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1.
J Am Heart Assoc ; 12(13): e027899, 2023 07 04.
Article in English | MEDLINE | ID: mdl-37345815

ABSTRACT

Background Internet-based participation has the potential to enhance pragmatic and decentralized trials, where representative study populations and generalizability to clinical practice are key. We aimed to study the differences between internet and noninternet/telephone participants in a large remote, pragmatic trial. Methods and Results In a subanalysis of the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) study, we compared internet participants with those who opted for noninternet participation. Study process measures examined included participant characteristics at consent, study medication adherence, and study retention. The clinical outcome examined was a composite of all-cause mortality, hospitalization for myocardial infarction, or hospitalization for stroke. Noninternet participants were older (mean 69.4 versus 67.4 years), more likely to be female (38.9% versus 30.2%), more likely to be Black (27.3% versus 6.0%) or Hispanic (11.1% versus 2.0%), and had a higher number of comorbid conditions. The composite clinical outcome was more than twice as high in noninternet participants. The hazard of nonadherence to the assigned aspirin dosage was 46% higher in noninternet participants than internet participants. Conclusions Noninternet participants differed from internet participants in notable demographic characteristics while having poorer baseline health. Over the course of ADAPTABLE, they also had worse clinical outcomes and greater likelihood of study drug nonadherence. These results suggest that trials focused on internet participation select for younger, healthier participants with a higher proportion of traditionally overrepresented patients. Allowing noninternet participation enhances diversity; however, additional steps may be needed to promote study retention and study medication adherence. Registration Information clinicaltrials.gov. Identifier: NCT02697916.


Subject(s)
Myocardial Infarction , Stroke , Female , Humans , Male , Aspirin/therapeutic use , Internet , Myocardial Infarction/drug therapy , Stroke/epidemiology , Stroke/drug therapy , Aged
2.
Dev Cell ; 56(7): 976-984.e3, 2021 04 05.
Article in English | MEDLINE | ID: mdl-33823136

ABSTRACT

Axon remodeling through sprouting and pruning contributes to the refinement of developing neural circuits. A prominent example is the pruning of developing sensory axons deprived of neurotrophic support, which is mediated by a caspase-dependent (apoptotic) degeneration process. Distal sensory axons possess a latent apoptotic pathway, but a cell body-derived signal that travels anterogradely down the axon is required for pathway activation. The signaling mechanisms that underlie this anterograde process are poorly understood. Here, we show that the tumor suppressor P53 is required for anterograde signaling. Interestingly loss of P53 blocks axonal but not somatic (i.e., cell body) caspase activation. Unexpectedly, P53 does not appear to have an acute transcriptional role in this process and instead appears to act in the cytoplasm to directly activate the mitochondrial apoptotic pathway in axons. Our data support the operation of a cytoplasmic role for P53 in the anterograde death of developing sensory axons.


Subject(s)
Axons/physiology , Sensory Receptor Cells/physiology , Tumor Suppressor Protein p53/physiology , Animals , Axons/enzymology , Axons/metabolism , Caspases/metabolism , Cells, Cultured , Cytoplasm/metabolism , Mice , Protein Domains , Sensory Receptor Cells/enzymology , Sensory Receptor Cells/metabolism , Tumor Suppressor Protein p53/chemistry , Tumor Suppressor Protein p53/genetics , bcl-X Protein/antagonists & inhibitors
3.
Neuron ; 103(3): 412-422.e4, 2019 08 07.
Article in English | MEDLINE | ID: mdl-31221560

ABSTRACT

Selective synaptic and axonal degeneration are critical aspects of both brain development and neurodegenerative disease. Inhibition of caspase signaling in neurons is a potential therapeutic strategy for neurodegenerative disease, but no neuron-specific modulators of caspase signaling have been described. Using a mass spectrometry approach, we discovered that RUFY3, a neuronally enriched protein, is essential for caspase-mediated degeneration of TRKA+ sensory axons in vitro and in vivo. Deletion of Rufy3 protects axons from degeneration, even in the presence of activated CASP3 that is competent to cleave endogenous substrates. Dephosphorylation of RUFY3 at residue S34 appears required for axon degeneration, providing a potential mechanism for neurons to locally control caspase-driven degeneration. Neuronally enriched RUFY3 thus provides an entry point for understanding non-apoptotic functions of CASP3 and a potential target to modulate caspase signaling specifically in neurons for neurodegenerative disease.


Subject(s)
Axons/pathology , Nerve Degeneration/pathology , Nerve Tissue Proteins/physiology , Animals , Axons/enzymology , Caspase 3/physiology , Cells, Cultured , Cytoskeletal Proteins , Enzyme Activation , Ganglia, Spinal/cytology , Ganglia, Spinal/embryology , Mice , Mice, Knockout , Nerve Degeneration/enzymology , Nerve Tissue Proteins/chemistry , Nerve Tissue Proteins/deficiency , Phosphorylation , Protein Processing, Post-Translational , Receptor, trkA/physiology , Sensory Receptor Cells/physiology , Structure-Activity Relationship
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 690-693, 2018 Jul.
Article in English | MEDLINE | ID: mdl-30440490

ABSTRACT

Osteosarcoma is the most common type of bone cancer. The primary means of osteosarcoma diagnosis is through evaluating plain x-rays. Using image analysis techniques, features that clinicians use to diagnose osteosarcoma can be quantified and studied using computer algorithms. In this paper, we classify benign tumor patients and osteosarcoma patients using both image features and metabolomic data. These two types of feature sets are processed with feature selection algorithms - recursive feature elimination and information gain. The selected features are then assessed by two classification models - random forest and support vector machine (SVM). The performances of the two models are evaluated and compared using receiver operating characteristic curves. The random forest classifier outperformed the SVM, with a sensitivity of .92 and a specificity of .78.


Subject(s)
Image Processing, Computer-Assisted , Metabolomics , Osteosarcoma/diagnostic imaging , Support Vector Machine , Algorithms , Humans , Osteosarcoma/classification , ROC Curve , Radiography , Sensitivity and Specificity , X-Rays
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