Scopus list of publications for November 6, 2022
Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder
(2023) Human Genetics and Genomics Advances, 4 (1), art. no. 100150, .
Kainer, D.a , Templeton, A.R.b , Prates, E.T.a , Jacboson, D.a , Allan, E.R.O.c , Climer, S.d , Garvin, M.R.a e
a Computational Systems Biology, Oak Ridge National Laboratory, Oak Ridge, TN, United States
b Department of Biology, Washington University – St Louis, St. Louis, MO, United States
c University of Nevada Reno, Reno, NV, United States
d Department of Computer Science, University of Missouri, St. Louis, MO, United States
e Williwaw Biosciences, LLC, Clarkston, MI, United States
Abstract
The heritability of autism spectrum disorder (ASD), based on 680,000 families and five countries, is estimated to be nearly 80%, yet heritability reported from SNP-based studies are consistently lower, and few significant loci have been identified with genome-wide association studies. This gap in genomic information may reside in rare variants, interaction among variants (epistasis), or cryptic structural variation (SV) and may provide mechanisms that underlie ASD. Here we use a method to identify potential SVs based on non-Mendelian inheritance patterns in pedigrees using parent-child genotypes from ASD families and demonstrate that they are enriched in ASD-risk genes. Most are in non-coding genic space and are over-represented in expression quantitative trait loci, suggesting that they affect gene regulation, which we confirm with their overlap of differentially expressed genes in postmortem brain tissue of ASD individuals. We then identify an SV in the GRIK2 gene that alters RNA splicing and a regulatory region of the ACMSD gene in the kynurenine pathway as significantly associated with a non-verbal ASD phenotype, supporting our hypothesis that these currently excluded loci can provide a clearer mechanistic understanding of ASD. Finally, we use an explainable artificial intelligence approach to define subgroups demonstrating their use in the context of precision medicine. © 2022 Oak Ridge National Laboratory, The Author(s)
Author Keywords
artificial intelligence; autism spectrum disorder; Genomic structural variation; kynurenine pathway; Mendelian inheritance; missing heritability; precision medicine
Funding details
National Institutes of HealthNIHRF1 AG053303
U.S. Department of EnergyUSDOE
Office of ScienceSCDE-AC05-00OR22725
Oak Ridge National LaboratoryORNL
UT-Battelle
Document Type: Article
Publication Stage: Final
Source: Scopus
Pooled image-base screening of mitochondria with microraft isolation distinguishes pathogenic mitofusin 2 mutations
(2022) Communications Biology, 5 (1), art. no. 1128, .
Yenkin, A.L.a b , Bramley, J.C.a b , Kremitzki, C.L.a b , Waligorski, J.E.a b , Liebeskind, M.J.a b , Xu, X.E.a b , Chandrasekaran, V.D.a b , Vakaki, M.A.a b , Bachman, G.W.a b , Mitra, R.D.a b , Milbrandt, J.D.a b , Buchser, W.J.a b
a Department of Genetics, Washington University School of Medicine, St Louis, MO, United States
b Functional Imaging for Variant Elucidation at the McDonnell Genome Institute, St Louis, MO, United States
Abstract
Most human genetic variation is classified as variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a method termed “Raft-Seq”. We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator Mitofusin 2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen. © 2022, The Author(s).
Document Type: Article
Publication Stage: Final
Source: Scopus
Marijuana use and DNA methylation-based biological age in young adults
(2022) Clinical Epigenetics, 14 (1), art. no. 134, .
Nannini, D.R.a , Zheng, Y.a , Joyce, B.T.a , Gao, T.a , Liu, L.b , Jacobs, D.R., Jr.c , Schreiner, P.c , Liu, C.d , Horvath, S.e f , Lu, A.T.e , Yaffe, K.g , Sidney, S.h , Greenland, P.a , Lloyd-Jones, D.M.a , Hou, L.a
a Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive, Suite 1400, Chicago, IL 60611, United States
b Division of Biostatistics, Washington University, St. Louis, MO, United States
c Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States
d Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
e Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
f Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States
g University of California at San Francisco School of Medicine, San Francisco, CA, United States
h Kaiser Permanente Division of Research, Oakland, CA, United States
Abstract
Background: Marijuana is the third most commonly used drug in the USA and efforts to legalize it for medical and recreational use are growing. Despite the increase in use, marijuana’s effect on aging remains understudied and understanding the effects of marijuana on molecular aging may provide novel insights into the role of marijuana in the aging process. We therefore sought to investigate the association between cumulative and recent use of marijuana with epigenetic age acceleration (EAA) as estimated from blood DNA methylation. Results: A random subset of participants from The Coronary Artery Risk Development in Young Adults (CARDIA) Study with available whole blood at examination years (Y) 15 and Y20 underwent epigenomic profiling. Four EAA estimates (intrinsic epigenetic age acceleration, extrinsic epigenetic age acceleration, PhenoAge acceleration, and GrimAge acceleration) were calculated from DNA methylation levels measured at Y15 and Y20. Ever use and cumulative marijuana-years were calculated from the baseline visit to Y15 and Y20, and recent marijuana use (both any and number of days of use in the last 30 days) were calculated at Y15 and Y20. Ever use of marijuana and each additional marijuana-year were associated with a 6-month (P < 0.001) and a 2.5-month (P < 0.001) higher average in GrimAge acceleration (GAA) using generalized estimating equations, respectively. Recent use and each additional day of recent use were associated with a 20-month (P < 0.001) and a 1-month (P < 0.001) higher GAA, respectively. A statistical interaction between marijuana-years and alcohol consumption on GAA was observed (P = 0.011), with nondrinkers exhibiting a higher GAA (β = 0.21 [95% CI 0.05, 0.36], P = 0.008) compared to heavy drinkers (β = 0.05 [95% CI − 0.09, 0.18], P = 0.500) per each additional marijuana-year. No associations were observed for the remaining EAA estimates. Conclusions: These findings suggest cumulative and recent marijuana use are associated with age-related epigenetic changes that are related to lifespan. These observed associations may be modified by alcohol consumption. Given the increase in use and legalization, these findings provide novel insight on the effect of marijuana use on the aging process as captured through blood DNA methylation. © 2022, The Author(s).
