bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S)
Authors: van Sleuwen Meike , Sun Haoqi , Eckhardt Christine , Neelagiri Anudeepthi , Tesh Ryan A. , Westmeijer Mike , Paixao Luis , Rajan Subapriya , Velpula Krishnamurthy Parimala , Sikka Pooja , Leone Michael J. , Panneerselvam Ezhil , Quadri Syed A. , Akeju Oluwaseun , Kimchi Eyal Y. , Westover M. Brandon 
Published: September 2021

OBJECTIVES:
Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

DESIGN:
Retrospective cohort study.

SETTING:
Single-center tertiary academic medical center.

PATIENTS:
Three-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.

INTERVENTIONS:
None.

MEASUREMENTS AND MAIN RESULTS:
We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.

CONCLUSIONS:
The E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.
Anisocoria and Poor Pupil Reactivity by Quantitative Pupillometry in Patients With Intracranial Pathology
Authors: Prescott Brenton R. , Saglam Hanife , Duskin Jonathan A. , Miller Matthew I. , Thakur Arnav S. , Gholap Eesha A. , Hutch Meghan R. , Smirnakis Stelios M. , Zafar Sahar F. , Dupuis Josée , Benjamin Emelia J. , Greer David M. , Ong Charlene J. 
Published: September 2021

OBJECTIVES:
To describe the prevalence and associated risk factors of new onset anisocoria (new pupil size difference of at least 1 mm) and its subtypes: new onset anisocoria accompanied by abnormal and normal pupil reactivities in patients with acute neurologic injuries.

DESIGN:
We tested the association of patients who experienced new onset anisocoria subtypes with degree of midline shift using linear regression. We further explored differences between quantitative pupil characteristics associated with first-time new onset anisocoria and nonnew onset anisocoria at preceding observations using mixed effects logistic regression, adjusting for possible confounders.

SETTING:
All quantitative pupil observations were collected at two neuro-ICUs by nursing staff as standard of care.

PATIENTS:
We conducted a retrospective two-center study of adult patients with intracranial pathology in the ICU with at least a 24-hour stay and three or more quantitative pupil measurements between 2016 and 2018.

MEASUREMENTS AND MAIN RESULTS:
We studied 221 patients (mean age 58, 41% women). Sixty-three percent experienced new onset anisocoria. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity occurring at any point during hospitalization was significantly associated with maximum midline shift (β = 2.27 per mm; p = 0.01). The occurrence of new onset anisocoria accompanied by objective evidence of normal pupil reactivity was inversely associated with death (odds ratio, 0.34; 95% CI, 0.16–0.71; p = 0.01) in adjusted analyses. Subclinical continuous pupil size difference distinguished first-time new onset anisocoria from nonnew onset anisocoria in up to four preceding pupil observations (or up to 8 hr prior). Minimum pupil reactivity between eyes also distinguished new onset anisocoria accompanied by objective evidence of abnormal pupil reactivity from new onset anisocoria accompanied by objective evidence of normal pupil reactivity prior to first-time new onset anisocoria occurrence.

CONCLUSIONS:
New onset anisocoria occurs in over 60% of patients with neurologic emergencies. Pupil reactivity may be an important distinguishing characteristic of clinically relevant new onset anisocoria phenotypes. New onset anisocoria accompanied by objective evidence of abnormal pupil reactivity was associated with midline shift, and new onset anisocoria accompanied by objective evidence of normal pupil reactivity had an inverse relationship with death. Distinct quantitative pupil characteristics precede new onset anisocoria occurrence and may allow for earlier prediction of neurologic decline. Further work is needed to determine whether quantitative pupillometry sensitively/specifically predicts clinically relevant anisocoria, enabling possible earlier treatments.
Unbiased immune profiling reveals a natural killer cell-peripheral nerve axis in fibromyalgia
Authors: Verma Vivek , Drury Gillian L. , Parisien Marc , Özdağ Acarli Ayşe N. , Al-Aubodah Tho-Alfakar , Nijnik Anastasia , wen Xia , Tugarinov Nicol , Verner Maria , Klares Richie , Linton Alexander , Krock Emerson , Morado Urbina Carlos E. , Winsvold Bendik , Fritsche Lars G. , Fors Egil A. ,   HUNT-All In Pain, Piccirillo Ciriaco , Khoutorsky Arkady , Svensson Camilla I. , Fitzcharles Mary A. , Ingelmo Pablo M. , Bernard Nicole F. , Dupuy Franck P. , Üçeyler Nurcan , Sommer Claudia , King Irah L. , Meloto Carolina B. , Diatchenko Luda 
Published: September 2021

