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8. Effectiveness of an Informal Home Care Support Intervention Program to Reduce Loneliness and Improve Quality of Life among Lonely Community-Dwelling Older Adults: A Feasibility Study

Author

Kousha, Ahmad, primary, Lotfalinezhad, Elham, additional, Nadrian, Haidar, additional, Andersen-Ranberg, Karen, additional, Freeman, Shannon, additional, Barati, Fatemeh, additional, Mosazadeh, Hasan, additional, Hashemiparast, Mina, additional, Jafarabadi, Mohamed Asghari, additional, Sohrabi, Ahmad, additional, and Honarvar, Mohammad Reza, additional

Published
2024
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12. Assessment of a combined mouthwash on pain relief in pericoronitis: a randomized clinical study.

Author

Arekhi, Neman, Mortazavi, Nazanin, Bahramnejad, Emad, Khouri, Vahid, Tajaldini, Mahboubeh, Asgari, Negar, and Sohrabi, Ahmad

Subjects
CHLORHEXIDINE, HETEROCYCLIC compounds, MOUTH, DATA analysis, STATISTICAL significance, PERIODONTAL disease, STATISTICAL sampling, VISUAL analog scale, AMOXICILLIN, RANDOMIZED controlled trials, DESCRIPTIVE statistics, METRONIDAZOLE, PAIN management, DRUG efficacy, ANALYSIS of variance, STATISTICS, DATA analysis software, MOUTHWASHES, EVALUATION, DISEASE complications
Abstract

Background: Pericoronitis, an inflammation near wisdom teeth, often occurs when they are partially emerged, especially in the lower jaw. Commonly, the gingiva partially envelops the tooth. Treatments vary from gingival surgery to extraction. This study assessed the efficacy of a mouthwash with Chlorhexidine, Benzydamine, Nanosilver, Amoxicillin, and Metronidazole for pain reduction and enhancement of maximum mouth opening in acute pericoronitis cases. Materials and methods: In this randomized controlled clinical trial conducted at the Gorgan Dental Faculty, 48 pericoronitis patients were randomized into two groups. The control group used a 0.12% chlorhexidine mouthwash, while the case group used a mouthwash containing Chlorhexidine, Benzydamine, Nanosilver, Amoxicillin, and Metronidazole. The study recorded Visual Analog Scale (VAS) scores for 7 days, and Maximum mouth opening (MMO) was measured at the start and after 7 days. The analysis was performed using SPSS v20. Results: In this study, we compared the effects of a combined mouthwash with those of a chlorhexidine mouthwash on pericoronitis in 48 patients, with an average age of 21.56 years. No significant difference in pain reduction was observed between the groups; however, both groups exhibited decreased pain and improved MMO post-treatment. The gender distribution was balanced across both groups. Conclusion: The results indicate that both chlorhexidine mouthwash and combined mouthwash significantly improved maximum mouth opening. Nonetheless, there were no notable differences in efficacy between the two groups. These findings suggest that these mouthwashes may be beneficial for oral hygiene, warranting further in-depth research. Trial registration: Registered on 12/03/2023, registration number IRCT20230104057046N1. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset.

Author

Shiri, Isaac, Salimi, Yazdan, Sirjani, Nasim, Razeghi, Behrooz, Bagherieh, Sara, Pakbin, Masoumeh, Mansouri, Zahra, Hajianfar, Ghasem, Avval, Atlas Haddadi, Askari, Dariush, Ghasemian, Mohammadreza, Sandoughdaran, Saleh, Sohrabi, Ahmad, Sadati, Elham, Livani, Somayeh, Iranpour, Pooya, Kolahi, Shahriar, Khosravi, Bardia, Bijari, Salar, and Sayfollahi, Sahar

Subjects
FEDERATED learning, COMPUTED tomography, PROGNOSTIC models, COVID-19, DATA privacy
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. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Author

Shiri, Isaac, primary, Salimi, Yazdan, additional, Saberi, Abdollah, additional, Pakbin, Masoumeh, additional, Hajianfar, Ghasem, additional, Avval, Atlas Haddadi, additional, Sanaat, Amirhossein, additional, Akhavanallaf, Azadeh, additional, Mostafaei, Shayan, additional, Mansouri, Zahra, additional, Askari, Dariush, additional, Ghasemian, Mohammadreza, additional, Sharifipour, Ehsan, additional, Sandoughdaran, Saleh, additional, Sohrabi, Ahmad, additional, Sadati, Elham, additional, Livani, Somayeh, additional, Iranpour, Pooya, additional, Kolahi, Shahriar, additional, Khosravi, Bardia, additional, Khateri, Maziar, additional, Bijari, Salar, additional, Atashzar, Mohammad Reza, additional, Shayesteh, Sajad P., additional, Babaei, Mohammad Reza, additional, Jenabi, Elnaz, additional, Hasanian, Mohammad, additional, Shahhamzeh, Alireza, additional, Ghomi, Seyed Yaser Foroghi, additional, Mozafari, Abolfazl, additional, Shirzad‐Aski, Hesamaddin, additional, Movaseghi, Fatemeh, additional, Bozorgmehr, Rama, additional, Goharpey, Neda, additional, Abdollahi, Hamid, additional, Geramifar, Parham, additional, Radmard, Amir Reza, additional, Arabi, Hossein, additional, Rezaei‐Kalantari, Kiara, additional, Oveisi, Mehrdad, additional, Rahmim, Arman, additional, and Zaidi, Habib, additional

Published
2024
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17. Risk factors and treatment outcome of smear positive pulmonary tuberculosis patients: A five-year study in the North of Iran.