Author Keywords
Aging; Alcohol; CARDIA; Epigenetic age acceleration; Marijuana
Funding details
National Institute on AgingNIAR01AG069120, R21AG063370
National Heart, Lung, and Blood InstituteNHLBI
American Heart AssociationAHA14SFRN20790000, 17SFRN33700278
Northwestern UniversityNUHHSN268201800003I
Kaiser Foundation Research InstituteKFRIHHSN268201800004I
University of MinnesotaUMNHHSN268201800006I
University of Alabama at BirminghamUABHHSN268201800005I, HHSN268201800007I
Document Type: Article
Publication Stage: Final
Source: Scopus
The effect of limb selection methods on gait analysis in Parkinson’s disease
(2022) Parkinsonism and Related Disorders, 104, pp. 81-84.
Baudendistel, S.T.a b , Schmitt, A.C.c , Balthaser, K.C.b , Wade, F.E.b , Hass, C.J.b
a Program in Physical Therapy, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
b Applied Neuromechanics Laboratory, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
c Department of Health, Human Performance & Recreation, University of Arkansas, Fayetteville, AR, United States
Abstract
Introduction: Asymmetry of motor symptoms is a common characteristic of Parkinson’s disease (PD), yet gait outcomes are often reported as limb averages or authors fail to report which limb is being analyzed. This study aimed to investigate how varying limb selection methods may impact statistical comparisons of common gait measures amongst fallers and non-fallers with PD. Methods: Overground walking data was collected on 53 fallers and 117 non-fallers during routine clinical visits. The relationship between limb selection method (left, right, most-affected, least-affected, and limbs averaged) and faller status (faller vs non-faller) on spatiotemporal gait parameters was analyzed using a mixed linear model. Results: Significant interactions between limb selection method and faller status were found for step time variability and swing time variability. Regardless of selection method, it was possible to discern significant differences between fallers and non-fallers. Yet, if researchers only analyze the least-affected limb during gait analysis, the differences between fallers and non-fallers are less apparent. Conclusion: In individuals experiencing uneven laterality of symptoms that affect gait, limb averaging may alter interpretation of statistical findings and mask compensation patterns. This study promotes a refined gait analysis process, particularly in individuals that present with possible asymmetric walking. Including limb selection methods in future studies encourages holistic and transparent analyses within the literature. © 2022 Elsevier Ltd
Author Keywords
Asymmetry; Gait; Parkinson’s disease; Walking
Funding details
National Institutes of HealthNIHT32-NS082128
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Document Type: Article
Publication Stage: Final
Source: Scopus
IoT cloud laboratory: Internet of Things architecture for cellular biology
(2022) Internet of Things (Netherlands), 20, art. no. 100618, .
Parks, D.F.a , Voitiuk, K.a , Geng, J.b , Elliott, M.A.T.a , Keefe, M.G.e , Jung, E.A.b , Robbins, A.b , Baudin, P.V.b , Ly, V.T.b , Hawthorne, N.b , Yong, D.b , Sanso, S.E.d , Rezaee, N.d , Sevetson, J.L.a , Seiler, S.T.a , Currie, R.d , Pollen, A.A.f h , Hengen, K.B.g , Nowakowski, T.J.e f , Mostajo-Radji, M.A.d , Salama, S.R.a c d , Teodorescu, M.b d , Haussler, D.a c d
a Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, United States
b Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, United States
c Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, United States
d UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, United States
e Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, United States
f The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, United States
g Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, United States
h Department of Neurology, University of California San Francisco, San Francisco, CA 94143, United States
Abstract
The Internet of Things (IoT) provides a simple framework to control online devices easily. IoT is now a commonplace tool used by technology companies but is rarely used in biology experiments. IoT can benefit cloud biology research through alarm notifications, automation, and the real-time monitoring of experiments. We developed an IoT architecture to control biological devices and implemented it in lab experiments. Lab devices for electrophysiology, microscopy, and microfluidics were created from the ground up to be part of a unified IoT architecture. The system allows each device to be monitored and controlled from an online web tool. We present our IoT architecture so other labs can replicate it for their own experiments. © 2022 The Author(s)
Author Keywords
Cloud biology; Cloud computing; Electrophysiology; Internet of things; Microfluidics; Microscopy
Funding details
SF 857
National Science FoundationNSFACI-1540112, ACI-1541349, CNS-1730158, NSF 2034037, OAC-1826967
National Institutes of HealthNIHR01MH120295
National Institute of Mental HealthNIMH
National Human Genome Research InstituteNHGRISCR_021353
University of CaliforniaUC
National Defense Science and Engineering GraduateNDSEG00002116, RM1HG011543, T32HG008345
Document Type: Article
Publication Stage: Final
Source: Scopus
Dentate gyrus morphogenesis is regulated by β-catenin function in hem-derived fimbrial glia
(2022) Development (Cambridge, England), 149 (21), .
