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 feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

Objective:To investigate the influencing factors of textbook outcomes in liver surgery (TOLS) after radical resection of gallbladder carcinoma.Methods:The retrospective case-control study was conducted. The clinicopathological data of 530 patients who underwent radical resection of gallbladder carcinoma in 15 medical centers, including the First Affiliated Hospital of Army Medical University et al, from January 2014 to January 2020 were collected. There were 209 males and 321 females, aged (61±10)years. Patients underwent radical resection of gallbladder carcinoma, including cholecystectomy, hepatectomy, invasive bile duct resection, and lymph node dissection. Observation indicators: (1) situations of TOLS; (2) influencing factors of TOLS. Measure-ment data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. The univariate analysis was conducted using the corresponding statistical methods based on data type, and variables with P<0.10 were included in multivariate analysis. Multivariate analysis was conducted using the Logistic stepwise regression model. Results:(1) Situations of TOLS. All 530 patients underwent radical resection of gallbladder carcinoma, and there were 498 cases achieving R 0 resection, 508 cases without ≥grade 2 intra-operative adverse events, 456 cases without postoperative grade B and grade C biliary leakage, 513 cases without postoperative grade B and grade C liver failure, 395 cases without severe com-plications within postoperative 90 days, 501 cases did not being re-admission caused by severe com-plications within postoperative 90 days. Of the 530 patients, 54.53%(289/530) of patients achieved postoperative TOLS, while 45.47%(241/530) of patients did not achieve postoperative TOLS. (2) Influencing factors of TOLS. Results of multivariate analysis showed that American Society of Anesthesiologists classification >grade Ⅱ, preoperative jaundice, T staging as T3?T4 stage, N staging as N2 stage, liver resection as right hemi-hepatectomy, and neoadjuvant therapy were independent factors influencing TOLS in patients undergoing radical resection of gallbladder carcinoma ( odds ratio=2.65, 1.87, 5.67, 5.65, 2.55, 3.34, 95% confidence interval as 1.22?5.72, 1.18?2.95, 2.51?12.82, 2.83?11.27, 1.41?4.63, 1.88?5.92, P<0.05). Conclusion:American Society of Anesthesiologists classification >grade Ⅱ, preoperative jaundice, T staging as T3?T4 stage, N staging as N2 stage, liver resection as right hemi-hepatectomy, and neoadjuvant therapy are independent factors influencing TOLS in patients undergoing radical resection of gallbladder carcinoma.

Objective:To study the influence of circular RNA hsa_circZDHHC21_004 on the proliferation of human small intestinal epithelial cells HIEC-6 after 60Co γ-rays exposure. Methods:HIEC-6 cells were exposed to 60Co γ-rays at 0, 5, 10, and 15 Gy with a dose rate of 1 Gy/min. The expression level of hsa_circZDHHC21_004 in the irradiated HIEC-6 cell was detected. Hsa_circZDHHC21_004 was knocked-down to investigate the influences of hsa_circZDHHC21_004 on the proliferation of irradiated HIEC-6 cells by CCK-8 assay and colony formation assay. Results:The expression level of hsa_circZDHHC21_004 in HIEC-6 cells was upregulated by (1.00±0.24), (1.34±0.28), (1.85±0.31), and (2.80±0.64) times of control after 0, 5, 10, and 15 Gy irradiation, respectively and there were significant difference between 10 or 15 Gy group and 0 Gy group ( F=10.86, P=0.008). Knockdown of hsa_circZDHHC21_004 significantly increased the proliferation rate of HIEC-6 cells at 24, 48, and 72 h after 10 Gy irradiation compared with non-irradiated control ( t=-6.25, -5.83, -7.75, P < 0.001). Under 2 and 5 Gy irradiation, the clone formation rates of the hsa_circZDHHC21_004 knockdown cells were significantly higher than those of the control ( t=-7.45, -8.83, P<0.01). Conclusions:Hsa_circZDHHC21_004 is increased after irradiation and influenced the proliferation of irradiated HIEC-6 cells.

