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Bahreiny SS, Ahangarpour A, Amraei M, Mansouri Z, Pirsadeghi A, Kazemzadeh R, Javidan M, Karamali N, Bastani MN, Dabbagh MR. Autoimmune thyroid disorders and polycystic ovary syndrome: Tracing links through systematic review and meta-analysis. J Reprod Immunol 2024; 163:104215. [PMID: 38402811 DOI: 10.1016/j.jri.2024.104215] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/22/2024] [Accepted: 02/11/2024] [Indexed: 02/27/2024]
Abstract
Polycystic Ovary Syndrome (PCOS) and Autoimmune Thyroiditis (AIT) are two prevalent endocrine disorders affecting women, often coexisting within the same patient population. This meta-analysis aims to systematically assess and synthesize the existing body of literature to elucidate the intricate relationship between PCOS and AIT. A systematic literature search for relevant observational studies was conducted in electronic databases such as Web of Science, Google Scholar, PubMed, Cochrane, and Scopus until March 2023. All Statistical analyses were performed using CMA Software v3.7 in a random-effects network meta-analysis. In addition, sensitivity and meta-regression analyses were conducted to identify sources of Heterogeneity based on related risk factors. Our meta-analysis included eighteen studies with 3657 participants, which revealed significant differences between PCOS patients and control groups. In particular, a considerable association was detected between PCOS and the presence of AIT (OR = 2.38; 95% CI: 1.63-3.49; P< 0.001) and elevated levels of TSH (SMD = 0.24; 95% CI: 0.06-0.42; P= 0.01), anti-TPO (SMD = 0.36; 95% CI: 0.19-0.53; P< 0.001), anti-TG (SMD = 1.24; 95% CI: 0.37-2.10; P< 0.001), and other positive serum antibodies compared to the control groups. The findings from this meta-analysis may contribute to enhanced diagnostic strategies like complete thyroid function tests, more targeted interventions, and improved patient care for individuals presenting with both PCOS and AIT. Additionally, identifying commonalities between these conditions may pave the way for future research directions, guiding the development of novel therapeutic approaches that address the interconnected nature of PCOS and AIT.
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Affiliation(s)
- Seyed Sobhan Bahreiny
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Medical Basic Sciences Research Institute, Physiology Research Center, Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Medicinal Plant Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Akram Ahangarpour
- Medical Basic Sciences Research Institute, Physiology Research Center, Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Medicinal Plant Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mahdi Amraei
- Department of Health Services Management, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zahra Mansouri
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; USERN Office, Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Ali Pirsadeghi
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Razieh Kazemzadeh
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Moslem Javidan
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Negin Karamali
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Immunology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad-Navid Bastani
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Medical Basic Sciences Research Institute, Physiology Research Center, Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mohammad Reza Dabbagh
- Medicinal Plant Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
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Shiri I, Salimi Y, Sirjani N, Razeghi B, Bagherieh S, Pakbin M, Mansouri Z, Hajianfar G, Avval AH, Askari D, Ghasemian M, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khosravi B, Bijari S, Sayfollahi S, Atashzar MR, Hasanian M, Shahhamzeh A, Teimouri A, Goharpey N, Shirzad-Aski H, Karimi J, Radmard AR, Rezaei-Kalantari K, Oghli MG, Oveisi M, Vafaei Sadr A, Voloshynovskiy S, Zaidi H. Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset. Med Phys 2024. [PMID: 38335175 DOI: 10.1002/mp.16964] [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: 10/05/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Nasim Sirjani
- Research and Development Department, Med Fanavarn Plus Co, Karaj, Iran
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Sara Bagherieh
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qom, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | | | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qom, Iran
| | - Saleh Sandoughdaran
- Department of Clinical Oncology, Royal Surrey County Hospital, Guildford, UK
| | - Ahmad Sohrabi
- Radin Makian Azma Mehr Ltd., Radinmehr Veterinary Laboratory, Iran University of Medical Sciences, Gorgan, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Bardia Khosravi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Sahar Sayfollahi
- Department of Neurosurgery, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical research development center, Qom University of Medical Sciences, Qom, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Neda Goharpey
- Department of radiation oncology, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
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Mansouri Z, Salimi Y, Akhavanallaf A, Shiri I, Teixeira EPA, Hou X, Beauregard JM, Rahmim A, Zaidi H. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [ 177Lu]Lu-DOTATATE radiopharmaceutical therapy. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06618-9. [PMID: 38267686 DOI: 10.1007/s00259-024-06618-9] [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: 11/13/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Eliluane Pirazzo Andrade Teixeira
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Xinchi Hou
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Mathieu Beauregard
- Cancer Research Centre and Department of Radiology and Nuclear Medicine, Université Laval, Quebec City, QC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Mansouri Z, Salimi Y, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study. Radiat Oncol 2024; 19:12. [PMID: 38254203 PMCID: PMC10804728 DOI: 10.1186/s13014-024-02409-6] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/17/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. METHODS A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. RESULTS The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. CONCLUSION Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Shiri I, Amini M, Yousefirizi F, Vafaei Sadr A, Hajianfar G, Salimi Y, Mansouri Z, Jenabi E, Maghsudi M, Mainta I, Becker M, Rahmim A, Zaidi H. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images. Med Phys 2024; 51:319-333. [PMID: 37475591 DOI: 10.1002/mp.16615] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. PURPOSE Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. METHODS The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. RESULTS In single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . CONCLUSION PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, USA
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ismini Mainta
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Minerva Becker
- Service of Radiology, Geneva University Hospital, Geneva, Switzerland
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
- Department of Radiology and Physics, University of British Columbia, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Salimi Y, Akhavanallaf A, Mansouri Z, Shiri I, Zaidi H. Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks. Eur Radiol 2023; 33:9411-9424. [PMID: 37368113 PMCID: PMC10667156 DOI: 10.1007/s00330-023-09839-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 12/07/2022] [Revised: 03/28/2023] [Accepted: 04/14/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions. METHODS The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed. RESULTS The model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was - 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, - 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were - 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, - 1.11 ± 2.90%, 2.34 ± 2.03%, respectively. CONCLUSION Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation. CLINICAL RELEVANCE STATEMENT We proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations. KEY POINTS • We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation. • Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation. • By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.