Parichha, A.a , Datta, D.a , Suresh, V.a , Chatterjee, M.b , Holtzman, M.J.c , Tole, S.a
a Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400005, India
b Amity Institute of Neuropsychology and Neurosciences, Amity University, Noida, 201303, India
c Pulmonary and Critical Care Medicine, Washington University, St. Louis, MO 63110, United States
Abstract
The dentate gyrus, a gateway for input to the hippocampal formation, arises from progenitors in the medial telencephalic neuroepithelium adjacent to the cortical hem. Dentate progenitors navigate a complex migratory path guided by two cell populations that arise from the hem, the fimbrial glia and Cajal-Retzius (CR) cells. As the hem expresses multiple Wnt genes, we examined whether β-catenin, which mediates canonical Wnt signaling and also participates in cell adhesion, is necessary for the development of hem-derived lineages. We report that, in mice, the fimbrial glial scaffold is disorganized and CR cells are mispositioned upon hem-specific disruption of β-catenin. Consequently, the dentate migratory stream is severely affected, and the dentate gyrus fails to form. Using selective Cre drivers, we further determined that β-catenin function is required in the fimbrial glial scaffold, but not in the CR cells, for guiding the dentate migration. Our findings highlight a primary requirement for β-catenin for the organization of the fimbrial scaffold and a secondary role for this factor in dentate gyrus morphogenesis. © 2022. Published by The Company of Biologists Ltd.
Author Keywords
Cajal-Retzius cells; Dentate gyrus; Dentate gyrus morphogenesis; Fimbrial scaffold; Hem-derived fimbrial glia; Mouse; β-Catenin
Document Type: Article
Publication Stage: Final
Source: Scopus
Autosomal dominant and sporadic late onset Alzheimer’s disease share a common in vivo pathophysiology
(2022) Brain: A Journal of Neurology, 145 (10), pp. 3594-3607.
Morris, J.C.a , Weiner, M.b , Xiong, C.c , Beckett, L.d , Coble, D.c , Saito, N.d , Aisen, P.S.e , Allegri, R.f , Benzinger, T.L.S.g , Berman, S.B.h , Cairns, N.J.i , Carrillo, M.C.j , Chui, H.C.e , Chhatwal, J.P.k , Cruchaga, C.l , Fagan, A.M.a , Farlow, M.m , Fox, N.C.n , Ghetti, B.o , Goate, A.M.p , Gordon, B.A.g , Graff-Radford, N.q , Day, G.S.q , Hassenstab, J.a , Ikeuchi, T.r , Jack, C.R.s , Jagust, W.J.t , Jucker, M.u v , Levin, J.w , Massoumzadeh, P.g , Masters, C.L.x , Martins, R.y , McDade, E.a , Mori, H.z , Noble, J.M.aa , Petersen, R.C.ab , Ringman, J.M.e , Salloway, S.ac , Saykin, A.J.ad , Schofield, P.R.ae , Shaw, L.M.af , Toga, A.W.ag , Trojanowski, J.Q.ah , Vöglein, J.ai , Weninger, S.aj , Bateman, R.J.a , Buckles, V.D.a
a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
b Department of Radiology, University of California at San Francisco, San Francisco, CA, United States
c Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
d Department of Public Health Sciences, School of Medicine, University of California; Davis, Davis, CA, USA
e Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
f Department of Cognitive Neurology, Neuropsychology and Neuropsychiatry, Institute for Neurological Research (FLENI)Buenos Aires, Argentina
g Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
h Department of Neurology and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, PA, United States
i College of Medicine and Health and the Living Systems Institute, University of Exeter, Exeter, United Kingdom
j Alzheimer’s Association, Chicago, IL, United States
k Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
l Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
m Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States
n Department of Neurodegenerative Disease and UK Dementia Research Institute, UCL Institute of Neurology, London, United Kingdom
o Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
p Ronald M. Loeb Center for Alzheimer’s Disease, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
q Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
r Department of Molecular Genetics, Brain Research Institute, Niigata UniversityNiigata, Japan
s Department of Radiology, Mayo Clinic, Rochester, MN, United States
t Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
u Cell Biology of Neurological Diseases Group, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
v Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
w DZNE Munich, Munich Cluster of Systems Neurology (SyNergy) and Ludwig-Maximilians-Universität, Munich, Germany
x Florey Institute, University of Melbourne, Melbourne, Australia
y Sir James McCusker Alzheimer’s Disease Research Unit, Edith Cowan University, Nedlands, Australia
z Department of Neuroscience, Osaka City University Medical School, Japan
aa Department of Neurology, Taub Institute for Research on Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
ab Department of Neurology, Mayo Clinic, Rochester, MN, United States
ac Department of Neurology, Butler Hospital and Alpert Medical School of Brown University, Providence, RI 02906, United States
ad Department of Radiology and Imaging Sciences and the Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
ae Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, Australia
af Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
ag Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
ah Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
ai German Center for Neurodegenerative Diseases (DZNE) and Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
aj Cambridge, MA, United States
Abstract
The extent to which the pathophysiology of autosomal dominant Alzheimer’s disease corresponds to the pathophysiology of ‘sporadic’ late onset Alzheimer’s disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer’s disease to late onset Alzheimer’s disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer’s disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer’s Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer’s disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer’s disease and late onset Alzheimer’s disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer’s disease than in late onset Alzheimer’s disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer’s disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer’s disease and late onset Alzheimer’s disease, supporting a shared pathobiological construct. © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Author Keywords
Alzheimer pathophysiology; biomarkers; rates of change
Document Type: Article
Publication Stage: Final
Source: Scopus
The use of virtual tools in narrowing the impact of health disparities in neurology
(2022) Frontiers in Pediatrics, 10, art. no. 1028833, .