Author

Golsha, Roghieh, Mazandarani, Mahdi, Sohrabi, Ahmad, Shirzad-Aski, Hesamaddin, Kamalinia, Hamidreza, Rezaeifar, Atefeh, and Fattahi, Mandana

Subjects
TUBERCULOSIS patients, TUBERCULOSIS, MEDICAL personnel, TREATMENT effectiveness, DEATH rate
Abstract

Background: It is essential to constantly review the risk factors and treatment outcomes of tuberculosis (TB). This study evaluated some important risk factors of TB over five years. Methods: Between 2013 and 2018, all available information and possible risk factors related to TB patients were analyzed from the TB registry program of the health district of Gorgan, Iran. Results: Among 349 TB patients, 194 (55.59%) were males and 167 (47.85%) had at least a comorbidity. The death rate was higher in the age group more than 65 years (p < 0.001), the low-educated group (P = 0.012), and patients with underlying diseases, especially diabetes (p < 0.001). In total, univariate and multivariate statistical analyzes showed that having comorbidity (OR = 4.34; 95% CI 1.49 - 13.49), as well as, being jobless (OR = 3.07; 95% CI 1.19 - 8.59) were the main factors influencing the adverse events. Conclusion: According to the study, aging, underlying diseases, and cultural poverty include a higher share of the main risk factors for active TB and/or treatment outcomes. By considering these risk factors and training the medical staff continually, we can reduce the time of TB diagnosis, and prevent it from spreading. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Effectiveness of an Informal Home Care Support Intervention Program (HoSIP) to Improve Loneliness and Quality of Life in Community-Dwelling Lonely Older Adults: A Feasibility Study (Preprint)

Author

Kousha, Ahmad, primary, Lotfalinezhad, Elham, additional, Nadrian, Haidar, additional, Andersen-Ranberg, Karen, additional, Freeman, Shannon, additional, Barati, Fatemeh, additional, Mosazadeh, Hassan, additional, Hashemiparast, Mina, additional, Asghari Jafarabadi, Mohamed, additional, Sohrabi, Ahmad, additional, and Honarvar, Mohammad Reza, additional

Published
2023
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26. Association between high-sensitive cardiac troponin level and coronary artery disease: A systematic review and meta-analysis.

Author

Fatemi, Alireza, Zahedi, Mahdi, Yazdooei, Yasmin, Daei, Maryam, Ansari, Mohammad Mostafa, Sohrabi, Ahmad, Azizinejad, Arash, Hssanpour, Mohammad Reza, Behrouzifar, Marzieh, and Babapour, Hanieh

Subjects
CORONARY artery disease, TROPONIN, GREY literature, PUBLICATION bias
Abstract

Background and objectives: Previous studies suggest a link between high-sensitive cardiac troponin (hs-cTn) levels and coronary artery disease (CAD). However, the nature of this relationship is disputed. To address this, we conducted a study to gather and assess evidence on the association between hs-cTn and CAD prediction. Data sources: Studies were systematically searched and collected from four databases and different types of gray literature to cover all available evidence. After the screening, the selected articles' quality and risk of bias assessment were evaluated. Synthesis method: Meta-analysis calculated std. mean difference on the extracted data. Furthermore, heterogeneity, sensitivity, subgroups, and publication bias analyses were assessed. Results: Twenty-two studies were included in this systematic review, with a total of 844 cases and 2101 control people. The results of the meta-analysis on nine studies showed a significant and positive association between hs-cTn levels and CAD (pooled std. mean difference = 0.44; 95% confidence interval = 0.14–0.73; p < 0.003), with no publication bias (p = 0.9170). Among subgroups, std. mean differences were notably different only when the data were stratified by region or risk of bias; however, subgroup analysis could not determine the source of heterogeneity. Conclusions: Available prospective studies indicate a strong association of hs-cTn with the risk of CAD and significant improvements in CAD prediction. Further investigations in both molecular and clinical fields with proper methodology and more detailed information are needed to discover more evidence and underlying mechanisms to clear the interactive aspects of hs-cTn level in CAD patients. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Design, implementation and evaluation of informal home care support intervention program for lonely older adults in the community: Protocol for a feasibility study

Author

Lotfalinezhad, Elham, primary, Nadrian, Haidar, additional, Kousha, Ahmad, additional, Andersen-Ranberg, Karen, additional, Asghari Jafarabadi, Mohamed, additional, Sohrabi, Ahmad, additional, Hashemiparast, Mina, additional, Honarvar, Mohammad Reza, additional, and Freeman, Shannon, additional

Published
2022
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28. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms:Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Author