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Affiliation(s)
- Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, CH_1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
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Riveira-Martin M, Akhavanallaf A, Mansouri Z, Bianchetto Wolf N, Salimi Y, Ricoeur A, Mainta I, Garibotto V, López Medina A, Zaidi H. Predictive value of 99mTc-MAA-based dosimetry in personalized 90Y-SIRT planning for liver malignancies. EJNMMI Res 2023; 13:63. [PMID: 37395912 DOI: 10.1186/s13550-023-01011-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 07/04/2023] Open
Abstract
BACKGROUND Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation absorbed doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT. RESULTS A total of 79 patients were analysed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method. Mean absorbed dose, tumour-to-normal ratio, and absorbed dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOI). Mann-Whitney U-test and Pearson's correlation coefficient were used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumour absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics. CONCLUSION This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distribution.
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Affiliation(s)
- Mercedes Riveira-Martin
- Genetic Oncology, Radiobiology and Radiointeraction Research Group, Galicia Sur Health Research Institute, Vigo, Spain
- Department of Radiology, Rehabilitation and Physiotherapy, Medicine School, Complutense University of Madrid, Madrid, Spain
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Nicola Bianchetto Wolf
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Alexis Ricoeur
- Service of Radiology, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Ismini Mainta
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland
- Centre for Biomedical Imaging (CIBM), Geneva, Switzerland
- Geneva Neuroscience Centre, Geneva University, Geneva, Switzerland
| | - Antonio López Medina
- Department of Medical Physics and RP, Hospital do Meixoeiro (GALARIA), Vigo, Spain.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospital, 1211, Geneva, Switzerland.
- Geneva Neuroscience Centre, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Hajianfar G, Sabouri M, Salimi Y, Amini M, Bagheri S, Jenabi E, Hekmat S, Maghsudi M, Mansouri Z, Khateri M, Hosein Jamshidi M, Jafari E, Bitarafan Rajabi A, Assadi M, Oveisi M, Shiri I, Zaidi H. Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance. Z Med Phys 2023:S0939-3889(23)00008-9. [PMID: 36932023 DOI: 10.1016/j.zemedi.2023.01.008] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/22/2022] [Accepted: 01/18/2023] [Indexed: 03/17/2023]
Abstract
PURPOSE Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Soroush Bagheri
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sepideh Hekmat
- Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hosein Jamshidi
- Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Esmail Jafari
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Ahmad Bitarafan Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Nejaddehbashi F, Radan M, Bayati V, Dianat M, Mard SA, Mansouri Z. Adipose-derived mesenchymal stem cells in emphysema: Comparison of inflammatory markers changes in response to intratracheal and systemic delivery method. Tissue Cell 2023; 80:102011. [PMID: 36603371 DOI: 10.1016/j.tice.2022.102011] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022]
Abstract
Cytokines are the most important inflammatory mediators and are well-known as the main cause of emphysema. Adipose-derived stem cells (ADSCs) as a cell-based treatment strategy could play a pivotal role in lung regeneration through anti-inflammatory and paracrine properties. Accordingly, the aim of this study was to the comparison of inflammation markers' improvement in response to the intratracheal and systemic delivery method of adipose-derived mesenchymal stem cells in emphysema. Forty-eight rats were divided into five groups including Control, Elastase (25 IU/kg, Intratracheal, at day first and 10th), Elastase+PBS, Intratracheal cell therapy (1 ×107, at day 28th), and Systemic cell therapy groups (1 ×107, Jugular vein, at day 28th). After 3 weeks, the blood gas analysis (PO2, PCO2 and pH), fibrinogen level, and C-reactive protein (CRP) concentrations were measured in all groups. In addition, inflammatory genes expression, and concentration levels of pro and anti-inflammatory cytokines (IL-6, IL-17, TNF-α, and TGF-β,) were evaluated using Real-time PCR and Elisa kits, respectively. The statistical analysis of our data shows that local administration leads to more significant treatment efficacy with decreased inflammation parameters such as WBC count and pro-inflammatory cytokines in comparison with systemic treatment. Besides, these results were approved by more reduction of CRP and fibrinogen concentration levels in blood samples of intra-tracheal AMSCs-treated rats compare with the systemic group. Moreover, the improvement in histopathology indexes of the local administrated group was significantly better than the systemic group. Accordingly, the obtained results suggest local administration as the most efficacious route for mesenchymal stem cells delivery in patients with emphysema.