Abstract
The pathophysiology of fibromyalgia syndrome (FMS) remains elusive, leading to a lack of objective diagnostic criteria and targeted treatment. We globally evaluated immune system changes in FMS by conducting multiparametric flow cytometry analyses of peripheral blood mononuclear cells and identified a natural killer (NK) cell decrease in patients with FMS. Circulating NK cells in FMS were exhausted yet activated, evidenced by lower surface expression of CD16, CD96, and CD226 and more CD107a and TIGIT. These NK cells were hyperresponsive, with increased CCL4 production and expression of CD107a when co-cultured with human leukocyte antigen null target cells. Genetic and transcriptomic pathway analyses identified significant enrichment of cell activation pathways in FMS driven by NK cells. Skin biopsies showed increased expression of NK activation ligand, unique long 16–binding protein, on subepidermal nerves of patients FMS and the presence of NK cells near peripheral nerves. Collectively, our results suggest that chronic activation and redistribution of circulating NK cells to the peripheral nerves contribute to the immunopathology associated with FMS.
ANGPTL3 as therapeutic target
Authors: Kersten Sander 
Published: September 2021

Purpose of review
Elevated LDL-C and triglycerides are important risk factors for the development of atherosclerotic cardiovascular disease. Although effective therapies for lipid lowering exist, many people do not reach their treatment targets. In the last two decades, ANGPTL3 has emerged as a novel therapeutic target for lowering plasma LDL-C and triglycerides. Here, an overview of the recent literature on ANGPTL3 is provided, focusing on the therapeutic benefits of inactivation of ANGPTL3 via monoclonal antibodies, antisense oligonucleotides, and other more nascent approaches. In addition, the potential mechanisms by which ANGPTL3 inactivation lowers plasma LDL-C are discussed.

Recent findings
ANGPTL3 is a factor secreted by the liver that inhibits lipoprotein lipase and other lipases via the formation of a complex with the related protein ANGPTL8. Large-scale genetic studies in humans have shown that carriers of loss-of-function variants in ANGPTL3 have lower plasma LDL-C and triglyceride levels, and are at reduced risk of atherosclerotic cardiovascular disease. Clinical studies in patients with different forms of dyslipidemia have demonstrated that inactivation of ANGPTL3 using monoclonal antibodies or antisense oligonucleotides markedly lowers plasma LDL-C and triglyceride levels.

Summary
Anti-ANGPTL3 therapies hold considerable promise for reducing plasma LDL-C and triglycerides in selected patient groups.
FastMix: A Versatile Multi-Omics Data Integration Pipeline for Cell Type-Specific Biomarker Inference
Authors: Zhang Yun , Sun Hao , Mandava Aishwarya , Aevermann Brian D. , Kollmann Tobias R. , Scheuermann Richard H. , Qiu Xing , Qian Yu 
Published: September 2021

AbstractWe developed a novel analytic pipeline -FastMix- to integrate flow cytometry, bulk transcriptomics, and clinical covariates for statistical inference of cell type-specific gene expression signatures.FastMixaddresses the “largep, smalln” problem via a carefully designed linear mixed effects model (LMER), which is applicable for both cross-sectional and longitudinal studies. With a novel moment-based estimator,FastMixruns and converges much faster than competing methods for big data analytics. The pipeline also includes a cutting-edge flow cytometry data analysis method for identifying cell population proportions. Simulation studies showed thatFastMixproduced smaller type I/II errors with more accurate parameter estimation than competing methods. When applied to real transcriptomics and flow cytometry data in two vaccine studies,FastMix-identified cell type-specific signatures were largely consistent with those obtained from the single cell RNA-seq data, with some unique interesting findings.
New insights from GWAS on longitudinal and cross-sectional BMI and related phenotypes in admixed children with Native American and European ancestries
Authors: Vicuña Lucas , Barrientos Esteban , Norambuena Tomás , Alvares Danilo , Gana Juan Cristobal , Leiva Valeria , Mericq Veronica , Meza Cristian , Pereira Ana , Santos José L. , Eyheramendy Susana 
Published: September 2021

AbstractBody-mass index (BMI) is a well-known marker of adiposity across all ages. The genetic architecture of BMI has been thoroughly studied among adults. In contrast, there are a few genome-wide association studies (GWAS) on children. Further, GWAS on children have been performed almost exclusively in Europeans at single ages. We aimed to better understand the genetic architecture of BMI trajectory across ages and how BMI is affected by Native American genetic ancestry. We performed cross-sectional and longitudinal GWAS for BMI-related traits on 904 admixed Chilean children with mostly European and Mapuche Native American genetic ancestry. We focused on BMI and two traits that occur at the minimum of the childhood BMI growth trajectory, namely, age at adiposity rebound (Age-AR) and BMI at adiposity rebound (BMI-AR). We found several variants in the immune geneHLA-DQB3that are strongly associated with BMI at ages 1.5-2.5 years old, but not at other ages. We also identified a variant in the sex-determining geneDMRT1significantly associated with Age-AR (P= 9.8 × 10−9). Further, BMI was significantly higher in Mapuche than in European children at all ages between 5.5 and 16.5 years old, but not before. Finally, Age-AR was significantly lower (P= 0.013) by 1.64 years in the Mapuche children compared with Europeans.
Acquired peripheral nerve injuries associated with severe COVID-19
Authors: Franz Colin K. , Murthy Nikhil K. , Malik George R. , Kwak Jean W. , D’Andrea Dom , Wolfe Alexis R. , Farr Ellen , Stearns Melanie A. , Deshmukh Swati , Tavee Jinny O. , Sun Fang , Swong Kevin N. , Rydberg Leslie , Cotton R. James , Wolfe Lisa F. , Walter James M. , Coleman John M. , Rogers John A. 
Published: September 2021