Le Pichon, J.-B.a , Horton, S.a , Abdelmoity, O.b , Hoffman, M.A.c , Cramer, E.d , Kishk, N.e , Hamada, S.f , Abdelmoity, A.a
a Division of Neurology, Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO, United States
b Washington University at St. Louis, Saint Louis, MO, United States
c Department of Pediatrics, Children’s Mercy Research Institute, Kansas City, MO, United States
d Division of Health Services / Outcomes Research, Children’s Mercy Research Institute, Kansas City, MO, United States
e Department of Neurology, Cairo University, Giza, Egypt
f Department of Neurosurgery, Ain Shams University, Cairo, Egypt
Abstract
The concept of Epilepsy Treatment Gap (ETG) refers to the proportion of people with epilepsy who are not being appropriately treated. The ETG in the USA approaches 10%, with historically underserved populations and rural populations disproportionately affected. The ETG in Low-and Middle-Income Countries (LMIC) is reported to be 5–10 times higher than in high-income countries. The growing availability of reliable internet access offers a unique opportunity to provide better care to children and adults with epilepsy. In this paper we explore various telehealth (TH) initiatives that have leveraged the availability of easy and free access to an internet connection in reducing the ETG in underserved regions of the world. We describe several interventions targeted to reach patients and providers in rural areas of the United States and in LMIC. First, we examine initiatives that were developed to improve patient access to coordinated care and education regarding epilepsy and seizures. Next, we describe an intervention designed to improve knowledge of epilepsy diagnosis and treatment for providers in LMIC. We conclude with a brief overview of the use of virtual tools in diminishing the ETG. 2022 Le Pichon, Horton, Abdelmoity, Hoffman, Cramer, Kishk, Hamada and Abdelmoity.
Author Keywords
education; epilpesy; neurology; telehealth; treatment gap; virtual medicine
Funding details
Health Resources and Services AdministrationHRSAH98MC332390100
Document Type: Article
Publication Stage: Final
Source: Scopus
A Scalable, Cell-based Method for the Functional Assessment of Ube3a Variants
(2022) Journal of Visualized Experiments: JoVE, (188), .
Stelzer, J.A.a , Yi, J.J.b
a Department of Neuroscience, Washington University School of Medicine
b Department of Neuroscience, Washington University School of Medicine;
Abstract
The increased use of sequencing in medicine has identified millions of coding variants in the human genome. Many of these variants occur in genes associated with neurodevelopmental disorders, but the functional significance of the vast majority of variants remains unknown. The present protocol describes the study of variants for Ube3a, a gene that encodes an E3 ubiquitin ligase linked to both autism and Angelman syndrome. Duplication or triplication of Ube3a is strongly linked to autism, whereas its deletion causes Angelman syndrome. Thus, understanding the valence of changes in UBE3A protein activity is important for clinical outcomes. Here, a rapid, cell-based method that pairs Ube3a variants with a Wnt pathway reporter is described. This simple assay is scalable and can be used to determine the valence and magnitude of activity changes in any Ube3a variant. Moreover, the facility of this method allows the generation of a wealth of structure-function information, which can be used to gain deep insights into the enzymatic mechanisms of UBE3A.
Document Type: Article
Publication Stage: Final
Source: Scopus
Age-related Huntington’s disease progression modeled in directly reprogrammed patient-derived striatal neurons highlights impaired autophagy
(2022) Nature Neuroscience, .
