How cy pres promotes transdisciplinary convergence science: an academic health center for women's cardiovascular and brain health.

Cardiovascular disease (CVD) is largely preventable, and the leading cause of death for men and women. Though women have increased life expectancy compared to men, there are marked sex disparities in prevalence and risk of CVD-associated mortality and dementia. Yet, the basis for these and female-male differences is not completely understood. It is increasingly recognized that heart and brain health represent a lifetime of exposures to shared risk factors (including obesity, hyperlipidemia, diabetes, and hypertension) that compromise cerebrovascular health. We describe the process and resources for establishing a new research Center for Women's Cardiovascular and Brain Health at the University of California, Davis as a model for: (1) use of the cy pres principle for funding science to improve health; (2) transdisciplinary collaboration to leapfrog progress in a convergence science approach that acknowledges and addresses social determinants of health; and (3) training the next generation of diverse researchers. This may serve as a blueprint for future Centers in academic health institutions, as the cy pres mechanism for funding research is a unique mechanism to leverage residual legal settlement funds to catalyze the pace of scientific discovery, maximize innovation, and promote health equity in addressing society's most vexing health problems.

Multi-omics Characterization of Epigenetic and Genetic Risk of Alzheimer Disease in Autopsied Brains from two Ethnic Groups.

Background: Both genetic variants and epigenetic features contribute to the risk of Alzheimer's disease (AD). We studied the AD association of CpG-related single nucleotide polymorphisms (CGS), which act as the hub of both the genetic and epigenetic effects, in Hispanics decedents and generalized the findings to Non-Hispanic Whites (NHW) decedents. Methods: First, we derived the dosage of the CpG site-creating allele of multiple CGSes in each 1 KB window across the genome and we conducted a sliding window association test with clinical diagnosis of AD in 7,155 Hispanics (3,194 cases and 3,961 controls) using generalized linear mixed models with the adjustment of age, sex, population structure, genomic relationship matrix, and genotyping batches. Next, using methylation and bulk RNA-sequencing data from the dorsolateral pre-frontal cortex in 150 Hispanics brains, we tested the cis- and trans-effects of AD associated CGS on brain DNA methylation to mRNA expression. For the genes with significant cis- and trans-effects, we checked their enriched pathways. Results: We identified six genetic loci in Hispanics with CGS dosage associated with AD at genome-wide significance levels: ADAM20 (Score=55.2, P= 4.06×10 -8 ), between VRTN (Score=-19.6, P= 1.47×10 -8 ) and SYNDIG1L (Score=-37.7, P= 2.25×10 -9 ), SPG7 (16q24.3) (Score=40.5, P= 2.23×10 -8 ), PVRL2 (Score=125.86, P= 1.64×10 -9 ), TOMM40 (Score=-18.58, P= 4.61×10 -8 ), and APOE (Score=75.12, P= 7.26×10 -26 ). CGSes in PVRL2 and APOE were also genome-wide significant in NHW. Except for ADAM20 , CGSes in all the other five loci were associated with Hispanic brain methylation levels (mQTLs) and CGSes in SPG7, PVRL2, and APOE were also mQTLs in NHW. Except for SYNDIG1L ( P =0.08), brain methylation levels in all the other five loci affected downstream RNA expression in the Hispanics ( P <0.05), and methylation at VRTN and TOMM40 were also associated with RNA expression in NHW. Gene expression in these six loci were also regulated by CpG sites in genes that were enriched in the neuron projection and synapse (FDR<0.05). Conclusions: We identified six CpG associated genetic loci associated with AD in Hispanics, harboring both genetic and epigenetic risks. However, their downstream effects on mRNA expression maybe ethnic specific and different from NHW.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.

The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.

The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease.

Despite the increasing demographic diversity of the United States' aging population, there remain significant gaps in post-mortem research investigating the ethnoracial heterogeneity in the neuropathological landscape of Alzheimer Disease (AD). Most autopsy-based studies have focused on cohorts of non-Hispanic White decedents (NHWD), with few studies including Hispanic decedents (HD). We aimed to characterize the neuropathologic landscape of AD in NHWD (n = 185) and HD (n = 92) evaluated in research programs across three institutions: University of California San Diego, University of California Davis, and Columbia University. Only persons with a neuropathologic diagnosis of intermediate/high AD determined by NIA Reagan and/or NIA-AA criteria were included. A frequency-balanced random sample without replacement was drawn from the NHWD group using a 2:1 age and sex matching scheme with HD. Four brain areas were evaluated: posterior hippocampus, frontal, temporal, and parietal cortices. Sections were stained with antibodies against Aß (4G8) and phosphorylated tau (AT8). We compared the distribution and semi-quantitative densities for neurofibrillary tangles (NFTs), neuropil threads, core, diffuse, and neuritic plaques. All evaluations were conducted by an expert blinded to demographics and group status. Wilcoxon's two-sample test revealed higher levels of neuritic plaques in the frontal cortex (p = 0.02) and neuropil threads (p = 0.02) in HD, and higher levels of cored plaques in the temporal cortex in NHWD (p = 0.02). Results from ordinal logistic regression controlling for age, sex, and site of origin were similar. In other evaluated brain regions, semi-quantitative scores of plaques, tangles, and threads did not differ statistically between groups. Our results demonstrate HD may be disproportionately burdened by AD-related pathologies in select anatomic regions, particularly tau deposits. Further research is warranted to understand the contributions of demographic, genetic, and environmental factors to heterogeneous pathological presentations.

