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Article ; Online: CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

Marefat, Abdolreza / Marefat, Mahdieh / Hassannataj Joloudari, Javad / Nematollahi, Mohammad Ali / Lashgari, Reza

2023 Volume 11, Page(s) 1025746

Abstract: COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected ... ...

Abstract	COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
MeSH term(s)	Humans ; COVID-19/diagnostic imaging ; X-Rays ; Health Personnel ; Tomography, X-Ray Computed
Language	English
Publishing date	2023-02-27
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2023.1025746
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Article ; Online: Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images.

Joloudari, Javad Hassannataj / Azizi, Faezeh / Nodehi, Issa / Nematollahi, Mohammad Ali / Kamrannejhad, Fateme / Hassannatajjeloudari, Edris / Alizadehsani, Roohallah / Islam, Sheikh Mohammed Shariful

Mathematical biosciences and engineering : MBE

2023 Volume 20, Issue 9, Page(s) 16236–16258

Abstract: COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in ... ...

Abstract	COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.
MeSH term(s)	Humans ; Artificial Intelligence ; COVID-19 Testing ; COVID-19/diagnostic imaging ; Neural Networks, Computer ; Tomography, X-Ray Computed
Language	English
Publishing date	2023-11-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2023725
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Article: GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis.

Hassannataj Joloudari, Javad / Azizi, Faezeh / Nematollahi, Mohammad Ali / Alizadehsani, Roohallah / Hassannatajjeloudari, Edris / Nodehi, Issa / Mosavi, Amir

Frontiers in cardiovascular medicine

2022 Volume 8, Page(s) 760178

Abstract: Background: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly ... ...

Abstract	Background: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods: Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset. Results: As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset. Conclusion: We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
Language	English
Publishing date	2022-02-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2781496-8
ISSN	2297-055X
ISSN	2297-055X
DOI	10.3389/fcvm.2021.760178
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Article ; Online: Body composition predicts hypertension using machine learning methods: a cohort study.

Nematollahi, Mohammad Ali / Jahangiri, Soodeh / Asadollahi, Arefeh / Salimi, Maryam / Dehghan, Azizallah / Mashayekh, Mina / Roshanzamir, Mohamad / Gholamabbas, Ghazal / Alizadehsani, Roohallah / Bazrafshan, Mehdi / Bazrafshan, Hanieh / Bazrafshan Drissi, Hamed / Shariful Islam, Sheikh Mohammed

Scientific reports

2023 Volume 13, Issue 1, Page(s) 6885

Abstract: We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body ... ...

Abstract	We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
MeSH term(s)	Male ; Humans ; Adult ; Middle Aged ; Aged ; Female ; Cohort Studies ; Bayes Theorem ; Electric Impedance ; Body Composition ; Machine Learning
Language	English
Publishing date	2023-04-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-34127-6
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Article ; Online: Prognosis prediction in traumatic brain injury patients using machine learning algorithms.

Khalili, Hosseinali / Rismani, Maziyar / Nematollahi, Mohammad Ali / Masoudi, Mohammad Sadegh / Asadollahi, Arefeh / Taheri, Reza / Pourmontaseri, Hossein / Valibeygi, Adib / Roshanzamir, Mohamad / Alizadehsani, Roohallah / Niakan, Amin / Andishgar, Aref / Islam, Sheikh Mohammed Shariful / Acharya, U Rajendra

Scientific reports

2023 Volume 13, Issue 1, Page(s) 960

Abstract: Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic ... ...

Abstract	Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
MeSH term(s)	Humans ; Brain Injuries, Traumatic/diagnosis ; Brain Injuries, Traumatic/therapy ; Prognosis ; Treatment Outcome ; Algorithms ; Machine Learning
Language	English
Publishing date	2023-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-28188-w
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Article ; Online: Effect of oral administration of GnRHa+nanoparticles of chitosan in oogenesis acceleration of goldfish Carassius auratus.

Kookaram, Kazem / Mojazi Amiri, Bagher / Dorkoosh, Farid Abedin / Nematollahi, Mohammad Ali / Mortazavian, Elaheh / Abed Elmdoust, Amirreza

Fish physiology and biochemistry

2021 Volume 47, Issue 2, Page(s) 477–486

Abstract: Several methods have been used to accelerate previtellogenesis and vitellogenesis stages in fish, including hormonal induction, sustained-release delivery systems, and oral delivery of gonadotropin-releasing hormone (GnRH). In this study, we proposed the ...