Shiri, Isaac, Salimi, Yazdan, Pakbin, Masoumeh, Hajianfar, Ghasem, Avval, Atlas Haddadi, Sanaat, Amirhossein, Mostafaei, Shayan, Akhavanallaf, Azadeh, Saberi, Abdollah, Mansouri, Zahra, Askari, Dariush, Ghasemian, Mohammadreza, Sharifipour, Ehsan, Sandoughdaran, Saleh, Sohrabi, Ahmad, Sadati, Elham, Livani, Somayeh, Iranpour, Pooya, Kolahi, Shahriar, Khateri, Maziar, Bijari, Salar, Atashzar, Mohammad Reza, Shayesteh, Sajad P., Khosravi, Bardia, Babaei, Mohammad Reza, Jenabi, Elnaz, Hasanian, Mohammad, Shahhamzeh, Alireza, Foroghi Ghomi, Seyaed Yaser, Mozafari, Abolfazl, Teimouri, Arash, Movaseghi, Fatemeh, Ahmari, Azin, Goharpey, Neda, Bozorgmehr, Rama, Shirzad-Aski, Hesamaddin, Mortazavi, Roozbeh, Karimi, Jalal, Mortazavi, Nazanin, Besharat, Sima, Afsharpad, Mandana, Abdollahi, Hamid, Geramifar, Parham, Radmard, Amir Reza, Arabi, Hossein, Rezaei-Kalantari, Kiara, Oveisi, Mehrdad, Rahmim, Arman, and Zaidi, Habib

Subjects
Radiomics, X-ray CT, Machine learning, COVID-19, Prognosis
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.

Published
2022
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29. The Role of Cognitive Control in Paranormal Beliefs: A Study Based on Performance in Go/No-go Task.

Author

Narmashiri, Abdolvahed, Hatami, Javad, Khosrowabadi, Reza, and Sohrabi, Ahmad

Subjects
CONTROL (Psychology), COGNITIVE ability, MULTIVARIATE analysis, TASK performance, HUMAN behavior
Abstract

Introduction: Cognitive control plays a role in human behavior and mental processes and affects paranormal beliefs. This study aims to investigate the role of cognitive control in paranormal beliefs using the go/no-go task. Methods: A total of 92 people were selected based on low, middle, and high scores in the revised paranormal belief scale (R-PBS) and assigned to 3 groups. The groups included 30 severe paranormal believers (13 females with a mean age of 25.3 years), 31 mild paranormal believers (14 females with a mean age of 26.4 years), and 31 skeptics (16 females with a mean age of 25.8 years). All participants were tested on the go/no-go task. A multivariate analysis of variance was conducted with the given groups (severe paranormal believers, mild paranormal believers, and skeptics) as the independent variable and the go/no-go subscales scores as dependent variables. Results: The findings showed a significant difference between the mean scores in errors of go (F(2, 89)=7.20, P=0.01), errors of no-go (F(2, 89)=11.81, P=0.01), and reaction time (F(2, 89)=21.46, P=0.01) between the groups. Conclusion: The severe and mild paranormal believers had lower accuracy and slower reaction times than the skeptics group. Therefore, severe paranormal believers and mild paranormal believers had a weakness in all go/no-go subscale scores. This finding suggests that paranormal beliefs may be related to poor cognitive control. [ABSTRACT FROM AUTHOR]

Published
2023
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30. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Author

Shiri, Isaac, primary, Salimi, Yazdan, additional, Pakbin, Masoumeh, additional, Hajianfar, Ghasem, additional, Avval, Atlas Haddadi, additional, Sanaat, Amirhossein, additional, Mostafaei, Shayan, additional, Akhavanallaf, Azadeh, additional, Saberi, Abdollah, additional, Mansouri, Zahra, additional, Askari, Dariush, additional, Ghasemian, Mohammadreza, additional, Sharifipour, Ehsan, additional, Sandoughdaran, Saleh, additional, Sohrabi, Ahmad, additional, Sadati, Elham, additional, Livani, Somayeh, additional, Iranpour, Pooya, additional, Kolahi, Shahriar, additional, Khateri, Maziar, additional, Bijari, Salar, additional, Atashzar, Mohammad Reza, additional, Shayesteh, Sajad P., additional, Khosravi, Bardia, additional, Babaei, Mohammad Reza, additional, Jenabi, Elnaz, additional, Hasanian, Mohammad, additional, Shahhamzeh, Alireza, additional, Foroghi Ghomi, Seyaed Yaser, additional, Mozafari, Abolfazl, additional, Teimouri, Arash, additional, Movaseghi, Fatemeh, additional, Ahmari, Azin, additional, Goharpey, Neda, additional, Bozorgmehr, Rama, additional, Shirzad-Aski, Hesamaddin, additional, Mortazavi, Roozbeh, additional, Karimi, Jalal, additional, Mortazavi, Nazanin, additional, Besharat, Sima, additional, Afsharpad, Mandana, additional, Abdollahi, Hamid, additional, Geramifar, Parham, additional, Radmard, Amir Reza, additional, Arabi, Hossein, additional, Rezaei-Kalantari, Kiara, additional, Oveisi, Mehrdad, additional, Rahmim, Arman, additional, and Zaidi, Habib, additional

