Objective To observe the clinical effect of traditional Chinese medicine bone-setting technique using spinal,pelvi-lower extremity line to treat patients with knee osteoarthritis(KOA).Methods 426 patients with KOA were all from the First Affiliated Hospital of Hebei University of Traditional Chinese Medicine.They were randomly divided into experimental group(384 cases,57 cases of elimination,shedding and termination)by computer generated sequence.Traditional Chinese bone setting techniques were applied with spinal-pelvic-lower limb force line(divided into three parts:lumbar fixed point reduction method,hip joint push-pull and extension method and knee peripheral tendon recovery method every 3 days.2 weeks)treatment;The control group was the waiting treatment group(48 cases,6 cases were eliminated,abscission,termination),which was only used for clinical observation for 2 weeks.The main outcome index was WOMAC pain score.Secondary outcome measures were WOMAC stiffness score,functional score,standardized score and quality of life score(SF-12).The test time points were baseline,2 weeks after enrollment,and follow-up(14 weeks after enrollment).The control group was at baseline and 2 weeks after enrollment.Results Compared with baseline,WOMAC pain score,stiffness score,functional score and standardized score were all decreased in 2 groups 2 weeks after enrollment(P<0.05),but the experimental group was significantly decreased compared with the control group(P<0.001).SF-12 quality of life scores were all higher than before(P<0.001),but the experimental group was significantly higher than the control group(P<0.001).At follow-up,compared with 2 weeks after enrollment,WOMAC pain scores were increased(P<0.001),WOMAC stiffness,joint function and standardized scores were decreased(P<0.001),and SF-12 scores were increased(P<0.001).Conclusion The use of spinal-pelvi-lower extremity line of traditional Chinese medicine bone-setting technique in the treatment of KOA is effective in improving the knee joint function and improving the quality of life of patients,but the short-term effect of pain relief is good,and the long-term effect is not good.Its safety is good,and it can be considered in clinical application for KOA patients with joint dysfunction as the main manifestation.

Objective:To investigate the prognostic relationship between serum KL-6 level and rheumatoid arthritis(RA)combined with interstitial lung disease(RA-ILD).Methods:In Affiliated Hospital of Inner Mongolia Medical University from January 2018 to January 2021,seventy patients with RA were selected and divided into RA-ILD group(34 cases)and RA(36 cases)accord-ing to whether they were complicated with ILD,clinicopathological characteristics of the two groups of patients were analyzed by uni-variate analysis,focusing on the comparison of the expression level of KL-6 in the serum of the two groups and the correlation analysis with laboratory indexes,lung function and imaging,multivariate Logistic regression analysis risk factors affecting RA-ILD.Results:There were significant differences in gender,smoking history,age,age of onset,course of disease,DAS28,KL-6,WBC,UA,RF,anti-CCP antibody,TLC%Pred and DLCO%Pred between the two groups(P<0.05).In RA-ILD group,the proportion of clinical symp-toms was more prominent,joint pain was more prominent,and the thickening of pleural adhesion was more prominent on HRCT;Spearson correlation analysis showed that the level of serum KL-6 in RA-ILD group was significantly negatively correlated with TLC%Pred,VC%Pred and DLCO%Pred(P<0.05);there was a significant positive correlation with RF and anti-CCP antibody levels(P<0.05);there was no significant correlation with FEV1%Pred and FEV1/FVC(P>0.05);expression level of KL-6 in RA-ILD group was significantly higher than that in RA group before treatment,and there was no significant difference after treatment(P>0.05);Fol-lowed up for one year,six patients died in RA-ILD group.The expression level of KL-6 in death group was significantly higher than that in non death group(P<0.05);multivariate Logistic regression analysis showed that old age,male,smoking,increased anti CCP antibody and KL-6 were independent risk factors for RA-ILD(P<0.05);the maximum area under the ROC curve predicted by serum KL-6 was 0.802,the 95%CI was 0.743～0.866,the sensitivity was 0.853,the specificity was 0.827,and the maximum area under the ROC curve detected jointly was 0.854,indicating that the prediction ability of the model was strong.Conclusion:Serum KL-6 levels in RA-ILD patients are significantly increased.KL-6 can be used as a serological index for early detection of ILD,and combined with lung function detection can detect ILD earlier.Early diagnosis is of great significance for treatment and prognosis.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.

Objective:To explore the effect of fully automatic image segmentation of adenoid and nasopharyngeal airway by deep learning model based on U-Net network. Methods:From March 2021 to March 2022, 240 children underwent cone beam computed tomography（CBCT） in the Department of Otolaryngology, Head and Neck Surgery, General Hospital of Shenzhen University. 52 of them were selected for manual labeling of nasopharynx airway and adenoid, and then were trained and verified by the deep learning model. After applying the model to the remaining data, compare the differences between conventional two-dimensional indicators and deep learning three-dimensional indicators in 240 datasets. Results:For the 52 cases of modeling and training data sets, there was no significant difference between the prediction results of deep learning and the manual labeling results of doctors（P>0.05）. The model evaluation index of nasopharyngeal airway volume: Mean Intersection over Union（MIOU） s （86.32±0.54）%; Dice Similarity Coefficient（DSC）: （92.91±0.23）%; Accuracy: （95.92±0.25）%; Precision: （91.93±0.14）%; and the model evaluation index of Adenoid volume: MIOU: （86.28±0.61）%; DSC: （92.88±0.17）%; Accuracy: （95.90±0.29）%; Precision: （92.30±0.23）%. There was a positive correlation between the two-dimensional index A/N and the three-dimensional index AV/（AV+NAV） in 240 children of different age groups（P<0.05）, and the correlation coefficient of 9-13 years old was 0.74. Conclusion:The deep learning model based on U-Net network has a good effect on the automatic image segmentation of adenoid and nasopharynx airway, and has high application value. The model has a certain generalization ability.
Child ; Humans ; Adolescent ; Adenoids/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Pharynx ; Cone-Beam Computed Tomography ; Nose

