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.

Living systematic review (LSR) is a systematic review methodology that incorporates regular updates to integrate new evidence, aiming to rapidly reflect the latest research findings. Although LSRs are increasingly adopted in clinical fields, their reporting quality remains inconsistent and lack of standardized guidelines. To standardize LSR reporting, the PRISMA 2020 statement has released an extension checklist (PRISMA-LSR). This paper describes the background of PRISMA-LSR release and main revisions, and interprets it with examples, with the goal of guiding future LSR research and enhancing reporting quality.

Objective:To investigate the relationship between NOD-like receptor protein 3 (NLRP3) gene promoter region-30 single nucleotide polymorphism (SNP) (rs3738448) G/T and dilated cardiomyopathy (DCM) and its related risk factors.Methods:A case-control study method was used to collect 137 patients and 140 healthy controls; polymerase chain reaction-restriction endonuclease fragment length polymorphism technology combined with sequence alignment after DNA sequencing was used for data statistics; After Hardy-Weinberg balance test, the χ 2 test was used for correlation analysis; logistic regression was used to analyze the correlation between multiple risk factors and the SNP site and the incidence of DCM; SNPinfo database was used to predict and analyze the transcription factors affected by the SNP. Results:A total of GG and GT genotypes were detected at this SNP locus, and their genotype distributions were in line with Hardy-Weinberg equilibrium ( P>0.05). At the same time, the difference between the DCM group and the control group was significant ( P<0.05). Multivariate logistic regression analysis indicated that mean arterial pressure, C-reactive protein and B-type brain natriuretic peptide were independent risk factors for the onset of DCM (all P<0.05). The incidence of DCM in -30(RS3738448)G/T genotype GT group was 2.243 times higher than that in GG group (95% CI: 1.043-4.827, P<0.05). Logistic regression analysis under dominant, recessive and additive genetic models showed that there was a correlation between the dominant inheritance of SNP and the occurrence of DCM ( OR=0.44, AIC=370.4, BIC=381.3, P<0.05). Conclusions:The -30 (rs3738448) G/T SNP in the promoter region of the NLRP3 gene is associated with the pathogenesis of DCM, and provides population genetic data for the study of polymorphisms in the promoter region of NLRP3 gene.

Objective:To compare and analyze the predictive value of different inflammatory factors and tumor markers in intrahepatic cholangiocarcinoma and to develop a new and effective preoperative prognostic scoring system.Methods:102 and 72 cases with intrahepatic cholangiocarcinoma who underwent radical surgery in Tianjin Medical University Cancer Institute and Hospital and the Affiliated Hospital of Weifang Medical University were selected as the experimental group and the validation group, respectively. Clinicopathological and follow-up data were collected. Cox proportional-hazards model was used to analyze the predictive value of different prognostic markers. The relationship between prognostic markers and clinicopathological data was analyzed by rank sum test, χ2 or Fisher's exact test. Results:Among the direct inflammatory factors, tumor markers and combined inflammatory factors, prognostic inflammatory index (PII), carbohydrate antigen (CA) 19-9 and systemic inflammation score (SIS) were the most significant predictive factors for postoperative survival outcomes in patients with intrahepatic cholangiocarcinoma. The prognostic inflammatory and tumor score (PITS) was proposed as a new prognostic scoring system for intrahepatic cholangiocarcinoma. PII and CA19-9 were included into the scoring criteria for prognostic stratification of patients. PITS was an independent predictor of tumor-free survival and overall survival in patients with intrahepatic cholangiocarcinoma. Patients with high-grade PITS had later tumor grade and higher frequency of vascular invasion.Conclusion:PITS is highly effective prognostic scoring system for patients with intrahepatic cholangiocarcinoma. In addition, PITS is recommended for preoperative prognostic stratification in patients with intrahepatic cholangiocarcinoma.

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.

Objective To investigate the current situation of mobile phone addiction among nursing undergraduates in Wuhan,and to analyze influencing factors.Methods 240 nursing undergraduates from three universities in Wuhan were selected from January to April 2015 by convenience sampling method,and were evaluated about their situation on mobile phone addiction by questionnaire of mobile phone addiction tendency scale.Single factor analysis and Logistic multiple regression analysis were used for the influencing factors.Results 240 questionnaires were handed out with 232 retrieved and with effective rate 96.67％.Among the 232 nursing undergraduates,mobile phone addiction rate was 30.60％,with scores in the mobile phone addiction tendency scale (48.33 ± 14.23) points.It was revealed in the single factor analysis that differences appeared whether they were in single-parent family,the only child in the family,from a single-parent family or in a love relation (x2=7.45,4.63,12.59;P＜0.05);it was revealed in the Logistic regression analysis that influencing factors included whether they were in single-parent family,the only child or in a love relation.Conclusions Mobile phone addiction rate is relatively high among nursing undergraduates,with those who are in single-parent family,the only child or in a love relation more likely to be addicted.Guidance and education should be strengthened to nursing students,especially to those who are in single-parent family,the only child and who are in a love relation.