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Affiliation(s)
- Fereshteh Nejaddehbashi
- Cellular and Molecular Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Radan
- Cellular and Molecular Research Center & Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Vahid Bayati
- Cellular and Molecular Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mahin Dianat
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Seyyed Ali Mard
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zahra Mansouri
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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10
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Salimi Y, Shiri I, Akavanallaf A, Mansouri Z, Arabi H, Zaidi H. Fully automated accurate patient positioning in computed tomography using anterior-posterior localizer images and a deep neural network: a dual-center study. Eur Radiol 2023; 33:3243-3252. [PMID: 36703015 PMCID: PMC9879741 DOI: 10.1007/s00330-023-09424-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 11/29/2022] [Accepted: 01/02/2023] [Indexed: 01/28/2023]
Abstract
OBJECTIVES This study aimed to improve patient positioning accuracy by relying on a CT localizer and a deep neural network to optimize image quality and radiation dose. METHODS We included 5754 chest CT axial and anterior-posterior (AP) images from two different centers, C1 and C2. After pre-processing, images were split into training (80%) and test (20%) datasets. A deep neural network was trained to generate 3D axial images from the AP localizer. The geometric centerlines of patient bodies were indicated by creating a bounding box on the predicted images. The distance between the body centerline, estimated by the deep learning model and ground truth (BCAP), was compared with patient mis-centering during manual positioning (BCMP). We evaluated the performance of our model in terms of distance between the lung centerline estimated by the deep learning model and the ground truth (LCAP). RESULTS The error in terms of BCAP was - 0.75 ± 7.73 mm and 2.06 ± 10.61 mm for C1 and C2, respectively. This error was significantly lower than BCMP, which achieved an error of 9.35 ± 14.94 and 13.98 ± 14.5 mm for C1 and C2, respectively. The absolute BCAP was 5.7 ± 5.26 and 8.26 ± 6.96 mm for C1 and C2, respectively. The LCAP metric was 1.56 ± 10.8 and -0.27 ± 16.29 mm for C1 and C2, respectively. The error in terms of BCAP and LCAP was higher for larger patients (p value < 0.01). CONCLUSION The accuracy of the proposed method was comparable to available alternative methods, carrying the advantage of being free from errors related to objects blocking the camera visibility. KEY POINTS • Patient mis-centering in the anterior-posterior direction (AP) is a common problem in clinical practice which can degrade image quality and increase patient radiation dose. • We proposed a deep neural network for automatic patient positioning using only the CT image localizer, achieving a performance comparable to alternative techniques, such as the external 3D visual camera. • The advantage of the proposed method is that it is free from errors related to objects blocking the camera visibility and that it could be implemented on imaging consoles as a patient positioning support tool.
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Affiliation(s)
- Yazdan Salimi
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Isaac Shiri
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Azadeh Akavanallaf
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Zahra Mansouri
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Hossein Arabi
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Habib Zaidi
- grid.150338.c0000 0001 0721 9812Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Geneva University Neurocenter, Geneva University, Geneva, Switzerland ,grid.4494.d0000 0000 9558 4598Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands ,grid.10825.3e0000 0001 0728 0170Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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11
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Zare N, Maghsoudi N, Mirbehbahani SH, Foolad F, Khakpour S, Mansouri Z, Khodagholi F, Ghorbani Yekta B. Prenatal Methamphetamine Hydrochloride Exposure Leads to Signal Transduction Alteration and Cell Death in the Prefrontal Cortex and Amygdala of Male and Female Rats' Offspring. J Mol Neurosci 2022; 72:2233-2241. [PMID: 36056281 DOI: 10.1007/s12031-022-02062-2] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022]
Abstract
In the last decade, there has been a great increase in methamphetamine hydrochloride (METH) abuse by pregnant women that exposes fetus and human offspring to a wide variety of developmental impairments that may be the underlying causes of future psychosocial issues. Herein, we investigated whether prenatal METH exposure with different doses (2 and 5 mg/kg) could influence neuronal cell death and antioxidant level in the different brain regions of adult male and female offspring. Adult male and female Wistar rats prenatally exposed to METH (2 or 5 mg/kg) and/or saline was used in this study. At week 12, adult rats' offspring were decapitated to collect different brain region tissues including amygdala (AMY) and prefrontal cortices (PFC). Western blot analysis was performed to evaluate the apoptosis- and autophagy-related markers, and enzymatic assay was used to measure the level of catalase and also reduced glutathione (GSH). Our results showed that METH exposure during pregnancy increased the level of apoptosis (BAX/Bcl-2 and Caspase-3) and autophagy (Beclin-1 and LC3II/LC3I) in the PFC and AMY areas of both male and female offspring's brain. Also, we found an elevation in the GSH content of all both mentioned brain areas and catalase activity of PFC in the offspring's brain. These changes were more significant in female offspring. Being prenatally exposed to METH increased cell death at least partly via apoptosis and autophagy in AMY and PFC of male and female offspring's brain, while the antioxidant system tried to protect cells in these regions.
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Affiliation(s)
- Nayereh Zare
- Department of Anatomical Sciences and Cognitive Neuroscience, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nader Maghsoudi
- Neurobiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Hamidreza Mirbehbahani
- Neurobiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Forough Foolad
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Shahrzad Khakpour
- Department of Physiology, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Zahra Mansouri
- Neurobiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fariba Khodagholi
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Batool Ghorbani Yekta
- Department of Physiology, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran. .,Herbal Pharmacology Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
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12
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Jaseb K, Khaksar MA, Mansouri Z, Barani B, Ghashghaeepour M, Ghanavat M, Jaseb B, Poorchini P. Bloodstream Infections in Febrile Hematologic Oncology Patients in a Referral Hospital in Ahvaz, Iran: A Descriptive Cross-Sectional Study. Iran J Med Sci 2022; 47:612-614. [PMID: 36380983 PMCID: PMC9652492 DOI: 10.30476/ijms.2022.94503.2583] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/24/2022] [Accepted: 06/10/2022] [Indexed: 06/16/2023]
Affiliation(s)
- Kaveh Jaseb
- Department of Hematology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mohammad Ali Khaksar
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zahra Mansouri
- Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Bahar Barani
- Department of Hematology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Ghashghaeepour
- Department of Hematology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Majid Ghanavat
- Cancer Prevention Research center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Bahar Jaseb
- Department of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Pedram Poorchini