Biochemical analyses of tau and other neuronal markers in the submandibular gland and frontal cortex across stages of Alzheimer disease.

Hyperphosphorylation of the microtubule-associated protein tau is hypothesized to lead to the development of neurofibrillary tangles in select brain regions during normal aging and in Alzheimer disease (AD). The distribution of neurofibrillary tangles is staged by its involvement starting in the transentorhinal regions of the brain and in final stages progress to neocortices. However, it has also been determined neurofibrillary tangles can extend into the spinal cord and select tau species are found in peripheral tissues and this may be depended on AD disease stage. To further understand the relationships of peripheral tissues to AD, we utilized biochemical methods to evaluate protein levels of total tau and phosphorylated tau (p-tau) as well as other neuronal proteins (i.e., tyrosine hydroxylase (TH), neurofilament heavy chain (NF-H), and microtubule-associated protein 2 (MAP2)) in the submandibular gland and frontal cortex of human cases across different clinicopathological stages of AD (n = 3 criteria not met or low, n = 6 intermediate, and n = 9 high likelihood that dementia is due to AD based on National Institute on Aging-Reagan criteria). We report differential protein levels based on the stage of AD, anatomic specific tau species, as well as differences in TH and NF-H. In addition, exploratory findings were made of the high molecular weight tau species big tau that is unique to peripheral tissues. Although sample sizes were small, these findings are, to our knowledge, the first comparison of these specific protein changes in these tissues.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathy (CAA). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that approximately correlated with gold-standard human CERAD-like WSI scoring (p = 0.07 ± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers.

Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.

Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-ß analyses on digitized human brain slides.

Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-ß deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-ß-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-ß deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-ß plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.

TDP-43 Pathology in the Setting of Intermediate and High Alzheimer's Disease Neuropathologic Changes: A Preliminary Evaluation Across Ethnoracial Groups.

BACKGROUND: Transactive Response DNA Binding Protein 43 kDa (TDP-43) pathology is frequently found in cases with Alzheimer's disease (AD). TDP-43 pathology is associated with hippocampal atrophy and greater AD severity denoted by cognition and clinical representation. Current TDP-43 pathology studies are predominantly based on non-Hispanic White cohorts. OBJECTIVE: We sought to evaluate the presence of TDP-43 pathology across ethnoracial groups utilizing the National Alzheimer's Coordinating Center; a database containing data from over 29 institutions across the United States. Cases (N = 1135: Hispanics/Latinos = 29, African Americans/Black Americans = 51, Asians/Asian Americans = 10, American Indians/Alaskan Natives = 2, non-Hispanic White = 1043) with intermediate/high AD having data on TDP-43 pathology in the amygdala, hippocampus, entorhinal cortex, and neocortex were included. METHODS: TDP-43 pathology frequency in each neuroanatomic region among ethnoracial groups were compared using generalized linear mixed effects models with center as a random effect adjusting for age at death, education, and gender. RESULTS: Although groups were imbalanced, there was no significant difference across ethnoracial groups based on TDP-43 pathology (p = 0.84). With respect to neuroanatomical regions evaluated, there were no significant differences across ethnoracial groups (p-values > 0.06). There were also no significant differences for age at death and gender ratios across ethnoracial groups based on TDP-43 pathology. Although not statistically significant, TDP-43 pathology was present less often in Hispanic/Latinos (34%) when compared to non-Hispanic Whites (46%). CONCLUSION: While this is a preliminary evaluation, it highlights the need for diverse cohorts and on TDP-43 pathology research across ethnoracial groups. This is the first study to our knowledge having a focus on the neuroanatomical distribution of TDP-43 deposits in Hispanic/Latino decedents with AD.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathies (CAAs). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that correlated with gold-standard CERAD-like WSI scoring (p=0.07± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

Assessment of Current Practices Across Alzheimer's Disease Research Centers Biorepositories.

