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Xian J, Thalwitzer KM, McKee J, Sullivan KR, Brimble E, Fitch E, Toib J, Kaufman MC, deCampo D, Cunningham K, Pierce SR, Goss J, Rigby CS, Syrbe S, Boland M, Prosser B, Fitter N, Ruggiero SM, Helbig I. Delineating clinical and developmental outcomes in STXBP1-related disorders. Brain 2023; 146:5182-5197. [PMID: 38015929 PMCID: PMC10689925 DOI: 10.1093/brain/awad287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/31/2023] [Accepted: 08/18/2023] [Indexed: 11/30/2023] Open
Abstract
STXBP1-related disorders are among the most common genetic epilepsies and neurodevelopmental disorders. However, the longitudinal epilepsy course and developmental end points, have not yet been described in detail, which is a critical prerequisite for clinical trial readiness. Here, we assessed 1281 cumulative patient-years of seizure and developmental histories in 162 individuals with STXBP1-related disorders and established a natural history framework. STXBP1-related disorders are characterized by a dynamic pattern of seizures in the first year of life and high variability in neurodevelopmental trajectories in early childhood. Epilepsy onset differed across seizure types, with 90% cumulative onset for infantile spasms by 6 months and focal-onset seizures by 27 months of life. Epilepsy histories diverged between variant subgroups in the first 2 years of life, when individuals with protein-truncating variants and deletions in STXBP1 (n = 39) were more likely to have infantile spasms between 5 and 6 months followed by seizure remission, while individuals with missense variants (n = 30) had an increased risk for focal seizures and ongoing seizures after the first year. Developmental outcomes were mapped using milestone acquisition data in addition to standardized assessments including the Gross Motor Function Measure-66 Item Set and the Grasping and Visual-Motor Integration subsets of the Peabody Developmental Motor Scales. Quantification of end points revealed high variability during the first 5 years of life, with emerging stratification between clinical subgroups. An earlier epilepsy onset was associated with lower developmental abilities, most prominently when assessing gross motor development and expressive communication. We found that individuals with neonatal seizures or early infantile seizures followed by seizure offset by 12 months of life had more predictable seizure trajectories in early to late childhood compared to individuals with more severe seizure presentations, including individuals with refractory epilepsy throughout the first year. Characterization of anti-seizure medication response revealed age-dependent response over time, with phenobarbital, levetiracetam, topiramate and adrenocorticotropic hormone effective in reducing seizures in the first year of life, while clobazam and the ketogenic diet were effective in long-term seizure management. Virtual clinical trials using seizure frequency as the primary outcome resulted in wide range of trial success probabilities across the age span, with the highest probability in early childhood between 1 year and 3.5 years. In summary, we delineated epilepsy and developmental trajectories in STXBP1-related disorders using standardized measures, providing a foundation to interpret future therapeutic strategies and inform rational trial design.
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Affiliation(s)
- Julie Xian
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kim Marie Thalwitzer
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
- Division of Pediatric Epileptology, Centre for Pediatric and Adolescent Medicine, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Jillian McKee
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katie Rose Sullivan
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Elise Brimble
- Ciitizen Natural History Registry, Invitae, San Francisco, CA 94017, USA
| | - Eryn Fitch
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jonathan Toib
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michael C Kaufman
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
| | - Danielle deCampo
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kristin Cunningham
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Samuel R Pierce
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | | | - Steffen Syrbe
- Division of Pediatric Epileptology, Centre for Pediatric and Adolescent Medicine, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Michael Boland
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA
| | - Benjamin Prosser
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Physiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Nasha Fitter
- Ciitizen Natural History Registry, Invitae, San Francisco, CA 94017, USA
| | - Sarah M Ruggiero
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USA
- Epilepsy and Neurodevelopmental Disorders Center (ENDD), Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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Kather C, Shofer FS, Park JI, Bogen D, Pierce SR, Kording K, Nilan KA, Zhang H, Prosser LA, Johnson MJ. Quantifying interaction with robotic toys in pre-term and full-term infants. Front Pediatr 2023; 11:1153841. [PMID: 37928351 PMCID: PMC10622661 DOI: 10.3389/fped.2023.1153841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/29/2023] [Indexed: 11/07/2023] Open
Abstract
Infants born pre-term are at an increased risk for developmental, behavioral, and motor delay and subsequent disability. When these problems are detected early, clinical intervention can be effective at improving functional outcomes. Current methods of early clinical assessment are resource intensive, require extensive training, and do not always capture infants' behavior in natural play environments. We developed the Play and Neuro Development Assessment (PANDA) Gym, an affordable, mechatronic, sensor-based play environment that can be used outside clinical settings to capture infant visual and motor behavior. Using a set of classification codes developed from the literature, we analyzed videos from 24 pre-term and full-term infants as they played with each of three robotic toys designed to elicit different types of interactions-a lion, an orangutan, and an elephant. We manually coded for frequency and duration of toy interactions such as kicking, grasping, touching, and gazing. Pre-term infants gazed at the toys with similar frequency as full-term infants, but infants born full-term physically engaged more frequently and for longer durations with the robotic toys than infants born pre-term. While we showed we could detect differences between full-term and pre-term infants, further work is needed to determine whether differences seen were primarily due to age, developmental delays, or a combination.
