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Showing 2 results for Yaghoubi
Death Anxiety and Related Factors in Nurses during the COVID-19 Pandemic: A systematic Review
Mohammad Javad Ghazanfari, Raziyeh Chaghian Arani, Amirabbas Mollaei, Aghil Mollaei, Atefeh Falakdami, Poorya Takasi, Pooyan Ghorbani Vajargah, Shaqayeq Esmaeili, Hedayat Jafari, Tahereh Yaghoubi, Samad Karkhah,
Volume 10, Issue 3 (10-2022)
Abstract
Background and Objectives: High workload, insufficient resources, and many stressors in the workplace have led to the imposition of physical and psychological pressures on nurses, which exposes them to death anxiety (DA). This systematic review aimed to assess the DA and factors associated with its in nurses during the COVID-19 pandemic.
Material and Methods: An extensive search was conducted on Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database (SID) databases via keywords such asincluding "Death", "Death Anxiety", "Nurses", and "COVID-19", from December 2019 to November 10, 2021.
Results: 818 nurses were enrolled in four papers. The mean age and work experience of nurses Nurses’ mean age and work experiences were 31.21 (SD=5.43) and 7.60 (SD=6.73) years, respectively. The mean DA of nurses during the COVID-19 pandemic was 7.30 (SD=2.23). Also, 31.05% of nurses had a high level of DADA level during the COVID-19 pandemic. Age, sex, work experience, working hours per week, childbearing, several patients needing end‑of‑life care, direct participation in resuscitation operations, cases of direct participation in resuscitation operations, cases of patient death, depression, mental health status, and life satisfaction were influential factors in DA nurses during the COVID-19 pandemic.
Conclusion: Thus, nursing policymakers should pay special attention to these factors related to the use of nurses' health maintenance and promotion programs to increase the quality of nursing care for COVID-19 patients. Also, it is recommended that psychological and communication support be provided to nurses during the COVID-19 pandemic.

Presenting a neural network-based framework for drug-target interaction prediction
Mehran Nosrati , Mahdi Yaghoubi ,
Volume 13, Issue 1 (9-2025)
Abstract
Background: Identifying drug-target interactions (DTIs) is a central focus in pharmaceutical research, as accurately recognizing these interactions can play a crucial role in developing modern and targeted therapies. In recent years, numerous deep learning-based models have been introduced to predict these interactions. However, several challenges remain. Existing methods often fail to incorporate the three-dimensional structures of drugs and proteins alongside their SMILES and FASTA sequences, resulting in lower prediction accuracy. Furthermore, many approaches utilize only partial sequence data, thereby overlooking critical information. This lack of spatial and comprehensive sequence awareness ultimately limits the accurate modeling of molecular interactions and binding mechanisms.
Methods: In this study, we introduced TGATS2S-v1 and TGATS2S-v2, two novel deep learning frameworks designed to address the critical challenge of Drug-Target Interaction (DTI) prediction by integrating 3D structural information of both drugs and target proteins alongside their canonical sequence representations (SMILES and FASTA). The proposed methods leveraged three-dimensional structural information to enhance DTI prediction and were tested on the Davis dataset.
Results: The results of the proposed methods were thoroughly analyzed. By integrating 3D structural data, the predictive power of the models improved significantly. Evaluations showed that these models consistently outperformed advanced baseline models, delivering higher accuracy and robustness in all cases. The proposed model achieves state-of-the-art performance, improving PR-AUC by over 20%.
Conclusion: These findings indicate that incorporating 3D structural information plays a pivotal role in overcoming the limitations of previous models and paves the way for the discovery of more effective drugs and therapeutic advancements.

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