Obese individuals even with normal glucose tolerance(NGT)are at higher risk for developing type 2 diabetes(T2D),and obesity is associated with inflammation.However,mechanisms linking inflammation to beta-cell function...Obese individuals even with normal glucose tolerance(NGT)are at higher risk for developing type 2 diabetes(T2D),and obesity is associated with inflammation.However,mechanisms linking inflammation to beta-cell function and insulin sensitivity in NGT individuals are not fully understood.We aimed to investigate the relationships between inflammation-related proteins(IRPs)and insulin dynamics in NGT subjects.The explorations were conducted using data from 1109 non-diabetes subjects aged 40–44 with normal or excess body weight and 21 Chinese NGT subjects aged 22–32 with accurate metabolic assessment.IRPs were detected with Olink technology.Insulin sensitivity and beta-cell function were evaluated with hyperinsulinemic–euglycemic clamp and hyperglycemic clamp.Eight associators were identified with obesity in NGT subjects,among which MCP-3,IL-6,TWEAK,HGF,and CST5 also showed associations in non-diabetes people.Four IRPs were linked to insulin sensitivity,with IL-24 being a novel finding.Seven IRPs were related to beta-cell function,including novel associators CD244,CD40,and IL-15RA.Moreover,most IRPs were interconnected,with IL-6 as the hub.In conclusion,insulin sensitivity and beta-cell function are related to IRPs involved in chemotaxis,activation of immune cells,and cell proliferation,which might provide valuable information for the understanding of the mechanisms associated with T2D pathogenesis.展开更多
Identification of the phenotypes of fruits is critical for understanding complex genetic traits.Computed to-mography(CT)imaging technology enables the noninvasive acquisition of three-dimensional images of fruit inter...Identification of the phenotypes of fruits is critical for understanding complex genetic traits.Computed to-mography(CT)imaging technology enables the noninvasive acquisition of three-dimensional images of fruit interiors,thus providing a robust data foundation for phenotypic analysis.Accurate segmentation of internal fruit tissues is essential,as it directly influences the accuracy and reliability of the results.Current methods are not optimized for the unique features of plant fruit images.This study introduces XFruitSeg,which is a general deep learning model for segmenting plant fruit CT images.The model uses a U-shaped encoder-decoder architecture and integrates multitask learning.A large convolutional kernel network,RepLKNet,expands the receptive field for feature extraction.Multiscale skip connections and a deep supervision mechanism improve the model's ca-pacity to learn features of various sizes,and a contour feature learning branch specifically targets the interor-ganizational boundaries.An optimized composite loss function enhances the model's robustness when applied to imbalanced categories.Additionally,a dataset named XrayFruitData was established,which contains high-resolution images of twelve plant fruit varieties,with accurate annotations for orange,mangosteen,and durian fruits for model evaluation.Compared with four mainstream advanced models,XFruitSeg achieved su-perior segmentation performance on the orange,mangosteen,and durian datasets,with mean Dice coefficients of 95.21%,93.24%,and 94.70%and mean intersection over union(mIoU)scores of 91.09%,87.91%,and 90.35%,respectively.The results of extensive ablation experiments demonstrate the effectiveness of each component.Therefore,the proposed XFruitSeg model has been proven to be beneficial for high-precision analysis of internal fruit phenotyping traits.展开更多
基金supported by grants from the National Key Research and Development Program of China(2022YFA1004804)Shanghai Municipal Key Clinical Specialty,Shanghai Research Center for Endocrine and MetabolicDiseases(2022ZZ01002)+6 种基金National Natural Science Foundation of China(NSFC)major international(regional)joint research project(81220108006)to W.J.the Excellent Young Scientists Fund of NSFC(82022012)General Fund of NSFC(82270907)Major Program of NSFC(92357305)Innovative Research Team of High-level Local Universities in Shanghai(SHSMU-ZDCX20212700)Hong Kong Scholars Program(XJ2013035)Two Hundred Program from Shanghai Jiao Tong University School of Medicine to H.L.
文摘Obese individuals even with normal glucose tolerance(NGT)are at higher risk for developing type 2 diabetes(T2D),and obesity is associated with inflammation.However,mechanisms linking inflammation to beta-cell function and insulin sensitivity in NGT individuals are not fully understood.We aimed to investigate the relationships between inflammation-related proteins(IRPs)and insulin dynamics in NGT subjects.The explorations were conducted using data from 1109 non-diabetes subjects aged 40–44 with normal or excess body weight and 21 Chinese NGT subjects aged 22–32 with accurate metabolic assessment.IRPs were detected with Olink technology.Insulin sensitivity and beta-cell function were evaluated with hyperinsulinemic–euglycemic clamp and hyperglycemic clamp.Eight associators were identified with obesity in NGT subjects,among which MCP-3,IL-6,TWEAK,HGF,and CST5 also showed associations in non-diabetes people.Four IRPs were linked to insulin sensitivity,with IL-24 being a novel finding.Seven IRPs were related to beta-cell function,including novel associators CD244,CD40,and IL-15RA.Moreover,most IRPs were interconnected,with IL-6 as the hub.In conclusion,insulin sensitivity and beta-cell function are related to IRPs involved in chemotaxis,activation of immune cells,and cell proliferation,which might provide valuable information for the understanding of the mechanisms associated with T2D pathogenesis.
基金This work was supported by the National Key R&D Program of China(2023ZD04073)Sanya Yazhou Bay Science and Technology City(SCKJ-JYRC-2023-25)+1 种基金the National Natural Science Foundation of China(32360116)the Research Project of the Collaborative Innovation Center of Hainan University(XTCX2022NYB01).
文摘Identification of the phenotypes of fruits is critical for understanding complex genetic traits.Computed to-mography(CT)imaging technology enables the noninvasive acquisition of three-dimensional images of fruit interiors,thus providing a robust data foundation for phenotypic analysis.Accurate segmentation of internal fruit tissues is essential,as it directly influences the accuracy and reliability of the results.Current methods are not optimized for the unique features of plant fruit images.This study introduces XFruitSeg,which is a general deep learning model for segmenting plant fruit CT images.The model uses a U-shaped encoder-decoder architecture and integrates multitask learning.A large convolutional kernel network,RepLKNet,expands the receptive field for feature extraction.Multiscale skip connections and a deep supervision mechanism improve the model's ca-pacity to learn features of various sizes,and a contour feature learning branch specifically targets the interor-ganizational boundaries.An optimized composite loss function enhances the model's robustness when applied to imbalanced categories.Additionally,a dataset named XrayFruitData was established,which contains high-resolution images of twelve plant fruit varieties,with accurate annotations for orange,mangosteen,and durian fruits for model evaluation.Compared with four mainstream advanced models,XFruitSeg achieved su-perior segmentation performance on the orange,mangosteen,and durian datasets,with mean Dice coefficients of 95.21%,93.24%,and 94.70%and mean intersection over union(mIoU)scores of 91.09%,87.91%,and 90.35%,respectively.The results of extensive ablation experiments demonstrate the effectiveness of each component.Therefore,the proposed XFruitSeg model has been proven to be beneficial for high-precision analysis of internal fruit phenotyping traits.
