Citations

Study Design: Case presentation. Objective: The interlaminar endoscopic lateral recess decompression (IE-LRD) represents a novel surgical alternative to consider in carefully selected cases, particularly when patients do not respond to conservative treatment. Background: Lumbar spinal stenosis (LSS) constitutes one of the most prevalent spinal conditions in the elderly population around the world. A myriad of causes have been elucidated, and although infrequent, vertebral compression fractures (VCF) may also cause LSS. Conservative treatment might be the first option; however, patients often require surgery. Methods: A 76-year-old woman with a medical history of dementia consulted for radicular pain in her left leg during the past 6 months, without any favorable response to conservative treatment. Neurological examination revealed a positive straight leg raising test on the left. A spine MRI revealed a VCF in L5, linked with spondylolisthesis (grade I) at L5–S1. An IE-LRD was planned, as the patient did not consent to a classical open surgery. The incision was placed over the inferior medial edge of the left L4 lamina. Following sequential dilatators, the working channel was located on the inferomedial edge of the left L4 lamina. Lateral fluoroscopy (LF) guided the entire procedure. During the procedure, laminotomy was extended cranially and caudally to ensure sufficient vertebral decompression. Results: After surgery, the patient was mobilized in the first 6 hours and discharged in the next 24 hours. Her lower back pain score improved significantly to 2–3, while her leg VAS pain improved to 0–1. After a follow-up for 18 months, the patient did not manifest complications or recurrences, and no additional treatment was carried out. Conclusion: (1) IE-LRD might be a useful and safe surgical procedure for the surgical management of LSS caused by VCF. (2) Consider this surgical alternative for elderly patients without a previous response to conservative medical treatment.

Introduction There is uncertainty about the number of shark bites on humans in Florida (FL). Methods The International Shark Attack File (ISAF) and Global Shark Attack File (GSAF) databases (2016–2024) were compared with the non-inpatient FL Ambulatory and Emergency Department Patient Data database (AHCA) (2018–2023) and the National Emergency Medical Services (EMS) Information System (NEMSIS) (2016–2024). Results Between 2016–2024 there were 244 FL shark encounters reported in ISAF (mean 27.1 per year), 214 in GSAF (mean 23.8 per year), and 293 in a merged ISAF and GSAF (mean 32.6 per year). Between 2020–2023, there were 373 FL shark encounters reported in AHCA (mean 62.2 per year) compared with 114 encounters reported in NEMSIS (mean 22.8 per year) for 2020–2024. The rate ratio for AHCA FL shark encounters compared to the merged ISAF and GSAF was 1.91 (95% CI 1.64–2.23); p < .001. The percentage of shark encounters in Volusia County, FL, compared with all of FL shark encounters showed no significant differences between the ISAF and GSAF (40.2% and 33.6%, respectively) ( p = .16) and between the AHCA and NEMSIS (7.2% and 7.5%, respectively) ( p = 1.0). However, when comparing either ISAF or GSAF with either AHCA or NEMSIS, there were significant differences ( p < .001 for all). Discussion The AHCA dataset identified about twice as many shark bites as either the ISAF or GSAF. Many FL shark encounters did not require EMS or admission to the hospital. The discrepancy in the number of shark encounters attributed to Volusia County, FL, varied based on the dataset being examined.

Abstract Glioblastoma (GBM) is a lethal, treatment-resistant brain cancer characterized by diffuse brain invasion, complex neuron-glioma signaling, and cellular, molecular, and spatial heterogeneity. Surgery is the current mainstay of treatment, aimed at maximizing tumor cytoreduction, decompressing the brain to improve neurological function, and providing material for analyses to inform prognostication and adjuvant treatment selection. To accurately model GBM surgical resection and tissue sampling in preclinical animal models, we developed the Maximal Safe Intracranial Surgery (MSIS) system. MSIS integrated a miniaturized neurosurgical resection device and enabled a safe, tunable, and reproducible approach for intracranial tumor resection, multi-regional biopsy, and longitudinal tissue sampling in murine models, with stereotactic, sterile, and high-viability tissue collection. MSIS was applied across five distinct orthotopic brain tumor models to reproducibly generate histologically and radiologically representative models of post-surgical GBM. Similar to humans, increasing the extent of resection correlated with improved animal survival, and recurrent growth patterns varied significantly by model type. Transcriptional profiles of matched primary vs. post-surgical recurrent tumors revealed enrichment of margin-associated transcriptional states consistent with neuron-glioma signaling. Overall, the MSIS system offers opportunities to model the standard-of-care, dissect the spatial and temporal dynamics of GBM, and pair therapeutic discovery with contextual tumor pathobiology.