Published
2022
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32. Injury burden in individuals aged 50 years or older in the Eastern Mediterranean region, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Author

Al-Hajj, Samar, primary, Farran, Sarah H, additional, Sibai, Abla Mehio, additional, Hamadeh, Randah R, additional, Rahimi-Movaghar, Vafa, additional, Al-Raddadi, Rajaa M, additional, Sadeghian, Farideh, additional, Ghodsi, Zahra, additional, Alhajyaseen, Wael, additional, Abu-Rmeileh, Niveen ME, additional, Abbasi, Behzad, additional, Abbasi-Kangevari, Mohsen, additional, Abdoli, Amir, additional, Abdulrahman, Salam Abdulqadir, additional, Abubaker Ali, Hiwa, additional, Abu-Gharbieh, Eman, additional, Afzal, Muhammad Sohail, additional, Ahadinezhad, Bahman, additional, Ahmad, Sajjad, additional, Ahmadi, Ali, additional, Ahmed, Ali, additional, Ahmed, Haroon, additional, Ahmed Rashid, Tarik, additional, Ajami, Marjan, additional, Al Hamad, Hanadi, additional, Alanezi, Fahad Mashhour, additional, Alanzi, Turki M, additional, Alghnam, Suliman A, additional, Alhalaiqa, Fadwa Naji, additional, Ali, Beriwan Abdulqadir, additional, Ali, Liaqat, additional, Alimohamadi, Yousef, additional, Alinia, Cyrus, additional, Aljunid, Syed Mohamed, additional, Almustanyir, Sami, additional, Amin, Tarek Tawfik, additional, Amini, Saeed, additional, Amiri, Sohrab, additional, Amirzade-Iranaq, Mohammad Hosein, additional, Anoushirvani, Ali Arash, additional, Ansari-Moghaddam, Alireza, additional, Anvari, Davood, additional, Aqeel, Muhammad, additional, Arabloo, Jalal, additional, Arab-Zozani, Morteza, additional, Arulappan, Judie, additional, Aryannejad, Armin, additional, Asgary, Saeed, additional, Asghari-Jafarabadi, Mohammad, additional, Ashraf, Tahira, additional, Athari, Seyyed Shamsadin, additional, Atout, Maha Moh'd Wahbi, additional, Azadmanjir, Zahra, additional, Azadnajafabad, Sina, additional, Azangou-Khyavy, Mohammadreza, additional, Azari Jafari, Amirhossein, additional, Azzam, Ahmed Y Y, additional, Bagherieh, Sara, additional, Bakhshaei, Mohammad Hossein, additional, Batiha, Abdul-Monim Mohammad, additional, Bedi, Neeraj, additional, Bhagavathula, Akshaya Srikanth, additional, Bhutta, Zulfiqar A, additional, Bibi, Sadia, additional, Bijani, Ali, additional, Butt, Nadeem Shafique, additional, Chegini, Zahra, additional, Da'ar, Omar B, additional, Dahlawi, Saad M A, additional, Delavari, Sajad, additional, Dianatinasab, Mostafa, additional, Didehdar, Mojtaba, additional, Djalalinia, Shirin, additional, Doaei, Saeid, additional, Dodangeh, Milad, additional, Eghbalian, Fatemeh, additional, Ehsani-Chimeh, Elham, additional, El Sayed, Iman, additional, El Sayed Zaki, Maysaa, additional, Elhabashy, Hala Rashad, additional, Elhadi, Muhammed, additional, Eskandarieh, Sharareh, additional, Etemadimanesh, Azin, additional, Ezati Asar, Mohamad, additional, Falahi, Shahab, additional, Farzadfar, Farshad, additional, Fatehizadeh, Ali, additional, Fattahi, Hamed, additional, Foroutan, Masoud, additional, Ghafourifard, Mansour, additional, Ghamari, Seyyed-Hadi, additional, Ghanbari, Reza, additional, Ghanei Gheshlagh, Reza, additional, Ghashghaee, Ahmad, additional, Gholamalizadeh, Maryam, additional, Ghorbani, Alireza, additional, Gilani, Syed Amir, additional, Gohari, Kimiya, additional, Goleij, Pouya, additional, Grivna, Michal, additional, Gubari, Mohammed Ibrahim Mohialdeen, additional, Haddadi, Rasool, additional, Hafezi-Nejad, Nima, additional, Haj-Mirzaian, Arvin, additional, Halwani, Rabih, additional, Hameed, Sajid, additional, Hanif, Asif, additional, Hasaballah, Ahmed I, additional, Hassan, Amr, additional, Hassanian-Moghaddam, Hossein, additional, Hassanipour, Soheil, additional, Hassankhani, Hadi, additional, Hayat, Khezar, additional, Heidari, Mohammad, additional, Hosseini, Mohammad-Salar, additional, Hosseini, Mostafa, additional, Hosseinzadeh, Mehdi, additional, Househ, Mowafa, additional, Hoveidamanesh, Soodabeh, additional, Huijer, Huda Abu-Saad, additional, Imani, Behzad, additional, Irvani, Seyed Sina Naghibi, additional, Jabbarinejad, Roxana, additional, Jahani, Mohammad Ali, additional, Jalili, Mahsa, additional, Javadi Mamaghani, Amirreza, additional, Jeddi, Seyed Ali, additional, Joukar, Farahnaz, additional, Kabir, Ali, additional, Kalankesh, Laleh R, additional, Kalankesh, Leila R, additional, Kalhor, Rohollah, additional, Kamran, Aziz, additional, Keikavoosi-Arani, Leila, additional, Keykhaei, Mohammad, additional, Khader, Yousef