Molecular Pharmacology, Cancer System Biology, Cancer Stem cells, Medical Education, E-Learning
21
Scopus Publications
Scopus Publications
Predicting ICU Stay Duration for Hemodialysis Patients with Poisoning: A Study Comparing Deep Learning with Machine Learning Models Khadijeh Moulaei, Shahin Shadnia, Mohammad Reza Afrash, Babak Mostafazadeh, Heliya Rafsanjani, Peyman Erfan Talab Evini, Mohanna Sharifi, Mohadeseh Sarbaz Bardsiri, Hadi Kazemi-Arpanahi, Babak Sabet, Mohammad Parvin, Vahideh Montazeri, Amir Hossein Daeechini, Mitra Rahimi Medical Journal of Bakirkoy, 2025 Objective: This study compares the effectiveness of machine learning and deep learning models in predicting hemodialysis patients' length of stay in the intensive care unit (ICU).Methods: This retrospective cohort study used data from 980 poisoned patients undergoing hemodialysis.A variety of eight well-known machine learning [support vector machine, extreme gradient boosting, random forest (RF), decision tree] and deep learning (deep neural network, feedforward neural network, long short-term memory, convolutional neural network) models were employed.Results: Feature importance analyses using Shapley Additive exPlanation and local interpretable model-agnostic explanation methodologies identified Glasgow Coma Scale (GCS <8), intubation, acute kidney injury, PO 2 , blood urea nitrogen, metabolic acidosis, and number of hemodialysis sessions as key predictors of ICU stay duration in poisoned hemodialysis patients, with intubation score, GCS score, and ICU admission type being the most significant predictors.Overall, the RF model displayed exceptional performance across various metrics. Conclusion:Our findings emphasize the importance of neurological status, respiratory function, and renal injury in predicting ICU duration, offering valuable insights for clinical decision-making and resource allocation in this high-risk population.
Targeting Cancer Stem Cells and Hedgehog Pathway: Enhancing Cisplatin Efficacy in Ovarian Cancer With Metformin Emad Jafarzadeh, Vahideh Montazeri, Shima Aliebrahimi, Ahmad Habibian Sezavar, Mohammad H. Ghahremani, Seyed Nasser Ostad Journal of Cellular and Molecular Medicine, 2025 Ovarian cancer (OC) remains a leading cause of gynaecological cancer deaths due to late diagnosis and the emergence of resistance to platinum‐based chemotherapy, like cisplatin (Cis). Here, we investigated the potential of metformin (Met), a drug commonly used for type 2 diabetes, to overcome Cis resistance in OC. Our findings revealed a synergistic effect of Met with Cis in inhibiting cell viability, proliferation and colony/sphere formation capacity in both cisplatin‐sensitive (A2780) and ‐resistant (A2780/CDDP) ovarian cancer cell lines. This synergistic action triggered apoptosis through DNA damage, S‐phase cell cycle arrest and modulation of autophagy. Met also significantly decreased the expression of pluripotency transcription factors (Oct‐4, Sox2 and Nanog), indicating its potential to target cancer stem cells (CSCs). Furthermore, the combination therapy downregulated multidrug resistance protein 1 (MDR1) and excision repair cross‐complementation group 1 (ERCC1) expression, thereby sensitising resistant cells to Cis‐induced cytotoxicity. Additionally, the combination treatment suppressed the Hedgehog (Hh) signalling pathway, which is an important factor in inhibiting CSCs. Our study highlights the potential of the Met signalling pathway to synergise with Cis, overcoming therapeutic resistance in OC by targeting diverse cellular processes, including CSCs, and warrants further investigation in preclinical models.
