Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE:Patients with intracranial stenosis-related large-vessel occlusion (ICAS-LVO) may experience better outcomes with stent placement compared with stand-alone mechanical thrombectomy (MT). This study evaluates the safety and clinical outcomes of self-expanding stents (SES) versus balloon-mounted stents (BMS) in patients with ICAS-LVO treated with MT and stent placement. MATERIALS AND METHODS/METHODS:This secondary analysis of the Registry of Emergent Large-Vessel Occlusion Due to Intracranial Stenosis, a multicenter observational study, included patients with ICAS-LVO from 25 stroke centers who underwent stent placement. Patients were stratified by stent type (SES or BMS). The primary end point was 90-day mRS = 0-2. Secondary outcomes included successful reperfusion, recurrent stroke, and infarct volume. Symptomatic intracranial hemorrhage was the primary safety outcome. Inverse probability-weighting was adjusted for confounders. RESULTS:= .001), particularly in patients without prestenting angioplasty (14% versus 1%). CONCLUSIONS:SES and BMS demonstrated comparable safety and clinical outcomes in patients with ICAS-LVO. However, SES were linked to higher rates of restenosis and recurrent strokes, potentially influenced by the absence of prestenting angioplasty. Further research is needed to refine stent-placement strategies in this population.

In eukaryotes, each ribosomal subunit includes a ribosomal protein (RP) that is encoded as a fusion protein with ubiquitin (Ub). In yeast, each Ub-RP fusion requires processing by deubiquitylating enzymes (DUBs) to generate ribosome assembly-competent RPs and contribute to the cellular Ub pool. However, how Ub-RP fusions are processed by DUBs in human cells remains unclear. Here, we discovered that Ub-RPs are substrates of the Ub-fusion degradation (UFD) pathway in human cells via lysine 29 and 48 (K29/K48)-specific ubiquitylation and proteasomal degradation. We identified a pool of DUBs that catalytically process Ub-RPs, as well as DUBs that physically occlude Ub-RP interaction with UFD pathway Ub E3 ligases to prevent their degradation in a non-catalytic manner. Our results suggest that DUBs both process and stabilize Ub-RPs, whereas the UFD pathway regulates levels of Ub-RPs that cannot be fully processed by DUBs to fine-tune protein homeostasis.

Cardiovascular collapse and arrest in the periprocedural setting and intensive care unit differ from arrests in other contexts (such as out-of-hospital or hospital ward) because clinicians almost always witness the event, and the most likely precipitating cause may be known. In comparison to other settings, the response can be timelier and more focused on treating the underlying cause(s). Since many patients deteriorate over minutes to hours, clinicians can evaluate the patient expeditiously, generate a diagnosis, and initiate appropriate treatment more rapidly than in other arrest circumstances. This iteration of Perioperative Resuscitation and Life Support (PeRLS) employs Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) methodology to review the most recent evidence on preventing and managing cardiac arrest during the perioperative period. Furthermore, many of the recommendations and algorithms may also be applicable to areas outside the operating room, such as the intensive care unit and emergency room.

Worldwide, over half of all individuals with dementia are undiagnosed. In the United States, racial, ethnic, and economic inequities mirror global findings, with higher rates of missed and delayed diagnosis and poorer diagnostic quality among minoritized and disadvantaged groups. For example, delayed diagnosis is more prevalent among people identifying as non-Hispanic Black or Latino than non-Hispanic White. Systematic efforts to improve detection can increase diagnosis rates; there is broad consensus that earlier detection and initiation of focused care and support services benefit both affected individuals and their loved ones. Systemic under-detection and its contributions to persistent population-level suffering underscore the importance of early detection of dementia as a key public health issue. Improving early detection calls for comprehensive, coordinated responses from local, regional, and national public health systems in partnership with health care delivery systems and community-based organizations. The Public Health Center of Excellence on Early Detection of Dementia (PHCOE on EDD), funded by the Centers for Disease Control and Prevention (CDC), is a national resource to promote understanding and implementation of evidence-based and evidence-informed public health strategy for early detection of dementia. We, together with the PHCOEs on Dementia Risk Reduction and Dementia Caregiving, and nearly four dozen state and local initiatives, seek to operationalize the priorities of the Building Our Largest Dementia Infrastructure for Alzheimer's Act and National Healthy Brain Initiative, established by federal legislation in 2018 and 2024. Our efforts support the CDC's mandate to build a national public health infrastructure for brain health and dementia.