Abstract	Several methods have been used to accelerate previtellogenesis and vitellogenesis stages in fish, including hormonal induction, sustained-release delivery systems, and oral delivery of gonadotropin-releasing hormone (GnRH). In this study, we proposed the oral administration of GnRH analog + nanoparticles of chitosan to accelerate oogenesis in goldfish as a model fish in reproductive biology and aquaculture. In this regard, adult female goldfish were fed with six experimental groups: chitosan, 50 μg GnRHa/kg b.w., 100 μg GnRHa/kg b.w., chitosan + 50 μg GnRHa/kg b.w., and chitosan + 100 μg GnRHa/kg b.w., and diet without any additive as the control for 40 days in triplicate. Every 10 days, ovarian samples were collected, and gonadosomatic index (GSI), oocyte diameter (OD), zona radiata thickness (Zr), and diameter of the follicular layer (Fl) were measured to assess ovarian developmental stage for each treatment. Additionally, blood sampling was done to measure serum 17β-estradiol concentration at the end of the experiment. All parameters remained unchanged during the experiment in the chitosan-fed group. In the group fed with 100 μg GnRH or chitosan nanoparticle + 100 μg GnRHa, these parameters in general were increased. However, the effects in 50 μg GnRHa or chitosan nanoparticle + 50 μg GnRHa treatments were uncertain; they affected serum E2 levels as a trend toward a significant increase was observed in goldfish treated with chitosan nanoparticle + 100 μg GnRHa. Finally, the results indicated the oral administration of chitosan + 100 μg GnRHa/kg b.w. significantly accelerated the oocyte development and growth of ovary.
MeSH term(s)	Administration, Oral ; Animals ; Chitosan/chemistry ; Female ; Goldfish ; Gonadotropin-Releasing Hormone/administration & dosage ; Gonadotropin-Releasing Hormone/chemistry ; Gonadotropin-Releasing Hormone/pharmacology ; Nanoparticles/chemistry ; Oocytes/drug effects ; Oocytes/growth & development ; Oogenesis/drug effects
Chemical Substances	Gonadotropin-Releasing Hormone (33515-09-2) ; Chitosan (9012-76-4)
Language	English
Publishing date	2021-02-10
Publishing country	Netherlands
Document type	Clinical Trial, Veterinary ; Journal Article
ZDB-ID	292907-7
ISSN	1573-5168 ; 0920-1742
ISSN (online)	1573-5168
ISSN	0920-1742
DOI	10.1007/s10695-021-00926-9
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Article ; Online: The toxicological effect of Ruta graveolens extract in Siamese fighting fish: a behavioral and histopathological approach.

Forsatkar, Mohammad Navid / Nematollahi, Mohammad Ali / Brown, Culum

Ecotoxicology (London, England)

2016 Volume 25, Issue 4, Page(s) 824–834

Abstract: The effects of pharmacological waste on aquatic ecosystems are increasingly highlighted in ecotoxicology research. Many of these products are designed for human physiology but owing to the conservative nature of vertebrate evolution they also tend to ... ...

Abstract	The effects of pharmacological waste on aquatic ecosystems are increasingly highlighted in ecotoxicology research. Many of these products are designed for human physiology but owing to the conservative nature of vertebrate evolution they also tend to have effects on aquatic organisms and fishes in particular when they find their way into aquatic systems via wastewater effluent. One area of research has focused on reproductive control and the associated hormone treatments. Many of these hormones affect the reproductive physiology of fishes and may cause feminization of male reproductive traits. Alternative medicines have also been widely used particularly in traditional cultures but few of these alternative treatments have been assessed with respect to their potential impact on aquatic ecosystems. Rue (Ruta graveolens) has been used as a male contraceptive in traditional medicines but its effects on fish behavior and reproductive anatomy have yet to be established. Here we show that treating Siamese fighting fish, Betta splendens, with extract of rue has a significant effect on key aggressive/reproductive behaviors and the propensity to explore novel objects (boldness). In all cases the respective behaviors were reduced relative to controls and sham injected fish. Histological analysis of the testes revealed that rue exposure reduced the number of spermatozoa but increased the number of spermatocytes relative to controls.
MeSH term(s)	Animals ; Behavior, Animal/drug effects ; Male ; Perciformes/physiology ; Plant Extracts/toxicity ; Ruta ; Spermatozoa/drug effects ; Testis/drug effects
Chemical Substances	Plant Extracts
Language	English
Publishing date	2016-02-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	34042-x
ISSN	1573-3017 ; 0963-9292
ISSN (online)	1573-3017
ISSN	0963-9292
DOI	10.1007/s10646-016-1639-5
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Article ; Online: Quantity discrimination in parental fish: female convict cichlid discriminate fry shoals of different sizes.

Forsatkar, Mohammad Navid / Nematollahi, Mohammad Ali / Bisazza, Angelo

Animal cognition

2016 Volume 19, Issue 5, Page(s) 959–964

Abstract: Numerical abilities have been found to be adaptive in different contexts, including mating, foraging, fighting assessment and antipredator strategies. In species with parental care, another potential advantage is the possibility to adjust parental ... ...