Saleh, additional, Khalilov, Rovshan, additional, Khammarnia, Mohammad, additional, Khan, Ejaz Ahmad, additional, Khan, Maseer, additional, Khan, Moien AB, additional, Khanali, Javad, additional, Khayat Kashani, Hamid Reza, additional, Khosravizadeh, Omid, additional, Kolahi, Ali-Asghar, additional, Koohestani, Hamid Reza, additional, Koolaji, Sogol, additional, Lami, Faris Hasan, additional, Lasrado, Savita, additional, Madadizadeh, Farzan, additional, Magdy Abd El Razek, Muhammed, additional, Mahdavi, Mahdi, additional, Mahjoub, Soleiman, additional, Mahmoodpoor, Ata, additional, Mahmoudi, Razzagh, additional, Makki, Alaa, additional, Malekpour, Mohammad-Reza, additional, Malekzadeh, Reza, additional, Malih, Narges, additional, Malik, Ahmad Azam, additional, Mansour-Ghanaei, Fariborz, additional, Mansouri, Borhan, additional, Mansournia, Mohammad Ali, additional, Masoumi, Seyedeh Zahra, additional, Mehrabi Nasab, Entezar, additional, Menezes, Ritesh G, additional, Mesregah, Mohamed Kamal, additional, Mirmoeeni, Seyyedmohammadsadeq, additional, Mirza-Aghazadeh-Attari, Mohammad, additional, Mohammadi, Esmaeil, additional, Mohammadi, Heidar, additional, Mohammadi, Mokhtar, additional, Mohammadi, Seyyede Momeneh, additional, Mohammadian-Hafshejani, Abdollah, additional, Mohammed, Teroj Abdulrahman, additional, Moludi, Jalal, additional, Momtazmanesh, Sara, additional, Moradi, Maryam, additional, Moradi, Masoud, additional, Moradpour, Farhad, additional, Moradzadeh, Rahmatollah, additional, Mortazavi, Nazanin, additional, Mortazavi, Seyede Salehe, additional, Najafi, Houshang, additional, Najafpour, Zhila, additional, Natto, Zuhair S, additional, Nazari, Javad, additional, Nouraei, Hasti, additional, Nouri, Fatemeh, additional, Okati-Aliabad, Hassan, additional, Omar Bali, Ahmed, additional, Omer, Emad, additional, Pashazadeh Kan, Fatemeh, additional, Pazoki Toroudi, Hamidreza, additional, Piracha, Zahra Zahid, additional, Pourbagher-Shahri, AliMohammad, additional, Pourtaheri, Naeimeh, additional, Rabiee, Mohammad, additional, Rabiee, Navid, additional, Raeghi, Saber, additional, Rafieemehr, Hassan, additional, Rafiei, Sima, additional, Rahim, Fakher, additional, Rahimi-Dehgolan, Shahram, additional, Rahmani, Amir Masoud, additional, Rahmanian, Vahid, additional, Ramezanzadeh, Kiana, additional, Rao, Chythra R, additional, Rashedi, Vahid, additional, Rashidi, Amirfarzan, additional, Rashidi, Mahsa, additional, Rashidi, Mohammad-Mahdi, additional, Rasul, Azad, additional, Rawassizadeh, Reza, additional, Rayegani, Seyed-Mansoor, additional, Rezaei, Nazila, additional, Rezaei, Negar, additional, Rezaei, Saeid, additional, Rezaei, Zahed, additional, Rezaeian, Mohsen, additional, Rezapur-Shahkolai, Forouzan, additional, Rezazadeh-Khadem, Sahba, additional, Saadati, Mohammad, additional, Sabour, Siamak, additional, Saddik, Basema, additional, Saeed, Umar, additional, Saeedi Moghaddam, Sahar, additional, Safarpour, Hamid, additional, Safdarian, Mahdi, additional, Sahebkar, Amirhossein, additional, Salamati, Payman, additional, Salem, Marwa Rashad, additional, Samy, Abdallah M, additional, Sanadgol, Nima, additional, Saqib, Muhammad Arif Nadeem, additional, Sarikhani, Yaser, additional, Sarveazad, Arash, additional, Sathian, Brijesh, additional, Sayyah, Mehdi, additional, Sepanlou, Sadaf G, additional, Shahabi, Saeed, additional, Shaikh, Masood Ali, additional, Shaker, Elaheh, additional, Shams-Beyranvand, Mehran, additional, Sharifian, Sakineh, additional, Shati, Mohsen, additional, Shehata, Hatem Samir, additional, Sheidaei, Ali, additional, Sheikhbahaei, Sara, additional, Sheikhi, Rahim Ali, additional, Shobeiri, Parnian, additional, Siabani, Soraya, additional, Sivandzadeh, Gholam Reza, additional, Sofi-Mahmudi, Ahmad, additional, Sohrabi, Ahmad, additional, Tabaeian, Seidamir Pasha, additional, Tabary, Mohammadreza, additional, Tabibian, Elnaz, additional, Taheri, Ensiyeh, additional, Taheri, Majid, additional, Tehrani-Banihashemi, Arash, additional, Tiyuri, Amir, additional, Tohidast, Seyed Abolfazl, additional, Ullah, Saif, additional, Vahedi, Parviz, additional, Valadan Tahbaz, Sahel, additional, Valizadeh, Rohollah, additional, Varmaghani, Mehdi, additional, Vo, Bay, additional, Waheed, Yasir, additional, Yahyazadeh Jabbari, Seyed Hossein, additional, Yazdanpanah, Fereshteh, additional, Yousefi, Zabihollah, additional, Yusefi, Hossein, additional, Zahirian Moghadam, Telma, additional, Zandian, Hamed, additional, Zare, Iman, additional, Ziapour, Arash, additional, and Mokdad, Ali H, additional

