 instances of cSSI (2.5%). The first study cohort was utilized for both model training and internal validation, using the 15 shared features present in both datasets. Random search with cross‐validation was used to choose the type of machine learning (ML) model and its hyperparameters, with area under the receiver operating characteristic curve (AUC) as the validation metric. The second study cohort was then used to evaluate testing accuracy (external validation) and generalization capability of this model. The predictive importance of each feature was evaluated using permutation importance applied to the second cohort. Risk stratification cutoff scores were defined based on centiles over the first patient cohort and evaluated on the second cohort. Results The ML model with the highest validation AUC was an XGBoost classifier. This trained model achieved an AUC of 0.78 on the training data and a 0.72 cross‐validated AUC. When tested on data from the second study cohort, it achieved an AUC of 0.69. The features showing the strongest predictive generalization, as measured by permutation importance, were length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Using the second cohort to test our model, the model shows a 22.7% positive predictive value (PPV) of cSSI among women in the highest risk group, and a 0.8% PPV among women in the bottom risk group, compared with 2.5% cSSI incidence for the study population at large. Conclusion We developed and validated a predictive ML model for cSSI with information available to healthcare professionals at the time of hospital discharge. This model suggests that the most predictive risk factors for cSSI include length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Additionally, the model provides reasonable risk stratifications for individual patients. This tool may be used for identifying subgroups at higher risk of cSSI and identifying those who may require closer surveillance or clinical intervention.