Abstract Varlilumab is a monoclonal antibody with agonistic activity on CD27. The efficacy of single-agent PD-1 inhibitors on aggressive B-cell non-Hodkin lymphoma is limited. Preclinical and phase 1 data suggest that Varlilumab is safe and may have synergistic activity with PD-1 inhibitors. In the DIAL study, we explored whether dual immunomodulatory therapy with Varlilumab and Nivolumab in patients with relapsed/refractory aggressive B-NHL was safe and effective. We conducted a randomized phase II clinical trial on adult patients with relapsed/refractory aggressive B-NHL. Patients were randomized to receive Nivolumab monotherapy in the control group and Varlilumab combined with Nivolumab in the experimental group. Crossover to the experimental group was allowed upon progression in the control group. The primary endpoint was the overall response rate based on the Lymphoma Response to Immunomodulatory Therapy Criteria (LYRIC). 54 patients underwent randomization, and 51 received assigned intervention and were included in the intention-to-treat analysis. Objective response was observed in 6 out of 51 patients (ORR 12%). There were 4 responders (ORR 16%) in the Nivolumab group and 2 responders (ORR 7.6%) in the Nivolumab and Varlilumab combination group (p = 0.3). None of the 7 patients that crossed over to the experimental group responded to the addition of Varlilumab. The ORR of patients who were previously exposed to CAR-T cell therapy was 13% (5 out of 39) compared to 8.3% (1 out of 12) in the unexposed patients (p > 0.99). The rate of treatment emergent adverse events was similar between groups, and there was no fatal TEAE in the study. The addition of Varlilumab to Nivolumb did not improve outcomes in patients with aggressive B-NHL. Even though this trial was not designed to evaluate immunotherapy as a rescue therapy after CAR-T failure, most patients (76%) had previously received CAR-T therapy, and their outcomes were equally poor.

Introduction Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase involved in immune signaling and has been implicated in tumor-intrinsic resistance to DNA-damaging therapies. Although SYK activity has been associated with poor outcomes in triple-negative breast cancer (TNBC), its role in resistance to combined radiotherapy and immune checkpoint blockade remains unclear. Methods SYK expression and clinical relevance were assessed across multiple breast cancer cohorts, including radiotherapy-treated datasets. Integrated transcriptomic analyses were used to characterize SYK-associated immune states. The functional effects of pharmacologic SYK inhibition were evaluated using in vitro assays and an immunocompetent bilateral 4T1 tumor model in female BALB/c mice treated with radiotherapy and PD-1 blockade. Treatment-associated immune remodeling was assessed by flow cytometry and transcriptomic profiling. Results Across multiple cohorts, elevated SYK expression, particularly in TNBC, was associated with inferior clinical outcomes and immune dysregulation. Pharmacologic SYK inhibition enhanced tumor responses to radiotherapy and PD-1 blockade at both irradiated and distant tumor sites. These effects were accompanied by coordinated remodeling of the tumor immune microenvironment, including suppression of neutrophil-associated signatures and reductions in immunosuppressive myeloid populations, including M2-like macrophages and myeloid-derived suppressor cells. Discussion Pharmacologic SYK inhibition enhances responses to radiotherapy and PD-1 blockade in preclinical TNBC models and is accompanied by remodeling of the immunosuppressive myeloid compartment. These findings support further investigation of SYK-targeted strategies to improve responses to combined radiotherapy and immune checkpoint blockade.

