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Article ; Online: Computational modeling multiple conformational states of proteins with residual dipolar coupling data.

Abdollahi, Hamed / Prestegard, James H / Valafar, Homayoun

2023 Volume 82, Page(s) 102655

Abstract: Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, ...

Abstract	Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.
MeSH term(s)	Models, Molecular ; Nuclear Magnetic Resonance, Biomolecular/methods ; Proteins/chemistry ; Molecular Conformation ; Computer Simulation
Chemical Substances	Proteins
Language	English
Publishing date	2023-07-14
Publishing country	England
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2023.102655
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Article ; Online: Polydopamine contained hydrogel nanocomposites with combined antimicrobial and antioxidant properties for accelerated wound healing.

Abdollahi, Mahin / Andablib, Sina / Ghorbani, Roghayeh / Afshar, Davoud / Gholinejad, Mohammad / Abdollahi, Hamed / Akbari, Ali / Nikfarjam, Nasser

International journal of biological macromolecules

2024 , Page(s) 131700

Abstract: Overproduction of reactive oxygen species (ROS) in infected wounds induces a tremendous inflammatory reaction to delay wound healing. To address this problem, we designed a multifunctional polyacrylamide/PVA-based hydrogel containing synthesized poly(1- ... ...

Abstract	Overproduction of reactive oxygen species (ROS) in infected wounds induces a tremendous inflammatory reaction to delay wound healing. To address this problem, we designed a multifunctional polyacrylamide/PVA-based hydrogel containing synthesized poly(1-glycidyl-3-butylimidazolium salicylate) (polyGBImSal) and fabricated polydopamine-coated polyphenolic nanosheet (PDA@PNS) for wound dressing. The PDA@PNS particles were designed to induce I) antioxidant and anti-inflammatory features through ROS-scavenging and II) cell adhesive properties by the existing polydopamine into the hydrogels. The poly(ionic liquid)-based polyGBImSal was designed to allocate effective hydrogel antimicrobial activity. The fabricated hydrogel nanocomposites showed excellent properties in the swelling ratio, cell adhesiveness, protein adsorption, and anti-inflammatory, proving their general performance for application in wound healing. Furthermore, these hydrogels showed high antimicrobial activity (over 95 %) against three common wound-infecting pathogenic microbes: Escherichia coli, Staphylococcus aureus, and Candida albicans. The healing process of full-thickness dermal wounds in rats was accelerated by applying hydrogel nanocomposites with 0.5 wt% of PDA@PNS and 28 wt% of polyGBImSal. The wound closure contraction attained full closure, reaching 100 %, after 14 days, contrasted with the control group employing commercial wound dressing (Tegaderm), which achieved a closure rate of 68 % within the equivalent timeframe. These results make these hydrogel nanocomposites promising candidates for multifunctional wound dressing applications.
Language	English
Publishing date	2024-04-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	282732-3
ISSN	1879-0003 ; 0141-8130
ISSN (online)	1879-0003
ISSN	0141-8130
DOI	10.1016/j.ijbiomac.2024.131700
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Article ; Online: Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia.

Rezaei, Hamed / Mirzaie-Asl, Asghar / Abdollahi, Mohammad Reza / Tohidfar, Masoud

PloS one

2023 Volume 18, Issue 5, Page(s) e0285657

Abstract: The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study ... ...

Abstract	The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.
MeSH term(s)	Petunia ; Germination ; Hydrogen Peroxide ; Seeds ; Neural Networks, Computer ; Sterilization ; Disinfectants
Chemical Substances	Hydrogen Peroxide (BBX060AN9V) ; Disinfectants
Language	English
Publishing date	2023-05-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0285657
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Article ; Online: The influence of Hesperidin on memory, learning and oxidative stress parameters in rat model of utreoplacental insufficiency

Hamed Abdollahi / Mohsen Forouzanfar

Fiyz̤, Vol 25, Iss 1, Pp 704-

2021 Volume 713

Abstract: Background: Utreoplacental Insufficiency (UPI) causes impaired fetal brain development and induces oxidative stress, which ultimately leads to intrauterine growth restriction. Due to the antioxidant properties of Hesperidin (HES), the study aimed to ... ...