AbstractWe diagnosed 66 peripheral nerve injuries in 34 patients who survived severe COVID-19. We combine our latest data with published case series re-analyzed here (117 nerve injuries; 58 patients) to provide a comprehensive accounting of lesion sites. The most common are ulnar (25.1%), common fibular (15.8%), sciatic (13.1%), median (9.8%), brachial plexus (8.7%) and radial (8.2%) nerves at sites known to be vulnerable to mechanical loading. Protection of peripheral nerves should be prioritized in the care of COVID-19 patients. To this end, we report proof of concept data of a wearable, wireless pressure sensor to provide real time monitoring in the intensive care unit setting.
Diagnostic surveillance of high-grade gliomas: towards automated change detection using radiology report classification
Authors: Noto Tommaso Di , Atat Chirine , Teiga Eduardo Gamito , Hegi Monika , Hottinger Andreas , Cuadra Meritxell Bach , Hagmann Patric , Richiardi Jonas 
Published: September 2021

AbstractNatural Language Processing (NLP) on electronic health records (EHRs) can be used to monitor the evolution of pathologies over time to facilitate diagnosis and improve decision-making. In this study, we designed an NLP pipeline to classify Magnetic Resonance Imaging (MRI) radiology reports of patients with high-grade gliomas. Specifically, we aimed to distinguish reports indicating changes in tumors between one examination and the follow-up examination (treatment response/tumor progression versus stability). A total of 164 patients with 361 associated reports were retrieved from routine imaging, and reports were labeled by one radiologist. First, we assessed which embedding is more suitable when working with limited data, in French, from a specific domain. To do so, we compared a classic embedding techniques, TF-IDF, to a neural embedding technique, Doc2Vec, after hyperparameter optimization for both. A random forest classifier was used to classify the reports into stable (unchanged tumor) or unstable (changed tumor). Second, we applied the post-hoc LIME explainability tool to understand the decisions taken by the model. Overall, classification results obtained in repeated 5-fold cross-validation with TF-IDF reached around 89% AUC and were significantly better than those achieved with Doc2Vec (Wilcoxon signed-rank test,P= 0.009). The explainability toolkit run on TF-IDF revealed some interesting patterns: first, words indicating change such asprogressionwere rightfully frequent for reports classified as unstable; similarly, words indicating no change such asnotwere frequent for reports classified as stable. Lastly, the toolkit discovered misleading words such asT2which are clearly not directly relevant for the task. All the code used for this study is made available.
Reddit discussions about buprenorphine associated precipitated withdrawal in the era of fentanyl
Authors: Spadaro Anthony , Sarker Abeed , Hogg-Bremer Whitney , Love Jennifer S , O’Donnell Nicole , Nelson Lewis S. , Perrone Jeanmarie 
Published: September 2021

