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BACKGROUND AND OBJECTIVES: Although the international community collectively seeks to reduce global temperature rise to less than 1.5°C before 2100, irreversible environmental changes have already occurred, and as the planet warms, these changes will continue to occur. As we witness the effects of a warming planet on human health, it is imperative that neurologists anticipate how the epidemiology and incidence of neurologic disease may change. In this review, we organized our analysis around 3 key themes related to climate change and neurologic health: extreme weather events and temperature fluctuations, emerging neuroinfectious diseases, and pollutant impacts. Across each of these themes, we appraised and reviewed recent literature relevant to neurologic disease and practice. METHODS: Studies were identified using search terms relating to climate change, pollutants, and neurologic disease in PubMed, OVID MEDLINE, EMBASE, PsycInfo, and gray literature. Studies published between 1990 and 2022 were included if they pertained to human incidence or prevalence of disease, were in English, and were relevant to neurologic disease. RESULTS: We identified a total of 364 articles, grouped into the 3 key themes of our study: extreme weather events and temperature fluctuations (38 studies), emerging neuroinfectious diseases (37 studies), and pollutant impacts (289 studies). The included studies highlighted the relationships between neurologic symptom exacerbation and temperature variability, tick-borne infections and warming climates, and airborne pollutants and cerebrovascular disease incidence and severity. DISCUSSION: Temperature extremes and variability both associated with stroke incidence and severity, migraine headaches, hospitalization in patients with dementia, and multiple sclerosis exacerbations. Exposure to airborne pollutants, especially PM2.5 and nitrates, associated with stroke incidence and severity, headaches, dementia risk, Parkinson disease, and MS exacerbation. Climate change has demonstrably expanded favorable conditions for zoonotic diseases beyond traditional borders and poses the risk of disease in new, susceptible populations. Articles were biased toward resource-rich regions, suggesting a discordance between where research occurs and where changes are most acute. As such, 3 key priorities emerged for further study: neuroinfectious disease risk mitigation, understanding the pathophysiology of airborne pollutants on the nervous system, and methods to improve delivery of neurologic care in the face of climate-related disruptions.

The aim of this study was to characterize the body composition of patients undergoing neoadjuvant chemotherapy (NACT) for epithelial ovarian cancer (EOC), identify factors associated with sarcopenia at diagnosis, and evaluate the impact of pretreatment sarcopenia and changes in body composition parameters during therapy on perioperative and disease-related outcomes.Patients undergoing NACT for EOC between 2008 and 2020 were identified. Pre-treatment and post-treatment contrast-enhanced CT scans were reviewed to determine skeletal muscle index (SMI) and visceral adipose tissue (VAT) area at the mid-fourth lumbar vertebral level. SMI and VAT were analyzed for association with clinical and treatment variables.174 patients were identified. Mean pretreatment SMI and VAT were 38.3 cmPatients with lower pretreatment SMI tend to undergo less complex surgery than those with higher SMI despite NACT. Decrease in VAT may be a potential indicator of worse OS. Information on body composition can aid in clinical decision making in patients with EOC.

Myocarditis is a disease caused by inflammation of the heart that can progress to dilated cardiomyopathy, heart failure, and eventually death in many patients. Several etiologies are implicated in the development of myocarditis including autoimmune, drug-induced, infectious, and others. All causes lead to inflammation which causes damage to the myocardium followed by remodeling and fibrosis. This review aims to summarize recent findings in biomarkers for myocarditis and highlight the most promising candidates.Current methods of diagnosing myocarditis, including imaging and endomyocardial biopsy, are invasive, expensive, and often not done early enough to affect progression. Research is being done to find biomarkers of myocarditis that are cost-effective, accurate, and prognostically informative. These biomarkers would allow for earlier screening for myocarditis, as well as earlier treatment, and a better understanding of the disease course for specific patients. Early diagnosis of myocarditis with biomarkers may allow for prompt treatment to improve outcomes in patients.