Predicting Trunk Muscle Activity in Chronic Low Back Pain: Development of a Supervised Machine Learning Model Sara Salamat, Vahideh Montazeri, Saeed Talebian Journal of Modern Rehabilitation, 2025 Introduction: Recently, machine learning adoption has significantly increased across various applications, including the prediction of diseases based on a person’s clinical profile. Accordingly, this study develops and evaluates a supervised machine learning method to predict trunk muscle activity in people with chronic low back pain.Materials and Methods: This was a secondary data analysis from a subgroup of people with nonspecific chronic low back pain. The correlation between labeled data and the output data of muscle activity level was measured through surface electromyography. The result showed a good correlation, suggesting the potential utility of this approach in distinguishing individuals with low back pain from pain-free controls.Results: To validate the performance of the developed machine learning, the results were compared with the SPSS software, version 17. The model’s predictive performance was further assessed using various evaluation methods, including the area under the receiver operating characteristics curve. The study’s findings indicate that the model achieved area under the curve values ranging from 0.5 to 0.9 across all muscles and different tasks for people with back pain. In contrast, the pain-free group exhibited area under the curve values between 0.4 and 0.8.Conclusion: The supervised machine learning approach using logistic regression may offer clinically meaningful predictions in defining the differences in trunk muscle activity between individuals with non-specific chronic low back pain and pain-free controls. While the obtained results demonstrate promise, further studies need to enhance the model’s performance and achieve a more accurate estimation of muscle activity levels.
Combating Drug Resistance in Lung Cancer: Exploring the Synergistic Potential of Metformin and Cisplatin in a Novel Combination Therapy; A Systematic Review Emad Jafarzadeh, Behnam Omidi Sarajar, Armineh Rezaghol Lalani, Nima Rastegar-Pouyani, Shima Aliebrahimi, Vahideh Montazeri, Mohammad H Ghahremani, Seyed Nasser Ostad Current Topics in Medicinal Chemistry, 2025 Introduction: The persistent drug resistance observed in lung cancer necessitates innovative strategies to improve therapeutic outcomes. This review investigates the potential of combining metformin (Met) and cisplatin (Cis) to overcome drug resistance and enhance treatment efficacy. Cis's limitations, including drug resistance and adverse effects, coupled with Met’s established safety profile, form the backdrop for this exploration. Methods: Systematic literature searches across major databases identified relevant studies exploring the synergistic effects of Met and Cis in the context of drug-resistant lung cancer. Data extraction encompassed diverse facets, including treatment protocols, cellular responses, and mechanistic insights. The synthesis of these findings sheds light on the potential of this combination therapy to combat drug resistance. Results: Numerous in vitro and in vivo studies have demonstrated the ability of the Met + Cis combination to sensitize drug-resistant lung cancer cells. The co-treatment consistently showed enhanced inhibition of cell proliferation, elevated apoptosis rates, and attenuated migration and invasion capabilities compared to monotherapies. Mechanistically, Met’s modulatory effect on key pathways, such as AMPK-mTOR and ROS-mediated signaling, appears to underlie its ability to counter drug resistance. Conclusion: The Met + Cis combination holds promise as an innovative strategy to counter drug resistance in lung cancer. By harnessing the synergistic effects of these agents, combination therapy offers a novel approach to enhance treatment efficacy and mitigate the challenges posed by drug-resistant lung cancer. Although further clinical validation is required, the Met + Cis synergy represents a promising avenue in the pursuit of improved lung cancer therapy outcomes.
Elucidating the Inhibitory Potential of Statins Against Oncogenic c-Met Tyrosine Kinase Through Computational and Cell-based Studies Elham Ahmad Alizadeh, Leila Karami, Fahimeh Ghasemi, Amir Shadboorestan, Mohammad Reza Torabi, Vahideh Montazeri, Shima Aliebrahimi, Seyed Nasser Ostad Iranian Journal of Pharmaceutical Research, 2025 Background: The cellular mesenchymal-epithelial transition (c-Met) receptor, a member of the receptor tyrosine kinase family, is a novel therapeutic target for treating many cancers, including stomach cancer. Overexpression of c-Met and/or high levels of hepatocyte growth factor (HGF) correlate with poor prognosis. Statins, as LDL-lowering agents, are exploited to obtain anti-cancer effects via a wide range of pleiotropic effects. Objectives: The present study aimed to discover the most effective statin as a c-Met signaling inhibitor through computational and experimental approaches. Methods: Two main computational approaches, i.e., machine learning (ML) model and molecular dynamics (MDs) simulation, were followed by cytotoxicity, flow cytometric analysis, and western blot assay on AGS and MKN-45 gastric cancer cells. Results: The machine learning section was founded on developing tree-based classification algorithms to predict the biological activities of the proposed statin structures as c-Met receptor inhibitors. In the second step, molecular docking and MD simulation were utilized to estimate the biomolecular interactions. The proposed classification models reveal that all structures have more than 200 nM biological activities. Machine learning led the experiment to find fluvastatin and pitavastatin as the two compounds with the highest inhibitory effects. In cell-based assays, both tested statins exhibited cytotoxicity and induced apoptosis, accompanied by sub-G1 accumulation in gastric cancer cells. However, no significant reduction in c-Met phosphorylation was observed by western blot. Conclusions: No relation between the statins’ inhibitory effect and the c-Met pathway on cancerous cells could be reported.
