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Albricker ACL, Freire CMV, Santos SND, Alcantara MLD, Cantisano AL, Porto CLL, Amaral SID, Veloso OCG, Morais Filho DD, Teodoro JAR, Petisco ACGP, Saleh MH, Barros MVLD, Barros FS, Engelhorn ALDV, Engelhorn CA, Nardino ÉP, Silva MADM, Biagioni LC, Souza AJD, Sarpe AKP, Oliveira ACD, Moraes MRDS, Francisco Neto MJ, Françolin PC, Rochitte CE, Iquizli R, Santos AASMDD, Muglia VF, Naves BDL. Recommendation Update for Vascular Ultrasound Evaluation of Carotid and Vertebral Artery Disease: DIC, CBR and SABCV - 2023. Arq Bras Cardiol 2023; 120:e20230695. [PMID: 37991060 DOI: 10.36660/abc.20230695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023] Open
Affiliation(s)
- Ana Cristina Lopes Albricker
- Centro Universitário de Belo Horizonte (UniBH), Belo Horizonte, MG - Brasil
- IMEDE - Instituto Mineiro de Ultrassonografia, Belo Horizonte, MG - Brasil
| | - Claudia Maria Vilas Freire
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Empresa Brasileira de Serviços Hospitalares (UBSERH), Brasília, DF - Brasil
| | | | | | | | | | | | - Orlando Carlos Glória Veloso
- Rede UnitedHealth Group (UHG), Rio de Janeiro, RJ - Brasil
- Hospital Pasteur, Rio de Janeiro, RJ - Brasil
- Hospital Américas, Rio de Janeiro, RJ - Brasil
- Hospital de Clínicas Mário Lioni, Rio de Janeiro, RJ - Brasil
| | | | | | | | | | | | | | | | | | - Érica Patrício Nardino
- Faculdade de Medicina do ABC Paulista, SP - Brasil
- Faculdade de Medicina Unoeste, Guarujá, SP - Brasil
| | | | | | | | | | | | | | | | - Peter Célio Françolin
- Instituto do Coração (InCor) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP - Brasil
| | - Carlos Eduardo Rochitte
- Instituto do Coração (InCor) da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP - Brasil
- Hospital do Coração (Hcor), São Paulo, SP - Brasil
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Zhang J, Ryu JY, Tirado SR, Dickinson LD, Abosch A, Aziz-Sultan MA, Boulos AS, Barrow DL, Batjer HH, Binyamin TR, Blackburn SL, Chang EF, Chen PR, Colby GP, Cosgrove GR, David CA, Day AL, Folkerth RD, Frerichs KU, Howard BM, Jahromi BR, Niemela M, Ojemann SG, Patel NJ, Richardson RM, Shi X, Valle-Giler EP, Wang AC, Welch BG, Williams Z, Zusman EE, Weiss ST, Du R. A Transcriptomic Comparative Study of Cranial Vasculature. Transl Stroke Res 2023:10.1007/s12975-023-01186-w. [PMID: 37612482 DOI: 10.1007/s12975-023-01186-w] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/06/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
In genetic studies of cerebrovascular diseases, the optimal vessels to use as controls remain unclear. Our goal is to compare the transcriptomic profiles among 3 different types of control vessels: superficial temporal artery (STA), middle cerebral arteries (MCA), and arteries from the circle of Willis obtained from autopsies (AU). We examined the transcriptomic profiles of STA, MCA, and AU using RNAseq. We also investigated the effects of using these control groups on the results of the comparisons between aneurysms and the control arteries. Our study showed that when comparing pathological cerebral arteries to control groups, all control groups presented similar responses in the activation of immunological processes, the regulation of intracellular signaling pathways, and extracellular matrix productions, despite their intrinsic biological differences. When compared to STA, AU exhibited upregulation of stress and apoptosis genes, whereas MCA showed upregulation of genes associated with tRNA/rRNA processing. Moreover, our results suggest that the matched case-control study design, which involves control STA samples collected from the same subjects of matched aneurysm samples in our study, can improve the identification of non-inherited disease-associated genes. Given the challenges associated with obtaining fresh intracranial arteries from healthy individuals, our study suggests that using MCA, AU, or paired STA samples as controls are feasible strategies for future large-scale studies investigating cerebral vasculopathies. However, the intrinsic differences of each type of control should be taken into consideration when interpreting the results. With the limitations of each control type, it may be most optimal to use multiple tissues as controls.