BACKGROUND: Cardiac amyloidosis (CA) is an infiltrative cardiomyopathy with poor prognosis absent appropriate treatment. Elevated native myocardial T(1) and T(2) have been reported for CA, and tissue characterization by cardiac MRI may expedite diagnosis and treatment. Cardiac Magnetic Resonance Fingerprinting (cMRF) has the potential to enable tissue characterization for CA through rapid, simultaneous T(1) and T(2) mapping. Furthermore, cMRF signal timecourses may provide additional information beyond myocardial T(1) and T(2). METHODS: Nine CA patients and five controls were scanned at 3T using a prospectively gated cMRF acquisition. Two cMRF-based analysis approaches were examined: (1) relaxometric-based linear discriminant analysis (LDA) using native T(1) and T(2), and (2) signal timecourse-based LDA. The Fisher coefficient was used to compare the separability of patient and control groups from both approaches. Leave-two-out cross-validation was employed to evaluate the classification error rates of both approaches. RESULTS: Elevated myocardial T(1) and T(2) was observed in patients vs controls (T(1) 1395±121 vs 1240±36.4 ms, p

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/, and the data are freely available through Open Science Framework (10.17605/OSF.IO/RU4D2)., Measurement(s) regulation of gene expression • sequence feature annotation • lnc_RNA • tissue-specific expression of lncRNA • Expression Quantitative Trait Locus Technology Type(s) digital curation • computational modeling technique Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12827597

Despite significant research efforts, clinical practice for arterial bypass surgery has been stagnant, and engineered grafts continue to face postimplantation challenges. Here, we describe the development and application of a durable small-diameter vascular graft with tailored regenerative capacity. We fabricated small-diameter vascular grafts by electrospinning fibrin tubes and poly(ε-caprolactone) fibrous sheaths, which improved suture retention strength and enabled long-term survival. Using surface topography in a hollow fibrin microfiber tube, we enable immediate, controlled perfusion and formation of a confluent endothelium within 3–4 days in vitro with human endothelial colony-forming cells, but a stable endothelium is noticeable at 4 weeks in vivo. Implantation of acellular or endothelialized fibrin grafts with an external ultrathin poly(ε-caprolactone) sheath as an interposition graft in the abdominal aorta of a severe combined immunodeficient Beige mouse model supports normal blood flow and vessel patency for 24 weeks. Mechanical properties of the implanted grafts closely approximate the native abdominal aorta properties after just 1 week in vivo. Fibrin mediated cellular remodeling, stable tunica intima and media formation, and abundant matrix deposition with organized collagen layers and wavy elastin lamellae. Endothelialized grafts evidenced controlled healthy remodeling with delayed and reduced macrophage infiltration alongside neo vasa vasorum-like structure formation, reduced calcification, and accelerated tunica media formation. Our studies establish a small-diameter graft that is fabricated in less than 1 week, mediates neotissue formation and incorporation into the native tissue, and matches the native vessel size and mechanical properties, overcoming main challenges in arterial bypass surgery.

BackgroundIn single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.FindingsTo accelerate this process, we have developed IKAP—an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, natural killer cells, and monocytes in 2 peripheral blood mononuclear cell datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types, thereby delineating cell identities in a multi-layered ontology.ConclusionsBy tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-sequencing analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-sequencing data.

We have assembled a comprehensivelongnon-codingRNAknowledgebase (lncRNAKB) of 77,199 annotated human lncRNAs (224,286 transcripts) by methodically integrating widely used lncRNAs resources. To facilitate functional characterization of lncRNAs, we employed Genotype-Tissue Expression (GTEx) project to provide tissue-specific gene expression profiles of lncRNAs in 31 solid organ tissues. Additional information includes network analysis to identify co-expressed gene modules to potentially delineate lncRNA function. Tissue-specificity, phylogenetic conservation scores and coding potential for lncRNAs are included. Finally, using whole genome sequencing data from GTEx, expression quantitative trait loci (cis-eQTL) regulated lncRNAs were calculated in all tissues. lncRNAKB is available athttp://www.lncrnakb.org.

In single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by marker genes are two separate steps for investigating cell identity. However, results in clustering greatly affect the ability to differentiate between cell groups. We develop IKAP – an algorithm identifying major cell groups that improves differentiating by tuning parameters for clustering. Using multiple datasets, we demonstrate IKAP improves identification of major cell types and facilitates cell ontology curation.