Published
2022
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35. Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 Patients

Author

Shiri, Isaac, primary, Salimi, Yazdan, additional, Saberi, Abdollah, additional, Pakbin, Masoumeh, additional, Hajianfar, Ghasem, additional, Avval, Atlas Haddadi, additional, Sanaat, Amirhossein, additional, Akhavanallaf, Azadeh, additional, Mostafaei, Shayan, additional, Mansouri, Zahra, additional, Askari, Dariush, additional, Ghasemian, Mohammadreza, additional, Sharifipour, Ehsan, additional, Sandoughdaran, Saleh, additional, Sohrabi, Ahmad, additional, Sadati, Elham, additional, Livani, Somayeh, additional, Iranpour, Pooya, additional, Kolahi, Shahriar, additional, Khosravi, Bardia, additional, Khateri, Maziar, additional, Bijari, Salar, additional, Atashzar, Mohammad Reza, additional, Shayesteh, Sajad P., additional, Babaei, Mohammad Reza, additional, Jenabi, Elnaz, additional, Hasanian, Mohammad, additional, Shahhamzeh, Alireza, additional, Gholami, Seyed Yaser Foroghi, additional, Mozafari, Abolfazl, additional, Shirzad-Aski, Hesamaddin, additional, Movaseghi, Fatemeh, additional, Bozorgmehr, Rama, additional, Goharpey, Neda, additional, Abdollahi, Hamid, additional, Geramifar, Parham, additional, Radmard, Amir Reza, additional, Arabi, Hossein, additional, Rezaei-Kalantari, Kiara, additional, Oveisi, Mehrdad, additional, Rahmim, Arman, additional, and Zaidi, Habib, additional

Published
2021
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36. COVID-19 Prognostic Modeling Using CT Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients

Author

Shiri, Isaac, primary, Salimi, Yazdan, additional, Pakbin, Masoumeh, additional, Hajianfar, Ghasem, additional, Avval, Atlas Haddadi, additional, Sanaat, Amirhossein, additional, Mostafaei, Shayan, additional, Akhavanallaf, Azadeh, additional, Saberi, Abdollah, additional, Mansouri, Zahra, additional, Askari, Dariush, additional, Ghasemian, Mohammadreza, additional, Sharifipour, Ehsan, additional, Sandoughdaran, Saleh, additional, Sohrabi, Ahmad, additional, Sadati, Elham, additional, Livani, Somayeh, additional, Iranpour, Pooya, additional, Kolahi, Shahriar, additional, Khateri, Maziar, additional, Bijari, Salar, additional, Atashzar, Mohammad Reza, additional, Shayesteh, Sajad P., additional, Khosravi, Bardia, additional, Babaei, Mohammad Reza, additional, Jenabi, Elnaz, additional, Hasanian, Mohammad, additional, Shahhamzeh, Alireza, additional, Gholami, Seyed Yaser Foroghi, additional, Mozafari, Abolfazl, additional, Teimouri, Arash, additional, Movaseghi, Fatemeh, additional, Ahmari, Azin, additional, Goharpey, Neda, additional, Bozorgmehr, Rama, additional, Shirzad-Aski, Hesamaddin, additional, Mortazavi, Rozbeh, additional, Karimi, Jalal, additional, Mortazavi, Nazanin, additional, Besharat, Sima, additional, Afsharpad, Mandana, additional, Abdollahi, Hamid, additional, Geramifar, Parham, additional, Radmard, Amir Reza, additional, Arabi, Hossein, additional, Rezaei-Kalantari, Kiara, additional, Oveisi, Mehrdad, additional, Rahmim, Arman, additional, and Zaidi, Habib, additional