In 1984, the National Institute on Aging developed the Alzheimer's disease centers program. The main goal of these centers is to advance the understanding of Alzheimer's disease and related dementias (ADRD) through comprehensive patient evaluations and cutting-edge research in pathology, laboratory medicine, education, and scientific discovery. The neuropathology core of the Alzheimer's Disease Research Centers (ADRCs) collects postmortem brain tissue from consented donors ranging from cognitively normal individuals to those with late-stage dementia, whose samples and data can be shared around the world to further advance knowledge, diagnosis, and to eventually find cures for ADRD. Although recommended guidelines for biorepositories exist, we aimed to understand the current practices within neuropathology cores across the ADRCs. A survey was developed that focused on information related to sample processing methods, biospecimen requests, financial costs related to the repository, and data management. This survey was distributed to 28 current and former ADRC neuropathology cores. The survey obtained a response rate of 82% (23/28). Although most centers were consistent in responses related to sample processing and storage, they varied widely in processes by which neuropathological samples are shared and cost recovery mechanisms. The results of this survey provide benchmark data on practices within neuropathology cores across ADRCs and the overlap with biorepository best practices. Future studies focused on understanding factors that may influence current practices (such as available funds and personnel) are need to aid in minimizing barriers to optimally follow best practices. Sharing these data among ADRCs will allow for improvement in workflows and working toward cures for ADRD.

LATE-NC staging in routine neuropathologic diagnosis: an update.

An international consensus report in 2019 recommended a classification system for limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC). The suggested neuropathologic staging system and nomenclature have proven useful for autopsy practice and dementia research. However, some issues remain unresolved, such as cases with unusual features that do not fit with current diagnostic categories. The goal of this report is to update the neuropathologic criteria for the diagnosis and staging of LATE-NC, based primarily on published data. We provide practical suggestions about how to integrate available genetic information and comorbid pathologies [e.g., Alzheimer's disease neuropathologic changes (ADNC) and Lewy body disease]. We also describe recent research findings that have enabled more precise guidance on how to differentiate LATE-NC from other subtypes of TDP-43 pathology [e.g., frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS)], and how to render diagnoses in unusual situations in which TDP-43 pathology does not follow the staging scheme proposed in 2019. Specific recommendations are also made on when not to apply this diagnostic term based on current knowledge. Neuroanatomical regions of interest in LATE-NC are described in detail and the implications for TDP-43 immunohistochemical results are specified more precisely. We also highlight questions that remain unresolved and areas needing additional study. In summary, the current work lays out a number of recommendations to improve the precision of LATE-NC staging based on published reports and diagnostic experience.

BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.

As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid-ß pathology classification model into a unified framework (without incurring significant computation complexity) to identify pathologies, visualize their distributions, and quantify each type of pathologies in segmented GM/WM regions, respectively.

Plasma biomarkers predict cognitive trajectories in an ethnoracially and clinically diverse cohort: Mediation with hippocampal volume.

Introduction: We examine whether the association between key plasma biomarkers (amyloid ß [aß] 42/40, total tau (t-tau), neurofilament light [NfL]) and cognitive trajectories (executive function [EF] and episodic memory [EM]) is mediated through neurodegeneration. Methods: All participants were recruited from the University of California, Davis-Alzheimer's Disease Research Center (n = 473; baseline age range = 49-95 years, 60% women). We applied an accelerated longitudinal design to test latent growth models for EF and EM, and path and mediation analyses. Age was centered at 75 years, and all models were adjusted for sex, education, and ethnicity. Results: HV differentially mediated the association aß 42/40 and NfL on EF and EM level and change. Hippocampal volume (HV) did not mediate the association between t-tau and cognitive performance. Discussion: Neurodegeneration as represented with HV selectively mediates the association between key non-invasive plasma biomarkers and cognitive trajectories in an ethnoracially and clinically diverse community-based sample.

Frequency of LATE neuropathologic change across the spectrum of Alzheimer's disease neuropathology: combined data from 13 community-based or population-based autopsy cohorts.