IMPORTANCE/UNASSIGNED:Open-label trials of antiseizure medications (ASMs) and devices suggest seizure reduction in focal treatment-resistant epilepsy (FTRE) may demonstrate treatment-related disease-modifying effects. Understanding FTRE trends can provide insight into treatment responses. OBJECTIVE/UNASSIGNED:To determine whether seizure frequency in FTRE improves over time. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:The Human Epilepsy Project 2 was a prospective, observational, multicenter study of patients with FTRE from May 2018 to September 2021 who were followed up for 18 to 36 months at 10 US-based comprehensive epilepsy centers. Analysis was performed from 2021 to 2024. Study data included seizure frequency, medication use, device use, surgeries tracked using daily electronic diaries, monthly check-ins, medical record review, and case report forms. Eligibility criteria included focal epilepsy diagnosis, age between 16 and 65 years, and failure of 4 or more ASMs (≥2 due to seizure control failure). Participants were recruited as a volunteer sample. EXPOSURES/UNASSIGNED:Participants were treated with multiple interventions at their physicians' discretion. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was seizure frequency trends, evaluated by quantifying seizure freedom rates and frequency reductions. Medication and device treatment responses were assessed by tracking ASM and device changes. RESULTS/UNASSIGNED:Of 196 approached participants, 146 met eligibility criteria and were included in the study. Mean (SD) participant age was 40 (12) years, and epilepsy was diagnosed at a mean (SD) age of 19.8 (13.6) years. The cohort had 84 (57.5%) female participants. A total of 35 participants had implantable devices; 1 had epilepsy surgery during the study. Of 146 participants, 128 provided sufficient seizure data for analysis, and 2 were excluded as outliers. Seizure frequency was reduced in 86 participants (68.3%) during the second half of study participation compared to the first half. In the overall cohort, mean modeled monthly seizure frequency percentage reduction was 68.73% (95% CI, 52.92%-84.54%). From 0 to 12 months (cohort 1), mean modeled percentage reduction was 67.76% (95% CI, 19.42%-116.09%); for 12 to 24 months (cohort 2), 36.00% (95% CI, 9.27%-53.46%); and for longer than 24 months (cohort 3), 66.03% (95% CI, 48.25%-83.80%) (all P < .001). An ASM was added in 69 participants (54.7%), of whom 46 (66.7%) experienced seizure frequency reduction, including seizure freedom. Seizure trajectories in participants with devices did not significantly differ from those without devices. CONCLUSIONS AND RELEVANCE/UNASSIGNED:Findings from the HEP2 study imply that FTRE improves over time, ASM additions had low probability of achieving seizure freedom but contributed to seizure reduction, and device-treated participants exhibited similar seizure trajectories to those without devices. Whether improvements reflected the natural history of FTRE or active management remains unclear, but our findings suggest cautious interpretation of open-label studies positing disease-modifying effects and further research into FTRE treatment response.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