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Affiliation(s)
- Collin Kather
- Rehabilitation Robotics Lab, Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States
| | - Frances S. Shofer
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeong Inn Park
- Rehabilitation Robotics Lab, Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel Bogen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Samuel R. Pierce
- Department of Physical Therapy, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Konrad Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Kathleen A. Nilan
- Division of Neonatology, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Huayan Zhang
- Division of Neonatology, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Neonatology, Guangzhou Women’s and Children’s Medical Center, Guangzhou, China
| | - Laura A. Prosser
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, United States
- Division of Rehabilitation Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Michelle J. Johnson
- Rehabilitation Robotics Lab, Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
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Deo R, Dubin RF, Ren Y, Murthy AC, Wang J, Zheng H, Zheng Z, Feldman H, Shou H, Coresh J, Grams M, Surapaneni AL, Bhat Z, Cohen JB, Rahman M, He J, Saraf SL, Go AS, Kimmel PL, Vasan RS, Segal MR, Li H, Ganz P. Proteomic cardiovascular risk assessment in chronic kidney disease. Eur Heart J 2023; 44:2095-2110. [PMID: 37014015 PMCID: PMC10281556 DOI: 10.1093/eurheartj/ehad115] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/21/2023] [Accepted: 02/16/2023] [Indexed: 04/05/2023] Open
Abstract
AIMS Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. METHODS AND RESULTS Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. CONCLUSION In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.
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Affiliation(s)
- Rajat Deo
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Ruth F Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Yue Ren
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Ashwin C Murthy
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Jianqiao Wang
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haotian Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Harold Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Josef Coresh
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Morgan Grams
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Aditya L Surapaneni
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Zeenat Bhat
- Division of Nephrology, University of Michigan, 5100 Brehm Tower, 1000 Wall Street, Ann Arbor, MI 48105, USA
| | - Jordana B Cohen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
- Renal, Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, 831 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Mahboob Rahman
- Department of Medicine, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Wearn Bldg. 3 Floor. Rm 352, Cleveland, OH 44106, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, SL 18, New Orleans, LA 70112, USA
| | - Santosh L Saraf
- Division of Hematology and Oncology, University of Illinois at Chicago, 1740 West Taylor Street, Chicago, IL 60612, USA
| | - Alan S Go
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA
- Departments of Epidemiology, Biostatistics and Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Section of Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Mark R Segal
- Department of Epidemiology and Biostatistics, University of California, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94143, USA
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Peter Ganz
- Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, 1001 Potrero Avenue, 5G1, San Francisco, CA 94110, USA
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Tutwiler V, Singh J, Litvinov RI, Bassani JL, Purohit PK, Weisel JW. Rupture of blood clots: Mechanics and pathophysiology. Sci Adv 2020; 6:eabc0496. [PMID: 32923647 PMCID: PMC7449685 DOI: 10.1126/sciadv.abc0496] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/02/2020] [Indexed: 05/07/2023]
Abstract
Fibrin is the three-dimensional mechanical scaffold of protective blood clots that stop bleeding and pathological thrombi that obstruct blood vessels. Fibrin must be mechanically tough to withstand rupture, after which life-threatening pieces (thrombotic emboli) are carried downstream by blood flow. Despite multiple studies on fibrin viscoelasticity, mechanisms of fibrin rupture remain unknown. Here, we examined mechanically and structurally the strain-driven rupture of human blood plasma-derived fibrin clots where clotting was triggered with tissue factor. Toughness, i.e., resistance to rupture, quantified by the critical energy release rate (a measure of the propensity for clot embolization) of physiologically relevant fibrin gels was determined to be 7.6 ± 0.45 J/m2. Finite element (FE) simulations using fibrin material models that account for forced protein unfolding independently supported this measured toughness and showed that breaking of fibers ahead the crack at a critical stretch is the mechanism of rupture of blood clots, including thrombotic embolization.
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Affiliation(s)
- Valerie Tutwiler
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jaspreet Singh
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rustem I. Litvinov
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russian Federation
| | - John L. Bassani
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA
| | - Prashant K. Purohit
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA
| | - John W. Weisel
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, USA
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