Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.

Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.

Objective:To screen the aging genes closely associated with pelvic organ prolapse(POP)by bioinformatics techniques,and to clarify the potential clinical significance and value of key genes.Methods:Gene Expression Omnibus(GEO)Database was used to download the datasets GSE53868 and GSE151188 for POP-related genes with the keyword"pelvic organ prolapse".The aging-related genes were obtained from Aging Atlas,CellAge,and the Human Ageing Genomic Resources(HAGR)Databases;the intersection of genes related with POP in two groups provided a list of differentially expressed genes(DEGs)associated with aging in POP;gene Set Enrichment Analysis(GSEA)was conducted with R software version 4.2.1;Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis of DEGs were conducted by the Database for Annotation,Visualization and Integrated Discovery(DAVID);the protein-protein interaction(PPI)network was constructed with Cytoscape 3.9.1 software;the top 10 Hub genes were selected by cytoHubba plugin;the infiltration of 22 types of immune cells in the patients in POP group and control group was analyzed by CIBERSORT deconvolution method using R software;the key genes were further screened by LASSO regression algorithm;the correlation and diagnostic efficacy between key genes and immune cell infiltration were analyzed.Results:From the Aging Atlas,CellAge,and HAGR Databases,724 aging-related genes were identified.Intersection with the POP expression profile yielded an aging gene expression matrix related to POP containing 624 genes,and 29 POP-related DEGs were identified after differential analysis,including 2 upregulated genes and 27 downregulated genes.The GSEA results showed that the upregulated pathways were mainly related to diabetes and cellular senescence,whereas the downregulated pathways included Alzheimer's disease and hypoxia-inducible factor-1(HIF-1)signaling pathways.The GO functional enrichment analysis mainly enriched in the biological processes such as the response of the cells to lipopolysaccharide,inflammatory response,and negative regulation of cell proliferation.The KEGG signaling pathway enrichment analysis mainly enriched in interleukin-17(IL-17),tumor necrosis factor(TNF),and nuclear factor-kappa B(NF-κB)signaling pathways.The PPI network analysis got 10 Hub genes including interleukin-6(IL-6),interleukin-1B(IL-1B),prostaglandin-endoperoxide synthase 2(PTGS2),and NF-kappa-B inhibitor alpha(NFKBIA).The CIBERSORT deconvolution method results showed a relatively higher infiltration proportion of neutrophils and activated mast cells in the patients in POP group,the activated mast cells had a positive correlation with most of the DEGs(r>0.5)and the macrophages had a significant positive correlation with IL-1B(r>0.6).The key genes Jun D proto-oncogene(JUND),Snail homolog 1(SNAI1),amphiregulin(AREG),Lamin A/C(LMNA),and superoxide dismutase 2(SOD2)selected by LASSO regression analysis had high diagnostic efficacies,and the area under receiver operating characteristic curve(ROC)(AUC)were all greater than 0.75.Conclusion:During the aging process,the genes such as JUND,SNAI1,AREG,LMNA,and SOD2 may participate in the pathophysiology of POP through various pathways,including inflammation-related pathways,transcription regulation,and affecting collagen secretion and metabolism,thereby influence the connective tissue support function and promote the occurrence and development of POP.