Knowledge, attitudes, and practices of adolescent depression among parents of children diagnosed with depression in Ningbo City, eastern China.

Objective: To investigate parental knowledge, attitudes, and practices (KAP) toward adolescent depression. Methods: A cross-sectional survey was conducted between October 2022 and October 2023 at The First Affiliated Hospital of Ningbo University among parents of adolescents diagnosed with depression. A self-administered questionnaire was used to collect the parents' demographic characteristics and KAP toward adolescent depression. Results: A total of 522 questionnaires were collected from parents of depressed adolescents. Among the participants, 383 (73.37%) were female. In addition, 426 participants (81.61%) had children aged 14-18. The mean knowledge, attitude, and practice scores were 9.09 ± 2.37 (possible range: 0-12), 37.04 ± 4.11 (possible range: 11-55), and 31.53 ± 3.84 (possible range: 8-40), respectively. There were significant positive correlations between knowledge and attitude (r = 0.225, p < 0.001), knowledge and practice (r = 0.240, p < 0.001), and attitude and practice (r = 0.381, p < 0.001). The path analysis showed significant direct effects of knowledge on attitude (ß = 0.422, p < 0.001) and practice (ß = 0.283, p < 0.001). There was an indirect effect of knowledge on practice through attitude (ß = 0.131, p = 0.004). Attitude directly impacted practice (ß = 0.311, p < 0.001). Conclusion: Parents of adolescents diagnosed with depression exhibited moderate KAP regarding adolescent depression. The study underscored the importance of targeted interventions to improve parental KAP in supporting adolescents with depression. Moreover, future research should explore additional factors influencing parental attitudes and behaviors toward adolescent depression to develop more effective interventions.

Validating automated eye disease screening AI algorithm in community and in-hospital scenarios.

Purpose: To assess the accuracy and robustness of the AI algorithm for detecting referable diabetic retinopathy (RDR), referable macular diseases (RMD), and glaucoma suspect (GCS) from fundus images in community and in-hospital screening scenarios. Methods: We collected two color fundus image datasets, namely, PUMCH (556 images, 166 subjects, and four camera models) and NSDE (534 images, 134 subjects, and two camera models). The AI algorithm generates the screening report after taking fundus images. The images were labeled as RDR, RMD, GCS, or none of the three by 3 licensed ophthalmologists. The resulting labels were treated as "ground truth" and then were used to compare against the AI screening reports to validate the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the AI algorithm. Results: On the PUMCH dataset, regarding the prediction of RDR, the AI algorithm achieved overall results of 0.950 ± 0.058, 0.963 ± 0.024, and 0.954 ± 0.049 on sensitivity, specificity, and AUC, respectively. For RMD, the overall results are 0.919 ± 0.073, 0.929 ± 0.039, and 0.974 ± 0.009. For GCS, the overall results are 0.950 ± 0.059, 0.946 ± 0.016, and 0.976 ± 0.025. Conclusion: The AI algorithm can work robustly with various fundus camera models and achieve high accuracies for detecting RDR, RMD, and GCS.

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

Robust Collaborative Learning of Patch-Level and Image-Level Annotations for Diabetic Retinopathy Grading From Fundus Image.

Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network-based algorithms treat DR grading as a classification task via image-level annotations. However, these algorithms have not fully explored the valuable information in the DR-related lesions. In this article, we present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bidirectionally exchange the fine-grained lesion and image-level grade information. As a result, it exploits more discriminative features for DR grading. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience. By testing on datasets of different distributions (such as label and camera), we prove that our algorithm is robust when facing image quality and distribution variations that commonly exist in real-world practice. We inspect the proposed framework through extensive ablation studies to indicate the effectiveness and necessity of each motivation. The code and some valuable annotations are now publicly available.

MiRNA-199a-5p targets WNT2 to regulate depression through the CREB/BDNF signaling in hippocampal neuron.

