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Fulltext The Effect of Doxycycline on Achilles Tendon Repair in a Rat Model

Sobhani-Eraghi A, Panahi M, Shirani A, Pazoki-Toroudi H

Malays Orthop J, 2020 Nov;14(3):155-160.
PMID: 33403077 DOI: 10.5704/MOJ.2011.024

Introduction: Doxycycline is a commonly used antibiotic that is also a potent inhibitor of matrix metalloproteinase (MMPs). The use of doxycycline in repairing tendon lesions has been previously investigated and conflicting findings have been reported on its effectiveness. In this study, we sought to evaluate the effects of exposure to doxycycline on Achilles tendon repair.
Materials and Methods: Twenty healthy rats of the same breed and gender were randomly assigned to two groups of sham, and Doxycycline group therapy. The rats underwent a surgical intervention in which a 2mm incision was performed on the lateral sides of the right Achilles tendons. The treatment group received oral gavage administrations of 50mg/kg/day of doxycycline for 30 days. After this duration, tissue samples were taken from the site of the injuries, which were then histologically evaluated for alignment of the collagen fibres, inflammation reaction, cellular density, and fibroblastic activity.
Results: The histological assessment of the tissue samples, revealed significant changes in the repaired tissues of the treatment group in comparison to the sham group; namely more irregularity in the alignment of the collagen fibres, increased cellular density, and increased fibroblastic activity. However, only the alignment of the collagen fibres reached the statistical significance.
Conclusion: The results of this study indicate that exposure to doxycycline may result in the improvement of repair of the Achilles tendon injuries, especially collagen filament integrity.
Cytoprotective effects of antioxidant supplementation on mesenchymal stem cell therapy

Panahi M, Rahimi B, Rahimi G, Yew Low T, Saraygord-Afshari N, Alizadeh E

J Cell Physiol, 2020 10;235(10):6462-6495.
PMID: 32239727 DOI: 10.1002/jcp.29660

Mesenchymal stem cells (MSCs) are earmarked as perfect candidates for cell therapy and tissue engineering due to their capacity to differentiate into different cell types. However, their potential for application in regenerative medicine declines when the levels of the reactive oxygen and nitrogen species (RONS) increase from the physiological levels, a phenomenon which is at least inevitable in ex vivo cultures and air-exposed damaged tissues. Increased levels of RONS can alter the patterns of osteogenic and adipogenic differentiation and inhibit proliferation, as well. Besides, oxidative stress enhances senescence and cell death, thus lowering the success rates of the MSC engraftment. Hence, in this review, we have selected some representatives of antioxidants and newly emerged nano antioxidants in three main categories, including chemical compounds, biometabolites, and protein precursors/proteins, which are proved to be effective in the treatment of MSCs. We will focus on how antioxidants can be applied to optimize the clinical usage of the MSCs and their associated signaling pathways. We have also reviewed several paralleled properties of some antioxidants and nano antioxidants which can be simultaneously used in real-time imaging, scaffolding techniques, and other applications in addition to their primary antioxidative function.
Fulltext Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.

Sci Rep, 2021 Jul 20;11(1):15152.
PMID: 34285263 DOI: 10.1038/s41598-021-93957-4
Fulltext Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, et al.

Sci Rep, 2018 Oct 18;8(1):15364.
PMID: 30337603 DOI: 10.1038/s41598-018-33755-7

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
Fulltext Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, et al.

Sensors (Basel), 2018 Jul 31;18(8).
PMID: 30065216 DOI: 10.3390/s18082464

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models

Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, et al.

Sci Total Environ, 2018 Sep 01;634:853-867.
PMID: 29653429 DOI: 10.1016/j.scitotenv.2018.04.055

The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
Fulltext A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Tien Bui D, Shirzadi A, Shahabi H, Chapi K, Omidavr E, Pham BT, et al.

Sensors (Basel), 2019 May 29;19(11).
PMID: 31146336 DOI: 10.3390/s19112444

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
Poster presentations

Aksu F, Topacoglu H, Arman C, Atac A, Tetik S, Hasanovic A, et al.

Surg Radiol Anat, 2009 Sep;31 Suppl 1:95-229.
PMID: 27392492 DOI: 10.1007/BF03371486

Conference abstracts: Malaysia in affiliation
(1). PO-211. AGE-SPECIFIC STRESS-MODULATED
CHANGES OF SPLENIC IMMUNOARCHITECTURE
IN THE GROWING BODY. Marina Yurievna Kapitonova, Syed Baharom Syed Ahmad Fuad, Flossie Jayakaran; Faculty of Medicine, Universiti Teknologi MARA, Shah Alam, Malaysia
[email protected]
(2). PO-213. A DETAILED OSTEOLOGICAL STUDY OF THE ANOMALOUS GROOVES NEAR THE
MASTOID NOTCH OF THE SKULL. ISrijit Das, 2Normadiah Kassim, lAzian Latiff, IFarihah Suhaimi, INorzana Ghafar, lKhin Pa Pa Hlaing, lIsraa Maatoq, IFaizah Othman; I Department of Anatomy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia; 2 Department of Anatomy, Universiti Malaya, Kuala Lumpur, Malaysia. das_sri [email protected]
(3). PO-21S. FIRST LUMBRICAL MUSCLE OF THE
PALM: A DETAILED ANATOMICAL STUDY WITH
CLINICAL IMPLICATIONS. Srijit Das, Azian Latiff, Parihah Suhaimi, Norzana Ghafar, Khin Pa Pa Hlaing, Israa Maatoq, Paizah Othman; Department of Anatomy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia. [email protected]
(4). PO-336. IMPROVEMENT IN EXPERIMENTALLY
INDUCED INFRACTED CARDIAC FUNCTION
FOLLOWING TRANSPLANTATION OF HUMAN
UMBILICAL CORD MATRIX-DERIVED
MESENCHYMAL CELLS. lSeyed Noureddin Nematollahi-Mahani, lMastafa Latifpour, 2Masood Deilami, 3Behzad Soroure-Azimzadeh, lSeyed
Hasan Eftekharvaghefi, 4Fatemeh Nabipour, 5Hamid
Najafipour, 6Nouzar Nakhaee, 7Mohammad Yaghoobi, 8Rana Eftekharvaghefi, 9Parvin Salehinejad, IOHasan Azizi; 1 Department of Anatomy, Kerman University of Medical Sciences, Kerman, Iran; 2 Department of Cardiosurgery, Hazrat-e Zahra Hospital, Kerman, Iran; 3 Department of Cardiology, Kerman University of Medical Sciences, Kerman, Iran; 4 Department of Pathology, Kerman University of Medical Sciences, Kerman, Iran; 5 Department of Physiology, Kerman University of Medical Sciences, Kerman, Iran; 6 Department of Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran; 7 Department
of Biotechnology, Research Institute of Environmental Science, International Center for Science, High Technology & Environmental Science, Kerman, Iran; 8 Students Research Center, Kerman University of Medical Sciences, Kerman, Iran; 9 Institute of Bioscience, University Putra Malaysia,
Kuala Lumpur, Malaysia; 10 Department of Stem Cell, Cell Science Research Center, Royan Institute, ACECR, Tehran, Iran. [email protected]
(5).