Objective:To investigate the metabolite changes in rat plasma after total body irradiation (TBI) and to explore dose classification based on radiation sensitive metabolites.Methods:The differential metabolites induced by radiation were screened and verified by metabolomics. In the discovery stage, 50 SD rats were irradiated with 0, 1, 2, 3, 5 and 8 Gy of 60Co γ-rays. In the verification stage, 25 rats were irradiated with 0, 0.5, 2.5, 4 and 6 Gy. Peripheral blood samples were collected 4 h after irradiation, and plasma was separated. Radiation-induced differential metabolites were identified and their concentrations were determined. Receiver operating characteristic (ROC) curve of the differential metabolites was used to classify dose range. Results:In the discovery stage, 8 radiation-induced differential metabolites in rat plasma were identified and four of them (cytosine, L-hexylcarnitine, Linoelaidylcarnitine and L-palmitylcarnitine) were upregulated, which was confirmed in the verification stage. The area under the curve (AUC) for the specific dose was >0.75. After combining these four metabolites, the AUC value to classify the radiation dose of 0 Gy versus >0 Gy, <2 Gy versus ≥2 Gy, <5 Gy versus ≥5 Gy were 0.96, 1 and 0.94, respectively.Conclusions:The metabolites in rat plasma changed significantly at 4 h after TBI, where 8 differential metabolites were identified. Cytosine, L-hexylcarnitine, linoelaidylcarnitine and L-palmiylcarnitine were stably over-expressed in the plasma after irradiation. The combination of these four compounds had high classification accuracy and thus may applicable as radiation sensitive biomarkers for dose classification.

Objective:To screen the indicators of retrospective dose estimation, based on 5 cytogenetic methods to assess the victim followed-up at 4 year after 192Ir radiation accident in Nanjing. Methods:The chromosome aberration (dic + r) assay, cytokinesis block micronucleus (MN) and nucleoplasmic bridge (NPB) assay, fluorescence in situ hybridization (FISH)-based and G banding-based translocation analysis were used to retrospective biological dose estimation. Results:The estimated doses of FISH-based and G banding -based analysis were 1.45 and 1.21 Gy respectively, which was similar to the biological dose estimated short time after the accident. However, the estimated doses by chromosome aberration, micronucleus and nucleoplasmic bridge method were 0.56, 0.45 and 0.41 Gy respectively, which were lower than the corresponding biodose. Correction factors were used to adjust the biodose.Conclusions:In the 4th years after exposure, the estimated biological doses by FISH-based and G banding-based translocation were consistent with the biodose.Therefore, the two methods were suitable for retrospective dose estimation, while correction factors should be considered in chromosome aberration method for retrospective dose estimation.

Objective:To explore the effects of ultraviolet B (UVB) on the premature senescence of human immortalized keratinocytes HaCaT cells and the possible underlying molecular mechanism.Methods:HaCaT cells were exposed with UVB of different doses (20, 50, 80 and 100 mJ/cm 2). At 72 h after exposure, cellular morphology was observed by Giemsa staining, cell proliferation was detected by clone formation assay, and the proportion of premature senescence cells was detected by β-galactosidase staining. The number change of lysosomes was detected by Lyso-Tracker Red fluorescence probe at 24, 48 and 72 h after exposure. Cell migration was measured by scratch test at 24 h and 48 h after exposure. The protein expressions of p53 and p16 related to premature senescence were detected by Western blot assay at 72 h after exposure. Results:After UVB exposure, HaCaT cells showed a premature senescence phenotype. At 72 h after exposure, the cell volume increased ( F=115.18, P<0.05), the cell proliferation ability decreased ( F=410.32, P<0.05), the activity of β-galactosidase increased ( F=16.31, P<0.05), and the expressions of P53 and P16 increased. In addition, the number of lysosomes increased at 24, 48, and 72 h after exposure ( F=17.65, 38.36, 13.66, P<0.05), and cell migration capacity was inhibited at 24 and 48 h after exposure ( F=8.21, 11.48, P<0.05). Conclusions:UVB exposure can induce premature senescence of HaCaT cells by increasing the expression of p53 and p16 proteins.

Objective:To screen radiosensitive lipid metabolites in rat plasma and analyze their metabolic pathways in order to provide scientific basis for radiation damage biomarker.Methods:The whole body irradiation of 60Co γ rays was performed to rats with different doses of 0, 1, 3 and 5 Gy. The changes of lipids in plasma were detected by untargeted lipidomics method based on liquid chromatography coupled mass spectrometry. Results:Twenty plasma lipids were identified as the potential radiosensitive biomarkers at 7 days after irradiation, including 13 over-expressed lipids and 7 down-expressed lipids, where 12 lipids well responded to radiation doses.Conclusions:Lipid metabolites in rat plasma are significantly changed after exposure to γ rays, and the metabolic pathways of sphingolipid, glycerophospholipid and glycosylphosphatidylinositol (GPI) are significantly enriched.