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Renwick JRM, Preobrazenski N, Giudice MD, Swinton PA, Gurd BJ. Including supramaximal verification reduced uncertainty in VO 2peak response rate. Appl Physiol Nutr Metab 2024; 49:41-51. [PMID: 37611323 DOI: 10.1139/apnm-2023-0137] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Many reports describe using a supramaximal verification phase-exercising at a power output higher than the highest power output recorded during an incremental cardiopulmonary test-to validate VO2max. The impact of verification phases on estimating the proportion of individuals who increased VO2peak in response to high-intensity interval training (HIIT) remains an underexplored area in the individual response literature. This analysis investigated the influence of same-day and separate-day verification phases during repeated measurements (incremental tests-INCR1 and INCR2; incremental tests + supramaximal verification phases-INCR1+ and INCR2+) of VO2peak on typical error (TE) and the proportion of individuals classified as responders (i.e., the response rate) following 4 weeks of HIIT (n = 25) or a no-exercise control period (n = 9). Incorporation of supramaximal verification consistently reduced the standard deviation of individual response, TE, and confidence interval (CI) widths. However, variances were statistically similar across all groups (p > 0.05). Response rates increased when incorporating either one (INCR1 to INCR1+; 24%-48%, p = 0.07) or two (INCR2 to INCR2+; 28%-48%, p = 0.063) supramaximal verification phases. However, response rates remained unchanged when either zero-based thresholds or smallest worthwhile difference response thresholds were used (50% and 90% CIs, all p > 0.05). Supramaximal verification phases reduced random variability in VO2peak response to HIIT. Compared with separate-day testing (INCR2 and INCR2+), the incorporation of a same-day verification (INCR1+) reduced CI widths the most. Researchers should consider using a same-day verification phase to reduce uncertainty and better estimate VO2peak response rate to HIIT.
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Affiliation(s)
- John R M Renwick
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Nicholas Preobrazenski
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON K7L 3N6, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Michael D Giudice
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Paul A Swinton
- School of Health Sciences, Robert Gordon University, Aberdeen AB10 7QE, UK
| | - Brendon J Gurd
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON K7L 3N6, Canada
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Ye H, Fan Z, Chai G, Li G, Wei Z, Hu J, Sheng X, Chen L, Zhu X. Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography. Front Neurosci 2021; 15:653965. [PMID: 34017235 PMCID: PMC8129191 DOI: 10.3389/fnins.2021.653965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 01/15/2021] [Accepted: 04/06/2021] [Indexed: 11/29/2022] Open
Abstract
Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60–145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Guohong Chai
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zixuan Wei
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Nabet BY, Esfahani MS, Moding EJ, Hamilton EG, Chabon JJ, Rizvi H, Steen CB, Chaudhuri AA, Liu CL, Hui AB, Almanza D, Stehr H, Gojenola L, Bonilla RF, Jin MC, Jeon YJ, Tseng D, Liu C, Merghoub T, Neal JW, Wakelee HA, Padda SK, Ramchandran KJ, Das M, Plodkowski AJ, Yoo C, Chen EL, Ko RB, Newman AM, Hellmann MD, Alizadeh AA, Diehn M. Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition. Cell 2020; 183:363-376.e13. [PMID: 33007267 PMCID: PMC7572899 DOI: 10.1016/j.cell.2020.09.001] [Citation(s) in RCA: 189] [Impact Index Per Article: 47.3] [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: 02/14/2020] [Revised: 06/18/2020] [Accepted: 08/28/2020] [Indexed: 12/30/2022]
Abstract
Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.
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Affiliation(s)
- Barzin Y Nabet
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Mohammad S Esfahani
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Everett J Moding
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Emily G Hamilton
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Jacob J Chabon
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Hira Rizvi
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chloe B Steen
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Aadel A Chaudhuri
- Department of Radiation Oncology, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Chih Long Liu
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Angela B Hui
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Diego Almanza
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Program in Cancer Biology, Stanford University, Stanford, CA, USA
| | - Henning Stehr
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Linda Gojenola
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Rene F Bonilla
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Michael C Jin
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Young-Jun Jeon
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Department of Integrative Biotechnology, Sungkyunkwan University, Suwon, Republic of Korea
| | - Diane Tseng
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Cailian Liu
- Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Taha Merghoub
- Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell School of Medicine, New York, NY, USA; Parker Institute for Cancer Immunotherapy at MSK, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joel W Neal
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Heather A Wakelee
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Sukhmani K Padda
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Kavitha J Ramchandran
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Millie Das
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Department of Medicine, VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Andrew J Plodkowski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christopher Yoo
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Emily L Chen
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Ryan B Ko
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Aaron M Newman
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Matthew D Hellmann
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell School of Medicine, New York, NY, USA; Parker Institute for Cancer Immunotherapy at MSK, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Ash A Alizadeh
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
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