Citations

Abstract Objectives/Background The intent of this review is to provide practical guidance for trainees considering a headache medicine fellowship and examine key factors that contribute to program selection. Headache medicine continues to gain appeal as a neurologic subspecialty, and the number of headache fellowship programs across the country has paralleled that growth. Residents who develop interest in the field must decide whether to pursue fellowship for more specialized training. Establishing a framework for navigating this process can help trainees better evaluate programs and make informed decisions. Methods This narrative review was conducted as an initiative by the New Investigators and Trainees Special Interest Group of the American Headache Society in collaboration with a group of academic leaders and headache clinicians who participated in headache fellowship and/or the match process. Topics were selected based on themes identified in existing literature on fellowship selection across other disciplines. Headache‐specific information was obtained from a review of literature on headache education and fellowship, along with relevant professional society and program institution websites. This information was supplemented by the authors' perspectives when available evidence was limited. Results Physicians choose headache medicine for various reasons. This career path derives from inspiration through mentorship received during residency, exposure to a diverse patient population during training, and the desire to provide new therapies and procedural interventions. Headache medicine fellowships offer structured and in‐depth training through clinical, educational, professional, and scholarly activities. Programs may vary in size, structure, support, and patient care settings. These factors can help trainees decide whether to pursue fellowship and what to evaluate in different programs. Conclusion This review identifies key factors in pursuing a headache medicine fellowship, allowing trainees to seek programs that support professional interests and career aspirations.

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder that increases risk for premature coronary artery disease and has accessible and effective interventions. The Dutch lipid clinic network is currently the most used diagnostic criterion; however, genetic sequencing provides a definitive diagnosis of FH. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify individuals who were genotype positive for FH. METHODS: Participants were recruited from 3 geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; and Jacksonville, FL). Participants underwent Exome+ sequencing (dba Helix, San Mateo, CA) and return of results for specific genetic findings in APOB , LDLR , or PCSK9 . A chart review was performed to collect demographics, personal, and family cardiovascular history. RESULTS: At the time of the study, 84 413 participants were enrolled in the Tapestry study. Annotation and interpretation of all variants in genes for FH resulted in the identification of 419 likely pathogenic and pathogenic variants (prevalence, 0.50%), which included 116 APOB , 298 LDLR , and 5 PCSK9 . Sixty-six percent were female, the mean body mass index was 27.3, with 12.3% reporting a history of diabetes. Hypertriglyceridemia (≥150 mg/dL) was present in 39.5% and reduced HDL (<50 mg/dL) was present in 56.7% of patients. 27.5% of patients were not on cholesterol-lowering medications, and only 10% of FH carriers were at goal low-density lipoprotein cholesterol levels. A history of coronary artery disease was reported in 22.4% of the cohort. Nearly 90% of these participants were newly diagnosed carriers of FH. Only 30.8% of confirmed genetic diagnoses of FH satisfied clinical (Dutch lipid clinic network) criteria for a diagnosis. CONCLUSIONS: Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have familial hypercholesterolemia. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05212428.

Background: Meniscal preservation has been demonstrated to contribute to long-term knee health and has been a successful intervention in isolation and in patients with anterior cruciate ligament reconstruction (ACLR). The long-term results of meniscal repair in the setting of revision ACLR have yet to be documented. Purpose: To report the incidence of meniscal repair failures at the 6-year follow-up in a cohort of patients who underwent concurrent revision ACLR and primary meniscal repair. Study Design: Prospective cohort study; Level of evidence, 2. Methods: All revision ACLRs with concomitant primary meniscal repair cases from a multicenter group between 2006 and 2011 were selected. Six-year follow-up was obtained to determine whether any subsequent surgery had occurred since their initial revision ACLR. If so, operative reports were obtained, whenever possible, to verify pathological condition and treatment. Results: In total, 221 patients from 1234 revision ACLRs underwent concurrent primary meniscal repairs (18% of the cohort). There were 238 repairs performed: 173 medial and 65 lateral. The majority of these repairs (n = 181; 76%) were performed with an all-inside technique. Six-year surgical follow-up was obtained in 77% (171/221) of the cohort, or 189 of 238 (79%) of the repairs (136 medial, 53 lateral). The meniscal repair failure rate, defined as reoperation, was 16% (31/189) at 6 years. Of the 31 failures, 28 were medial (24 all-inside, 4 inside-out; 28/136 = 20.6% failure rate) and 3 were lateral (2 all-inside, 1 inside-out; 3/53 = 5.7% failure rate). Three medial failures were treated in conjunction with a subsequent repeat revision ACLR. Medial tears underwent reoperation for failure at a significantly higher rate than lateral tears (20.6% vs 5.7%; P = .01) and had a significantly shorter survival time compared with lateral tears ( P = .02). No difference was found between the failure and nonfailure groups when it came to tear type, tear length, repair technique utilized, suture/implant type, or number of sutures used between the 2 groups. Conclusion: Meniscal repair in the revision ACLR setting has a 16% failure rate at 6 years. Failure rates for medial tears (20.6%) were found to be higher than that for lateral tears (5.7%), which aligns with previous studies in both the revision and primary ACLR setting.