Published
2021
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37. The global scientific publications on gut microbiota in type 2 diabetes; a bibliometric, Scientometric, and descriptive analysis

Author

Dehghanbanadaki, Hojat, primary, Aazami, Hossein, additional, Ejtahed, Hanieh Sadat, additional, Sohrabi, Ahmad, additional, Raftar, Shahrbanoo Keshavarz Azizi, additional, Tarashi, Samira, additional, Tabatabaei-Malazy, Ozra, additional, Bahramali, Golnaz, additional, Siadat, Seyed Davar, additional, Esfahani, Ensieh Nasli, additional, and Razi, Farideh, additional

Published
2021
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38. Factors predicting good outcome of intravenous thrombolysis in stroke patients before rt-PA administration

Author

Mehrpour, Masoud, Afrakhteh, Motahareh, Shojaei, Seyedeh Fahimeh, Sohrabi, Ahmad, Ashayeri, Rezan, Esmaeili, Sara, and Bahadori, Maryam

Subjects
lcsh:Internal medicine, ischemic stroke, rt-pa, cardiovascular diseases, lcsh:RC31-1245, thrombolytic therapy
Abstract

Background: To determine whether it is possible to predict intravenous thrombolytic therapy (IVT) outcome after 3 months in acute ischemic stroke patients who are candidate to receive recombinant tissue plasminogen activator (rt-PA), before rt-PA administration based on their risk factors and some available laboratory results. Methods: We enrolled 118 ischemic stroke patients who were treated with standard dose of Alteplase in our hospital. Baseline characteristics, door-to-needle time (DTN), onset-to-treatment time (OTT), the National Institute Health Stroke Scale (NIHSS), systolic and diastolic blood pressure on admission, history of diabetes, hypertension, dyslipidemia, coronary artery disease (CAD), previous ischemic stroke, atrial fibrillation (AF), laboratory results were retrospectively collected. The modified Rankin Scale (mRS) was recorded after 3 months of admission and patients were divided into good (mRS£ 2) and poor (mRS>2) outcome groups. Chi-square test and t-test were used for categorical and continuous variables, respectively. Predictors for outcome after 3 months were studied by multivariable logistic regression. Results: Good outcome was seen in 60 (51) patients and poor outcome was seen in 58 (49) patients. Significant predictors for outcome at 3 months according to multivariable regression analysis were NIHSS score (odds ratio OR, 0.61; 95% confidence interval CI, 0.498-0.750; p

Published
2019

39. Factors predicting the outcome of intravenous thrombolysis in stroke patients before rt-PA administration

Author

Mehrpour, Masoud, Afrakhte, Motahare, Shojaei, Seyedeh Fahimeh, Sohrabi, Ahmad, Ashayeri, Rezan, Esmaeili, Sara, and Bahadori, Maryam

Subjects
Thrombolytic therapy, Ischemic stroke, Original Article, rt-PA, cardiovascular diseases
Abstract

Background: To determine whether it is possible to predict intravenous thrombolytic therapy (IVT) outcome after 3 months in acute ischemic stroke patients who are candidate to receive recombinant tissue plasminogen activator (rt-PA), before rt-PA administration based on their risk factors and some available laboratory results. Methods: We enrolled 118 ischemic stroke patients who were treated with standard dose of Alteplase in our hospital. Baseline characteristics, door-to-needle time (DTN), onset-to-treatment time (OTT), the National Institute Health Stroke Scale (NIHSS), systolic and diastolic blood pressure on admission, history of diabetes, hypertension, dyslipidemia, coronary artery disease (CAD), previous ischemic stroke, atrial fibrillation (AF), laboratory results were retrospectively collected. The modified Rankin Scale (mRS) was recorded after 3 months of admission and patients were divided into good (mRS 2) and poor (mRS>2) outcome groups. Chi-square test and t-test were used for categorical and continuous variables, respectively. Predictors for outcome after 3 months were studied by multivariable logistic regression. Results: Good outcome was seen in 60 (51%) patients and poor outcome was seen in 58 (49%) patients. Significant predictors for outcome at 3 months according to multivariable regression analysis were NIHSS score (odds ratio [OR], 0.61; 95% confidence interval [CI], 0.498-0.750; p

Published
2019

40. The Application of Bi-clustering and Bayesian Network for Gene Sets Network Construction in Breast Cancer Microarray Data.

Author

Sohrabi, Ahmad, Saraygord-Afshari, Neda, and Roudbari, Masoud

Subjects
*INFERENTIAL statistics, *MICROARRAY technology, *CANCER patients, *GENE expression profiling, *DESCRIPTIVE statistics, *STATISTICAL models, *CLUSTER analysis (Statistics), *BREAST tumors, *ALGORITHMS
Abstract

Background: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. Results: After preprocessing, clustering was performed on the data with the dimension (4710 x 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets. Conclusion: According to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent. [ABSTRACT FROM AUTHOR]

Published
2022
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43. Detection Rate of Colorectal Polyps in Symptomatic Candidates of Colonoscopy: When Should We Do a Total Colonoscopy?