Oh, Y.M.a , Lee, S.W.a , Kim, W.K.a , Chen, S.a , Church, V.A.a , Cates, K.a , Li, T.a b , Zhang, B.a b , Dolle, R.E.c , Dahiya, S.d , Pak, S.C.e , Silverman, G.A.e , Perlmutter, D.H.e , Yoo, A.S.a b
a Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States
b Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, United States
c Department of Biochemistry, Washington University School of Medicine, St. Louis, MO, United States
d Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
e Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Huntington’s disease (HD) is an inherited neurodegenerative disorder with adult-onset clinical symptoms, but the mechanism by which aging drives the onset of neurodegeneration in patients with HD remains unclear. In this study we examined striatal medium spiny neurons (MSNs) directly reprogrammed from fibroblasts of patients with HD to model the age-dependent onset of pathology. We found that pronounced neuronal death occurred selectively in reprogrammed MSNs from symptomatic patients with HD (HD-MSNs) compared to MSNs derived from younger, pre-symptomatic patients (pre-HD-MSNs) and control MSNs from age-matched healthy individuals. We observed age-associated alterations in chromatin accessibility between HD-MSNs and pre-HD-MSNs and identified miR-29b-3p, whose age-associated upregulation promotes HD-MSN degeneration by impairing autophagic function through human-specific targeting of the STAT3 3′ untranslated region. Reducing miR-29b-3p or chemically promoting autophagy increased the resilience of HD-MSNs against neurodegeneration. Our results demonstrate miRNA upregulation with aging in HD as a detrimental process driving MSN degeneration and potential approaches for enhancing autophagy and resilience of HD-MSNs. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
Funding details
National Institute on AgingNIAR01NS107488
National Institute of Neurological Disorders and StrokeNINDS
Hereditary Disease FoundationHDFRF1AG056296
Cure Alzheimer’s FundCAF
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Inferring the dynamical effects of stroke lesions through whole-brain modeling
(2022) NeuroImage: Clinical, 36, art. no. 103233, .
Idesis, S.a , Favaretto, C.b c , Metcalf, N.V.d , Griffis, J.C.d , Shulman, G.L.d e , Corbetta, M.b c d e f , Deco, G.a g
a Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, Catalonia, Barcelona, 08005, Spain
b Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, Padova, 35129, Italy
c Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, Padova, 35128, Italy
d Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, United States
e Department of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, United States
f VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, Padova, 35129, Italy
g Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, Catalonia, Barcelona, 08010, Spain
Abstract
Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions. © 2022 The Authors
Author Keywords
Dynamical effects; Generative model; Stroke; Structural disconnection; Whole-brain model
Funding details
55403
CUP C94I20000420007
MSCA-ITN-ETNH2020-860563
MART_ECCELLENZA18_01
PID2019-105772GB-I00/AEI/10.13039/501100011033
Fondazione Cassa di Risparmio di Padova e Rovigo
Ministerio de Ciencia, Innovación y UniversidadesMCIUANR-17-HBPR-0001
Ministero della SaluteRF-2008 -12366899
Ministero dell’Istruzione, dell’Università e della RicercaMIUR
Fundação Bial361/18, RF-2019-12369300
Document Type: Article
Publication Stage: Final
Source: Scopus
Ignoring the Unknown: Attentional Suppression of Unpredictable Visual Distraction
(2022) Journal of Experimental Psychology: Human Perception and Performance, .
Ma, X., Abrams, R.A.
Department of Psychological and Brain Sciences, Washington University in St. Louis, United States
Abstract
Recent findings have shown that people are capable of proactively inhibiting salient visual distractors in a scene when they know the color of the distractor, enhancing efficient search. Investigations of this suppression effect have concluded that it is not possible to suppress a distractor of an unknown color, implying a mechanism that operates only on a first-order, feature-specific level. However, with a modification to the search task, we show here for the first time that people can indeed suppress salient uniquely colored distractors even when not knowing their color in advance. The task requires participants to search for the most prevalent of several shapes in the display. In two experiments the presence of an unpredictable-color singleton facilitated search. An experiment with briefly presented probes confirmed proactive prevention of capture by the distractor. The results reveal a second-order or global-salience-based suppressivemechanismthat facilitates visual processing. © 2022 American Psychological Association
Author Keywords
Attentional capture; Attentional suppression; Visual attention; Visual search
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Wulf Hanson, S.a , Abbafati, C.b , Aerts, J.G.c , Al-Aly, Z.d e , Ashbaugh, C.a , Ballouz, T.f , Blyuss, O.g h , Bobkova, P.i , Bonsel, G.j , Borzakova, S.k l , Buonsenso, D.m n , Butnaru, D.o , Carter, A.a , Chu, H.p , De Rose, C.m , Diab, M.M.q r , Ekbom, E.s , El Tantawi, M.t , Fomin, V.u , Frithiof, R.v , Gamirova, A.w , Glybochko, P.V.x , Haagsma, J.A.y , Haghjooy Javanmard, S.z , Hamilton, E.B.a , Harris, G.aa , Heijenbrok-Kal, M.H.ab ac , Helbok, R.ad , Hellemons, M.E.c , Hillus, D.ae , Huijts, S.M.af , Hultström, M.v ag , Jassat, W.ah , Kurth, F.ai aj , Larsson, I.