Abstract	Numerical abilities have been found to be adaptive in different contexts, including mating, foraging, fighting assessment and antipredator strategies. In species with parental care, another potential advantage is the possibility to adjust parental behavior in relation to the numerosity of the progeny. The finding that many fish vary their parental investment in relation to brood size advocates the existence of a mechanism for appraising offspring number, an aspect that has never been directly investigated. Here we tested the ability of parental female convict cichlid (Amatitlania nigrofasciata) to discriminate between two fry groups differing in number by measuring time spent attempting to recover groups of fry experimentally displaced from the next. Females spent more time trying to recover the fry from larger groups when tested with contrasts 6 versus 12 (1:2) and 6 versus 9 fry (2:3); however, they showed no preference in the 6 versus 8 (3:4) contrast, suggesting that this task exceeds their discrimination capacity.
Language	English
Publishing date	2016-09
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1466332-6
ISSN	1435-9456 ; 1435-9448
ISSN (online)	1435-9456
ISSN	1435-9448
DOI	10.1007/s10071-016-0997-y
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Book ; Online: GSVMA

Joloudari, Javad Hassannataj / Azizi, Faezeh / Nematollahi, Mohammad Ali / Alizadehsani, Roohallah / Hassannataj, Edris / Mosavi, Amir

A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset

2021

Abstract: Coronary heart disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side ... ...

Abstract	Coronary heart disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Hence, this paper provides a new hybrid machine learning model called Genetic Support Vector Machine and Analysis of Variance (GSVMA). The ANOVA is known as the kernel function for SVM. The proposed model is performed based on the Z-Alizadeh Sani dataset. A genetic optimization algorithm is used to select crucial features. In addition, SVM with Anova, Linear SVM, and LibSVM with radial basis function methods were applied to classify the dataset. As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold cross-validation technique with 35 selected features on the Z-Alizadeh Sani dataset. Therefore, the genetic optimization algorithm is very effective for improving accuracy. The computer-aided GSVMA method can be helped clinicians with CAD diagnosis. Comment: 14 pages, 10 figures
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2021-07-23
Publishing country	us
Document type	Book ; Online
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Article: Transgenerational disrupting impacts of atrazine in zebrafish: Beneficial effects of dietary spirulina.

Hedayatirad, Maryam / Mirvaghefi, Alireza / Nematollahi, Mohammad Ali / Forsatkar, Mohammad Navid / Brown, Culum

Comparative biochemistry and physiology. Toxicology & pharmacology : CBP

2019 Volume 230, Page(s) 108685

Abstract: In a range of fish species, offspring sustainability is much dependent to their mother's investment into the egg yolk. A healthy environment helps broodfish to produce normal quality offspring. However, deviation from optimal conditions can disturb body ... ...

Abstract	In a range of fish species, offspring sustainability is much dependent to their mother's investment into the egg yolk. A healthy environment helps broodfish to produce normal quality offspring. However, deviation from optimal conditions can disturb body functions that effect the next generation. Here, zebrafish (Danio rerio) was employed to investigate the transgenerational impacts of an immunotoxic and endocrine disruptor, atrazine (AZ). In addition, the possible ameliorated effects of a nutraceutical, Arthrospira platensis (spirulina- SP), was considered. Adult females were either exposed to 0 (Cn), 5 (AZ5), and 50 (AZ50) μg/L AZ or fed SP-supplemented diet (10 g/kg; SP). In combination treatments, fish were also exposed to AZ and fed SP (SP-AZ5 and SP-AZ50). Embryos were obtained after 28 d of exposure. Exposure to AZ50 caused females to produce eggs with significantly lower fertilization and hatching. No changes were observed in the concentrations of thyroid hormones. AZ significantly increased cortisol response and reduced levels of immunoglobulin, lysozyme and complement activities in females and their offspring. SP-AZ5 and SP-AZ50 females, however, resisted to the toxic effects of AZ, produced embryos with lower cortisol content and higher immunity competence. Bactericidal activity of the embryos also showed the transgenerational antimicrobial effects of SP along with the AZ immunotoxicity. Overall, these results indicate that AZ could have long lasting toxic effects on fish, and that dietary SP-supplementation could ameliorate AZ induced transgenerational toxic effects.
MeSH term(s)	Animals ; Atrazine/toxicity ; Biomarkers/metabolism ; Dietary Supplements ; Endocrine Disruptors/toxicity ; Female ; Maternal Inheritance ; Spirulina/metabolism ; Thyroid Gland/metabolism ; Thyroid Hormones/metabolism ; Water Pollutants, Chemical/toxicity ; Zebrafish/embryology ; Zebrafish/metabolism
Chemical Substances	Biomarkers ; Endocrine Disruptors ; Thyroid Hormones ; Water Pollutants, Chemical ; Atrazine (QJA9M5H4IM)
Language	English
Publishing date	2019-12-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	189285-x
ISSN	1532-0456 ; 0306-4492 ; 0742-8413
ISSN	1532-0456 ; 0306-4492 ; 0742-8413
DOI	10.1016/j.cbpc.2019.108685
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