ObjectiveTeam-based learning (TBL) is an increasingly popular teaching method in medical education. However, TBL hasn’t been well-studied in the ophthalmology clerkship context. This study was to examine the impact of modified TBL in such context and to assess the student evaluations of TBL.MethodsNinety-nine students of an 8-year clinical medicine program from Zhongshan Ophthalmic Centre, Sun Yat-sen University, were randomly divided into four sequential units and assigned to six teams with the same faculty. The one-week ophthalmology clerkship module included traditional lectures, gross anatomy and a TBL module. The effects of the TBL module on student performance were measured by the Individual Readiness Assurance Test (IRAT), the Group Readiness Assurance Test (GRAT), the Group Application Problem (GAP) and final examination scores (FESs). Students’ evaluations of TBL were measured by a 16-item questionnaire. IRAT and GRAT scores were compared using a paired t-test. One-way analysis of variance (ANOVA) and subgroup analysis compared the effects among quartiles that were stratified by the Basic Ophthalmology Levels (BOLs). The BOLs were evaluated before the ophthalmology clerkship.ResultsIn TBL classes, the GRAT scores were significantly higher than the IRAT scores in both the full example and the BOL-stratified groups. It highlighted the advantages of TBL compared to the individual learning. Quartile-stratified ANOVA comparisons showed significant differences at FES scores (P < 0.01). In terms to IRAT, GRAT and GAP scores, there was no significant result. Moreover, IRAT scores only significantly differed between the first and fourth groups. The FES scores of the first three groups are significantly higher than the fourth group. Gender-specific differences were significant in FES but not the IRAT. Overall, 57.65% of student respondents agreed that TBL was helpful. Male students tended to rate TBL higher than female students.ConclusionThe application of modified TBL to the ophthalmology clerkship curriculum improved students’ performance and increased students’ engagement and satisfaction. TBL should be further optimized and developed to enhance the educational outcomes among multi-BOLs medical students.

Background: Studies have shown that mini-αA can protect retinal pigment epithelium (RPE) cells from apoptosis. However, no in vivo study concerning the anti-apoptotic function of mini-αA has been conducted yet. Methods: MTT assay, HE staining and TUNEL assay were used to assess levels of cells, and an animal model was established to examine the protective effects of mini-αA against NaIO3-induced RPE cell apoptosis. Western blot analysis and RT-qPCR were performed to explore the possible mechanism of mini-αA’s protective function against NaIO3-induced RPE cell apoptosis. Results: Results from in vivo and animal experiments showed that mini-αA antagonized NaIO3-induced RPE cell apoptosis. Further investigation into how mini-αA provided protection against NaIO3-induced RPE cell apoptosis showed that mini-αA reduced NaIO3-induced RPE cell apoptosis and autophagy. In addition, unfolded protein response was also involved in the protective effects of mini-αA against NaIO3-induced RPE cell apoptosis. Conclusions: mini-αA can antagonize RPE cell apoptosis induced by NaIO3. A possible mechanism is by inhibition of apoptosis by repressing autophagy and endoplasmic reticulum stress.

This study suggests that the curative rate of TN following MVD is higher in the MRTA-positive group. Venous compression and no neurovascular contact that were negative on MRTA image are poor prognostic factors for surgical outcome of TN. Thus, preoperative MRTA serves as a useful tool in patient selection and outcome prediction.

Orbital CG is a rare expansive cystic condition and nearly always occurs in the lateral region of the superior orbital ridge within the frontal diploic space. This condition shows a marked preponderance in middle-aged males. The findings that computed tomography scan did not reveal bone erosion in patient 1, and magnetic resonance imaging examination showed moderate signal intensity, rather than high signal intensity, on T1-weighted images in patient 2 indicated that these represented unusual presentations. Surgical excision has a high success rate with a low incidence of recurrence.

We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.