-M.v , Lipcsey, M.v , Liu, C.ak , Loflin, C.D.aa , Malinovschi, A.al , Mao, W.q am , Mazankova, L.an , McCulloch, D.ao , Menges, D.f , Mohammadifard, N.ap , Munblit, D.h aq , Nekliudov, N.A.w , Ogbuoji, O.am , Osmanov, I.M.k ar , Peñalvo, J.L.as at , Petersen, M.S.au av , Puhan, M.A.f aw , Rahman, M.ax , Rass, V.ad , Reinig, N.a , Ribbers, G.M.ab , Ricchiuto, A.ay , Rubertsson, S.v az , Samitova, E.an ar , Sarrafzadegan, N.ap ba , Shikhaleva, A.i , Simpson, K.E.a , Sinatti, D.m , Soriano, J.B.bb bc , Spiridonova, E.w , Steinbeis, F.ae , Svistunov, A.A.x , Valentini, P.m , Van De Water, B.J.bd be , Van Den Berg-Emons, R.ab , Wallin, E.v , Witzenrath, M.ai bf , Wu, Y.a , Xu, H.bg , Zoller, T.ae , Adolph, C.bh bi , Albright, J.a , Amlag, J.O.a , Aravkin, A.Y.a bj bk , Bang-Jensen, B.L.a , Bisignano, C.a , Castellano, R.a , Castro, E.a , Chakrabarti, S.a bl , Collins, J.K.a , Dai, X.a bk , Daoud, F.a , Dapper, C.a , Deen, A.a , Duncan, B.B.bm , Erickson, M.a , Ewald, S.B.a , Ferrari, A.J.a bn , Flaxman, A.D.a bk , Fullman, N.a , Gamkrelidze, A.bo , Giles, J.R.a , Guo, G.a , Hay, S.I.a bk , He, J.a , Helak, M.a , Hulland, E.N.a bl , Kereselidze, M.bo , Krohn, K.J.a , Lazzar-Atwood, A.a , Lindstrom, A.bn bp , Lozano, R.a bk , Malta, D.C.bq , Månsson, J.a , Mantilla Herrera, A.M.bn br , Mokdad, A.H.a bk , Monasta, L.bs , Nomura, S.bt bu , Pasovic, M.a , Pigott, D.M.a bk , Reiner, R.C., Jr.a bk , Reinke, G.a , Ribeiro, A.L.P.bv bw , Santomauro, D.F.a bn bx , Sholokhov, A.a , Spurlock, E.E.a by , Walcott, R.bz , Walker, A.a , Wiysonge, C.S.ca cb , Zheng, P.a bk , Bettger, J.P.cc , Murray, C.J.L.a bk , Vos, T.a bk
a Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
b Department of Juridical and Economic Studies, La Sapienza University, Rome, Italy
c Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
d John T. Milliken Department of Internal Medicine, Washington University in St Louis, St Louis, MO, United States
e Clinical Epidemiology Center, US Department of Veterans Affairs, St Louis, MO, United States
f Epidemiology, Biostatistics, and Prevention Institute, University of Zürich, Zurich, Switzerland
g Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
h Department of Pediatrics and Pediatric Infectious Diseases, I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
i Clinical Medicine (Pediatric Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
j EuroQol Research Foundation, Rotterdam, Netherlands
k Pirogov Russian National Research Medical University, Moscow, Russian Federation
l Research Institute for Healthcare Organization and Medical Management, Moscow Healthcare Department, Moscow, Russian Federation
m Department of Woman and Child Health and Public Health, Agostino Gemelli University Polyclinic IRCCS, Rome, Italy
n Global Health Research Institute, Catholic University of Sacred Heart, Rome, Italy
o I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
q Center for Policy Impact in Global Health, Duke University, Durham, NC, United States
r Department of Surgery, Duke University, Durham, NC, United States
s Uppsala University Hospital, Uppsala, Sweden
t Pediatric Dentistry and Dental Public Health Department, Alexandria University, Alexandria, Egypt
u Rector’s Office, I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
v Department of Surgical Sciences, Anesthesiology, and Intensive Care Medicine, Uppsala University, Uppsala, Sweden
w Clinical Medicine (General Medicine Profile), I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
x Administration Department, I. M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
y Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
z Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
aa School of Nursing, Duke University, Durham, NC, United States
ab Department of Rehabilitation Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
ac Neurorehabilitation, Rijndam Rehabilitation, Rotterdam, Netherlands
ad Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
ae Department of Infectious Diseases and Respiratory Medicine, Charité Medical University Berlin, Berlin, Germany
af Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
ag Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
ah Department of Public Health Surveillance and Response, National Institute for Communicable Diseases, Johannesburg, South Africa
ai Department of Infectious Diseases and Respiratory Medicine, Charité University Medical Center Berlin, Berlin, Germany
aj Department of Clinical Research and Tropical Medicine, Bernhard-Nocht Institute of Tropical Medicine, Hamburg, Germany
ak Department of Epidemiology, Harvard University, Boston, MA, United States
al Department of Medical Sciences, Uppsala University, Uppsala, Sweden
am Duke Global Health Institute, Duke University, Durham, NC, United States
an Russian Medical Academy of Continuous Professional Education, Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation
ao Department of Medicine, University of Washington, Seattle, United States
ap Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
aq National Heart and Lung Institute, Imperial College London, London, United Kingdom
ar ZA Bashlyaeva Children’s Municipal Clinical Hospital, Moscow, Russian Federation
as Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
at Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States
au Department of Occupational Medicine and Public Health, Faroese Hospital System, Torshavn, Faroe Islands
av Centre of Health Science, University of Faroe Islands, Torshavn, Faroe Islands
aw Department of Epidemiology, Johns Hopkins University, Baltimore, MD, United States
ax Department of Internal Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
ay Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
az Department of Surgical Sciences, Hedenstierna Laboratory, Uppsala University, Uppsala, Sweden
ba School of Population and Public Health, University of British Columbia, Vancouver, Canada
bb Hospital Universitario de la Princesa, Madrid, Spain
bc Centro de Investigación Biomédica en Red Enfermedades Respiratorias, Center for Biomedical Research in Respiratory Diseases Network, Madrid, Spain
bd Department of Global Health and Social Medicine, Harvard University, Boston, MA, United States