Abstract	Background: Utreoplacental Insufficiency (UPI) causes impaired fetal brain development and induces oxidative stress, which ultimately leads to intrauterine growth restriction. Due to the antioxidant properties of Hesperidin (HES), the study aimed to outcome this compound on cognitive impairment and serum level of catalase, antioxidant capacity of total and malondialdehyde following uterine-placental insufficiency in rats. Materials and Methods: Thirty pregnant Wistar rats were randomly divided into 5 groups: control group, UPI+NS (Utreoplacental insufficiency+normal saline), UPI+HES25 (Utreoplacental insufficiency+Hesperidin 25 mg/kg), and UPI+HES50 (Utreoplacental insufficiency+Hesperidin 50mg/kg), UPI+HES100 (Utreoplacental insufficiency+ Hesperidin 100mg/kg). UPI was induced by obstruction of the anterior uterine arteries on day 18 of gestation. Hesperidin or normal saline gavage was performed from day 12 to 18 of gestation. Evaluation of working memory, avoidant learning and anxiety-like behaviors and then evaluation of serum levels of catalase, total antioxidant capacity and malondialdehyde content were performed in one-month-old pups. Results: There was a significant decrease in working and avoidance memory, catalase levels, total antioxidants capacity with increased levels of anxiety and malondialdehyde in the UPI+ NS group compared to the control group (P<0.05). While in the HES-treated groups, there was a significant increase in working and avoidance memory, catalase level and total antioxidant capacity with a decrease in anxiety and malondialdehyde levels compared to the UPI+NS group (P<0.05(. Conclusion: Hesperidin can improve memory and cognitive impairments in the model of uterine-placental insufficiency of rats by reducing oxidative stress damage.
Keywords	intrauterine growth retardation ; hesperidin ; oxidative stress ; anxiety ; memory- short term ; Medicine (General) ; R5-920
Subject code	150
Language	Persian
Publishing date	2021-04-01T00:00:00Z
Publisher	Kashan University of Medical Sciences and Health Services
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.

Rezaei, Hamed / Mirzaie-Asl, Asghar / Abdollahi, Mohammad Reza / Tohidfar, Masoud

PloS one

2023 Volume 18, Issue 11, Page(s) e0293754

Abstract: The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a ... ...

Abstract	The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.
MeSH term(s)	Plant Growth Regulators ; Petunia ; Reproducibility of Results ; Algorithms ; Machine Learning
Chemical Substances	Plant Growth Regulators
Language	English
Publishing date	2023-11-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0293754
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Article: Combination therapy of cisplatin and resveratrol to induce cellular aging in gastric cancer cells: Focusing on oxidative stress, and cell cycle arrest.

Rahimifard, Mahban / Baeeri, Maryam / Mousavi, Taraneh / Azarnezhad, Asaad / Haghi-Aminjan, Hamed / Abdollahi, Mohammad

Frontiers in pharmacology

2023 Volume 13, Page(s) 1068863

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2023-01-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2022.1068863
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Article ; Online: Effects of flaxseed supplementation on weight loss, lipid profiles, glucose, and high-sensitivity C-reactive protein in patients with coronary artery disease: A systematic review and meta-analysis of randomized controlled trials.

Sabet, Hamid Reza / Ahmadi, Mohammad / Akrami, Mehdi / Motamed, Mahsa / Keshavarzian, Omid / Abdollahi, Mozhan / Rezaei, Mehdi / Akbari, Hamed

Clinical cardiology

2024 Volume 47, Issue 1, Page(s) e24211

Abstract: This meta-analysis aimed to evaluate the effects of flaxseed supplementation on weight loss, lipid profiles, high-sensitivity C-reactive protein (hs-CRP), and glucose levels in patients with coronary artery disease (CAD). A systematic search was ... ...

Abstract	This meta-analysis aimed to evaluate the effects of flaxseed supplementation on weight loss, lipid profiles, high-sensitivity C-reactive protein (hs-CRP), and glucose levels in patients with coronary artery disease (CAD). A systematic search was performed using various online databases, including Scopus, PubMed, Web of Science, EMBASE, and Cochrane Library, to identify relevant randomized controlled trials (RCTs) until June 2023. To evaluate heterogeneity among the selected studies, the Q-test and I
MeSH term(s)	Humans ; Coronary Artery Disease ; C-Reactive Protein ; Flax ; Glucose ; Randomized Controlled Trials as Topic ; Cholesterol, HDL ; Weight Loss ; Dietary Supplements
Chemical Substances	C-Reactive Protein (9007-41-4) ; Glucose (IY9XDZ35W2) ; Cholesterol, HDL
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Meta-Analysis ; Systematic Review ; Journal Article ; Review
ZDB-ID	391935-3
ISSN	1932-8737 ; 0160-9289
ISSN (online)	1932-8737
ISSN	0160-9289
DOI	10.1002/clc.24211
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Article ; Online: Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia.

Hamed Rezaei / Asghar Mirzaie-Asl / Mohammad Reza Abdollahi / Masoud Tohidfar

PLoS ONE, Vol 18, Iss 5, p e

2023 Volume 0285657

Abstract	The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Enhancing petunia tissue culture efficiency with machine learning

Hamed Rezaei / Asghar Mirzaie-Asl / Mohammad Reza Abdollahi / Masoud Tohidfar

PLoS ONE, Vol 18, Iss 11, p e

A pathway to improved callogenesis.

2023 Volume 0293754

Abstract	The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic ...
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Synthesis and characterization of actively HER-2 Targeted Fe

Babaye Abdollahi, Behnaz / Ghorbani, Marjan / Hamishehkar, Hamed / Malekzadeh, Reza / Farajollahi, Alireza

BioImpacts : BI

2022 Volume 13, Issue 1, Page(s) 17–29

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2022-01-15
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2604624-6
ISSN	2228-5660 ; 2228-5652
ISSN (online)	2228-5660
ISSN	2228-5652
DOI	10.34172/bi.2022.23682
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