Objective To prepare T cell immunoglobulin domain and mucin domain 1 protein-1(TIM-1)-Fc fusion protein,and to investigate the intervention effect of TIM-1-FC fusion protein on ovabumin(OVA)-induced asthma mice as well as its po-tential mechanism.Methods Through genetic engineering,pcDNA3.1(+)-TIM-1-Fc plasmid was constructed and stably transfected into CHO cells.Cell culture supernatant was collected and purified,and TIM-1-Fc fusion protein was ob-tained.Mouse model of allergic asthma was established by intraperitoneal injection of OVA aluminum hydroxide solution.The mice were randomly divided into control group,asthma group and TIM-1-Fc intervention group.The TIM-1-Fc intervention group included TIM-1-Fc nasal drop group and TIM-1-Fc injection group.20 min before each intervention,40 μL ovalbumin sa-line solution(OVA-NS)was used for nasal drops.Protein TIM-1-Fc nasal drops group(1 μg/40 μL TIM-1-Fc nasal drops was administrated in each mouse)and TIM-1-Fc injection group(6 μg/200 μL TIM-1-Fc was injected intraperitoneally in each mouse)were interfered with TIM-1-Fc fusion,once a day for 7 days.Normal saline was used as replacement in the control group.Hematoxylin-eosin staining(HE)was used to observe the pathological changes of lung tissue.Flow cytometry was used to detect the proportion of type 2 helper T cells(Th2),type 17 helper T cells(Th17),regulatory T cells(Treg)and the levels of related cytokines in peripheral blood of mice.Results TIM-1-Fc fusion protein and OVA-induced allergic asthma mouse model was successfully constructed.Compared with asthma group,TIM-1-Fc fusion protein could significantly reduce airway inflam-matory injury and lung tissue injury in asthmatic mice.TIM-1-Fc fusion protein intervention could significantly reduce the pro-portion of TIM-1 CD4+T cells and TIM-1+Th17 cells in peripheral blood,increase the number of TIM-1+Treg cells,signifi-cantly reduce the proportion of Th2 and Th17 cells,and increase the proportion of Treg cells.It could modulate Th1/Th2 and Th17/Treg immune imbalances in asthma.Conclusion TIM-1-Fc fusion protein improves airway inflammation and lung tissue injury in OVA-induced allergic asthma mice,and its mechanism may be related to the immune regulation of Th1/Th2 and Th17/Treg by TIM-1-Fc fusion protein.

Background:The musk glands of adult male Chinese forest musk deer(Moschus berezovskii Flerov,1929)(FMD),which are considered as special skin glands,secrete a mixture of sebum,lipids,and proteins into the musk pod.Together,these components form musk,which plays an important role in attracting females during the breeding season.However,the relationship between the musk glands and skin of Chinese FMD remains undiscovered.Here,the musk gland and skin of Chinese FMD were examined using histological analysis and RNA sequencing(RNA-seq),and the expression of key regulatory genes was evaluated to determine whether the musk gland is derived from the skin.Methods:A comparative analysis of musk gland anatomy between juvenile and adult Chinese FMD was conducted.Then,based on the anatomical structure of the musk gland,skin tissues from the abdomen and back as well as musk gland tissues were obtained from three juvenile FMD.These tissues were used for RNA-seq,hematoxylin-eosin(HE)staining,immunohistochemistry(IHC),western blot(WB),and quantitative real-time polymerase chain reaction(qRT-PCR)experiments.Results:Anatomical analysis showed that only adult male FMD had a complete glandular organ and musk pod,while juvenile FMD did not have any well-developed musk pods.Transcriptomic data revealed that 88.24%of genes were co-expressed in the skin and musk gland tissues.Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway analysis found that the genes co-expressed in the abdomen skin,back skin,and musk gland were enriched in biological development,endocrine system,lipid metabolism,and other pathways.Gene Ontology(GO)enrichment analysis indicated that the genes expressed in these tissues were enriched in biological processes such as multicellular development and cell division.Moreover,the Metascape predictive analysis tool demonstrated that genes expressed in musk glands were skin tissue-specific.qRT-PCR and WB revealed that sex-determining region Y-box protein 9(Sox9),Caveolin-1(Cav-1),and androgen receptor(AR)were expressed in all three tissues,although the expression levels differed among the tissues.According to the IHC results,Sox9 and AR were expressed in the nuclei of sebaceous gland,hair follicle,and musk gland cells,whereas Cav-1 was expressed in the cell membrane.Conclusions:The musk gland of Chinese FMD may be a derivative of skin tissue,and Sox9,Cav-1,and AR may play significant roles in musk gland development.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.