Hepatic fibrosis is a reversible pathological process in various chronic liver diseases and is closely associated with the development and progression of severe liver diseases such as liver cirrhosis and hepatocellular carcinoma， and it has emerged as a significant global health challenge. In recent years， studies have shown that histone lactylation， a newly discovered epigenetic modification， actively participates in regulating the progression of hepatic fibrosis. This article systematically reviews the core regulatory effect of histone lactylation modification in the interaction between inflammatory microenvironment and hepatic fibrosis， in order to clarify the cascade regulatory mechanism of “inflammation-hepatic fibrosis” and provide new insights for early diagnosis， targeted intervention， and prevention of malignant transformation in hepatic fibrosis.

Objective To investigate the role and molecular mechanism of ginsenoside Rb1(Rb1)in regulating neutrophil extracellular trapping networks(NETs)to intervene in atherosclerosis(AS).Methods In vivo:an AS model was constructed with ApoE knockout mice superimposed on a high-fat diet.The pathological and morphological changes of aortic root plaques were observed by HE staining and oil red O staining;Immunofluorescence labelling of neutrophils citrullinated histones(Cit-H3)and macrophages as well as IL-1β at the aortic root plaque site were used to assess the inflammatory infiltration.In vitro:NETs induced by PMA and cholesterol crystals were taken as models respectively.Direct effect of Rb1 against NETs formation assessed by Sytox staining and immunofluorescence staining with Cit-H3 and myeloperoxidase.Rb1 on ROS levels was assessed by DCFH-DA.Rb1 on histone H3 citrulline modification was assessed by Western blotting.Results In vivo:Rb1 significantly inhibited plaque formation,lipid deposition(P<0.05)and intra-plaque inflammatory infiltration(P<0.05).In vitro:Rb1 significantly inhibited NETs formation(P<0.05),neutrophil ROS levels(P<0.05),and Cit-H3 levels(P<0.05).Conclusions Rb1 significantly inhibited AS progression by inhibiting plaque NETs formation,which may be partly through the inhibition of histone H3 citrullination resulting from activation of the neutrophil oxidative stress pathway.

Objective To investigate the role and molecular mechanism of ginsenoside Rb1(Rb1)in regulating neutrophil extracellular trapping networks(NETs)to intervene in atherosclerosis(AS).Methods In vivo:an AS model was constructed with ApoE knockout mice superimposed on a high-fat diet.The pathological and morphological changes of aortic root plaques were observed by HE staining and oil red O staining;Immunofluorescence labelling of neutrophils citrullinated histones(Cit-H3)and macrophages as well as IL-1β at the aortic root plaque site were used to assess the inflammatory infiltration.In vitro:NETs induced by PMA and cholesterol crystals were taken as models respectively.Direct effect of Rb1 against NETs formation assessed by Sytox staining and immunofluorescence staining with Cit-H3 and myeloperoxidase.Rb1 on ROS levels was assessed by DCFH-DA.Rb1 on histone H3 citrulline modification was assessed by Western blotting.Results In vivo:Rb1 significantly inhibited plaque formation,lipid deposition(P<0.05)and intra-plaque inflammatory infiltration(P<0.05).In vitro:Rb1 significantly inhibited NETs formation(P<0.05),neutrophil ROS levels(P<0.05),and Cit-H3 levels(P<0.05).Conclusions Rb1 significantly inhibited AS progression by inhibiting plaque NETs formation,which may be partly through the inhibition of histone H3 citrullination resulting from activation of the neutrophil oxidative stress pathway.

Objective·To compare the performance of various deep learning methods in the segmentation and classification of colorectal polyp endoscopic images,and identify the most effective approach.Methods·Four colorectal polyp datasets were collected from three hospitals,encompassing 1 534 static images and 15 videos.All samples were pathologically validated and categorized into two types:serrated lesions and adenomatous polyps.Polygonal annotations were performed by using the LabelMe tool,and the annotated results were converted into integer mask formats.These data were utilized to train various architectures of deep neural networks,including convolutional neural network(CNN),Transformers,and their fusion,aiming to develop an effective semantic segmentation model.Multiple performance indicators for automatic diagnosis of colon polyps by different architecture models were compared,including mIoU,aAcc,mAcc,mDice,mFscore,mPrecision and mRecall.Results·Four different architectures of semantic segmentation models were developed,including two deep CNN architectures(Fast-SCNN and DeepLabV3plus),one Transformer architecture(Segformer),and one hybrid architecture(KNet).In a comprehensive performance evaluation of 291 test images,KNet achieved the highest mIoU of 84.59％,significantly surpassing Fast-SCNN(75.32％),DeepLabV3plus(78.63％),and Segformer(80.17％).Across the categories of"background","serrated lesions"and"adenomatous polyps",KNet's intersection over union(IoU)were 98.91％,74.12％,and 80.73％,respectively,all exceeding other models.Additionally,KNet performed excellently in key performance metrics,with aAcc,mAcc,mDice,mFscore,and mRecall reaching 98.59％,91.24％,91.31％,91.31％,and 91.24％,respectively,all superior to other models.Although its mPrecision of 91.46％was not the most outstanding,KNet's overall performance remained leading.In inference testing on 80 external test images,KNet maintained an mIoU of 81.53％,demonstrating strong generalization capabilities.Conclusion·The semantic segmentation model of colorectal polyp endoscopic images constructed by deep neural network based on KNet hybrid architecture,exhibits superior predictive performance,demonstrating its potential as an efficient tool for detecting colorectal polyps.

