Research article

Thyroid function and hematological alterations in cardiac catheterization workers: a pre-post observational study on x-ray exposure

  • Ionizing radiation is a common health risk encountered by healthcare professionals. Previous studies highlighted the potential adverse effects of X-ray exposure, such as metabolic dysfunctions, radiation-induced biological changes, and a susceptibility to infection. The present study examined the long-term effects of radiation exposure on the hematopoietic system and thyroid functions. A cohort of 40 healthcare professionals of various professions working in the cardiac catheterization center at Azadi Teaching Hospital were recruited for this study. After their consent, the necessary data were taken, and blood specimens were collected for the laboratory investigations. The recruits were divided based on x-ray exposure into pre- and post-exposure groups. Concerning the hematological parameters, no significant differences were observed between the groups, except MCH, which was statistically elevated in the post-exposure group (p = 0.000). Among the thyroid hormones, only free T4 was significantly increased in the post-exposed subjects (p = 0.000) as compared to non-exposed controls. Moreover, a positive correlation was observed between the ionizing radiation exposure dosage and T4 elevation (r = 0.362, p = 0.02). The findings collectively highlight the remarkable impact of long-term radiation exposure on thyroid functions and some hematological parameters. Therefore, regular monitoring of health professionals is suggested to avoid the detrimental effects of radiation on human health.

    Citation: Haliz Hussein, Hazhmat Ali, Zeki Mohamed, Majeed Mustafa, Khairi Abdullah, Asaad Alasady, Mayada Yalda. Thyroid function and hematological alterations in cardiac catheterization workers: a pre-post observational study on x-ray exposure[J]. AIMS Biophysics, 2025, 12(1): 43-53. doi: 10.3934/biophy.2025004

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  • Ionizing radiation is a common health risk encountered by healthcare professionals. Previous studies highlighted the potential adverse effects of X-ray exposure, such as metabolic dysfunctions, radiation-induced biological changes, and a susceptibility to infection. The present study examined the long-term effects of radiation exposure on the hematopoietic system and thyroid functions. A cohort of 40 healthcare professionals of various professions working in the cardiac catheterization center at Azadi Teaching Hospital were recruited for this study. After their consent, the necessary data were taken, and blood specimens were collected for the laboratory investigations. The recruits were divided based on x-ray exposure into pre- and post-exposure groups. Concerning the hematological parameters, no significant differences were observed between the groups, except MCH, which was statistically elevated in the post-exposure group (p = 0.000). Among the thyroid hormones, only free T4 was significantly increased in the post-exposed subjects (p = 0.000) as compared to non-exposed controls. Moreover, a positive correlation was observed between the ionizing radiation exposure dosage and T4 elevation (r = 0.362, p = 0.02). The findings collectively highlight the remarkable impact of long-term radiation exposure on thyroid functions and some hematological parameters. Therefore, regular monitoring of health professionals is suggested to avoid the detrimental effects of radiation on human health.



    We are pleased to present the edition in Mathematical Biosciences and Engineering of a Special Issue that highlights machine learning in molecular biology. Our aim is to report latest developments both in computational methods and analysis expanding the existed biological knowledge in molecular biological systems. We feature both web-based resources, which provide easy access to users, downloadable tools of particular use for in-house processing, and the inclusion into pipelines being developed in the laboratory.

    In this special issue, Zhu et al. [1] developed a new approach to computationally reconstruct the 3D structure of the X-chromosome during XCI, in which the chain of DNA beads representing a chromosome is stored and simulated inside a 3D cubic lattice. They first generated the 3D structures of the X-chromosome before and after XCI by applying simulated annealing and Metropolis-Hastings simulations. Then, Xist localization intensities on the X-chromosome (RAP data) are used to model the traveling speeds or acceleration between all bead pairs during the process of XCI. With their approach, the 3D structures of the X-chromosome at 3 hours, 6 hours, and 24 hours after the start of the Xist expression, which initiates the XCI process, have been reconstructed.

    Long noncoding RNAs (lncRNA) play important roles in gene expression regulation in diverse biological contexts. While lncRNA-gene interactions are closely related to the occurrence and development of cancers, the new target genes could be detected from known lncRNA regulated genes. Lu et al. [2] developed a method by using a biclustering approach for elucidating lncRNA-gene interactions, which allows for the identification of particular expression patterns across multiple datasets, indicating networks of lncRNA and gene interactions. Their method was applied and evaluated on the breast cancer RNA-seq datasets along with a set of known lncRNA regulated genes. Their method provides useful information for future studies on lncRNAs.

    RNA modification site prediction offers an insight into diverse cellular processing in the regulation of organisms. Deep learning can detect optimal feature patterns to represent input data other than feature engineering from traditional machine learning methods. Sun et al. [3] developed DeepMRMP (Multiple Types RNA Modification Sites Predictor), a predictor for multiple types of RNA modifications method, which is based on the bidirectional Gated Recurrent Unit (BGRU) and transfer learning. Using multiple RNA site modification data and correlation among them, DeepMRMP build predictor for different types of RNA modification sites. DeepMRMP identifies N1-methyladenosine (m1A), pseudouridine (Ψ), 5-methylcytosine (m5C) modification sites through 10-fold cross-validation of the RNA sequences of H. sapiens, M. musculus and S. cerevisiae,

    In biomedical research, near infrared spectroscopy (NIRS) is widely applied to analysis of active ingredients in medicinal fungi. Huang et al. [4] introduced an autonomous feature extraction method to model original NIRS vectors using attention based residual network (ABRN). Attention module in ABRN is employed to enhance feature wave bands and to decay noise. Different from traditional NIRS analysis methods, ABRN does not require any preprocessing of artificial feature selections which rely on expert experience. Comparing with other methods on various benchmarks and measurements, ABRN has better performance in autonomously extracting feature wave bands from original NIRS vectors, which can decrease the loss of tiny feature peaks.

    Selectively and non-covalently interact with hormone, the soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it's necessary to develop computational methods to accurately and efficiently identify HBP. In Tan et al.'s paper [5], a machine learning-based method named as HBPred2.0 was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. The proposed method yielded an overall accuracy of 97.15% in the 5-fold cross-validation test. A user-friendly webserver is also provided.

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    We hope that the readers will find this Special Issue helpful in identifying tools and analysis to help them in their study of particular molecular biological problems. In addition, this Issue is also providing an insight into current developments in bioinformatics where the articles describe the strategies being employed to exploring and interpreting sophisticate biological mechanisms, inferring underling relationships and interactions, predicting consequences from disturbance and building hypothesis in molecular biological systems.

    Last but not least, we thank all the authors contributing to this special issue, and editor May Zhao's help and excellent work.



    Conflict of interest



    The authors have no conflict of interest to declare.

    Author contributions



    Haliz Hussein and Hazhmat Ali conducted the experiments and drafted the manuscript. Zeki Mohamed analyzed the data. Majeed Mustafa and Khairi Abdullah collected data and recruited patients for the study. Asaad Alasady and Mayada Yalda conceptualized the study, performed critical analysis, and edited the manuscript. Hazhmat Ali supervised the project.

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