INTRODUCTION: This study mainly investigated the role of miR-199a-5p in depression. METHODS: qRT-PCR and western blotting were employed to detect the expressions of miR-199a-5p, CREB and BDNF. Sucrose preference test, forced swimming test, and tail suspension test were performed to evaluate depression-related symptoms. MTT assays and flow cytometry were used to examine the cell reproduction and apoptotic cells of hippocampal neuron. RESULTS: The data demonstrated that the expression levels of miR-199a-5p in the cerebrospinal fluids and serums of depression patient and the hippocampus of chronic unpredictable mild stress (CUMS) mouse were significantly increased. However, the expressions of WNT2, p-CREB, and BDNF were inhibited. In addition, miR-199a-5p-inhibitor enhanced sucrose preferences of CUMS mouse and decreased immobile time in sucrose preference test and forced swimming test. Knockdown of WNT2 attenuated the effects of miR-199a-5p-inhibitor on cell reproduction and apoptotic cells of hippocampal neuron and the expression of WNT2, p-CREB, and BDNF. CONCLUSION: MiR-199a-5p can target WNT2 to enhance the development of depression through regulation of the CREB/BDNF signaling. TRIAL REGISTRATION: JNU-Hos-49284.

Continuous Theta-Burst Stimulation Over the Right Orbitofrontal Cortex in Treatment-Resistant Obsessive-Compulsive Disorder Treatment: A Randomized Sham-Controlled Trial.

PURPOSE: Examining whether modulation of right orbitofrontal cortex (OFC) activity by continuous theta-burst stimulation (cTBS) affects obsessive-compulsive disorder (OCD) symptoms. PATIENTS AND METHODS: A total of 28 treatment-resistant OCD participants were treated with either active or sham cTBS of the OFC twice per day, for five days a week, for 2 weeks, in a double-blinded manner. Clinical response to treatment was determined using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). RESULTS: There were no statistically significant differences between the 2 groups after two weeks of treatment in the Yale-Brown Obsessive-Compulsive Scale score (group*time interaction, F2,20=0.996, p=0.387) and other secondary outcome measures, including anxiety symptoms and responder rates. Depressive symptoms improved significantly in the active group (p=0.027), but the significant difference disappeared at 6 weeks (p=0.089). CONCLUSION: This is the first randomized controlled study using cTBS in the right OFC to observe the improvement of treatment-resistant OCD symptoms. It is safe to use cTBS, but 2 weeks of treatment is not enough to achieve a curative effect. Future studies are needed to explore more advanced stimulation parameters suitable for the treatment of OCD. CLINICAL TRIAL REGISTRATION: www.chictr.org.cn, identifier ChiCTR2000034814.

Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker.

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients' survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients.

Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer.

The exact nature and dynamics of pancreatic ductal adenocarcinoma (PDAC) immune composition remains largely unknown. Desmoplasia is suggested to polarize PDAC immunity. Therefore, a comprehensive evaluation of the composition and distribution of desmoplastic elements and T-cell infiltration is necessary to delineate their roles. Here we develop a novel computational imaging technology for the simultaneous evaluation of eight distinct markers, allowing for spatial analysis of distinct populations within the same section. We report a heterogeneous population of infiltrating T lymphocytes. Spatial distribution of cytotoxic T cells in proximity to cancer cells correlates with increased overall patient survival. Collagen-I and αSMA+ fibroblasts do not correlate with paucity in T-cell accumulation, suggesting that PDAC desmoplasia may not be a simple physical barrier. Further exploration of this technology may improve our understanding of how specific stromal composition could impact T-cell activity, with potential impact on the optimization of immune-modulatory therapies.

Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma.

PURPOSE: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12-month overall survival status for GBM patients. METHODS: The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid-attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer-extracted texture features, namely, 48 segmentation-based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run-length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12-month survival status (a dichotomized version of overall survival at the 12-month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves. RESULTS: With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12-month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12-month survival status (with SFTA features on T1 postcontrast coronal scan). CONCLUSIONS: The authors evaluated the performance of five types of texture features in predicting GBM molecular subtypes and 12-month survival status. The authors' results show that texture features are predictive of molecular subtypes and survival status in GBM. These results indicate the feasibility of using tumor-derived imaging features to guide genomically informed interventions without the need for invasive biopsies.