AIM: To prepare a novel functionalized upconversion nanomaterial UCNPs-PEG-Tf through an improved method and observe its specific imaging phenomenon to choroidal melanoma cells(OCM-1).METHODS: NaYF4:Yb/Er(Up-conversion nanoparticles, UCNPs)was Prepared and plasma was treated to carboxylate the surface; then amino polyethylene glycol and transferrin(Tf)were respectively loaded to prepare UCNPs-PEG-Tf. Characterized them accordingly, its biocompatibility was tested accordingly, and the specific fluorescence phenomenon of OCM-1 was detected by fluorescence spectrophotometer and inverted fluorescence microscopy.RESULTS: Characterization confirmed successful synthesis of UCNPs, UCNPs-PEG, and further loading of Tf to form UCNPs-PEG-Tf. UCNPs-PEG-Tf showed excellent biocompatibility and emitted significant green fluorescence. Under the same conditions, fluorescence intensity measurement and observations from the inverted fluorescence microscope both demonstrated its significant specificity in imaging to OCM-1 cells.CONCLUSION: The synthesized novel functionalized UCNPs-PEG-Tf nanocomposite showed good biocompatibility and achieve specific imaging to OCM-1 cells.

Objective: To explore the association between negative life events and smartphone addiction among middle school students, so as to provide theoretical support and practical guidance for prevention and intervention of smartphone addiction among middle school students. Methods: Using cluster sampling, 8 890 students were selected to survey from 27 junior high schools and 3 senior high schools in a district of Shenzhen in 2022 (baseline) and 2023 (followup). Data were collected through selfresigned questionnaires on basic information, the Smartphone Addiction Scale-Short Version, and the Adolescent Selfrating Life Events Checklist. Mixedeffects models were employed to analyze the association. Results: Compared to 2022, the punishment scores of middle school students in 2023 [1.00 (0.00, 6.00) and 1.00 (0.00, 6.00)] decreased (Z=4.27), while the scores of interpersonal stress, learning stress and adaptation [4.00(0.00, 8.00), 4.00(0.00, 8.00); 4.00(1.00, 8.00), 5.00(2.00, 9.00); 2.00 (0.00, 6.00), 3.00 (0.00, 7.00)] increased (Z=-3.04, -8.36, -6.80) (P<0.01). Mixedeffects models revealed a positive doseresponse relationship between negative life events and smartphone addiction (OR=1.08-1.17, P<0.01). Stepwise regression showed independent positive effects of interpersonal stress (OR=1.05), academic stress (OR=1.03), and adaptation stress (OR=1.11) on smartphone addiction (P<0.01). Subgroup analysis of nonaddicted students in 2022 confirmed persistent associations for academic stress (OR=1.03) and adaptation (OR=1.07) (P<0.01). Conclusion Negative life events exhibit a positive doseresponse relationship with smartphone addiction, particularly interpersonal stress, academic stress, and adaptationrelated events.

Objective: To explore the longitudinal association between only-child status and smartphone addiction among middle school students, so as to provide a basis for establishing family intervention measures for smartphone addiction in middle school students. Methods: In October 2022 and October 2023, a preliminary survey and follow-up were conducted among 8 759 middle and high school students from 30 schools in a district of Shenzhen. A self-designed questionnaire was used to determine whether the students were the only-child, and the Chinese Version of the Smartphone Addiction Scale-Short Version (C-SAS-SV) was utilized to assess the students smartphone addiction status. A multilevel mixed-effects model and subgroup analysis were applied to examine the association between only-child status and smartphone addiction among middle school students. Results: During 2022 to 2023, the prevalence of smartphone addiction in the cohort of middle school students increased from 24.1% to 25.2%. Compared with only-child, non-only child were more likely to be addicted to smartphones (adjusted model: OR =1.2, 95% CI =1.1-1.4) and also scored higher on smartphone addiction (adjusted model: β =0.9, 95% CI =0.2-1.5)( P <0.05). Subgroup analysis further revealed that compared to baseline, non-only child demonstrated an increased prevalence of smartphone addiction (adjusted model: OR = 1.2 , 95% CI =1.0-1.5) and higher addiction scores (adjusted model: β =0.8, 95% CI =0.2-1.5) after one year( P <0.05). Conclusions Non-only child face higher risk of smartphone addiction. Under the current population policy, it is crucial to address smartphone addiction among middle school students who is not only child.

