Search

Background: To evaluate the effect and factors associated with the reactivation of retinopathy of prematurity (ROP) after intravitreal conbercept or aflibercept. Methods: We retrospectively reviewed the medical records of 176 eyes diagnosed with ROP and treated with anti-VEGF therapy between January 2018 and September 2022. The rate of reactivation and complications were assessed during the follow-up period. The factors of reactivation of ROP after intravitreal conbercept or aflibercept were analyzed on the basis of clinical factors and retinal parameters. Results: Reactivation of ROP occurred in 10 eyes (13.9%) after intravitreal conbercept and 13 eyes (12.5%) after intravitreal aflibercept (P = 0.79). The interval between injection and reactivation was significantly longer in the aflibercept group than in the conbercept group (15.50 ± 4.05 vs. 5.36 ± 0.50 weeks) (P < 0.001). The central retinal arteriolar equivalent (CRAE) of aggressive ROP was larger than that of type 1 prethreshold and threshold ROP before anti-VEGF therapy (P < 0.05). Zone I and stage 3 exhibited a positive correlation with the reactivation of retinopathy of prematurity (ROP) [odds ratio (OR) = 20.15, 5.02]. The changes in CRAE of pre-and post-therapy and gestational age were identified as potential protective factors for these outcomes (OR = 0.23, 0.49). Conclusions: Conbercept and aflibercept are effective for treating ROP. Aflibercept resulted in longer treatment intervals compared to conbercept. Zone, stage, and gestational age were associated with the reactivation of ROP. CRAE was associated with not only the severity of ROP but also its reactivation. Additionally, it may be an objective indicator in the early indication and follow-up of ROP. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%. [ABSTRACT FROM AUTHOR]

Copyright of Public Administration is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the role of change agents, city leaders, and their networks in facilitating interlocal collaboration in environmental governance. We propose an Agent Network Collaboration (ANC) model that brings individual managers, as change agents, back into the study of the interlocal agreement (ILA). We argue that the leadership transfer networks, the collection of city managers' career paths that connect cities, serve as an important channel for interlocal collaboration. With a dyadic panel data set of 14 cities in the Pearl River Delta region in China, we find substantive impacts of managers' career paths on the formation of ILAs. [ABSTRACT FROM AUTHOR]

In response to the increasing attention paid to environmental governance and leadership mobility, this study explores the interactions between leadership mobility and environmental governance performance. From the perspective of networks, this study aims to determine whether leadership mobility networks shape environmental governance outcomes. We argue that leadership transfer networks affect local water governance performance, which is particularly evident when leadership mobility occurs between cities with similar institutional environments. We collected managers' career data and water governance performance from forty-one cities located in the Pan-Yangtze River Delta region in China from 2011 to 2015. Methodologically, we employ spatial temporal autoregressive models to test the hypotheses and confirm the effects of the leadership transfer network on the homogeneity of water governance performance across the region. Theoretically, this study advances the institutional collective action framework in regional water governance by providing supplementary mechanisms from the perspective of agent network diffusion. [ABSTRACT FROM AUTHOR]

Flexible strain sensor as a measurement tool plays a significant role in agricultural development by long-term stable monitoring of the dynamic progress of plant growth. However, existing strain sensors still suffer from severe drawbacks, such as large hysteresis, insufficient fatigue resistance, and inferior stability, limiting their broad applications in the long-term monitoring of plant growth. Herein, we fabricate a novel conductive hydrogel strain sensor which is achieved through uniformly dispersing the conductive activated charcoal (AC) in high-viscosity polyvinyl alcohol (PVA) solution forming a continuous conductive network and simple preparation by freezing-thawing. The as-prepared strain sensor demonstrates low hysteresis (<1.5%), fatigue resistance (fatigue threshold of 40.87 J m−2), and long-term sensing stability upon mechanical cycling. We further exhibit the integration and application of PVA-AC strain sensor to monitor the growth of plants for 14 days. This work may offer an effective strategy for monitoring plant growth with conductive hydrogel strain sensor, facilitating the advancement of agriculture. [ABSTRACT FROM AUTHOR]