Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Recent studies found that forkhead box protein O1 (FoxO1) does not only demonstrate important biological functions in cell proliferation, gluconeogenesis, energy metabolism, and oxidative stress, but it also plays a vital role in the remodeling process of bones. FoxO1 can regulate bone mass by affecting osteoblasts, osteoclasts, and precursor cells. In this article, we review the role of FoxO1 in bone metabolism and elucidate its underlying mechanism.
Bone and Bones ; metabolism ; Cell Proliferation ; Forkhead Box Protein O1 ; Humans ; Osteoblasts ; Osteoclasts

OBJECTIVE：To optimize the p reparation technology of citronellol submicroemulsion. METHODS ：The content of citronellol in Citronellol submicroemulsion was determined by HPLC. Citronellol submicroemulsion by high-speed shearing dispersion-high pressure homogenization method ，with centrifugation stability constant （ke） and particle size were used as evaluation indexes. Its formulation and preparation technology were optimized and validated. Drug-loading amount and encapsulation rate of the preparation were detected. RESULTS ：The linear range of citronellol were 4-64 μg/mL（R 2＝0.999 9）. RSDs of precision ，stability（24 h）and reproducibility tests were all lower than 3％. The recoveries were 97.64％-101.97％（RSD＝ 2.28％，n＝3），97.71％-99.50％（RSD＝1.29％，n＝3），96.87％-101.48％（RSD＝2.86％，n＝3）. The optimal formulation included that total weight of soybean oil and medium chain triglycerides （1 ∶ 1，g/g）was 3.75 g，1.2％ soybean phospholipid was 0.6 g， cholesterol was 0.06 g，citronellol was 1.25 g，0.6 ％ sodium oleate was 0.3 g，15-hydroxystearic acid polyethylene glycol ester was 0.75 g，poloxamer 188 was 0.75 g，water added to 50 mL. After prepared by optimal technology at 4 ℃ which contained shearing speed of 13 000 r/min，lasting for 5 min， primary emulsion was adjusted to pH 7 with dilute hydro- chloric acid ，and homogenized with 600 Bar high pressure for 1434412440@qq.com 5 min. The parameters of Citronellol submicroemulsion accor- ding to optimal formulation and technology contained mean particle size of （91.05±0.26）nm，PDI of （0.20±0.01）， Zeta-potential of （－30.86±0.39）mV，average content of 649511230@qq.com citronellol（100.21±0.01）％，the drug-loading amount was （2.481 7 ± 0.000 7） mg/mL，the encapsulation rate was （99.27 ± 0.03）％ . CONCLUSIONS ：The optimal formulation and technology is stable and feasible.