Objective: To explore the potential causal association between adolescent compulsive behaviour and smartphone addiction based on longitudinal data, so as to provide reference for the establishment of adolescent smartphone addiction interventions. Methods: A preliminary survey and follow-up were conducted on 8 907 middle and high school students in a district of Shenzhen in 2022 and 2023, respectively. Compulsive behaviours were measured by using the Mental Health Inventory for Middle School Students-60 Items (MMHI-60), smartphone addiction was assessed by using the Smartphone Addiction Scale-Short Version ( SAS- SV), and the associations between compulsive behaviours and smartphone addiction were analysed by using multilevel mixed-effects models and subgroup analyses. Results: Smartphone addiction detection rates among middle school students were significantly associated with genders, father s education level, mother s education level, study load subgroups, and whether or not they were single-parent families, and there were statistical differences ( χ 2=17.21-175.34, P <0.05). Students with compulsive behaviours were 2.98 times more likely to develop smartphone addiction than those without compulsive behaviours ( OR=2.98, 95%CI=2.77-3.22, P <0.05). Subgroup analysis of middle school students without smartphone addiction in the first year found that compulsive behaviours significantly predicted smartphone addiction ( OR= 1.76 , 95%CI=1.54-2.01, P <0.05). Conclusion There is a potential causal association between obsessive-compulsive behaviours and smartphone addiction in middle school students, and obsessive-compulsive behaviours in middle school students could significantly predicted the occurrence of smartphone addiction.

Objective: To investigate the changing trends for associations of anxiety and depressive symptoms with smartphone addiction among middle school students, so as to provide a scientific basis for preventing smartphone addiction in middle school students. Methods: From 2022 to 2023, a method of combining convenient sampling with cluster sampling was used to select 8 923 middle school students from 27 junior high schools and 3 senior high schools in a district of Shenzhen City between September 2022 (baseline, T1) and September 2023 (follow up, T2). The Smartphone Addiction Scale-Short Version (SAS-SV), Patients Health Questionnaire-9 Item (PHQ-9), and Generalized Anxiety Disorder 7-item Scale (GAD-7) were administered to assess smartphone addiction, anxiety and depressive symptoms. Mixed effects models were used to analyze the association of anxiety and depressive symptoms with smartphone addiction among middle school students. Results: From September 2022 to September 2023, the reported prevalence of smartphone addiction increased from 24.22% to 25.25% ( χ 2=45.71); and smartphone addiction scores [ 24.00 (16.00, 32.00),25.00(16.00, 33.00)], anxiety symptom scores [2.00(0.00, 7.00),3.00(0.00, 7.00)] and depressive symptom scores[3.00(0.00, 8.00),5.00(0.00, 9.00)] all significantly increased ( Z =-17.43, -42.38, -41.57) (all P <0.05). There were statistically significant difference in the distribution of anxiety and depression symptom levels among middle school students in 2022 and 2023 ( χ 2=85.15, 106.85, both P <0.05). After adjusting for covariates such as age, gender and family background, mixed effects models revealed dose response associations of anxiety and depressive symptoms with smartphone addiction among middle school students:mild anxiety symptom( OR =3.22), moderate to severe anxiety symptom ( OR =5.36), mild depressive symptom ( OR =3.32) and moderate to severe depressive symptom ( OR =6.13) were significantly associated with higher risks of smartphone addiction (all P <0.05). Interaction effect analysis found that co existing anxiety and depressive symptoms synergistically increased addiction risk by 5.60 times ( OR =5.60) compared to the asymptomatic group, with 32% of the combined risk attributable to their interaction ( S=1.64, AP =0.32)(both P < 0.05 ). Conclusions Anxiety and depressive symptoms are significantly associated with smartphone addiction, exhibiting a synergistic effect. Attention should be paid to emotional issues and smartphone addiction among middle school students.

