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.

Objective:To explore the role of Caveolin-1 (CAV-1) in radiation-induced premature senescence of vascular endothelial cells.Methods:A cell model with stable knockdown of CAV-1 was constructed in human microvascular endothelial cells (HMEC-1) by lentiviral transfection using puromycin screening. The cells were divided into NC group and sh-CAV-1 group based on whether they were infected with lentivirus shRNA-CAV-1. The protein expression levels of CAV-1, p53 and p21 were detected by Western blot at 24, 48, and 72 h after 0, 2, and 4 Gy X-ray irradiation. The β-galactosidase staining kit was used to detect β-galactosidase in cells. CCK-8 kit was used to detect cell viability, and vascular endothelial cell function was detected by vascular tube-forming assay.Results:CAV-1 protein expression was significantly decreased at 48 h after 2 and 4 Gy X-ray irradiation ( t=3.50, 3.89, P < 0.05), and β-galactosidase in sh-CAV-1 group was significantly increased at 72 h after 0, 2 and 4 Gy X-ray irradiation ( t=12.91, 11.54, 6.04, P < 0.05) compared with the NC group. Knockdown of CAV-1 resulted in the decrease in the expression level of the cellular senescence-associated protein p53 protein ( t=4.09, 3.13, 3.43, P < 0.05), but increase in the expression level of p21 protein ( t=-3.63, -3.33, -3.06, P < 0.05). Compared with the NC group, knockdown CAV-1 significantly decreased cell viability ( t=2.97-25.89, P<0.05) and reduced vessel-forming capacity ( t=3.39-39.68, P < 0.05). Conclusions:CAV-1 is involved in the process of radiation-induced premature senescence of vascular endothelial cells through positive regulation of p53 and negative regulation of p21.

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.

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.

Objective:To explore the role of Caveolin-1 (CAV-1) in radiation-induced premature senescence of vascular endothelial cells.Methods:A cell model with stable knockdown of CAV-1 was constructed in human microvascular endothelial cells (HMEC-1) by lentiviral transfection using puromycin screening. The cells were divided into NC group and sh-CAV-1 group based on whether they were infected with lentivirus shRNA-CAV-1. The protein expression levels of CAV-1, p53 and p21 were detected by Western blot at 24, 48, and 72 h after 0, 2, and 4 Gy X-ray irradiation. The β-galactosidase staining kit was used to detect β-galactosidase in cells. CCK-8 kit was used to detect cell viability, and vascular endothelial cell function was detected by vascular tube-forming assay.Results:CAV-1 protein expression was significantly decreased at 48 h after 2 and 4 Gy X-ray irradiation ( t=3.50, 3.89, P < 0.05), and β-galactosidase in sh-CAV-1 group was significantly increased at 72 h after 0, 2 and 4 Gy X-ray irradiation ( t=12.91, 11.54, 6.04, P < 0.05) compared with the NC group. Knockdown of CAV-1 resulted in the decrease in the expression level of the cellular senescence-associated protein p53 protein ( t=4.09, 3.13, 3.43, P < 0.05), but increase in the expression level of p21 protein ( t=-3.63, -3.33, -3.06, P < 0.05). Compared with the NC group, knockdown CAV-1 significantly decreased cell viability ( t=2.97-25.89, P<0.05) and reduced vessel-forming capacity ( t=3.39-39.68, P < 0.05). Conclusions:CAV-1 is involved in the process of radiation-induced premature senescence of vascular endothelial cells through positive regulation of p53 and negative regulation of p21.

Objective:To explore classification models for radiosensitive lipid metabolites in human peripheral blood by combining lipidomics with multiple machine learning (ML) algorithms.Methods:Totally 97 peripheral blood samples were collected from 25 leukemia cases admitted to a general hospital in Beijing from March to September 2023 who were ready to undergo bone marrow transplantation, including 0 Gy blood samples before irradiation in the control group ( n=24), and 73 blood samples after irradiation at doses of 4, 8 and 12 Gy in the radiation group ( n=73), and the targeted lipidomic based on the ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) platform method to analyze the differences of different lipids between control and radiation groups. Then, lipids responsive to radiation doses of 0-12 Gy were identified using linear regression. Finally, classification models were constructed using five ML algorithms based on the training set, followed by the validation and evaluation of these models using the validation set. Results:Compared with the control group, the differences in the concentration changes of 62 lipids in 9 classes of lipid metabolites sensitive to radiation group were statistically significant ( t=-4.91 to 4.74, P<0.05), including sphingomyelins(SMs), cholesteryl esters(CEs), ceramides(Cers), phosphatidylinositols(PIs), hexosylceramides(HexCers), lysophosphatidylcholines (LysoPCs), phosphatidylcholines (PCOs), phosphatidylethanolamines (PEs), and lysophosphatidylethanolamines (LysoPEs). Twenty lipids responsive to radiation doses of 0-12 Gy were identified, namely 11 SMs, 7 CEs, 1 Cer, and 1 PI. The five models based on ML algorithms of decision tree (DT), support vector machine (SVM), light gradient boosting machine (Light GBM), random forest (RF), and K-nearest neighbors (KNN) all exhibited high goodness of fit (F1=0.69-1.00) and high sensitivity. The evaluation and validation metrics revealed that the RF-based model yielded the optimal radiation classification discrimination (sensitivity: 1.00; accuracy: 0.72; F1 score: 0.80). Conclusions:Lipid metabolites responsive to radiation and lipids responsive to radiation dose in human samples were identified using targeted lipidomics. The RF-based model can provide new ideas for exploring models of human radiosensitive lipid metabolites.