Abstract INTRODUCTION Glioblastoma (GBM) contains metabolically distinct cell lineages: treatment-sensitive cells (TSCs) dependent on glycolysis and treatment-resistant persister cells (TRPCs) reliant on lipid metabolism. Our integrative systems biology approach, combining spatial multi-omics and targeted analyses, uncovers lipid acquisition in TRPCs as a pivotal driver of GBM progression which is supported by a retrospective study of statin-treated patients, providing clinical evidence for the therapeutic potential of targeting lipid metabolism in GBM patients stratified based on high TRPC signatures. METHODS We applied spatial transcriptomics and proteomics combined with 3D reconstruction of the tumor microenvironment to map TRPC- and TSC-enriched regions. Immunohistochemically stained tissues were analyzed using CellProfiler and our custom SNAQ algorithm for single-cell neighborhood analysis. Lipid transfer from Tumor-Associated Myeloid Cells (TAMCs) to TRPCs was tracked via flow cytometry, holotomography, and time-lapse imaging. Genetic and pharmacological targeting of CCR2, CSF1R, and FABP3 was performed in vitro and in vivo. Finally, a retrospective analysis of 4,085 GBM patients assessed the impact of statin treatment in relation to TRPC signatures. RESULTS TRPCs exploit recruited TAMCs to acquire lipids, supporting their survival and immune evasion. These interactions are mediated by CCL2/7–CCR2 and CSF1–CSF1R signaling. Disruption of TRPC-TAM lipid homeostasis significantly remodeled the immune microenvironment, reduced tumor growth, and extended survival in preclinical models. Patients with high TRPC signatures typically had poor outcomes; however, retrospective analysis revealed that statin treatment was associated with improved survival in this subgroup. These findings position lipid trafficking as a metabolic vulnerability in GBM and highlight the therapeutic relevance of targeting TRPC–TAMC interactions. CONCLUSION Our study reveals lipid trafficking to TRPCs as a key mechanism driving GBM progression. Targeting TAMC-mediated lipid transfer disrupts immune suppression and tumor growth. Transcriptomic profiling may guide patient stratification, identifying those likely to benefit from lipid metabolism-targeted therapies such as statins.

Abstract BACKGROUND Gaseous hydrogen sulfide (H2S), a by-product of cysteine metabolism, inhibits cancer cell behavior and growth in glioblastoma (GBM) models. H2S production capacity (HPC) is decreased in human GBM specimens compared to non-tumor controls. Thus, boosting HPC is a novel strategy for GBM treatment. Suppression of thyroid hormone signaling increases production of H2S. Hypothesis: methimazole-induced hypothyroidism will increase tumor HPC to enhance chemotherapy efficacy in GBM. GOAL Proof of concept that methimazole, by reducing thyroid hormone signaling, can increase HPC in patients with recurrent/progressive GBM (rGBM). PRIMARY ENDPOINTS 10% increase in HPC and a 1.5-fold increase in peripheral blood sulfhydration signaling. METHODS This window of opportunity trial evaluates safety, HPC in plasma and tumor, and efficacy of methimazole + chemotherapy in patients with rGBM. rGBM patients planned for a resection begin methimazole 15 mg/d 5-7 days pre-op with investigator’s choice chemotherapy added 10-28 days post-op. Patients receive combination until progression. Pre- and post-op plasma and resected tumor tissue are assayed for HPC and proteins relevant to H2S production. Key eligibility: rGBM for whom a clinically-indicated resection is planned; normal thyroid function with no history of thyroid dysfunction. Patients may have received unlimited prior regimens including bevacizumab. RESULTS To date 15 patients (8 male), median age 56 years, enrolled. The trial was amended to increase methimazole to 25 mg/d; 3 patients have received this dose. Median TSH rose from 1.3 mU/ml to 20.5 at cycle 6. Free T3 and T4 did not fall significantly. Median plasma HPC rose 3-fold while tumor HPC did not rise significantly. No treatment-related grade 3-5 toxicities. Efficacy: PFS6 – 15% and OS12 – 21%. CONCLUSIONS Induction of subclinical hypothyroidism is feasible and safe in rGBM with significant increases in plasma but not tumor HPC. Further tumor correlative data for the dose-escalated methimazole will be presented.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a dismal 5-year survival rate of ~6.5%. Vasogenic cerebral edema is a severe complication that occurs in the majority of GBM patients, leading to a significant increase in intracranial pressure, neurological deficits, and increased mortality rates. GBM-associated edema is almost exclusively managed by the corticosteroid dexamethasone, the use of which is associated with immunosuppression and interference with radiation therapy. While there is considerable research on edema formation, little is known about how edema resolution occurs in GBM, particularly in response to dexamethasone or other anti-edema therapies. This gap in understanding highlights the need for new approaches to treat edema effectively. The water channel aquaporin-4 (AQP4), a key player in the brain’s water homeostasis, is upregulated in GBM compared to the normal brain. To investigate the role of AQP4 in GBM-associated edema, we used the RCAS/tv-a system to generate de novo GBM with different driver mutations in Aqp4 knockout (KO) and wild-type (WT) mice. Our results indicated that Aqp4 KO tumor-bearing mice had shortened survival and increased edema content. Additionally, Aqp4 loss decreased astrocyte coverage specifically in the tumor core but not the tumor periphery. Our single cell RNA-seq data suggested that Aqp4 loss led to an astrocytic shift towards a less reactive, progenitor-like state, while also decreasing the myeloid and CD8+ T cell population in the tumor microenvironment. Interestingly, tumor-bearing mice treated with anti-VEGFA antibody, which has anti-edema properties, showed increased expression of Aqp4 and reduced vessel leakage. This suggests that AQP4 plays a critical role in mediating the effects of anti-VEGFA therapy. Overall, this study highlights the important role of AQP4 in the resolution of edema, while also revealing its additional effects on astrocyte activation and the immune microenvironment in GBM.

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.