be Nursing and Midwifery Department, Seed Global Health, Boston, MA, United States
bf German Center for Lung Research, Berlin, Germany
bg Department of Family Medicine and Community Health, Duke University, Durham, NC, United States
bh Department of Political Science, University of Washington, Seattle, United States
bi Center for Statistics and the Social Sciences, University of Washington, Seattle, United States
bj Department of Applied Mathematics, University of Washington, Seattle, United States
bk Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, United States
bl Department of Global Health, University of Washington, Seattle, United States
bm Postgraduate Program in Epidemiology, Federal University of Rio Grande, Porto Alegre, Brazil
bn School of Public Health, University of Queensland, Brisbane, Australia
bo National Center for Disease Control and Public Health, Tbilisi, Georgia
bp School of Public Health, Queensland Centre for Mental Health Research, Wacol, Australia
bq Department of Maternal and Child Nursing and Public Health, Federal University of Minas Gerais, Belo Horizonte, Brazil
br West Moreton Hospital Health Services, Queensland Centre for Mental Health Research, Wacol, Australia
bs Clinical Epidemiology and Public Health Research Unit, Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy
bt Department of Health Policy and Management, Keio University, Tokyo, Japan
bu Department of Global Health Policy, University of Tokyo, Tokyo, Japan
bv Department of Internal Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
bw Centre of Telehealth, Federal University of Minas Gerais, Belo Horizonte, Brazil
bx Policy and Epidemiology Group, Queensland Centre for Mental Health Research, Wacol, Australia
by Department of Social and Behavioral Sciences, School of Public Health, Yale University, New Haven, CT, United States
bz Evans School of Public Policy and Governance, University of Washington, Seattle, United States
ca Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
cb HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
cc Department of Orthopedic Surgery, Duke University, Durham, NC, United States
Abstract
Importance: Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID). Objective: To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration. Design, Setting, and Participants: Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10501 hospitalized individuals and 42891 nonhospitalized individuals), the 10 collaborating cohort studies (10526 and 1906), and the 2 US electronic medical record databases (250928 and 846046). Data collection spanned March 2020 to January 2022. Exposures: Symptomatic SARS-CoV-2 infection. Main Outcomes and Measures: Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age. Results: A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months. Conclusions and Relevance: This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.. © 2022 American Medical Association. All rights reserved.
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Customizable, wireless and implantable neural probe design and fabrication via 3D printing
(2022) Nature Protocols, .
Parker, K.E.a b c d , Lee, J.e , Kim, J.R.a b c d , Kawakami, C.f , Kim, C.Y.e , Qazi, R.e , Jang, K.-I.g , Jeong, J.-W.e h , McCall, J.G.a b c d
a Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO, United States
b Department of Pharmaceutical and Administrative Sciences, University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO, United States
c Center for Clinical Pharmacology, University of Health Sciences and Pharmacy in St. Louis and Washington University School of Medicine, St. Louis, MO, United States
d Washington University Pain Center, Washington University in St. Louis, St. Louis, MO, United States
e School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
f Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology, Toyohashi, Japan
g Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
h KAIST Institute for Health Science and Technology, Daejeon, South Korea
Abstract
This Protocol Extension describes the low-cost production of rapidly customizable optical neural probes for in vivo optogenetics. We detail the use of a 3D printer to fabricate minimally invasive microscale inorganic light-emitting-diode-based neural probes that can control neural circuit activity in freely behaving animals, thus extending the scope of two previously published protocols describing the fabrication and implementation of optoelectronic devices for studying intact neural systems. The 3D-printing fabrication process does not require extensive training and eliminates the need for expensive materials, specialized cleanroom facilities and time-consuming microfabrication techniques typical of conventional manufacturing processes. As a result, the design of the probes can be quickly optimized, on the basis of experimental need, reducing the cost and turnaround for customization. For example, 3D-printed probes can be customized to target multiple brain regions or scaled up for use in large animal models. This protocol comprises three procedures: (1) probe fabrication, (2) wireless module preparation and (3) implantation for in vivo assays. For experienced researchers, neural probe and wireless module fabrication requires ~2 d, while implantation should take 30–60 min per animal. Time required for behavioral assays will vary depending on the experimental design and should include at least 5 d of animal handling before implantation of the probe, to familiarize each animal to their handler, thus reducing handling stress that may influence the result of the behavioral assays. The implementation of customized probes improves the flexibility in optogenetic experimental design and increases access to wireless probes for in vivo optogenetic research. © 2022, Springer Nature Limited.