- Department of Hematology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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13
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Salimi Y, Shiri I, Akhavanallaf A, Mansouri Z, Sanaat A, Pakbin M, Ghasemian M, Arabi H, Zaidi H. Deep Learning-based Calculation of Patient Size and Attenuation Surrogates from Localizer Image: Toward Personalized Chest CT Protocol Optimization. Eur J Radiol 2022; 157:110602. [DOI: 10.1016/j.ejrad.2022.110602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
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14
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Hoseinynejad K, Mard SA, Mansouri Z, Lamoochi Z, Kazemzadeh R. Efficacy of Chlorogenic Acid against Ethylene Glycol-Induced Renal Stone Model: The Role of NFKB-RUNX2-AP1-OSTERIX Signaling Pathway. Tissue Cell 2022; 79:101960. [DOI: 10.1016/j.tice.2022.101960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
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15
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Bayati V, Radan M, Dianat M, Mansouri Z, Souhrabi F. OXR1 signaling pathway as a possible mechanism of elastase-induced oxidative damage in pulmonary cells: the protective role of ellagic acid. Mol Biol Rep 2022; 49:8259-8271. [PMID: 35841468 DOI: 10.1007/s11033-022-07542-7] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/29/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND OBJECTIVE Oxidative stress is a process that occurs through free radicals on the cell membranes which causes damage to the cell and intracellular organelles, especially mitochondria membranes. H2O2 induced oxidative stress in human cells is of interest in toxicological research since oxidative stress plays a main role in the etiology of several pathological conditions. Neutrophil Elastase (Serine proteinase) is involved in the pathology process of emphysema as a respiratory disease through lung inflammation, and destruction of alveolar walls. The present study investigated the direct oxidative stress effects of Elastase in comparison with H2O2 on human lung epithelial cells (A549 cells) concerning the generation of reactive oxygen species (ROS) and modulation of oxidation resistance 1 (OXR1) and its downstream pathway using the well-known antioxidant Ellagic acid as an activator of antioxidant genes. MATERIALS AND METHODS The human pulmonary epithelial cells (A549) were divided into the nine groups including Negative control, Positive control (H2O2), Elastase (15, 30, and 60 mU/mL), Ellagic acid (10 μmol/L), and Elastase + Ellagic acid. Cytotoxicity, ROS generation, oxidative stress profile, level of reactive metabolites, and gene expression of OXR1 and its downstream genes were measured in all groups. RESULTS The obtained data demonstrated that Elastase exposure caused oxidative stress damage in a dose-depended manner which was associated with decreases in antioxidant defense system genes. Conversely, treatment with Ellagic acid as a potent antioxidant showed improved antioxidant enzyme activity and content which was in line with the upregulation of OXR1 signaling pathway genes. CONCLUSIONS The present findings can highlight the novel mechanism underlying the oxidative stress induced by Neutrophil Elastase through OXR1 and related genes. Moreover, the benefit of Ellagic acid on cytoprotection, resulting from its antioxidant properties was documented.
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Affiliation(s)
- Vahid Bayati
- Cellular and Molecular Research Center & Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Radan
- Cellular and Molecular Research Center & Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Mahin Dianat
- Department of Physiology, Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zahra Mansouri
- Department of Physiology, Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Farzaneh Souhrabi
- Department of Physiology, Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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16
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Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Khosravi B, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi SY, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 2022; 145:105467. [PMID: 35378436 PMCID: PMC8964015 DOI: 10.1016/j.compbiomed.2022.105467] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [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: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qum, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran
| | - Ehsan Sharifipour
- Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Sajad P. Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Bardia Khosravi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical Research Development Center, Qom University of Medical Sciences, Qum, Iran
| | - Seyaed Yaser Foroghi Ghomi
- Clinical Research Development Center, Shahid Beheshti Hospital, Qom University Of Medical Sciences, Qom, Iran
| | - Abolfazl Mozafari
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Movaseghi
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Azin Ahmari
- Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran
| | - Neda Goharpey
- Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rama Bozorgmehr
- Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Roozbeh Mortazavi
- Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Nazanin Mortazavi
- Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Sima Besharat
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mandana Afsharpad
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Parham Geramifar
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland,Geneva University Neurocenter, Geneva University, Geneva, Switzerland,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark,Corresponding author. Geneva University Hospital Division of Nuclear Medicine and Molecular Imaging, CH-1211, Geneva, Switzerland
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Mansouri Z, Tayebi A, Khalili R, Faizi F. Design and implementation of a follow-up and training program of health-promoting lifestyle after the coronary artery bypass graft. J Educ Health Promot 2022; 11:133. [PMID: 35677279 PMCID: PMC9170207 DOI: 10.4103/jehp.jehp_885_21] [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] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/06/2021] [Indexed: 06/15/2023]
Abstract
BACKGROUND Coronary artery bypass graft (CABG) plays an important role in reducing coronary heart disease mortality, but patients are still at risk after surgery. Consequences can be avoided if threatening behaviors are soon detected and lifestyles are promoted. Therefore, the present study aimed to evaluate, follow-up, and promote a healthy lifestyle in the patients. MATERIALS AND METHODS The present research was a quasi-experimental pre- and postintervention single-group study on 35 patients under the CABG at two hospitals affiliated to the Baqiyatallah University of Medical Sciences in Tehran from August 2020 to April 2021. The samples were selected using the purposive sampling method and the educational content was determined by creating an expert panel. We utilized the Health-promoting Lifestyle Profile II to collect data, and SPSS 22 to analyze them. RESULTS There was a significant difference between mean total scores of health-promoting lifestyle before and after the intervention and they reached from 138.7 ± 20 to 157.2 ± 18 (P < 0.0001). There was also a statistically significant difference between mean scores of nutrition (P < 0.003), physical activity (P < 0.0001), health responsibility (P < 0.0001), and stress management (P < 0.0001) before and after the intervention, but there was no statistically significant difference between mean scores of interpersonal relationships, and spiritual growth before and after the intervention. CONCLUSIONS The program had a positive effect on the health-promoting lifestyle scores of patients after CABG. It is possible to increase scores of healthy lifestyles in the patients by combining face-to-face and virtual training methods as well as involving family members and relatives of patients in training and follow-up programs.
