Citations

Abstract Cerebrospinal fluid (CSF) is a clear, transparent fluid derived from blood plasma that protects the brain and spinal cord against mechanical shock, provides buoyancy, clears metabolic waste and transports extracellular components to remote sites in the brain. Given its contact with the brain and the spinal cord, CSF is the most informative biofluid for studies of the central nervous system (CNS). In addition to other components, CSF contains extracellular vesicles (EVs) that carry bioactive cargoes (e.g., lipids, nucleic acids, proteins), and that can have biological functions within and beyond the CNS. Thus, CSF EVs likely serve as both mediators of and contributors to communication in the CNS. Accordingly, their potential as biomarkers for CNS diseases has stimulated much excitement for and attention to CSF EV research. However, studies on CSF EVs present unique challenges relative to EV studies in other biofluids, including the invasive nature of CSF collection, limited CSF volumes and the low numbers of EVs in CSF as compared to plasma. Here, the objectives of the International Society for Extracellular Vesicles CSF Task Force are to promote the reproducibility of CSF EV studies by providing current reporting and best practices, and recommendations and reporting guidelines, for CSF EV studies. To accomplish this, we created and distributed a world‐wide survey to ISEV members to assess methods considered ‘best practices’ for CSF EVs, then performed a detailed literature review for CSF EV publications that was used to curate methods and resources. Based on responses to the survey and curated information from publications, the CSF Task Force herein provides recommendations and reporting guidelines to promote the reproducibility of CSF EV studies in seven domains: (i) CSF Collection, Processing, and Storage; (ii) CSF EV Separation/Concentration; (iii) CSF EV Size and Number Measurements; (iv) CSF EV Protein Studies; (v) CSF EV RNA Studies; (vi) CSF EV Omics Studies and (vii) CSF EV Functional Studies.

Abstract Microglia, the major resident immune cells in the central nervous system, serve as the frontline soldiers against cerebral ischemic injuries, possibly along with metabolic alterations. However, signaling pathways involved in the regulation of microglial immunometabolism in ischemic stroke remain to be further elucidated. In this study, using single‐nuclei RNA sequencing, a microglial subcluster up‐regulated in ischemic brain tissues is identified, with high expression of Igf1 and Trem2, neuroprotective transcriptional signature and enhanced oxidative phosphorylation. Microglial depletion by PLX3397 exacerbates ischemic brain damage, which is reversed by repopulating the microglia with high Igf1 and Trem2 phenotype. Mechanistically, Igf1 serves as one of the major down‐stream molecules of Trem2, and Trem2‐Igf1 signaling axis regulates microglial functional and metabolic profiles, exerting neuroprotective effects on ischemic stroke. Overexpression of Igf1 and supplementation of cyclocreatine restore microglial glucometabolic levels and cellular functions even in the absence of Trem2. These findings suggest that Trem2‐Igf1 signaling axis reprograms microglial immunometabolic profiles and shifts microglia toward a neuroprotective phenotype, which has promising therapeutic potential in treating ischemic stroke.

AbstractBackgroundGiven the rarity of genetic frontotemporal dementia (FTD), researchers across the world have come together to form the FTD Prevention Initiative (FPI) in an effort to improve prevention trials design. As this initiative begins to bring together large‐scale data from worldwide cohort series, including ALLFTD in North America and GENFI in Europe and Canada, it is critical for FPI to quantify the level of relatedness between all participants. Here we provide the most recent update to these ongoing analyses.MethodsGenome‐wide SNP genotyping data from 1,684 ALLFTD and 568 GENFI participants was used to perform lineage analyses using PLINK. Briefly, QC was performed similarly in all datasets to remove individuals with low call rate and filter autosomal SNPs for missingness, frequency, and deviation from Hardy‐Weinberg equilibrium. Genetic ancestry was inferred by projecting genotyped samples into the principal components of the 1000 Genomes reference panel, using R package bigsnpr. Overlapping ALLFTD and GENFI genotyping data was then used, in a two‐stage approach, to calculate pairwise identity‐by‐descent (IBD) estimates and KING coefficients, followed by family‐network identification and pedigree reconstruction using PRIMUS.ResultsFirst, we calculated IBD estimates among all participants by restricting pairs to those with estimates>0.1875 (up to second‐degree relatives). Overall, we identified a total of 292 second‐degree family networks, including 168 ALLFTD and 120 GENFI families, mostly associated with pathogenic variants in the 3 major FTD‐causing genes. We also identified 4 family networks with participants enrolled in both the ALLFTD and GENFI series, as well as several multi‐site families within the ALLFTD consortium. This first, overall approach allowed us to predict close relationships even between individuals with different ancestral backgrounds, including at least 4 confirmed admixed families. More distant relationships were also detected within ALLFTD and GENFI by performing ancestry‐based analysis among participants with estimated European ancestry, using the KING‐robust algorithm.ConclusionsThese lineage analyses allowed us to identify, otherwise unknown, close (and distant) relatives from different study sites, as well as within the ALLFTD and GENFI series. This dataset will be a crucial resource to increase statistical accuracy and power in upcoming collaborative FPI studies.