Funding details
National Institutes of HealthNIHR01NS117899, R21DA055047
Brain and Behavior Research FoundationBBRF
National Alliance for Research on Schizophrenia and DepressionNARSADYI – 28565
National Research Foundation of KoreaNRFNRF-2020M3A9G8018572, NRF-2021R1A2C4001483
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Perinatal oxycodone exposure causes long-term sex-dependent changes in weight trajectory and sensory processing in adult mice
(2022) Psychopharmacology, .
Minakova, E.a , Mikati, M.O.b c d e f , Madasu, M.K.d e f , Conway, S.M.d e f , Baldwin, J.W.c g , Swift, R.G.b h , McCullough, K.B.b h , Dougherty, J.D.b h i , Maloney, S.E.b i , Al-Hasani, R.c d e f
a Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
b Department of Psychiatry, Washington University School of Medicine, Campus Box 8232, 660 South Euclid Avenue, St. Louis, MO 63110-1093, United States
c Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, United States
d Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
e Washington University Pain Management Center, Washington University School of Medicine, St. Louis, MO, United States
f Center for Clinical Pharmacology, University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO, United States
g Department of Biology, Washington University School of Medicine, St. Louis, MO, United States
h Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
i Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Rationale: In utero opioid exposure is associated with lower weight and a neonatal opioid withdrawal syndrome (NOWS) at birth, along with longer-term adverse neurodevelopmental outcomes and mood disorders. While NOWS is sometimes treated with continued opioids, clinical studies have not addressed if long-term neurobehavioral outcomes are worsened with continued postnatal exposure to opioids. In addition, pre-clinical studies comparing in utero only opioid exposure to continued post-natal opioid administration for withdrawal mitigation are lacking. Objectives: Here, we sought to understand the impact of continued postnatal opioid exposure on long term behavioral consequences. Methods: We implemented a rodent perinatal opioid exposure model of oxycodone (Oxy) exposure that included Oxy exposure until birth (short Oxy) and continued postnatal opioid exposure (long Oxy) spanning gestation through birth and lactation. Results: Short Oxy exposure was associated with a sex-specific increase in weight gain trajectory in adult male mice. Long Oxy exposure caused an increased weight gain trajectory in adult males and alterations in nociceptive processing in females. Importantly, there was no evidence of long-term social behavioral deficits, anxiety, hyperactivity, or memory deficits following short or long Oxy exposure. Conclusions: Our findings suggest that offspring with prolonged opioid exposure experienced some long-term sequelae compared to pups with opioid cessation at birth. These results highlight the potential long-term consequences of opioid administration as a mitigation strategy for clinical NOWS symptomology and suggest alternatives should be explored. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Author Keywords
Neonatal opioid withdrawal syndrome; Nociceptive processing; Opioid; Oxycodone; Sex differences
Funding details
National Institutes of HealthNIH
National Center for Advancing Translational SciencesNCATSULITR002345
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHDP50HD103525
Washington University School of Medicine in St. LouisWUSM20–186-9770
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Treatment preference changes after exposure to treatment in adults with chronic low back pain
(2022) PM and R, .
Lanier, V.M.a b , Lohse, K.R.a c , Hooker, Q.L.a , Francois, S.J.a , van Dillen, L.R.a b
a Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States
b Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO, United States
c Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Background: Patients’ pretreatment preferences can influence outcomes of nonpharmacologic treatments for musculoskeletal pain. Less is known about how patients’ treatment preferences change following exposure to treatment. Objective: To examine the effect of exposure to treatment and change in disability and pain on treatment preference ratings of two exercise-based treatments for people with chronic low back pain (LBP). Design: Secondary analysis of a subsample of participants from a randomized clinical trial. Setting: Academic research setting. Participants: Individuals with chronic LBP (n = 83). Interventions: 6 weekly sessions of motor skill training (MST) or strength and flexibility exercise (SFE). Main Outcome Measures: Prior to treatment, participants completed a treatment preference assessment measure (TPA) describing MST and SFE. Participants rated four attributes (effectiveness, acceptability/logicality, suitability/appropriateness, convenience) of each treatment on a 5-point Likert scale (0–4) with higher scores indicating higher ratings. An overall preference rating was calculated as the mean of the attribute ratings. The TPA was administered 12 months post treatment to reassess participants’ ratings of the treatment they received. Results: Participants who received MST rated their preference for MST higher 12 months post treatment and participants who received SFE rated their preference for SFE lower. Smaller improvements (to worsening) in pain were associated with a reduction in preference ratings in the SFE group, whereas the MST group generally increased their ratings regardless of pain. Changes in disability were not related to changes in preference ratings. Conclusions: Participants changed their preference ratings of two exercise-based treatments for LBP after exposure to the treatment. Participants who received the less familiar MST viewed this treatment more favorably 12 months post treatment, and this change was less contingent on changes in disability/pain than for participants in the SFE group. Assessing preference ratings at various times during treatment is crucial to understand a person’s preference for and perceptions of a treatment. © 2022 American Academy of Physical Medicine and Rehabilitation.
Funding details
National Institutes of HealthNIH
National Cancer InstituteNCIP30 CA091842
National Institute of Child Health and Human DevelopmentNICHD
National Center for Medical Rehabilitation ResearchNCMRRR01 HD047709, TL1 TR002344
Foundation for Physical TherapyFPT
Alvin J. Siteman Cancer Center
Document Type: Article
Publication Stage: Article in Press
Source: Scopus