Citations

AbstractObjectiveIn this observational study on a cohort of biopsy‐proven central nervous system demyelinating disease consistent with MS, we examined the relationship between early‐active demyelinating lesion immunopattern (IP) with subsequent clinical course, radiographic progression, and cognitive function.MethodsSeventy‐five patients had at least one early‐active lesion on biopsy and were pathologically classified into three immunopatterns based on published criteria. The median time from biopsy at follow‐up was 11 years, median age at biopsy ‐ 41, EDSS ‐ 4.0. At last follow‐up, the median age was 50, EDSS ‐ 3.0. Clinical examination, cognitive assessment (CogState battery), and 3‐Tesla‐MRI (MPRAGE/FLAIR/T2/DIR/PSIR/DTI) were obtained.ResultsIP‐I was identified in 14/75 (19%), IP‐II was identified in 41/75 (56%), and IP‐III was identified in 18/75 (25%) patients. Patients did not differ significantly by immunopattern in clinical measures at onset or last follow‐up. The proportions of disease courses after a median of 11 years were similar across immunopatterns, relapsing–remitting being most common (63%), followed by monophasic (32%). No differences in volumetric or DTI measures were found. CogState performance was similar for most tasks. A slight yet statistically significant difference was identified for episodic memory scores, with IP‐III patients recalling one word less on average.InterpretationIn this study, immunopathological heterogeneity of early‐active MS lesions identified at biopsy does not correlate with different long‐term clinical, neuroimaging or cognitive outcomes. This could be explained by the fact that while active white matter lesions are pathological substrates for relapses, MS progression is driven by mechanisms converging across immunopatterns, regardless of pathogenic mechanisms driving the acute demyelinated plaque.

Brain and spinal tumors affect 1 in 1000 people by 25 years of age, and have diverse histological, biological, anatomical and dissemination characteristics. A mortality of 30–40% means the majority are cured, although two-thirds have life-long disability, linked to accumulated brain injury that is acquired prior to diagnosis, and after surgery or chemo-radiotherapy. Only four drugs have been licensed globally for brain tumors in 40 years and only one for children. Most new cancer drugs in clinical trials do not cross the blood–brain barrier (BBB). Techniques to enhance brain tumor drug delivery are explored in this review, and cover those that augment penetration of the BBB, and those that bypass the BBB. Developing appropriate delivery techniques could improve patient outcomes by ensuring efficacious drug exposure to tumors (including those that are drug-resistant), reducing systemic toxicities and targeting leptomeningeal metastases. Together, this drug delivery strategy seeks to enhance the efficacy of new drugs and enable re-evaluation of existing drugs that might have previously failed because of inadequate delivery. A literature review of repurposed drugs is reported, and a range of preclinical brain tumor models available for translational development are explored.

Abstract Background: Wellness Rx is a community-academic partnership program developed through a community engagement and participatory model (CEP) framework, to improve health disparities in an under-resourced predominantly Black community. This program addresses health disparities in chronic disease and social determinants of health through interactive health education, empowerment, and neighborhood support. This program aims to increase access to resources, increase capacity and strengthen relationships with local organizations for sustainable health outcomes. Wellness Rx ran in the community for 4 years with financial and logistical support from an academic medical center. In year 5, the community participated in sustainability training to shift and transfer ownership to community members. Methods: A sustainability training program was developed and delivered to community members to allow for Wellness Rx to be community-led and self-directed. A core group of 12 community members volunteered to serve as the taskforce that would ultimately lead and direct Wellness Rx after the sustainability training. This training included Information sharing of program materials, key partnership contacts, resource lists, skills training on program implementation, volunteer management, grant writing, partnership development and leadership. Results: Since the adoption of the training program, the community has tailored the Wellness Rx to meet their needs and priorities, and encompasses topics within and outside of behavioral health. As leaders of the program, they have expanded their partnerships beyond existing networks and have secured funding for activities. Conclusion: Wellness Rx demonstrates the potential for a program to become self-directed after initial support from an academic medical center. Core components of Wellness Rx can be replicated and implemented as a power-sharing model for long-term sustainability. Citation Format: Manisha Salinas, Monica Albertie. Sustainability plan for Wellness Rx: A community-led initiative to reduce disparities and improve healthy lifestyles in an under-resourced Black community [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr B030.

Abstract Background Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality – for the specific purpose of assessing AI/ML improvements – is currently missing. Methods A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on “ AI in the Laboratory of the Future” prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. Results The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. Conclusions A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

AbstractBackgroundAs gene‐targeted therapies are increasingly being developed for Parkinson's disease (PD), identifying and characterizing carriers of specific genetic pathogenic variants is imperative. Only a small fraction of the estimated number of subjects with monogenic PD worldwide are currently represented in the literature and availability of clinical data and clinical trial‐ready cohorts is limited.ObjectiveThe objectives are to (1) establish an international cohort of affected and unaffected individuals with PD‐linked variants; (2) provide harmonized and quality‐controlled clinical characterization data for each included individual; and (3) further promote collaboration of researchers in the field of monogenic PD.MethodsWe conducted a worldwide, systematic online survey to collect individual‐level data on individuals with PD‐linked variants in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ‐1, as well as selected pathogenic and risk variants in GBA and corresponding demographic, clinical, and genetic data. All registered cases underwent thorough quality checks, and pathogenicity scoring of the variants and genotype–phenotype relationships were analyzed.ResultsWe collected 3888 variant carriers for our analyses, reported by 92 centers (42 countries) worldwide. Of the included individuals, 3185 had a diagnosis of PD (ie, 1306 LRRK2, 115 SNCA, 23 VPS35, 429 PRKN, 75 PINK1, 13 DJ‐1, and 1224 GBA) and 703 were unaffected (ie, 328 LRRK2, 32 SNCA, 3 VPS35, 1 PRKN, 1 PINK1, and 338 GBA). In total, we identified 269 different pathogenic variants; 1322 individuals in our cohort (34%) were indicated as not previously published.ConclusionsWithin the MJFF Global Genetic PD Study Group, we (1) established the largest international cohort of affected and unaffected individuals carrying PD‐linked variants; (2) provide harmonized and quality‐controlled clinical and genetic data for each included individual; (3) promote collaboration in the field of genetic PD with a view toward clinical and genetic stratification of patients for gene‐targeted clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