Abstract Patients with rare genetic disorders often wait years for a correct diagnosis, highlighting the urgent need for early and efficient triage for those at risk. Electronic health records (EHRs) are rich in useful information, yet the extent to which this data is sufficient to generate prediagnostic signals is uncertain. We trained machine learning models on billing codes and phenotypes extracted from clinical notes and applied these across eight conditions in a longitudinal cohort of roughly 3 million patient records from the Mayo Clinic, seeking early identification of conditions. The number of patients identified early varied by condition, ranging from 10% to 89% at 99% specificity. Median lead times exceeded one year prior to the first diagnostic billing code for most conditions, including Fabry disease (1,408 days), hereditary angioedema (2,755 days), neurofibromatosis type 1 (1,067 days), and hereditary hemorrhagic telangiectasia (2,320 days). We show that ML models trained on EHR data can prioritize patients for clinician review and confirmatory genetic testing and that the integration of diverse phenotypic data sources provides superior predictive value.

Abstract Background and Objectives Cryoprecipitated antihemophilic factor (cryoprecipitate), fibrinogen concentrate (FC), and pathogen‐reduced cryoprecipitated fibrinogen complex (PR cryoprecipitate) are sources of fibrinogen replacement, and each has advantages and disadvantages in terms of cost, efficacy, safety, and logistics of administration. This study examines the respective utilization frequencies of these different products at institutions in North America and the factors influencing the decision on which one(s) to adopt. Materials and Methods A survey was distributed via email to contacts for Advancement of Blood and Biotherapies (AABB)‐accredited blood banks/transfusion services. Survey questions covered topics including the availability and usage of FC, cryoprecipitate, and PR cryoprecipitate, as well as the perceived advantages of each product. Results A total of 53 responses were received from surveyed institutions (estimated 25% response rate). Over half (31/53, 58.5%) of respondents exclusively used cryoprecipitate, 30.2% (16/53) used two sources of fibrinogen replacement, and 7.5% (4/53) used all three (cryoprecipitate, FC, and PR cryoprecipitate). More than half of the institutions restricted FC to specific indications, primarily for cost containment. The most commonly cited advantages of FC over cryoprecipitate were storage convenience and rapid reconstitution. Conclusion Surveyed institutions in the United States and Canada primarily use cryoprecipitate for fibrinogen replacement, with variable usage of FC and limited usage of PR cryoprecipitate. The perception of FC being more expensive than cryoprecipitate predominated. Further studies comparing available fibrinogen replacement options would be valuable, particularly with consideration of product acquisition cost, wastage, impacts on utilization of other blood products, and patient outcomes.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

Abstract Global incidence and mortality rates for endometrial and cervical cancers have been steadily rising, while these rates for ovarian cancer have decreased. Although these cancers represent unique diseases and challenges, overlapping biology and responses to targeted and immunotherapies create opportunities to advance research through shared knowledge and resources. In 2025, investigators funded by National Cancer Institute (NCI) Specialized Programs of Research Excellence (SPORE) and other NCI-supported grants convened a hybrid meeting to identify challenges and knowledge gaps in translational research, highlight advances, define shareable resources and technologies, and foster collaboration among the Gynecologic Cancer SPORE sites, patient advocates, and NCI staff. Special sessions were arranged for presentations by Patient Advocates, Early-stage Investigators, Investigators Transitioning to Gynecologic Cancer Research. Meeting-wide workshops on Sharing Resources and Reducing Time to Activation of Clinical Trials and networking events were included. Collectively, the presentations converged on three overarching themes: (i) interception and early disease biology, (ii) development of innovative targeted and immune-based therapies, (iii) critical role of shared resources and collaborative infrastructure in accelerating translational progress. The unifying theme across disease sites was molecular advances to improve early detection/prevention and to drive therapeutic advancements. Novel findings discussed included: candidate precursor cells and mechanisms of high grade serous ovarian cancer development; therapeutic vaccines and their delivery methods for cervical dysplasia; identification of immune-targetable ovarian cancer antigens; various novel molecularly targeted drugs and immune-based agents for endometrial and ovarian cancers; and approaches to bypass drug resistance. Workshops delineated actionable strategies to further advance gynecologic cancer translational research.

ABSTRACT Purpose This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.