Author

Norouzi, Alireza, primary, Besharat, Sima, additional, Isapanah Amlashi, Fazel, additional, Nasrabadi, Maryam, additional, Gharanjik, Isan, additional, Ashkbari, Ali, additional, Riahi, Zoha, additional, Kaabe, Sajjad, additional, Shahabi Nasab, Iman, additional, Roshandel, Gholamreza, additional, Sohrabi, Ahmad, additional, Amiriani, Taghi, additional, and Semnani, Shahryar, additional

Published
2021
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44. Effect of Adding Intravenous Somatostatin to Rectal Indomethacin on Post-Endoscopic Retrograde Cholangiopancreatography (ERCP) Pancreatitis in High-risk Patients

Author

Norouzi, Alireza, primary, Ghasem Poori, Eshrat, additional, Kaabe, Sajjad, additional, Norouzi, Zahra, additional, Sohrabi, Ahmad, additional, Amlashi, Fazel I., additional, Tavasoli, Samaneh, additional, Besharat, Sima, additional, Ezabadi, Zahra, additional, and Amiriani, Taghi, additional

Published
2021
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45. EEG Coherence During Resting State Over Frontal Regions in Paranormal Beliefs.

Author

Narmashiri, Abdolvahed, Hatami, Javad, Khosrowabadi, Reza, and Sohrabi, Ahmad

Subjects
ELECTROENCEPHALOGRAPHY, FRONTAL lobe, EXTRASENSORY perception, ALPHA rhythm, EXECUTIVE function
Abstract

Introduction: Paranormal beliefs are defined as believing in extrasensory perception, precognition, witchcraft, and telekinesis, magical thinking, psychokinesis, superstitions. Previous studies corroborate that executive brain functions underpin paranormal beliefs. To test causal hypotheses, neurophysiological studies of brain activity are required. Method: A sample of 20 students (10 females, age: 22.50 ± 4.07 years) were included for the current study. The absolute power of resting-state EEG in intrahemispheric and interhemispheric coherence was analyzed with eyes opened. The paranormal beliefs were determined based on the total score of the Revised Paranormal Belief Scale (RPBS). Result: The results of this study demonstrated that there was a significant negative relationship between paranormal beliefs and EEG resting state in alpha band activity in the frontal lobe (left hemisphere), EEG coherence of alpha and beta1, beta2, and gamma band activities in the frontal lobe (right hemisphere) and coherence of alpha and beta1, beta2 and gamma band activities between frontal regions (two hemispheres). In addition, the results showed that coherence of alpha, alpha1, beta, and beta2 band activities between frontal lobe (right hemispheres) and EEG coherence of delta, alpha1, and band activities in the frontal lobe (two hemispheres) predicted paranormal beliefs. Conclusion: This study confirms connecting executive brain functions to paranormal beliefs, and determines that frontal brain functioning may contribute to paranormal beliefs. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Clinical Presentations of the Survivor and Non-survivor Hospitalized Patients with COVID-19 in the Golestan Province of Iran during the First Peak of the Epidemics.

Author

Abdolahi, Nafiseh, Norouzi, Alireza, Golsha, Roghieh, Khodabakhshi, Behnaz, Sohrabi, Ahmad, Gharib, Mohammad Hadi, Khandashpoor, Mahmoud, Tavassoli, Samane, Peivandi, Babak, Fazel, Abdolreza, Amlashi, Fazel Isapanah, Livani, Somayeh, Roshandel, Gholamreza, Besharat, Sima, and Shirzad-Aski, Hesamaddin

Subjects
COVID-19, SARS-CoV-2, SYMPTOMS, HOSPITAL patients, CORONAVIRUS diseases, COUGH, EPIDEMICS
Abstract

Background: Considering the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, which causes coronavirus disease 2019 (COVID-19), we aimed to report the clinical features of 427 patients with COVID-19 and the outcomes after one-month admission to major teaching hospitals in the northeast of Iran. Materials and Methods: Data of patients hospitalized with COVID-19 from 20 February 2020 to 20 April 2020 was analyzed using the R software. The cases and their outcomes were monitored up to one month following their admission. Results: Among 427 patients with a median age of 53 years (50.8% male), 81 (19%) were directly admitted to the ICU ward, and 68 (16%) died during the study. The mean (SD) lengths of hospital stay were significantly higher in the non-survivors (6 (9) days) than survivors (4 (5) days) (P = 0.018). Ventilation need was reported in 67.6% of the non-survivors and 0.8% of the survivors (P < 0.001). Cough (72.8%), fever (69.3%), and dyspnea (64.0%) were the most common symptoms. There were more comorbidities in the severe cases (73.5%) and non-survivor (77.5%). Liver and kidney damage were significantly more common in non-survivors. Ninety percent of the patients had at least one abnormal chest CT scan finding, including crazy paving and consolidation patterns (27.1%), followed by the ground-glass opacity (24.7%). Conclusion: Results showed that the patients’ age, underlying comorbidities, levels of SpO2, and laboratory findings at the time of admission may predict the progress of the disease and can be considered mortality-related factors. [ABSTRACT FROM AUTHOR]

Published
2022

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