AbstractThe DPYD gene encodes dihydropyrimidine dehydrogenase, the rate‐limiting enzyme for the metabolism of fluoropyrimidines 5‐fluorouracil and capecitabine. Genetic variants in DPYD have been associated with altered enzyme activity, therefore accurate detection and interpretation is critical to predict metabolizer status for individualized fluoropyrimidine therapy. The most commonly observed deleterious variation is the causal variant linked to the previously described HapB3 haplotype, c.1129‐5923C>G (rs75017182) in intron 10, which introduces a cryptic splice site. A benign synonymous variant in exon 11, c.1236G>A (rs56038477) is also linked to HapB3 and is commonly used for testing. Previously, these single‐nucleotide polymorphisms (SNPs) have been reported to be in perfect linkage disequilibrium (LD); therefore, c.1236G>A is often utilized as a proxy for the function‐altering intronic variant. Clinical genotyping of DPYD identified a patient who had c.1236G>A, but not c.1129‐5923C>G, suggesting that these two SNPs may not be in perfect LD, as previously assumed. Additional individuals with c.1236G>A, but not c.1129‐5923C>G, were identified in the Children's Mercy Data Warehouse and the All of Us Research Program version 7 cohort substantiating incomplete SNP linkage. Consequently, testing only c.1236G>A can generate false‐positive results in some cases and lead to suboptimal dosing that may negatively impact patient therapy and prospect of survival. Our data show that DPYD genotyping should include the functional variant c.1129‐5923C>G, and not the c.1236G>A proxy, to accurately predict DPD activity.

Background: While spontaneous coronary artery dissection (SCAD) patients can present with ventricular arrhythmias (VAs), the in-hospital outcomes among these patients are not well-established. Objective: This study aimed to analyze the in-hospital outcomes of patients with SCAD and concurrent VAs, and to determine the predictors of VAs occurrence in this population. Methods: Using the Nationwide Readmissions Database, our study included patients aged 18 years or older who had SCAD between 2017 and 2020. We categorized the cohort into two groups depending on the presence of VAs during hospitalization. The in-hospital outcomes were assessed between two groups. Through multivariable analysis, we further analyzed the predictors of VAs occurrence among SCAD patients. Results: Our study included 160 (13.45%) SCAD patients with concurrent VAs and 1,030 (86.55%) SCAD patients without concurrent VAs between 2017 and 2020. SCAD patients with concurrent VA were associated with higher rates of early mortality (11.88% vs. 2.04%, p<0.01), prolonged index hospital stay (≥7 days) (31.25% vs. 12.82%, p<0.01), and non-home discharge (21.88% vs. 6.50%, p<0.01). The length of hospital stay was longer in the SCAD with concurrent VAs group (6.93 days vs. 3.73 days, p<0.01), and the median cumulative cost of hospitalization was also higher in this group ($31,450 vs. $14,664, p<0.01). SCAD with concurrent VAs patients also had poorer in-hospital adverse events. In multivariable analysis, the independent predictors for VAs occurrence among SCAD patients were chronic liver disease (aOR: 2.18; 95% CI: 1.15- 4.12, p=0.02) and heart failure (aOR: 3.40; 95%CI: 2.30- 5.02, p<0.01). Conclusion: Ventricular arrhythmias among SCAD patients were associated with poorer in-hospital outcomes. Heart failure and chronic liver disease were the predictors of VAs in SCAD patients. This highlights the importance of closely monitor and aggressive VAs management among these high-risk patients.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [ P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.