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Siddharthan T, Grealis K, Kirkness JP, Ötvös T, Stefanovski D, Tombleson A, Dalzell M, Gonzalez E, Nakrani KB, Wenger D, Lester MG, Richmond BW, Fouras A, Punjabi NM. Quantifying ventilation by X-ray velocimetry in healthy adults. Respir Res 2023; 24:215. [PMID: 37649012 PMCID: PMC10469820 DOI: 10.1186/s12931-023-02517-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/18/2023] [Indexed: 09/01/2023] Open
Abstract
RATIONALE X-ray velocimetry (XV) has been utilized in preclinical models to assess lung motion and regional ventilation, though no studies have compared XV-derived physiologic parameters to measures derived through conventional means. OBJECTIVES To assess agreement between XV-analysis of fluoroscopic lung images and pitot tube flowmeter measures of ventilation. METHODS XV- and pitot tube-derived ventilatory parameters were compared during tidal breathing and with bilevel-assisted breathing. Levels of agreement were assessed using the Bland-Altman analysis. Mixed models were used to characterize the association between XV- and pitot tube-derived values and optimize XV-derived values for higher ventilatory volumes. MEASUREMENTS AND MAIN RESULTS Twenty-four healthy volunteers were assessed during tidal breathing and 11 were reassessed with increased minute ventilation with bilevel-assisted breathing. No clinically significant differences were observed between the two methods for respiratory rate (average Δ: 0.58; 95% limits of agreement: -1.55, 2.71) or duty cycle (average Δ: 0.02; 95% limits of agreement: 0.01, 0.03). Tidal volumes and flow rates measured using XV were lower than those measured using the pitot tube flowmeter, particularly at the higher volume ranges with bilevel-assisted breathing. Under these conditions, a mixed-model based adjustment was applied to the XV-derived values of tidal volume and flow rate to obtain closer agreement with the pitot tube-derived values. CONCLUSION Radiographically obtained measures of ventilation with XV demonstrate a high degree of correlation with parameters of ventilation. If the accuracy of XV were also confirmed for assessing the regional distribution of ventilation, it would provide information that goes beyond the scope of conventional pulmonary function tests or static radiographic assessments.
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Affiliation(s)
- Trishul Siddharthan
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA.
| | - Kyle Grealis
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | | | | | | | - Alex Tombleson
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Molly Dalzell
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Ernesto Gonzalez
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Kinjal Bhatt Nakrani
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | | | - Michael G Lester
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bradley W Richmond
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | | | - Naresh M Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
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Liu Y, Han J, Kong T, Xiao N, Mei Q, Liu J. DriverMP enables improved identification of cancer driver genes. Gigascience 2022; 12:giad106. [PMID: 38091511 PMCID: PMC10716827 DOI: 10.1093/gigascience/giad106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/30/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes. FINDINGS We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis. CONCLUSIONS The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.
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Affiliation(s)
- Yangyang Liu
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Jiyun Han
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Tongxin Kong
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Nannan Xiao
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Qinglin Mei
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
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