Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

Background: Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. Methods: This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Results: Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Conclusion: Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Improved dual-aggregation polyp segmentation network combining a pyramid vision transformer with a fully convolutional network.

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style. Concretely, in the transformer branch, we adopted the B3 variant of pyramid vision transformer v2 (PVTv2-B3) as an image encoder for capturing multi-scale global features and modeling long-distant interdependencies between them whilst designing an innovative multi-stage feature aggregation decoder (MFAD) to highlight critical local feature details and effectively integrate them into global features. In the decoder, the adaptive feature aggregation (AFA) block was constructed for fusing high-level feature representations of different scales generated by the PVTv2-B3 encoder in a stepwise adaptive manner for refining global semantic information, while the ResidualBlock module was devised to mine detailed boundary cues disguised in low-level features. With the assistance of the selective global-to-local fusion head (SGLFH) module, the resulting boundary details were aggregated selectively with these global semantic features, strengthening these hierarchical features to cope with scale variations of polyps. The FCN branch embedded in the designed ResidualBlock module was used to encourage extraction of highly merged fine features to match the outputs of the Transformer branch into full-size segmentation maps. In this way, both branches were reciprocally influenced and complemented to enhance the discrimination capability of polyp features and enable a more accurate prediction of a full-size segmentation map. Extensive experiments on five challenging polyp segmentation benchmarks demonstrated that the proposed Dua-PSNet owned powerful learning and generalization ability and advanced the state-of-the-art segmentation performance among existing cutting-edge methods. These excellent results showed our Dua-PSNet had great potential to be a promising solution for practical polyp segmentation tasks in which wide variations of data typically occurred.

Impact of intravitreal triamcinolone injection following epiretinal membrane peeling on ectopic inner foveal layer classification.

PURPOSE: This study aimed to evaluate the impact of intravitreal triamcinolone acetonide (TA) administration after peeling of idiopathic epiretinal membranes (iERM) on both anatomical and visual outcomes, utilizing the ectopic inner foveal layer (EIFL) staging scheme. METHODS: In this retrospective case-control study, we analyzed 43 eyes from 43 patients diagnosed with iERM between June 2019 and December 2021. All participants were categorized into the TA or control groups based on administering intravitreal TA injection following ERM peeling. We thoroughly reviewed the clinical data, including the preoperative and postoperative best-corrected visual acuity (BCVA), central foveal thickness (CFT), and macular cube volume (VOL), with ERM stages classified according to the EIFL staging scheme. RESULTS: The study enrolled 22 eyes in the TA and 21 in the control groups. Following a mean follow-up period of 11.07 ± 2.02 months, noteworthy improvements in EIFL stages were observed in both cohorts (p < 0.01), but without significant distinctions between groups. In the TA group, 63.64% of eyes demonstrated improvements in EIFL stages, while the control group exhibited 76.19% (p = 0.37). At the final visit, both groups experienced a noteworthy reduction in the postoperative CFT and VOL (p < 0.05), coupled with significant improvement in BCVA (p < 0.01). No substantial differences appeared between the two groups concerning BCVA, CFT, and VOL (all p > 0.05). CONCLUSIONS: Our study suggested that concurrent intravitreal TA injection following ERM removal did not provide additional benefits regarding anatomical and visual improvement in iERM cases classified as Stages 2 and 3.