Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

With the widespread application of ionizing radiation in many industries and the construction of nuclear power plants, the potentials for nuclear accidents is also increasing. In the event of a nuclear accident, rapid classification of a large population is generally involved, so accurate estimation of the radiation dose to the exposed population is the primary task of nuclear emergency response. Based on this need, World Health Organization and International Atomic Energy Agency have each established a worldwide network of biological dosimetry laboratories. In addition, regional networks of biological dosimetry laboratories have been established in the European Union, North America, Latin America and Asia. Based on the long-term organization of national training and assessment of biological dose estimation technology, China will also establish its own network of biological dosimetry laboratories in the future to cope with the emergency disposal needs of potential nuclear accidents. In this paper, the international biodosimetry network and related work will be reviewed, and the idea of establishing biodosimetry laboratory network in China will be elaborated.

Objective:To investigate the changes in the expression levels of long non-coding ribonucleic acids (lncRNAs) in human lymphocytes induced by low-dose ionizing radiation (LDIR) and the potential of lncRNAs as radiation biomarkers.Methods:Human immortalized lymphocytes (AHH-1) were irradiated with 0, 0.05, and 0.1 Gy of γ-rays at 24 h to extract RNAs for whole transcriptome sequencing. The sequencing was performed based on the 0, 0.05, and 0.1 Gy groups. The differentially expressed lncRNAs induced by LDIR were identified. The molecular functions, biological processes, and signaling pathway enrichment of differentially expressed genes were analyzed through the Gene Ontology (GO) analysis. Candidate lncRNAs were preliminarily validated using the qRT-PCR method. AHH-1 cells were irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy to extract the total RNAs at 4, 24, 48, 72, 96, and 120 h. The dose-response relationship of candidate lncRNAs was detected and analyzed. Peripheral blood sampled from eight healthy persons was irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy in vitro, followed by culturing for 24 h and 48 h to further verify the changes in the expression levels of radiation-responsive lncRNAs at the cellular level. Results:A total of 44 lncRNAs that were significantly up- or down-regulated after 0.05 and 0.1 Gy irradiation were initially identified through transcriptome sequencing. Among them, lncRNAs with over two-fold differential expression included SNHG1, SNHG15, NEAT1, and PRC1-AS1. At the cellular level, compared to 0 Gy, the relative expression level of PRC1-AS1 after 4 h to 48 h of γ-ray irradiation, was significantly elevated at 0.05, 0.075, and 0.1 Gy( t= -3.11 to 1.23, P < 0.05). In contrast, the relative expression level of NEAT1 was significantly up-regulated in a dose range of 0.02 to 0.1 Gy ( t=-2.47 to 2.10, P < 0.05). At the level of human peripheral blood, the relative expression levels of PRC1-AS1 and NEAT1 were significantly increased at 24 h after 0 to 0.2 Gy irradiation ( t=-3.79 to -1.96, P < 0.05). Conclusion:The PRC1-AS1 and NEAT1 with significant changes in expression levels serve as potential LDIR biomarkers.

Radiation health testing is one of the important basic work of radiation health. In order to strengthen the quality control of radiation health testing, various provinces have successively established radiation health quality control centers since 2009, which have played an important role in the standardization and normalization of radiation health testing. National policies and sustained incentives from various provinces will accelerate the construction of radiation health testing and quality control centers. It will improve the level of radiation health technology by competency validation for radiation health testing.

China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

With the widespread application of ionizing radiation in many industries and the construction of nuclear power plants, the potentials for nuclear accidents is also increasing. In the event of a nuclear accident, rapid classification of a large population is generally involved, so accurate estimation of the radiation dose to the exposed population is the primary task of nuclear emergency response. Based on this need, World Health Organization and International Atomic Energy Agency have each established a worldwide network of biological dosimetry laboratories. In addition, regional networks of biological dosimetry laboratories have been established in the European Union, North America, Latin America and Asia. Based on the long-term organization of national training and assessment of biological dose estimation technology, China will also establish its own network of biological dosimetry laboratories in the future to cope with the emergency disposal needs of potential nuclear accidents. In this paper, the international biodosimetry network and related work will be reviewed, and the idea of establishing biodosimetry laboratory network in China will be elaborated.

Objective:To investigate the changes in the expression levels of long non-coding ribonucleic acids (lncRNAs) in human lymphocytes induced by low-dose ionizing radiation (LDIR) and the potential of lncRNAs as radiation biomarkers.Methods:Human immortalized lymphocytes (AHH-1) were irradiated with 0, 0.05, and 0.1 Gy of γ-rays at 24 h to extract RNAs for whole transcriptome sequencing. The sequencing was performed based on the 0, 0.05, and 0.1 Gy groups. The differentially expressed lncRNAs induced by LDIR were identified. The molecular functions, biological processes, and signaling pathway enrichment of differentially expressed genes were analyzed through the Gene Ontology (GO) analysis. Candidate lncRNAs were preliminarily validated using the qRT-PCR method. AHH-1 cells were irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy to extract the total RNAs at 4, 24, 48, 72, 96, and 120 h. The dose-response relationship of candidate lncRNAs was detected and analyzed. Peripheral blood sampled from eight healthy persons was irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy in vitro, followed by culturing for 24 h and 48 h to further verify the changes in the expression levels of radiation-responsive lncRNAs at the cellular level. Results:A total of 44 lncRNAs that were significantly up- or down-regulated after 0.05 and 0.1 Gy irradiation were initially identified through transcriptome sequencing. Among them, lncRNAs with over two-fold differential expression included SNHG1, SNHG15, NEAT1, and PRC1-AS1. At the cellular level, compared to 0 Gy, the relative expression level of PRC1-AS1 after 4 h to 48 h of γ-ray irradiation, was significantly elevated at 0.05, 0.075, and 0.1 Gy( t= -3.11 to 1.23, P < 0.05). In contrast, the relative expression level of NEAT1 was significantly up-regulated in a dose range of 0.02 to 0.1 Gy ( t=-2.47 to 2.10, P < 0.05). At the level of human peripheral blood, the relative expression levels of PRC1-AS1 and NEAT1 were significantly increased at 24 h after 0 to 0.2 Gy irradiation ( t=-3.79 to -1.96, P < 0.05). Conclusion:The PRC1-AS1 and NEAT1 with significant changes in expression levels serve as potential LDIR biomarkers.

Radiation health testing is one of the important basic work of radiation health. In order to strengthen the quality control of radiation health testing, various provinces have successively established radiation health quality control centers since 2009, which have played an important role in the standardization and normalization of radiation health testing. National policies and sustained incentives from various provinces will accelerate the construction of radiation health testing and quality control centers. It will improve the level of radiation health technology by competency validation for radiation health testing.