Objective:To compare the effect of delaying progression of myopia in children between defocus incorporated multiple segments (DIMS) spectacle lens and orthokeratology.Methods:A nonrandomized controlled clinical study was conducted.A total of 390 children (390 eyes) with myopia who were treated in the First Affiliated Hospital of Zhengzhou University from January 2018 to December 2020 were included, with the spherical equivalent (SER) of -0.75 to -6.00 D. According to the willingness of patients and patients' guardians, the subjects were divided into DIMS group, orthokeratology group and single-vision spectacle lens group, with 130 cases (130 eyes) in each group, wearing DIMS spectacle lenses, standard or astigmatic design orthokeratology and conventional single-vision full-correction aspheric spectacle lenses, respectively.The SER of the eyes was measured using an automatic computerized refractometer in combination with subjective refraction before and one year after lens wear, and the axial length (AL) of the eyes was measured using IOLMaster.A total of 327 patients in the three groups met the inclusion and exclusion criteria, including 107 in the DIMS group, 112 in the orthokeratology group, and 104 in the single-vision spectacle lens group.All the right eyes were included in this study.The changes in SER and AL before and after wearing lenses for 1 year were compared among the three groups.The relationship between AL and SER changes and baseline data in the DIMS group was evaluated by Pearson linear correlation analysis.The study followed the Declaration of Helsinki, and the study protocol was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No.2023-KY-0174-002). The subjects and their guardians were fully aware of the purpose and methodology of the study, and voluntarily signed an informed consent form.Results:There were statistically significant overall differences in SER and AL at different time points among the three groups (SER: Fgroup=7.065, P=0.009; Ftime=183.730, P<0.001.AL: Fgroup=6.151, P=0.014; Ftime=175.290, P<0.001). One year later, the differences in SER and AL changes among the three groups were statistically significant ( F=7.065, P=0.009; F=6.151, P=0.014). The SER and AL of each group after 1 year was greater than the baseline, with the SER and AL and their changes significantly smaller in orthokeratology group and DIMS group than in single-vision spectacle lens group and greater in DIMS group than in orthokeratology group, showing statistically significant differences (all at P<0.05). Compared with single-vision spectacle lenses, wearing orthokeratology for 1 year can inhibit SER and AL progression by 58.3% and 59.0%, and wearing DIMS frame glasses for 1 year can inhibit SER and AL progression by 46.9% and 43.6%.The percentage of eyes with no change in SER was 5.77%(6/104), 24.11%(27/112) and 17.76%(19/107) in the single-vision spectacle lens group, orthokeratology group and DIMS group, respectively, and the percentage of AL was 0.00%(0/104), 8.93%(10/112) and 7.48%(8/107), respectively, showing statistically significant differences among the three groups ( χ2=9.316, 8.676; both at P<0.001). The AL change in the DIMS group was weakly negatively correlated with age ( r=-0.252, P=0.006). Conclusions:Wearing DIMS spectacle lenses is not as effective as orthokeratology in delaying myopia in children, but it is significantly better than wearing conventional single-vision spectacle lenses.

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:To investigate the risk factors and cumulative risk of myopia in children and adolescents, providing a basis for identifying cumulative risk factors in preventing and controlling myopia.Methods:Baseline data from the mental and physical health cohort of children and adolescents established in Inner Mongolia Autonomous Region were used. A stratified random cluster sampling method was adopted to select 138 974 students from fourth to twelfth grade as participants. Distance visual exams, refractive assessments, and questionnaires were conducted on the included students. Logistic regression analysis was used to evaluate each risk factor's impact on myopia's prevalence. The number of risk factors was summed to form a cumulative risk score, and logistic regression analysis was conducted to examine the association between the cumulative risk score and the prevalence of myopia. Additionally, the association between the cumulative risk score of myopic students and their degree of refractivity was analyzed using a generalized estimating equation.Results:The study found a high prevalence of myopia among children and adolescents at baseline (70.2%). Girls exhibited a higher prevalence (74.8%) than boys (65.6%), urban areas (74.3%) surpassed suburban ones (68.6%), and the incidence was greater in high schools (80.3%) compared to middle schools (75.3%), which, in turn, was higher than in elementary schools (57.7%) (all P<0.05). Analysis of risk factors revealed that children and adolescents experiencing improper reading and writing distances ( OR=1.10, 95% CI: 1.07-1.13), excessive homework ( OR=1.09, 95% CI: 1.06-1.12), insufficient sleep ( OR=1.10, 95% CI: 1.07-1.13), having myopic father ( OR=1.98, 95% CI: 1.91-2.05), having myopic mother ( OR=2.04, 95% CI: 1.97-2.10), or using classroom chairs not matched to their height faced ( OR=1.04, 95% CI: 1.01-1.07) increased myopia risks. Additionally, the prevalence and significant odds ratio of myopia increased with the increase in cumulative risk score, with every additional unit of cumulative risk score increasing the right eye's refractive error by -0.10 D. Conclusion:The presence of multiple factors and their comprehensive score increases the prevalence of myopia in children and adolescents.