Efficacy and Safety of Antidiabetic Agents for Major Depressive Disorder and Bipolar Depression: A Meta-Analysis of Randomized, Double-Blind, Placebo-Controlled Trials.

BACKGROUND: This meta-analysis aimed to determine the efficacy and safety of antidiabetic agents in the treatment of major depressive disorder and bipolar depression. METHODS: Randomized controlled trials (RCTs) of antidiabetic agents in major depressive disorder or bipolar depression were searched in three electronic databases and three clinical trial registry websites from their inception up to October 2023. The differences in changes in the depression rating scale scores from baseline to endpoint or pre-defined sessions, response rate, remission rate, rate of side effects and dropout rate between antidiabetic agents and placebo were meta-analyzed. RESULTS: Six RCTs involving 399 participants were included in the final meta-analysis, which did not find that antidiabetics outperformed the placebo in reducing depressive symptoms. The standardized mean difference (SMD) in the depression scores from baseline to endpoint was 0.25 (95% CI -0.1, 0.61). However, a subgroup analysis found a significant difference between antidiabetics and placebos in reducing depressive symptoms in Middle Eastern populations, with an SMD of 0.89 (95% CI 0.44, 1.34). CONCLUSIONS: The current meta-analysis does not support the efficacy of antidiabetics being superior to the placebo in the treatment of unipolar and bipolar depression. However, a subgroup analysis indicates that patients from the Middle East may benefit from adding an antidiabetic medication to their ongoing medication(s) for their depression. Larger studies with good-quality study designs are warranted.

Predicting the final clinical phenotype after the first attack of optic neuritis.

BACKGROUND AND OBJECTIVES: To evaluate the factors determining the final clinical phenotype after an initial isolated attack of optic neuritis (ON). ON could be an isolated event or the initial presentation of a chronic neuroimmunological condition. METHODS: This was a retrospective analysis of patients presenting to University Hospitals Cleveland Medical Center for an initial, isolated attack of ON. Final clinical phenotypes were idiopathic ON, multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein associated disease (MOGAD), or secondary ON (e.g. neurosarcoidosis). Several potential predictors at the time of initial presentation were compared among the different phenotypes to determine early predictors. Categorical variables were compared using Pearson χ2 or Fisher's exact test, and continuous variables were compared using independent t-test. RESULTS: Sixty-four patients met criteria (average age 41.3 ± 13.3, 78.1% females). Average time to final diagnosis was 8.3 months, and average follow-up was 47 months. The final phenotypes were MS (22, 34%), idiopathic ON (14, 22%), MOGAD (11, 17%), NMOSD (10, 16%), and secondary ON (7, 11%). White race, unilateral ON, short segment hyperintensity on orbital MRI, classical demyelination on brain MRI, and not requiring PLEX were associated with MS. Older age, poor steroid responsiveness, and requiring PLEX were associated with NMOSD. African American race, bilateral ON, papillitis on fundoscopy, long segment hyperintensity on orbital MRI, and normal brain MRI were associated with MOGAD. Normal or thinned retinal nerve fiber layer on OCT, short segment hyperintensity on orbital MRI, and normal brain MRI were associated with idiopathic ON. CONCLUSION: The final clinical phenotype may be predictable at the time of initial ON presentation. This requires a careful evaluation of patient demographics, treatment response, funduscopic findings, OCT, and orbital and brain MRIs. Utilizing early predictors in clinical practice could better inform prognosis and management decisions.

Application of Graphic Design with Computer Graphics and Image Processing: Taking Packaging Design of Agricultural Products as an Example.

With development of economy, all industries have undergone earthshaking changes. Various new technologies are starting to be employed in all aspects of life, and graphic design is no exception. The use of computer graphics and image processing technologies in graphic design can substantially improve design efficiency and make graphic design job more convenient to develop. The requirements for the quality of graphic design are higher. Quality inspection has become a necessary step in the production process, in which the detection of graphic design defects is an indispensable and important link. The traditional graphic design defect detection adopts the method of manual visual inspection, which has the disadvantages of poor stability, long consumption time, and high labor cost. As an efficient computer graphics and image processing technology, convolutional neural network has received extensive attention in graphic design defect detection because of its advantages of high speed, efficiency, and high degree of automation. Taking agricultural product packaging as an example, this paper studies application technology for graphic design defect detection with convolutional neural network (CNN). The main contents are as follows: construct the original YOLOv3 network model, input the graphic design images of agricultural product packaging into the network model in batches according to the computing power of the hardware equipment, train the YOLOv3 network, and deeply study and analyze the experimental results. The related improvement techniques are then given, based on the characteristics of agricultural product packaging design faults. The backbone network, multiscale feature map, a priori frame, and activation function of YOLOv3 are improved, and then performance of the improved model is verified by experiments.

Using Virtual Reality to Examine the Neural and Physiological Responses to Height and Perturbations in Quiet Standing.

We describe an experimental setup, which uses virtual reality to understand neural responses to height and perturbations in human postural control. This system could help clinicians develop better methods to alleviate symptoms from a significant fear of heights, especially in the elderly and those with movement disorders, such as Parkinson's disease. In our design, EEG and EKG systems monitor the participants' neural responses and heart activities respectively, while they try to maintain balance on a force plate in an induced virtual world, experiencing randomized height changes and perturbations. These responses are then analyzed to understand the participants' anxiety caused by height and postural challenges.