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Shinn EH, Busch BE, Jasemi N, Lyman CA, Toole JT, Richman SC, Symmans WF, Chavez-MacGregor M, Peterson SK, Broderick G. Network Modeling of Complex Time-Dependent Changes in Patient Adherence to Adjuvant Endocrine Treatment in ER+ Breast Cancer. Front Psychol 2022; 13:856813. [PMID: 35903747 PMCID: PMC9315289 DOI: 10.3389/fpsyg.2022.856813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 06/23/2022] [Indexed: 11/25/2022] Open
Abstract
Early patient discontinuation from adjuvant endocrine treatment (ET) is multifactorial and complex: Patients must adapt to various challenges and make the best decisions they can within changing contexts over time. Predictive models are needed that can account for the changing influence of multiple factors over time as well as decisional uncertainty due to incomplete data. AtlasTi8 analyses of longitudinal interview data from 82 estrogen receptor-positive (ER+) breast cancer patients generated a model conceptualizing patient-, patient-provider relationship, and treatment-related influences on early discontinuation. Prospective self-report data from validated psychometric measures were discretized and constrained into a decisional logic network to refine and validate the conceptual model. Minimal intervention set (MIS) optimization identified parsimonious intervention strategies that reversed discontinuation paths back to adherence. Logic network simulation produced 96 candidate decisional models which accounted for 75% of the coordinated changes in the 16 network nodes over time. Collectively the models supported 15 persistent end-states, all discontinued. The 15 end-states were characterized by median levels of general anxiety and low levels of perceived recurrence risk, quality of life (QoL) and ET side effects. MIS optimization identified 3 effective interventions: reducing general anxiety, reinforcing pill-taking routines, and increasing trust in healthcare providers. Increasing health literacy also improved adherence for patients without a college degree. Given complex regulatory networks’ intractability to end-state identification, the predictive models performed reasonably well in identifying specific discontinuation profiles and potentially effective interventions.
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Affiliation(s)
- Eileen H. Shinn
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Eileen H. Shinn,
| | - Brooke E. Busch
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Neda Jasemi
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cole A. Lyman
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - J. Tory Toole
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Spencer C. Richman
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - William Fraser Symmans
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mariana Chavez-MacGregor
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Susan K. Peterson
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
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Parthasarathy A, Wong NH, Weiss AN, Tian S, Ali SE, Cavanaugh NT, Chinsky TM, Cramer CE, Gupta A, Jha R, Johnson LK, Tuason ED, Klafehn LM, Krishnadas V, Musich RJ, Pfaff JM, Richman SC, Shumway AJ, Hudson AO. SELfies and CELLfies: Whole Genome Sequencing and Annotation of Five Antibiotic Resistant Bacteria Isolated from the Surfaces of Smartphones, An Inquiry Based Laboratory Exercise in a Genomics Undergraduate Course at the Rochester Institute of Technology. J Genomics 2019; 7:26-30. [PMID: 30820259 PMCID: PMC6389494 DOI: 10.7150/jgen.31911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/05/2019] [Indexed: 01/06/2023] Open
Abstract
Are touchscreen devices a public health risk for the transmission of pathogenic bacteria, especially those that are resistant to antibiotics? To investigate this, we embarked on a project aimed at isolating and identifying bacteria that are resistant to antibiotics from the screens of smartphones. Touchscreen devices have become ubiquitous in society, and it is important to evaluate the potential risks they pose towards public health, especially as it pertains to the harboring and transmission of pathogenic bacteria that are resistant to antibiotics. Sixteen bacteria were initially isolated of which five were unique (four Staphylococcus species and one Micrococcus species). The genomes of the five unique isolates were subsequently sequenced and annotated. The genomes were analyzed using in silico tools to predict the synthesis of antibiotics and secondary metabolites using the antibiotics and Secondary Metabolite Analysis SHell (antiSMASH) tool in addition to the presence of gene clusters that denote resistance to antibiotics using the Resistance Gene Identifier (RGI) tool. In vivo analysis was also done to assess resistance/susceptibility to four antibiotics that are commonly used in a research laboratory setting. The data presented in this manuscript is the result of a semester-long inquiry based laboratory exercise in the genomics course (BIOL340) in the Thomas H. Gosnell School of Life Sciences/College of Science at the Rochester Institute of Technology.
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Affiliation(s)
- Anutthaman Parthasarathy
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Narayan H Wong
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Amanda N Weiss
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Susan Tian
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Sara E Ali
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Nicole T Cavanaugh
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Tyler M Chinsky
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Chelsea E Cramer
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Aditya Gupta
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Rakshanda Jha
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Loryn K Johnson
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Elizabeth D Tuason
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Lauren M Klafehn
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Varada Krishnadas
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Ryan J Musich
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Jennifer M Pfaff
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Spencer C Richman
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - Alexandria J Shumway
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
| | - André O Hudson
- The Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester NY, USA
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