ABSTRACTBackgroundBuprenorphine is an evidence-based treatment for Opioid Use Disorder (OUD). Standard buprenorphine induction requires a period of opioid abstinence to minimize risk of precipitated opioid withdrawal (POW). Our objective was to study the impact of the increasing presence of fentanyl and its analogs in the opioid supply of the United States, on buprenorphine induction and POW, using social media data from Reddit.MethodsThis is a data-driven, mixed methods study of opioid-related forums, called subreddits, on Reddit to analyze posts related to fentanyl, POW, and buprenorphine induction. The posts were collected from seven subreddits using an application programming interface for Reddit. We applied natural language processing to identify subsets of salient posts relevant to buprenorphine induction, and performed manual, qualitative, thematic analyses of them.Results267,136 posts were retrieved from seven subreddits. Fentanyl mentions increased from 3 in 2013 to 3870 in 2020, and POW mentions increased from 2 (2012) to 332 (2020). Manual review of 384 POW-mentioning posts and 106 ‘Bernese method’ (a microdosing induction strategy) mentioning posts revealed common themes and peoples’ experiences. Specifically, presence of fentanyl caused POWs despite long abstinence durations, and alternative induction via microdosing were frequently recommended in peer-to-peer discussions.ConclusionsThis study found that increased social media chatter on Reddit about POW correlated with fentanyl mentions. A subset of posts described microdosing as a self-management strategy to avoid POW. Reddit posts suggest that people are utilizing these strategies to initiate buprenorphine due to challenges arising from fentanyl prevalence in the opioid supply.HighlightsIncrease in mentions of precipitated opioid withdrawal (POW) on Reddit from 2012 to 2021 was closely correlated with the increase in fentanyl mentions.Experiences of precipitated opioid withdrawal (POW) were described by individuals who reported sufficient periods of abstinence by standard buprenorphine induction protocols.People with Opioid Use Disorder (OUD) on Reddit are using and recommending microdosing strategies with buprenorphine to avoid POW.People who used fentanyl report experiencing POW following statistically longer periods of abstinence than people who use heroin.
Polygenic risk of any, metastatic, and fatal prostate cancer in the Million Veteran Program
Authors: Pagadala Meghana S. , Lynch Julie , Karunamuni Roshan , Alba Patrick R. , Lee Kyung Min , Agiri Fatai Y. , Anglin Tori , Carter Hannah , Gaziano J. Michael , Jasuja Guneet Kaur , Deka Rishi , Rose Brent S. , Panizzon Matthew S. , Hauger Richard L. , Seibert Tyler M. 
Published: September 2021

AbstractBackgroundGenetic scores may provide an objective measure of a man’s risk of prostate cancer and thus inform screening decisions. We evaluated whether a polygenic hazard score based on 290 genetic variants (PHS290) is associated with risk of prostate cancer in a diverse population, including Black men, who have higher average risk of prostate cancer death but are often treated as a homogeneous, high-risk groupMethodsThis was a retrospective analysis of Million Veteran Program (MVP), a national, population-based cohort study of United States military veterans conducted 2011-2021. Cox proportional hazards analyses tested for association of genetic and other risk factors (including self-reported race/ethnicity and family history) with age at death from prostate cancer, age at diagnosis of metastatic (nodal or distant) prostate cancer, and age at diagnosis of any prostate cancer.Results590,750 male participants were included. Median age at last follow-up was 69 years. PHS290 was associated with fatal prostate cancer in the full cohort and for each racial/ethnic group (p<10−10). Comparing men in the highest 20% of PHS290 to those in the lowest 20%, the hazard ratio for fatal prostate cancer was 4.42 [95% CI: 3.91-5.02]. When accounting for guideline-recommended risk factors (family history, race/ethnicity), PHS290 remained the strongest independent predictor of any, metastatic, and fatal prostate cancer.ConclusionsPHS290 stratified US veterans of diverse ancestry for lifetime risk of prostate cancer, including metastatic and fatal cancer. Predicting genetic risk of lethal prostate cancer with PHS290 might inform individualized decisions about prostate cancer screening.
Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data
Authors: Zhang Martin Jinye , Hou Kangcheng , Dey Kushal K. , Sakaue Saori , Jagadeesh Karthik A. , Weinand Kathryn , Taychameekiatchai Aris , Rao Poorvi , Pisco Angela Oliveira , Zou James , Wang Bruce , Gandal Michael , Raychaudhuri Soumya , Pasaniuc Bogdan , Price Alkes L. 
Published: September 2021

ABSTRACTGene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution without the need for annotation of individual cells to cell types; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =346K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.
Identification of Common genes and proteins in Alzheimer’s Disease, Multiple Sclerosis and Duchenne Muscular Dystrophy using in-silico methods
Authors: Singh Subhankar Kumar , Chakraborty Souvik , Maiti Tarasankar , Chakraborty Anuska 
Published: September 2021

ABSTRACTAlzheimer’s is a type of dementia symptom that slowly worsens over some time. In its early stages, memory loss is mild, but with late-stage Alzheimer’s, the patient loses the ability to carry on a conversation and respond to their environment. Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) characterized by chronic inflammation, demyelination, gliosis, and neuronal loss. Duchenne muscular dystrophy (DMD) is one of the most severe forms of inherited muscular dystrophies. It is the most common hereditary neuromuscular disease and does not exhibit a predilection for any race or ethnic group. In this study, several gene expression study data were analyzed and there were 557 Differentially Expressed Genes (DEGs) in all three chosen Datasets. A protein-protein interaction network was created using STRING and CytoHubba plug-in was used to identify the top ten genes which are POLR2A, SETD2, EFTUD2, RBM25, PRPF40A, CDK13, BPTF, THOC2, SNRNP70, and SCAF11. Online software Enrichr was used for Gene Ontology and KEGG pathway enrichment analysis to find out the biological process, molecular function, cellular component, and the pathways that are commonly affected in these diseases.