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卒中是中国居民致死和长期致残的主要原因，传统的治疗药物受限于血脑屏障阻碍、稳定性差、缺乏靶向性、低载药量，治疗效果可能欠理想。纳米材料作为一种新兴的、非侵入性、可实时控制的药物，已被广泛用于卒中的诊疗。纳米药物能够有效提高药物的生物相容性、稳定性、靶向性，并延长药物的半衰期，从而有效改善传统药物的疗效。近年来纳米材料在缺血性卒中诊疗领域取得了一定进展，但仍面临临床转化等挑战。本文主要综述纳米材料在缺血性卒中诊疗中的研究进展和挑战，旨在继续推动纳米材料在卒中诊疗中的研究与应用。 Abstract: Stroke is a major cause of death and long-term disability among residents in China. Traditional therapeutic drugs are limited by the obstruction of the blood-brain barrier, poor stability, lack of targeting, and low drug loading, which may result in suboptimal treatment outcomes. Nanomaterials, as an emerging, non-invasive, and real-time controllable drug delivery system, have been widely used in the diagnosis and treatment of stroke. Nanomedicines can effectively enhance the biocompatibility, stability, and targeting of drugs, as well as prolong their half-life, thereby significantly improving the efficacy of traditional medications. In recent years, there has been some progress in the application of nanomaterials in the field of ischemic stroke diagnosis and treatment, although challenges in clinical translation remain. This article primarily reviews the research progress and challenges of nanomaterials in the diagnosis and treatment of ischemic stroke, aiming to further promote the research and application of nanomaterials in stroke therapy.

Objective: To assess the associations between phthalates (PAEs) exposure and the levels of inflammatory cytokines in the ovarian microenvironment. Methods: 64 females who were undergoing in vitro fertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI-ET) from April to December in 2020 were included in this cross-sectional study. The levels of 10 kinds of phthalate metabolites (mPAEs) and 2 kinds factors in follicular fluid were measured. Multivariable linear regression model and further stratified analysis by age were conducted to analyze the associations between mPAEs and inflammatory factors. Results: There were significant positive associations between mono-butyl phthalate (MBP) and tumor necrosis factor-α (β=0.446, P=0.003) and interleukin-6 (β=0.425, P=0.003) in the follicular fluid. Similarly, there was a significant positive relationship between mono (2-ethyl-5-oxohexyl) phthalate (MEOHP) and tumor necrosis factor-α（β=0.411, P=0.019). In the stratified analysis, MBP still showed significantly the positive association with tumor necrosis factor-α (β=0.667, P= 0.000) and interleukin-6 (β=0.407, P=0.028) in the group aged≥30 years. In contrast, there were no correlations between mPAEs and inflammatory factors within the group aged ＜30 years. Conclusions: Our results suggest that there is strongly association between PAEs exposure and the altered levels of inflammatory factor, and that PAEs exposure may bring changes of inflammatory burden in the ovarian microenvironment. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office 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.)

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Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security 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.)

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Detection technology has been widely used for apple leaf diseases and pests in recent years, with the rapid development of deep learning. However, most of the existing identification equipment is deployed in the field, with limited network conditions and computing resources, whereas, a wide variety of diseases and pests, with limited recognition accuracy, which is not conducive to the development of digital agriculture. In this study, a fast identification (S-DenseNet-E) was proposed to realize the best effect on public data sets. The DenseNet's Dense module was selected to add an auxiliary model, in order to facilitate the identification of indistinguishable diseases. The S-DenseNet model was designed for the multi-diseases using the DenseNet121. The aggregation connection was adopted only in the output layer of the module in the S-DenseNet. The high reasoning speed of DenseNet121 was achieved to solve the dense connection. The average all-category F1-score of SDenseNet was 85.14%, the inference time was 33.03 ms, and the number of parameters was 1.1 M. Compared with DenseNet121, the average F1-score of all categories was improved by 0.13 by 4.28, the inference time was shortened by 32.87 ms, and the number of parameters was reduced by 6.96 M. An auxiliary model was used to assist S-DenseNet to identify the indistinguishable disease, which was built S-DenseNet-E of composite recognition framework. Furthermore, the one-vote strategy was adopted under two-model voting. The S-DenseNet-E maintained the high recognition of the S-DenseNet for the multi-diseases, while the effective recognition of the indistinguishable disease indicated a low inference time and small parameters. S-DenseNet-E achieved an average F1-score of 85.86% in all categories, an F1-score of 70.10% for indistinguishable disease, an inference time of 38.92 ms, and a parameter of 2.2 M. Compared with the S-DenseNet, the SDenseNet-E improved the F1-score by 4.28 on indistinguishable diseases, and the inference time by 5.89 ms. Compared with the DenseNet121, S-DenseNet-E shared an average F1-score improvement of 0.85 percentage points in all categories, an F1- score improvement of 5.18 percentage points in indistinguishable disease, a reduction in the number of parameters by 5.86 M, where the inference time was reduced by 26.98 ms. Therefore, the S-DenseNet-E presented better recognition performance for two complex situations, namely, apples suffering from multiple diseases and unidentifiable diseases, where was required fewer computational resources. The practical application of the model was also verified in the field. Specifically, 1 730 apple leaf disease images were collected on site in Banan District, Chongqing, China. Three types were divided into healthy, Scab, and rust leaves. The improved model was compared with other models for experiments. The S-DensseNet model showed a recognition accuracy of 91.91% for healthy apple leaves, 91.83% for Scab apple leaves, and 98.43% for Trust apple leaves. The S-DensseNet model also demonstrated the least inference time, only requiring 42.72 ms. The experimental results show that the S-DenseNet and S-DenseNet-E can be expected to run on embedded edge computing devices, while more accurately identifying the multiple diseases and unidentifiable diseases of apple leaves, and fully meeting the actual production needs of apple orchards. The finding has an important significance for the development of digital agriculture. [ABSTRACT FROM AUTHOR]

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The influence of water on the growth and development of grapes cannot be ignored. In order to provide a theoretical reference for drought risk management and loss reduction and quality assurance of table grapes, the Bohai Rim region was taken as a study area. The crop water deficit index, which was calculated using daily meteorological data from 60 meteorological stations in the region from 1981 to 2014, was taken as an index to explore the drought of table grapes. By the crop water deficit index of the table grapes in each growth period in the region, the drought frequency and station ratio of the table grapes in each growth period were obtained. Then we evaluated the risk of drought based on the spatial and temporal distribution of table grapes in the region. The results showed that the matching degree was low in the time dimension between the water requirement of table grapes in the Bohai Rim region and precipitation, and table grapes in the region had a certain risk of drought in each growth stage. The new shoot growth period and fruit ripe period of table grapes were high-risk periods of drought in the region, when droughts were more frequently and more severely to occur. The central and southern part of Hebei province were high-risk area for drought, and where the risk of severe droughts is higher for table grapes. Table grapes in the Bohai Rim region had the most widely range of drought during the ripening period. [ABSTRACT FROM AUTHOR]

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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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.)

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