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Affiliation(s)
- Zahra Mansouri
- Department of Medical-Surgical Nursing, Nursing Faculty, Student Research Committee, Baqiyatallah University of Medical Science, Tehran, Iran
| | - Ali Tayebi
- Department of Medical-Surgical Nursing, Nephrology and Urology Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Robabe Khalili
- Department of Medical-Surgical Nursing, Behavioral Sciences Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Fakhrudin Faizi
- Department of Medical-Surgical Nursing, Atherosclerosis Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
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Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, Moradi S, Mansouri Z, Pakbin M, Sandoughdaran S, Abdollahi H, Radmard AR, Rezaei‐Kalantari K, Ghelich Oghli M, Zaidi H. COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. Int J Imaging Syst Technol 2022; 32:12-25. [PMID: 34898850 PMCID: PMC8652855 DOI: 10.1002/ima.22672] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/18/2021] [Accepted: 10/17/2021] [Indexed: 05/17/2023]
Abstract
We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research CenterIran University of Medical SciencesTehranIran
| | - Dariush Askari
- Department of Radiology TechnologyShahid Beheshti University of Medical SciencesTehranIran
| | - Shakiba Moradi
- Research and Development DepartmentMed Fanavaran Plus Co.KarajIran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
| | - Masoumeh Pakbin
- Clinical Research Development CenterQom University of Medical SciencesQomIran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research CenterShahid Beheshti University of Medical SciencesTehranIran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied MedicineKerman University of Medical SciencesKermanIran
| | - Amir Reza Radmard
- Department of RadiologyShariati Hospital, Tehran University of Medical SciencesTehranIran
| | - Kiara Rezaei‐Kalantari
- Rajaie Cardiovascular Medical and Research CenterIran University of Medical SciencesTehranIran
| | - Mostafa Ghelich Oghli
- Research and Development DepartmentMed Fanavaran Plus Co.KarajIran
- Department of Cardiovascular SciencesKU LeuvenLeuvenBelgium
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalGenevaSwitzerland
- Geneva University NeurocenterGeneva UniversityGenevaSwitzerland
- Department of Nuclear Medicine and Molecular ImagingUniversity of Groningen, University Medical Center GroningenGroningenNetherlands
- Department of Nuclear MedicineUniversity of Southern DenmarkOdenseDenmark
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Salimi Y, Shiri I, Akhavanallaf A, Mansouri Z, Saberi Manesh A, Sanaat A, Pakbin M, Askari D, Sandoughdaran S, Sharifipour E, Arabi H, Zaidi H. Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging. Insights Imaging 2021; 12:162. [PMID: 34743251 PMCID: PMC8572075 DOI: 10.1186/s13244-021-01105-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 08/10/2021] [Accepted: 10/09/2021] [Indexed: 01/07/2023] Open
Abstract
Background Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01105-3.
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Affiliation(s)
- Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Zahra Mansouri
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abdollah Saberi Manesh
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qom, Iran
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ehsan Sharifipour
- Neuroscience Research Center, Qom University of Medical Sciences, Qom, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Mansouri Z, Dianat M, Radan M, Badavi M. Ellagic Acid Ameliorates Lung Inflammation and Heart Oxidative Stress in Elastase-Induced Emphysema Model in Rat. Inflammation 2021; 43:1143-1156. [PMID: 32103438 DOI: 10.1007/s10753-020-01201-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [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: 02/06/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the most important factors in the progress of cardiovascular disease (CVD) which is associated with limited airflow and alveolar demolition. The aim of this study is to investigate the possible protective effect of ellagic acid (EA), as a natural anti-oxidant, against pulmonary arterial hypertension (PAH) and development of lung and heart injuries induced by elastase. Sixty healthy male Sprague-Dawley rats (150-180 g) were divided into six groups: control (saline 0.9%, 1 ml/kg, by gavage), porcine pancreatic elastase (PPE) (25 UI/kg, intratracheal), EA (10, 15, and 30 mg/kg, gavage), PPE + EA (30 mg/kg, by gavage). Lead II electrocardiogram was used to evaluate the inotropic and chronotropic parameters of rat heart using Bio-Amp device and the LabChart software. The anti-oxidant levels (superoxide dismutase, catalase, and glutathione) and malondialdehyde were measured by appropriate kits, and right ventricular systolic pressure (RVSP) was recorded by the PowerLab system and measured by the LabChart software (ADInstruments). Elastase administration caused an increase in RVSP which was in line with elevated inflammatory cells and cytokines, as well as lipid peroxidation, and decreased anti-oxidant levels. Also, electrocardiogram parameters significantly changed in elastase group compared with control rats. Co-treatment with EA not only restored elastase-depleted anti-oxidant levels and prevented pulmonary arterial hypertension but also improved cardiac chronotropic and inotropic properties. Our results documented that elastase administration leads to pulmonary arterial hypertension and EA, as an anti-inflammatory and anti-oxidant factor, can protect development of lung and heart injuries induced by elastase.