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) and Alzheimer's disease neuropathologic change (ADNC) are each associated with substantial cognitive impairment in aging populations. However, the prevalence of LATE-NC across the full range of ADNC remains uncertain. To address this knowledge gap, neuropathologic, genetic, and clinical data were compiled from 13 high-quality community- and population-based longitudinal studies. Participants were recruited from United States (8 cohorts, including one focusing on Japanese-American men), United Kingdom (2 cohorts), Brazil, Austria, and Finland. The total number of participants included was 6196, and the average age of death was 88.1 years. Not all data were available on each individual and there were differences between the cohorts in study designs and the amount of missing data. Among those with known cognitive status before death (n = 5665), 43.0% were cognitively normal, 14.9% had MCI, and 42.4% had dementia-broadly consistent with epidemiologic data in this age group. Approximately 99% of participants (n = 6125) had available CERAD neuritic amyloid plaque score data. In this subsample, 39.4% had autopsy-confirmed LATE-NC of any stage. Among brains with "frequent" neuritic amyloid plaques, 54.9% had comorbid LATE-NC, whereas in brains with no detected neuritic amyloid plaques, 27.0% had LATE-NC. Data on LATE-NC stages were available for 3803 participants, of which 25% had LATE-NC stage > 1 (associated with cognitive impairment). In the subset of individuals with Thal Aß phase = 0 (lacking detectable Aß plaques), the brains with LATE-NC had relatively more severe primary age-related tauopathy (PART). A total of 3267 participants had available clinical data relevant to frontotemporal dementia (FTD), and none were given the clinical diagnosis of definite FTD nor the pathological diagnosis of frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). In the 10 cohorts with detailed neurocognitive assessments proximal to death, cognition tended to be worse with LATE-NC across the full spectrum of ADNC severity. This study provided a credible estimate of the current prevalence of LATE-NC in advanced age. LATE-NC was seen in almost 40% of participants and often, but not always, coexisted with Alzheimer's disease neuropathology.

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, weighs each contribution, and is robust to noisy labels. We collected 100,495 annotations on 20,099 candidate amyloid beta neuropathologies (cerebral amyloid angiopathy (CAA), and cored and diffuse plaques) from three institutions, independently annotated by five experts. DL methods trained on a consensus-of-two strategy yielded 12.6-26% improvements by area under the precision recall curve (AUPRC) when compared to those that learned individualized annotations. This strategy surpassed individual-expert models, even when unfairly assessed on benchmarks favoring them. Moreover, ensembling over individual models was robust to hidden random annotators. In blind prospective tests of 52,555 subsequent expert-annotated images, the models labeled pathologies like their human counterparts (consensus model AUPRC = 0.74 cored; 0.69 CAA). This study demonstrates a means to combine multiple ground truths into a common-ground DL model that yields consistent diagnoses informed by multiple and potentially variable expert opinions.

Neuropathology Studies of Dementia in US Persons other than Non-Hispanic Whites.

Alzheimer's disease (AD) and vascular dementia are two of the most prevalent dementias that afflict the aging population in the United States (US). Studies have made great strides in understanding the neuropathology of these diseases; however, many studies are conducted in the context of non-Hispanic whites (NHWs), and few include the rapidly growing underrepresented populations that reside in the US. We sought to characterize current knowledge of the neuropathologic landscape of AD and vascular dementia of the largest growing US minority groups, namely Latinos/Hispanics, Black Americans, and Asian Americans, compared with NHWs being the majority group. It is vital to note these historic categories are social constructs and cultural and social associations may underlie differences.  We conducted a literature search utilizing specific criteria to yield neuropathology papers that addressed the demographics and neuropathologies of relevance, then collated the findings into this review. We reveal that while there has been much progress in neuropathological research involving Latinos/Hispanics and Black Americans in the past decade, no cohesive conclusions could be extrapolated from the existing data due to the dearth of minority participants and even smaller amount of information related to the heterogeneity within each minority group, especially Latinos/Hispanics. Furthermore, we reveal an even greater scarcity in neuropathological studies involving Asian Americans, also a very heterogeneous group. We hope the presented findings will illuminate the paucity of minority representation in not just neuropathological research but the field of clinical research overall and serve to inspire clinicians and researchers to help reduce the health disparities underrepresented groups in the US face.

Fatty acid oxidation fuels glioblastoma radioresistance with CD47-mediated immune evasion.

Glioblastoma multiforme (GBM) remains the top challenge to radiotherapy with only 25% one-year survival after diagnosis. Here, we reveal that co-enhancement of mitochondrial fatty acid oxidation (FAO) enzymes (CPT1A, CPT2 and ACAD9) and immune checkpoint CD47 is dominant in recurrent GBM patients with poor prognosis. A glycolysis-to-FAO metabolic rewiring is associated with CD47 anti-phagocytosis in radioresistant GBM cells and regrown GBM after radiation in syngeneic mice. Inhibition of FAO by CPT1 inhibitor etomoxir or CRISPR-generated CPT1A-/-, CPT2-/-, ACAD9-/- cells demonstrate that FAO-derived acetyl-CoA upregulates CD47 transcription via NF-κB/RelA acetylation. Blocking FAO impairs tumor growth and reduces CD47 anti-phagocytosis. Etomoxir combined with anti-CD47 antibody synergizes radiation control of regrown tumors with boosted macrophage phagocytosis. These results demonstrate that enhanced fat acid metabolism promotes aggressive growth of GBM with CD47-mediated immune evasion. The FAO-CD47 axis may be targeted to improve GBM control by eliminating the radioresistant phagocytosis-proofing tumor cells in GBM radioimmunotherapy.