The extremely low bioavailability of oral paclitaxel (PTX) mainly due to the complicated gastrointestinal environment, the obstruction of intestinal mucus layer and epithelium barrier. Thus, it is of great significance to construct a coordinative delivery system which can overcome multiple intestinal physicochemical obstacles simultaneously. In this work, a high-density PEGylation-based glycocholic acid-decorated micelles (PTX@GNPs) was constructed by a novel polymer, 9-Fluorenylmethoxycarbonyl-polyethylene glycocholic acid (Fmoc-PEG-GCA). The Fmoc motif in this polymer could encapsulate PTX via π‒π stacking to form the core of micelles, and the low molecular weight and non-long hydrophobic chain of Fmoc ensures the high-density of PEG. Based on this versatile and flexible carriers, PTX@GNPs possess mucus trapping escape ability due to the flexible PEG, and excellent intestine epithelium targeting attributed to the high affinity of GCA with apical sodium-dependent bile acid transporter. The in vitro and in vivo results showed that this oral micelle could enhance oral bioavailability of PTX, and exhibited similar antitumor efficacy to Taxol injection via intravenous route. In addition, oral PTX@GNPs administered with lower dosage within shorter interval could increase in vivo retention time of PTX, which supposed to remodel immune microenvironment and enhance oral chemotherapy efficacy by synergistic effect.

Peptide‒drug conjugates (PDCs) are drug delivery systems consisting of a drug covalently coupled to a multifunctional peptide via a cleavable linker. As an emerging prodrug strategy, PDCs not only preserve the function and bioactivity of the peptides but also release the drugs responsively with the cleavable property of the linkers. Given the ability to significantly improve the circulation stability and targeting of drugs in vivo and reduce the toxic side effects of drugs, PDCs have already been extensively applied in drug delivery. Herein, we review the types and mechanisms of peptides, linkers and drugs used to construct PDCs, and summarize the clinical applications and challenges of PDC drugs.

Objective : To study smile exposure in 20- to 30-year-olds with convex facial profiles and to explore the correlation between smile exposure and other aesthetic indicators, as well as psychological factors. Methods : This study obtained ethical approval from the hospital. After they gave informed consent and portrait authorization, 80 young subjects aged 20-30 with convex facial profiles had their dynamic postural smile and spontaneous laughter recorded. The videos were imported into Photoshop CC software, key frames were selected, and the smile exposure was measured. The three-dimensional information of the subject's face in a resting state was obtained, the relevant aesthetic indicators were measured, and the satisfaction degree of their smile and laughter were evaluated. Correlation analysis was conducted for smile exposure and the relevant aesthetic indicators and subjective psychological evaluation. Results : There were statistically significant differences in smile exposure, smile patterns, relevant aesthetic indicators and subjective psychological evaluation between males and females aged 20 to 30 (P<0.05). There was a large gap between males and females in the average opening degree when laughing, males having 5 times that of females, while the average gingival exposure height of the maxillary central incisor in males was approximately 1/2 of that in females. The average nasolabial angle of males (99.80° ± 7.96°) was larger than that of females (96.26° ± 7.31°) (P<0.05), while the average ratio of upper lip length to the length of the lower 1/3 of the face of males (33.73% ± 2.35%) was less than that of females (38.57% ± 2.76%) (P<0.05). The average psychological score of males (57.75±13.46) was higher than that of females (53.69±17.95) (P<0.05). The ratios of maxillary teeth and gingival exposure to oral fissure in the postural smile were positively correlated with the nasolabial angle. The oral fissure width in spontaneous laughter was negatively correlated with psychological score. Conclusion Convex-faced males and females aged 20 to 30 have different smile exposures and smile patterns. Males tend to smile more openly with a larger opening and less gingival exposure, while females tend to grin with a small opening and more gingival exposure. Smile exposure is positively correlated with nasolabial angle and negatively correlated with smile satisfaction.

Tumor immunotherapy is a therapeutic modality that uses immunological principles and methods to activate and enhance the body''s immune system to generate immune response for the removal of tumour cells. Many new immunotherapeutic agents have demonstrated effective anti-tumour capabilities, yet their clinical use is challenging due to the complex mechanisms of tumour immune escape. Meanwhile, these drugs would accumulate in different tissues and organs in the human body and be unable to achieve precise and specific targeting therapeutic effects, resulting in serious immune-related adverse effects, which greatly hinders the clinical potential of immunotherapy.Nanodrug delivery systems can deliver immunotherapeutic drugs to target tissues or specific immune cells precisely, thereby enhancing immune effects and reducing side effects.This paper reviews the research progress of nanodrug delivery systems in tumour immunotherapy in recent years based on the regulatory mechanism of the anti-tumour immune response, with a prospect of the challenges and development in this field.