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Affiliation(s)
- Zahra Mansouri
- Department of Physiology, Faculty of Medicine, Persian Gulf Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mahin Dianat
- Department of Physiology, Faculty of Medicine, Persian Gulf Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Maryam Radan
- Department of Physiology, Faculty of Medicine, Persian Gulf Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mohammad Badavi
- Department of Physiology, Faculty of Medicine, Persian Gulf Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Vafazadeh R, Mansouri Z, Willis AC. Nickel(II) Complex with a Flexidentate Ligand Derived from Acetohydrazide: Synthesis, Structural Characterization and Hirshfeld Surface Analysis. Acta Chim Slov 2020. [DOI: 10.17344/acsi.2019.5539] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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22
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Vafazadeh R, Mansouri Z, Willis AC. Nickel(II) Complex with a Flexidentate Ligand Derived from Acetohydrazide: Synthesis, Structural Characterization and Hirshfeld Surface Analysis. Acta Chim Slov 2020; 67:516-521. [PMID: 33855577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023] Open
Abstract
The mononuclear Ni(II) complex [Ni(Lp)2(CH3OH)2]Cl2 has been synthesized by reacting 1-(5-hydroxy-3-methyl-5-phenyl-4,5-dihydro-1H-pyrazol-1-yl)ethan-1-one ligand (HL) with NiCl2·6H2O in methanol solution. In the reaction, the tridentate ligand, HL, was converted in situ into 4-hydroxy-4-phenylbut-3-en-2-ylidene)acetohydrazid ligand, (pyrazole, Lp). The pyrazole ligand acts as bidentate neutral ligand and the hydroxyl group is left uncoordinated. The structure of the Ni(II) complex has been established by X-ray crystallography. The Ni(II) is six-coordinate and has a distorted octahedral geometry. It is bonded by two nitrogen and by two oxygen atoms of the two pyrazole ligands and two oxygen atoms of methanol molecules. The Hirshfeld surface analysis and the 2D the fingerprint plot are used to analyses all of the intermolecular contacts in the crystal structures. The main intermolecular contacts are H/H and Cl/H interactions.
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Mahboobian MM, Mohammadi M, Mansouri Z. Development of thermosensitive in situ gel nanoemulsions for ocular delivery of acyclovir. J Drug Deliv Sci Technol 2020. [DOI: 10.1016/j.jddst.2019.101400] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Azimi S, Mansouri Z, Bakhtiari S, Tennant M, Kruger E, Rajabibazl M, Daraei A. Does green tea consumption improve the salivary antioxidant status of smokers? Arch Oral Biol 2017; 78:1-5. [DOI: 10.1016/j.archoralbio.2017.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 02/01/2017] [Accepted: 02/03/2017] [Indexed: 10/20/2022]
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Mohammadpour Lashkari F, Totonchi M, Zamanian MR, Mansouri Z, Sadighi Gilani MA, Sabbaghian M, Mohseni Meybodi A. 46,XX males: a case series based on clinical and genetics evaluation. Andrologia 2016; 49. [PMID: 27882599 DOI: 10.1111/and.12710] [Citation(s) in RCA: 6] [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] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2016] [Indexed: 11/28/2022] Open
Abstract
46,XX male sex reversal syndrome is one of the rarest sex chromosomal aberrations. The presence of SRY gene on one of the X chromosomes is the most frequent cause of this syndrome. Based on Y chromosome profile, there are SRY-positive and SRY-negative forms. The purpose of our study was to report first case series of Iranian patients and describe the different clinical appearances based on their genetic component. From the 8,114 azoospermic and severe oligozoospermic patients referred to Royan institute, we diagnosed 57 cases as sex reversal patients. Based on the endocrinological history, we performed karyotyping, SRY and AZF microdeletion screening. Patients had a female karyotype. According to available hormonal reports of 37 patients, 16 cases had low levels of testosterone (43.2%). On the other hand, 15 males were SRY positive (90.2%), while they lacked the spermatogenic factors encoding genes on Yq. Commencing the testicular differentiation in males, the SRY gene is considered to be very important in this process. Due to homogeneous results of karyotyping and AZF deletion, there are both positive and negative SRY cases that show similar sex reversal phenotypes. Evidences show that there could be diverse phenotypic differences that could be raised from various reasons.
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Affiliation(s)
- F Mohammadpour Lashkari
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - M Totonchi
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - M R Zamanian
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - Z Mansouri
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - M A Sadighi Gilani
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.,Department of Urology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - M Sabbaghian
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - A Mohseni Meybodi
- Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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Nabipour AR, Alizadeh A, Saadat-Hosseini M, Mansouri Z, Shamsoddini L, Nakhaee N. Correlates of waterpipe smoking among Iranian university students and the role of religiosity. Int J Psychiatry Med 2016. [PMID: 28629297 DOI: 10.1177/0091217417696735] [Citation(s) in RCA: 6] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Waterpipe smoking among youth and adolescents in Iran has gained in popularity. The aim of this study was to investigate the relationship between waterpipe smoking and different dimensions of religiosity in a sample of students attending two major universities in South East Iran. A total of 682 students completed a waterpipe and cigarette smoking questionnaire along with the Duke University Religion Index. The lifetime prevalence of dual cigarette and waterpipe use was 48.3%, with prevalence of current use (within the last 30 days) of 24.9%. The proportions of lifetime and current waterpipe-only users were 27.0% and 18.8%, respectively. Students who participated more often in private religious activities were less likely to report engaging in waterpipe smoking (odds ratio: 0.82; 95% confidence interval: 0.71-0.98). A higher level of attendance of religious services was negatively associated with dual cigarette and waterpipe smoking (odds ratio: 0.71; 95% confidence interval: 0.54-0.93). Waterpipe-only use was significantly higher among males, students who had lower grade point averages, those who reported having a close friend or a family member who was a waterpipe smoker. To conclude, it is possible that religious observance may have a protective role in lowering waterpipe usage among Iranian university students.
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Affiliation(s)
- Amir Reza Nabipour
- 1 Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | | | | | | | | | - Nouzar Nakhaee
- 1 Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran
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Mohammadpour Lashkari F, Mohseni Meybodi A, Mansouri Z, Kalantari H, Farahmand K, Vaziri H. The association between (8390G>A) single nucleotide polymorphism in APOE gene with Alzheimer’s and Parkinson disease. Egyptian Journal of Medical Human Genetics 2016. [DOI: 10.1016/j.ejmhg.2015.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Mansouri Z, Bakhtiari S, Noormohamadi R. Extensive Focal Epithelial Hyperplasia: A Case Report. Iran J Pathol 2015; 10:300-305. [PMID: 26351501 PMCID: PMC4539750] [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] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Accepted: 05/31/2014] [Indexed: 06/05/2023]
Abstract
Focal epithelial hyperplasia (FEH) or Heck's disease is a rare viral infection of the oral mucosa caused by human papilloma virus especially subtypes 13 or 32. The frequency of this disease varies widely from one geographic region and ethnic groups to another. This paper reports an Iranian case of extensive focal epithelial hyperplasia. A 35-year-old man with FEH is described, in whom the lesions had persisted for more than 25 years. The lesion was diagnosed according to both clinical and histopathological features. Dental practitioner should be aware of these types of lesions and histopathological examination together and a careful clinical observation should be carried out for a definitive diagnosis.