BACKGROUND:In vivo biomarkers that can detect long-term neuropathologies from repetitive head impact (RHI) exposure are needed, especially for the neurodegenerative tauopathy chronic traumatic encephalopathy (CTE). Here, we evaluated plasma p-tau217 as a potential biomarker for CTE p-tau pathology, and examined the concordance between plasma p-tau217 and Aβ pathology in an at-risk for CTE sample. METHOD/METHODS:The sample included 180 male former football players (120 professional, 60 college), and 56 asymptomatic men without RHI (i.e., controls). Participants completed blood draws, 18F-florbetapir (Aβ+=SUVR≥1.10), and 18F-flortaucipir PET. Traumatic encephalopathy syndrome (TES) diagnoses were made. Single molecule array for plasma p-tau217 (ALZpath) was performed (≥0.6 cutoff used to maximize sensitivity). Nine participants had post-mortem tissue. ANCOVA examined group differences in p-tau217 (football vs controls; TES-CTE no, TES-CTE suggestive, TES-CTE possible/probable). Multivariable regression models tested associations between p-tau217 and florbetapir/flortaucipir PET. Covariates included age, race and APOE e4. RESULT/RESULTS:Sample characteristics are in Table 1. p-tau217 concentrations were higher in former football players compared to controls (est. marginal mean difference=-0.217, p = 0.005). There were no group differences in Aβ-PET SUVR. No differences were found across TES-CTE certainty levels. In football players, higher p-tau217 was associated with higher Aβ-PET SUVR (B=1.380, 95%CI[0.597-2.155], p = 0.001) but not when Aβ+ (n = 17) participants and those with kidney/liver disease (n = 5) were excluded. Aβ+ participants had the highest p-tau217 (Figure 1). When compared against Aβ-PET, several false Aβ-positives (high p-tau217, Aβ-) were identified, including one extreme outlier (assay related) and a cluster of Aβ- participants with p-tau217 between 0.60-1.0. There were no associations with flortaucipir SUVR (frontal, mesial temporal, left parietal). Two extreme p-tau217 outliers had autopsy-confirmed CTE stage III (AD-, Table 2). Of the remaining donors, all were AD- and four had CTE (stages II-IV) with ptau217 between 0.125-0.449. CONCLUSION/CONCLUSIONS:Plasma p-tau217 has usefulness in quantifying Aβ pathology but restricted utility for detection of CTE. In this at-risk for CTE sample, p-tau217 and Aβ-PET were associated at the group level. At the individual level, false Aβ-positives (and negatives) existed, including Aβ- participants with high p-tau217. We will explore whether this discrepancy is due to disease or peripheral interference with the N-terminal binding in p-tau assays.

OBJECTIVE:Central to the development of novel antiseizure medications (ASMs) is testing of antiseizure activity in preclinical models. Although various well-established models exist, their predictive validity across the spectrum of clinical epilepsies has been less clear. We sought to establish the translational concordance of commonly used preclinical models to define models with the highest predictive clinical validity for focal onset seizures (FOS). METHODS:The Praxis Analysis of Concordance (PAC) framework was implemented to assess the translational concordance between preclinical and clinical ASM response for 32 US Food and Drug Administration-approved ASMs. Preclinical ASM responses in historically used seizure models were collected. Protective indices based on reported median tolerability and median efficacy values were calculated for each ASM in each preclinical model. A weighted scale representing relative antiseizure effect was used to grade preclinical ASM response for each seizure model. Data depth was further scored based on the number of evaluated ASMs with publicly available data. Established reports of clinical ASM use in patients with FOS were similarly evaluated, and a weighted scale representing prescribing patterns and perceived efficacy was used to grade clinical ASM response. To assess the predictive validity of preclinical models, a unified translational scoring matrix was developed to assign a concordance score spanning the spectrum from complete discordance (-1) to complete concordance (1) between preclinical and clinical ASM responses. Scores were summed and normalized to generate a global translational concordance score. RESULTS:The preclinical models with the highest translational concordance and greatest data depth for FOS were rodent maximal electroshock seizure (MES), mouse audiogenic seizure, mouse 6 Hz (32 mA), and rat amygdala kindling. SIGNIFICANCE/CONCLUSIONS:The PAC-FOS framework highlights mouse MES, mouse audiogenic, and mouse 6 Hz (32 mA) as three acute seizure models consistently demonstrating high predictive validity for FOS. We provide a pragmatic decision tree approach to support efficient resource utilization for novel ASM discovery for FOS.