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Affiliation(s)
- Jianing Zhang
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Jee-Yeon Ryu
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Selena-Rae Tirado
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | | | - Aviva Abosch
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, USA
| | - M Ali Aziz-Sultan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Alan S Boulos
- Department of Neurosurgery, Albany Medical Center, Albany, NY, USA
| | - Daniel L Barrow
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - H Hunt Batjer
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, USA
| | | | - Spiros L Blackburn
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - P Roc Chen
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX, USA
| | - Geoffrey P Colby
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Carlos A David
- Department of Neurosurgery, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Arthur L Day
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX, USA
| | - Rebecca D Folkerth
- Department of Forensic Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Kai U Frerichs
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Brian M Howard
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Behnam R Jahromi
- Department of Neurosurgery, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Mika Niemela
- Department of Neurosurgery, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - Steven G Ojemann
- Department of Neurosurgery, University of Colorado, Denver, CO, USA
| | - Nirav J Patel
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Xiangen Shi
- Department of Neurosurgery, Affiliated Fuxing Hospital, Capital Medical University, Beijing, China
| | | | - Anthony C Wang
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - Babu G Welch
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | | | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rose Du
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
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Blain Y, Alessandrino F, Scortegagna E, Balcacer P. Transplant renal artery stenosis: utilization of machine learning to identify ancillary sonographic and doppler parameters to predict stenosis in patients with graft dysfunction. Abdom Radiol (NY) 2023; 48:2102-2110. [PMID: 36947204 DOI: 10.1007/s00261-023-03872-7] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE To determine if ancillary sonographic and Doppler parameters can be used to predict transplant renal artery stenosis in patients with renal graft dysfunction. MATERIALS AND METHODS IRB-approved, HIPAA-compliant retrospective study included 80 renal transplant patients who had renal US followed by renal angiogram between January 2018 and December 2019. A consensus read of two radiologists recorded these parameters: peak systolic velocity, persistence of elevated velocity, grayscale narrowing, parvus tardus, delayed systolic upstroke, angle of the systolic peak (SP angle), and aliasing. Univariate analysis using t-test or chi-square was performed to determine differences between patients with and without stenosis. P values under 0.05 were deemed statistically significant. We used machine learning algorithms to determine parameters that could better predict the presence of stenosis. The algorithms included logistic regression, random forest, imbalanced random forest, boosting, and CART. All 80 cases were split between training and testing using stratified sampling using a 75:25 split. RESULTS We found a statistically significant difference in grayscale narrowing (p = 0.0010), delayed systolic upstroke (p = 0.0002), SP angle (p = 0.0005), and aliasing (p = 0.0024) between the two groups. No significant difference was found for an elevated peak systolic velocity (p = 0.1684). The imbalanced random forest (IRF) model was selected for improved accuracy, sensitivity, and specificity. Specificity, sensitivity, AUC, and normalized Brier score for the IRF model using all parameters were 73%, 81%, 0.82, and 69 in the training set, and 78%, 58%, 0.78, and 80 in the testing set. VIMP assessment showed that the combination of variables that resulted in the most significant change of the training set performance was that of grayscale narrowing and SP angle. CONCLUSION Elevated peak systolic velocity did not discriminate between patients with and without TRAS. Adding ancillary parameters into the machine learning algorithm improved specificity and sensitivity similarly in the training and testing sets. The algorithm identified the combination of lumen narrowing coupled with the angle of the systolic peak as better predictor of TRAS. This model may improve the accuracy of ultrasound for transplant renal artery stenosis.
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Affiliation(s)
- Yamile Blain
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA.
| | - Francesco Alessandrino
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
| | - Eduardo Scortegagna
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
| | - Patricia Balcacer
- Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA
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