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Affiliation(s)
- Zahra Mansouri
- Dept. of Oral Medicine, Dental School, Shahid Beheshti University, Tehran, Iran
| | - Sedigheh Bakhtiari
- Dept. of Oral Medicine, Dental School, Shahid Beheshti University, Tehran, Iran
| | - Robab Noormohamadi
- Dept. of Oral Medicine, Dental School, Shahid Beheshti University, Tehran, Iran
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Kalantari H, Asia S, Totonchi M, Vazirinasab H, Mansouri Z, Zarei Moradi S, Haratian K, Gourabi H, Mohseni Meybodi A. Delineating the association between isodicentric chromosome Y and infertility: a retrospective study. Fertil Steril 2014; 101:1091-6. [DOI: 10.1016/j.fertnstert.2013.12.048] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 12/28/2013] [Accepted: 12/30/2013] [Indexed: 02/07/2023]
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Baharvand M, Khodadoustan A, Mansouri Z, Mortazavi H. Combination therapy of atypical odontalgia with fluoxetine and clonazepam: Report of an effective prescription. Dent Hypotheses 2014. [DOI: 10.4103/2155-8213.133438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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31
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Yousefpour M, Naderi N, Mansouri Z, Janahmadi M, Alizadeh AM, Motamedi F. The comparison of the effects of acute and repeated morphine administration on fast synaptic transmission in magnocellular neurons of supraoptic nucleus, plasma vasopressin levels, and urine volume of male rats. Iran J Pharm Res 2014; 13:975-85. [PMID: 25276199 PMCID: PMC4177659] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The activity of the magnocellular neurons (MCNs) of supraoptic nucleus (SON) is regulated by a variety of excitatory and inhibitory inputs. Opioids are one of the important compounds that affect these inputs at SON synapses. In this study, whole-cell patch clamp recording of SON neurons was used to investigate the effect of acute and repeated morphine administration on spontaneous inhibitory and excitatory post synaptic currents (sIPSCs and sEPSCs) in MCNs. While acute bath application of morphine to brain slice of intact rat produced an increase in sEPSCs frequency and a decrease in sIPSCs frequency, repeated in-vivo administration of morphine produced opposite effect. Moreover, repetitive i.c.v. administration of morphine for three consecutive days caused significant increase in urine volume, but had no significant alteration in water consumption compared to control group. The increase in urine volume was consistent with a significant decrease in plasma arginine vasopressin (AVP) levels after repetitive i.p. morphine administration. The results suggest that acute administration of morphine stimulates whereas repeated administration of morphine inhibits the MCNs. Morphine-induced MCN inhibition could result in diminished plasma AVP levels and eventually an increase in urine volume of rats.
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Affiliation(s)
- Mitra Yousefpour
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences.
- Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences.
- Department of Physiology, Faculty of Medicine, Army University of Medical Sciences.
| | - Nima Naderi
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences.
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Shahid Beheshti University of Medical Sciences.
| | - Zahra Mansouri
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences.
| | - Mahyar Janahmadi
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences.
- Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences.
| | | | - Fereshteh Motamedi
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences.
- Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences.
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Kalantari H, Madani T, Zari Moradi S, Mansouri Z, Almadani N, Gourabi H, Mohseni Meybodi A. Cytogenetic analysis of 179 Iranian women with premature ovarian failure. Gynecol Endocrinol 2013; 29:588-91. [PMID: 23656387 DOI: 10.3109/09513590.2013.788625] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The importance of chromosomal abnormalities in etiology of premature ovarian failure (POF) is well known but in many cases, POF still remains idiopathic. We investigated the frequency and type of chromosomal aberrations in Iranian women diagnosed with idiopathic POF. Standard cytogenetic analysis was carried out in a total of 179 patients. Karyotype analysis of these patients revealed that 161 (89.95%) patients had normal female karyotype and 18 (10.05%) patients had abnormal karyotypes. The abnormal karyotypes included sex reverse sex determining region Y (SRY) negative (five Cases), X chromosome mosaicism (five cases), abnormal X chromosomes (three cases), abnormal autosomes (three cases) and X-autosome translocation (two cases). The overall prevalence of chromosomal abnormalities was 10.05% in this first large-scale report of chromosomal aberrations in Iranian women with POF. The results confirm previous observations and emphasis on the critical role of X chromosome abnormalities as one of the possible etiologies for POF.
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Affiliation(s)
- Hamid Kalantari
- Department of Genetics at Reproductive Biomedicine Research Center, Royan Institute for Reproductive biomedicine, ACECR, Tehran, Iran
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Zarei Moradi S, Mohseni Meybodi A, Gourabi H, Mozdarani H, Mansouri Z. Chromosome abnormalities and viability of vitrified eight-cell mouse embryos at presence of two different cryoprotectants at different storage durations. Cell J 2013; 14:254-63. [PMID: 23577304 PMCID: PMC3593929] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Accepted: 06/26/2012] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Experiments were conducted to find the differences between post-thaw viability and chromosome aberrations in eight-cell mouse embryos at presence of dimethyl sulfoxide (DMSO) and 1, 2-propanediol (PROH) as croprotectants in different storage durations. MATERIALS AND METHODS In this case-control study, a total number of 720 mouse embryos from about 250 NMRI mice were vitrified with 30% PROH or DMSO; each diluted with a solution containing 30% ficol plus 0.5 M sucrose. Embryos were exposed to the solutions for 0.5 minute at 25℃ followed by cooling in liquid nitrogen, then after appropriate storage duration, they were rapidly warmed. Besides, there were 100 mouse embryos for each cryoprotectant group (totally 200 embryos) as control. Embryo survival was assessed by in vitro development, and chromosome abnormalities were analyzed by Giemsa staining. RESULTS The proportion of mitotic abnormalities in PROH/DMSO vitrified embryos was significantly higher than unfrozen control group. This was confirmed also by a reduced viability of the embryos as judged by a culture at the blastocyst stage (p<0.05 in all test groups). CONCLUSION It can be deduced that long term cryopreservation may result in chromosomal abnormalities and/or low viability.