Abstract Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders. We aimed to develop a pipeline for diagnosing Alzheimer's disease and other tauopathies, including corticobasal degeneration, globular glial tauopathy, Pick’s disease, and progressive supranuclear palsy. We used deep learning (DL)-based approach called clustering-constrained-attention multiple instance learning (CLAM) on whole slide images (WSIs) of tau immunohistochemistry in three brain regions from 120 patients. We also augmented gradient-weighted class activation mapping (Grad-CAM) to the model for visualizing cellular-level evidence in the model’s decisions. The model using the sections of cingulate and superior frontal gyri achieved the highest area under the curve (0.970±0.037) and diagnostic accuracy (0.873±0.087). Grad-CAM showed the highest attention in known pathognomonic tau lesions for each disease (e.g., Pick bodies for Pick’s disease). Our findings supported the feasibility of the DL-based approach for the classification task on WSIs, which encouraged further investigation, especially focusing on clinicopathological correlation studies.

Abstract Background: Inclusion of historically underrepresented populations in research has been a problem existing for many years. The recent Covid pandemic has exposed the significant cost of gaps in access to care. There remains a need to reevaluate current research paradigm frameworks to consider tailored approaches that accommodate any location that would ease participant burden and maintain retention. Current decentralized research models include home and mobile options. However, barriers exist, and certain demographics remain elusive to recruitment into research studies. Aim: In this paper, we aim to identify a theoretical framework that will allow for more inclusivity in research. Methodology: Current utilized community-based decentralized research models are compared, barriers in recruitment and retention of interested participants are examined, and a framework individualized to study participants is offered. Results: The proposed framework expands upon the complex adaptive systems theory to incorporate community-based research which considers dividing studies into individual study visit components that may differ in location. It allows for ease of research participant access to care and mutual collaboration among providers and institutions. This new theoretical model expansion outlines a novel approach to community-based participatory research for increased representation and inclusion. Conclusion: Investigators should carefully consider creative and effective solutions to overcome barriers to research participation access, increase diversity and representation, and provide tailored public health interventions. Minimally risk study intervention visits may occur in multiple community-driven locations such as: community resource centers, local physician offices, mobile research units, remote electronic visits, home settings, and a combination of the above (hybrid model). Breaking studies into individual study visit components expands the reach of science into the communities that have long been difficult to reach. Citation Format: Jennifer M. Crook, Manisha Salinas, Parisa Fathi, Jessica Otero Machuca, Emelina Asto Flores, Monica Albertie, Noreen Stephenson, Chelsea Reeves, Folakemi Odedina. Expanding current decentralized research model concepts for targeted community health interventions [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A060.

AbstractBackgroundMonitoring changes in cognitive impairment is an important aspect of care for aging patients. Traditionally, this is done with pen‐and‐paper assessments such as the Mini‐Mental State Exam (MMSE). EEG is a low‐cost and widely available neurophysiology tool that provides a data‐rich measure of brain health, but signal complexity has limited its impact. Advances in machine learning bring new possibilities to decode those complex signals and leverage EEG in patient care. Here, we engineer novel EEG features for assessing brain health with the [Banded Fractal Variability] (BFV) technique. We use [BFV] and other EEG metrics to assess patients’ cognitive function via a continuous score (a [Cognitive Impairment Index], or [CII]). We validate the diagnostic value of the EEG features by comparing our model against reduced models lacking the EEG features.MethodThe patient sample (N=97, mean (sd) age = 69.1 (11.0), 52.6% female) was collected at the Pacific Brain Health Center. Diagnostic categories included subjective cognitive impairment (n=44), mild cognitive impairment (n=26), and dementia (n=27). Mixed disease (AD, DLB, FTD, vascular, diabetes) was common in the impaired sample, and 39.6% were diagnosed with AD. We developed a continuous [CII] model trained on patients’ MMSE scores (range = 11 to 30, mean = 26.6) using a gradient boosting model with [Banded Fractal Variability] and other EEG features computed from clinical wake EEGs. Model development and testing took place within a rigorous nested cross‐validation (NCV) procedure to prevent overfitting. Performance was compared to reduced (no‐EEG features) models using the non‐parametric Wilcoxon sign‐rank test.ResultThe approach showed a Mean Absolute Error of 1.98 (sd = 0.28) across 5 test folds in the NCV outer loops, significantly outperforming both the baseline intercept‐only model (p = 0.031) and the demographics (age and years of education) model (p = 0.031).ConclusionThese preliminary results suggest that our [CII] derived from machine‐learning and quantitative EEG using [BFV] is a viable approach to assess and track patients’ cognitive impairment regardless of disease status across all impairment levels, raising the possibility that a brief EEG scan could shed light on patient health normally accessible only through extensive cognitive and neuropsychological testing.