Objective Great obstetrical syndrome(GOS)represents a group of pregnancy-related diseases that result in inadequate placentation.Most GOS cases end in preterm,either spontaneously or indicatively,and the use of antena...Objective Great obstetrical syndrome(GOS)represents a group of pregnancy-related diseases that result in inadequate placentation.Most GOS cases end in preterm,either spontaneously or indicatively,and the use of antenatal corticosteroids(ACS)is inevitably discussed.The placenta is an important,transient fetal-derived organ and is the embodiment of maternal or fetal well-being.However,few studies provide histological evidence of the placenta in GOS.This study aims to address these issues.Methods A total of 831 pregnant women were prospectively recruited.Placenta tissue was collected immediately and fixed with 4%paraformaldehyde solution for future H&E analysis.A novel checklist was devised to evaluate maternal vascular malperfusion sections on the basis of the commonly accepted Amsterdam placental workshop group consensus statement.Results A total of 131 patients were classified as having GOS.Comparisons between those with and without GOS revealed significant differences,including higher levels of distal villous hypoplasia,increased syncytial knots,accelerated villous maturation,and higher total scores in GOS.We found significant negative associations between GOS and neonatal weight,neonatal height,head circumference,placental surface area,placental volume,and placenta gross examination score.GOS neonates were 1.25 times more likely to have hyperbilirubinemia.Regarding the effect of ACS,a significant reduction in birthweight,height,and head circumference was observed,along with an increased risk of hyperbilirubinemia.Conclusion This study provides histological evidence of the GOS that supports the defective deep placentation hypothesis.Our research also contributes to benefit-risk consultation in the GOS,such as in cases of PE and FGR,where a balance between fetal lung maturation and short-term neonatal outcomes is crucial.展开更多
Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images wit...Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.展开更多
基金supported by the National Science Foundation of China(No.81873843)the National Science and Technology Pillar Program of China during the Twelfth Five-Year Plan Period(No.2014BA105B05)the Fundamental Research Funds for the Central Universities(No.2017 KFYXJJ102 and No.2019 KFYXKJC053).
文摘Objective Great obstetrical syndrome(GOS)represents a group of pregnancy-related diseases that result in inadequate placentation.Most GOS cases end in preterm,either spontaneously or indicatively,and the use of antenatal corticosteroids(ACS)is inevitably discussed.The placenta is an important,transient fetal-derived organ and is the embodiment of maternal or fetal well-being.However,few studies provide histological evidence of the placenta in GOS.This study aims to address these issues.Methods A total of 831 pregnant women were prospectively recruited.Placenta tissue was collected immediately and fixed with 4%paraformaldehyde solution for future H&E analysis.A novel checklist was devised to evaluate maternal vascular malperfusion sections on the basis of the commonly accepted Amsterdam placental workshop group consensus statement.Results A total of 131 patients were classified as having GOS.Comparisons between those with and without GOS revealed significant differences,including higher levels of distal villous hypoplasia,increased syncytial knots,accelerated villous maturation,and higher total scores in GOS.We found significant negative associations between GOS and neonatal weight,neonatal height,head circumference,placental surface area,placental volume,and placenta gross examination score.GOS neonates were 1.25 times more likely to have hyperbilirubinemia.Regarding the effect of ACS,a significant reduction in birthweight,height,and head circumference was observed,along with an increased risk of hyperbilirubinemia.Conclusion This study provides histological evidence of the GOS that supports the defective deep placentation hypothesis.Our research also contributes to benefit-risk consultation in the GOS,such as in cases of PE and FGR,where a balance between fetal lung maturation and short-term neonatal outcomes is crucial.
基金supported in part by the National Natural Science Foundation of China under Grant 62062061/in part by the Major Project Cultivation Fund of Xizang Minzu University under Grant 324112300447.
文摘Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.