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Affiliation(s)
- Shabnam Zarei Moradi
- 1. Department of Genetics at Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine,
ACECR, Tehran, Iran
| | - Anahita Mohseni Meybodi
- 1. Department of Genetics at Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine,
ACECR, Tehran, Iran
| | - Hamid Gourabi
- 1. Department of Genetics at Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine,
ACECR, Tehran, Iran
| | - Hossein Mozdarani
- 2. Department of Medical Genetics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Zahra Mansouri
- 1. Department of Genetics at Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine,
ACECR, Tehran, Iran
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Dabbagh A, Moghadam SF, Rajaei S, Mansouri Z, Manaheji HS. Can repeated exposure to morphine change the spinal analgesic effects of lidocaine in rats? J Res Med Sci 2011; 16:1361-5. [PMID: 22973332 PMCID: PMC3430028] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2011] [Accepted: 10/06/2011] [Indexed: 10/28/2022]
Abstract
BACKGROUND Chronic opium exposure leads to altered response to opioid compounds. The aim of this study was to assess the behavioral effects of opium tolerance on the analgesic effects of intrathecal lidocaine in rats. METHODS Twenty-four adult male Sprague Dawley rats with intrathecal (IT) catheters were divided into 3 groups of 8. The first group was morphine tolerant and received IT lidocaine (ML). Rats in the second group were not morphine tolerant and received IT lidocaine (L), while the third group consisted of not morphine tolerant rats that received IT placebo. Tail flick test was done and maximal possible antinociceptive effects (MPAE) were compared using analysis of variance (ANOVA). RESULTS While percent of MPAE significantly increased in the L group, it had a significant reduction in the ML group (P < 0.001). CONCLUSIONS After intrathecal lidocaine administration, a hyperalgesic response was seen in morphine tolerant rats and an analgesic response was seen in the lidocaine group.
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Affiliation(s)
- Ali Dabbagh
- Associate Professor, Fellowship in Cardiac Anesthesiology, Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran,
Corresponding Author: Ali Dabbagh E-mail:
| | | | - Samira Rajaei
- PhD Student, Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Mansouri
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Homa Shardi Manaheji
- Professor, Neuroscience Research Center and Department of Physiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Mozdarani H, Mansouri Z, Haeri SA. Cytogenetic radiosensitivity of g0-lymphocytes of breast and esophageal cancer patients as determined by micronucleus assay. J Radiat Res 2005; 46:111-116. [PMID: 15802866 DOI: 10.1269/jrr.46.111] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Enhanced chromosomal radiosensitivity is a feature of many cancer predisposition conditions, indicative of the important role of chromosomal alterations in carcinogenesis. In this study the cytokinesis-blocked micronucleous assay was used to compare the radiosensitivity of blood lymphocytes obtained from Iranian breast or esophageal cancer patients (n = 50, n = 16; respectively) with that of control individuals (n = 40). For each sample, one thousand binucleate lymphocytes were analyzed before and after in vitro exposure to 3 Gy of gamma rays. The radiation-induced frequency of micronucleus was significantly higher in the breast cancer group (261/1,000 binucleated cells) than in esophageal cancer group (241/1,000 binucleated cells, P < 0.01) or in the control group (240/1,000 binucleated cells, P < 0.01). The results indicate that breast cancer patients are more radiosensitive compared to normal healthy individuals or esophageal cancer patients. Increased radiosensitivity could be due to defects in DNA repair genes involved in breast cancer formation. Since patients with esophageal cancer did not show elevated radiosensitivity, it is assumed that the contribution of radiosensitivity-related genes to the development of esophageal cancer may be smaller than the contribution of those genes to breast cancer.
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Affiliation(s)
- Hossein Mozdarani
- Dept. of Medical Genetics, School of Medical Sciences, Tarbiat Modarres University, Tehran, Iran.
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Sacks PG, Parnes SM, Gallick GE, Mansouri Z, Lichtner R, Satya-Prakash KL, Pathak S, Parsons DF. Establishment and characterization of two new squamous cell carcinoma cell lines derived from tumors of the head and neck. Cancer Res 1988; 48:2858-66. [PMID: 2452013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Two human cell lines were established from untreated squamous cell carcinomas of the head and neck. Line 183 was derived from a head and neck squamous cell carcinoma of the tonsil and 1483 from a head and neck squamous cell carcinoma of the retromolar trigone. Both lines grow in a cobblestone pattern demonstrating their epithelial heritage. Immunofluorescence studies and one-dimensional polyacrylamide gel electrophoresis indicated that both lines contain cytokeratins. Line 1483 is more aggressive in nude mice, has a higher efficiency for anchorage-independent growth, expresses p21ras (product of the ras oncogene) at a higher level, and is more aneuploid than 183. 1483 also grows as a multicellular tumor spheroid. Line 1483, which was established from the primary tumor of a patient with nodal metastasis, thus displays more progressed characteristics than line 183, which was established from a patient with no clinically positive nodes.
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Affiliation(s)
- P G Sacks
- Department of Tumor Biology, University of Texas M.D. Anderson Hospital and Tumor Institute, Houston 77030
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