Using chitosan-coated magnetite nanoparticles as a drug carrier for opioid delivery against breast cancer Shima Aliebrahimi, Amir Farnoudian-Habibi, Fatemeh Heidari, Amir Amani, Vahideh Montazeri, Shiva Sabz Andam, Reza Saber, Ali Mohammad Alizadeh, Seyed Nasser Ostad Pharmaceutical Development and Technology, 2024 Over the past decades, opium derivatives have been discovered as new anti-cancer agents. In our study, Fe3O4 superparamagnetic nanoparticles (SPIONs) decorated with chitosan were loaded with papaverine or noscapine to surmount dug delivery-related obstacles. Modifying the magnetic nanoparticles (MNP) surface with polymeric materials such as chitosan prevents oxidation and provides a site for drug linkage, which renders them a great drug carrier. The obtained systems were characterized by DLS (20- 40 nm were achieved for MNPs and medicine loaded MNPs),TEM (spherical with average size of 11-20 nm)FTIR, XRD, and VSM (71.3 - 42.8 emu/g). Contrary to noscapine, papaverine-magnetic nanoparticles (MNPs) attenuated 4T1 murine breast cancer cell proliferation (11.50 ± 1.74 µg/ml) effectively compared to the free drug (62.35 ± 2.88 µg/ml) while sparing L-929 fibroblast cells (138.14 ± 4.38 µg/ml). Furthermore, SPION and SPION-chitosan displayed no cytotoxic activity. Colony-formation assay confirmed the long-term cytotoxicity of nanostructures. Both developed formulations promoted ROS production accompanied by late apoptotic cell death. The biocompatible nanoparticle exerted an augmenting effect to deliver papaverine to metastatic breast cancer cells.
The Impact of Cancer-Associated Fibroblasts on Drug Resistance, Stemness, and Epithelial-Mesenchymal Transition in Bladder Cancer: A Comparison between Recurrent and Non-Recurrent Patient-Derived CAFs Nima Rastegar-Pouyani, Vahideh Montazeri, Nikoo Marandi, Shima Aliebrahimi, Melika Andalib, Emad Jafarzadeh, Hamed Montazeri, Seyed Nasser Ostad Cancer Investigation, 2023 This study comparatively evaluated the possible effects of recurrent and non-recurrent patient-derived Cancer-Associated Fibroblasts (CAFs-R and -NR) on the bladder cancer cell line, EJ138. Both groups of CAFs increased cisplatin resistance and altered cell cycle distribution alongside induced resistance to apoptosis. Later, the scratch assay confirmed the cell migration-inducing effects of CAFs on cells. Nonetheless, only CAFs-R managed to increase sphere-formation and clonogenic levels in EJ138 cells, which were later validated by upregulating pluripotency transcription factors. Besides, CAFs-R also affected the expression levels of some of the EMT markers. Our study suggests that CAFs-R had stronger pro-tumorigenic effects on EJ138 cells.
Effect of heat stress on DNA damage: a systematic literature review Peymaneh Habibi, Seyed Naser Ostad, Ahad Heydari, Shima Aliebrahimi, Vahideh Montazeri, Abbas Rahimi Foroushani, Mohammad Reza Monazzam, Mahmoud Ghazi-Khansari, Farideh Golbabaei International Journal of Biometeorology, 2022
Evaluation of genistein effect on invasion of breast cancer stem cell-like cells Acta Medica Iranica, 2018
Association between NAT2 polymorphisms and prostate cancer Mandana Hasanzad, Seyed Amir Mohsen Ziaei, Vahideh Montazeri, Mahdi Afshari, Seyed Hamid Jamaldini, Mahdieh Imani, Mahshid Sattari, Leila Hashemian, Seyed Rouhollah Kalantar Moaetamed, Mohammad Samzadeh International Journal of Cancer Management, 2017