Objective To develop deep learning models for colonoscopy quality control using various deep learning architectures,and to delve into the decision-making mechanisms.Methods The colonoscopy images were selected from two datasets separately constructed by the HyperKvasir and Changshu Hospital Affiliated to Soochow University,encompassing intestines of varying degrees of cleanliness,polyps,and cecums.After image preprocessing and enhancement,transfer learning was carried out using the pre-trained models based on convolutional neural network(CNN)and Transformer.The model training adopted cross-entropy loss functions and Adam optimizer,and simultaneously implemented learning rate scheduling.To enhance model transparency,a thorough interpretability analysis was conducted using Grad-CAM,Guided Grad-CAM,and SHAP.The final model was converted to ONNX format and deployed on various equipment terminals to achieve real-time colonoscopy quality control.Results In a dataset of 3 831 colonoscopy images,EfficientNet model outperformed the other models on the test set,achieving an accuracy of 0.992 which was higher than those of the other models based on CNN(DenseNet121,ResNet50,VGG19)and Transformer(ViT,Swin,CvT),with a precision,recall rate,and F1 score of 0.991,0.989,and 0.990.On an external test set of 358 images,EfficientNet model had an average AUC,precision,and recall rate of 0.996,0.948,and 0.952,respectively.Although EfficientNet model is high-performing,some misjudgments still occurred.Interpretability analysis highlighted key image areas affecting decision-making.In addition,EfficientNet model was successfully converted to ONNX format and deployed on multiple platforms and devices,and it ensured real-time colonoscopy quality control with an inference speed of over 60 frames per second.Conclusion Among the 7 models developed for colonoscopy quality control based on CNN and Transformer,EfficientNet demonstrated exemplary performance across all categories and is deployed for real-time predictions on multiple terminals,aiming to provide patients with better medical care.

Background::The epidemic of overweight and obesity has become a worldwide public health problem. Cardiometabolic diseases may originate in childhood. We investigated the association between percent body fat (PBF) measured by the bioelectrical impedance assay and cardiometabolic risk (CMR) in pediatrics.Methods::This cross-sectional study involved 3819 subjects (6–17 years old) in Shanghai. We assessed the association between PBF and body mass index (BMI) with multiple CMR factors. We examined the risk for cardiometabolic abnormalities attributable to overweight and obesity based on age- and sex-specific PBF Z-scores and BMI Z-scores, respectively. Results::PBF, but not BMI, was positively associated with multiple CMR factors in males and females except for total cholesterol in females (all p < 0.05). Compared with the non-overweight group based on PBF, over-weight and obese subjects had increasingly higher odds ratio of dyslipidemia (2.90 (1.99–4.23), 4.59 (2.88–7.32) for males and 1.82 (1.20–2.75), 2.46 (1.47–4.11) for females) and elevated blood pressure (BP) (3.26 (2.35–4.51), 4.55 (2.92–7.09) for males and 1.59 (1.07–2.34), 3.98 (2.27–6.17) for females). Obesity females showed a higher likelihood for hyperglycemia (2.19 (1.24–3.84)) than non-overweight females. In both sexes, the predictive effect of PBF on dyslipidemia and elevated BP in adolescents was better than that in children. For hyperglycemia, the predictive effect of PBF was better in male adolescents and female children. There was no risk difference for cardiometabolic abnormalities attributable to BMI-based obesity categories. Conclusions::PBF but not BMI was associated with CMR. Overweight and obesity categories based on PBF had an increased risk for cardiometabolic abnormalities in children and adolescents.

Background::The epidemic of overweight and obesity has become a worldwide public health problem. Cardiometabolic diseases may originate in childhood. We investigated the association between percent body fat (PBF) measured by the bioelectrical impedance assay and cardiometabolic risk (CMR) in pediatrics.Methods::This cross-sectional study involved 3819 subjects (6–17 years old) in Shanghai. We assessed the association between PBF and body mass index (BMI) with multiple CMR factors. We examined the risk for cardiometabolic abnormalities attributable to overweight and obesity based on age- and sex-specific PBF Z-scores and BMI Z-scores, respectively. Results::PBF, but not BMI, was positively associated with multiple CMR factors in males and females except for total cholesterol in females (all p < 0.05). Compared with the non-overweight group based on PBF, over-weight and obese subjects had increasingly higher odds ratio of dyslipidemia (2.90 (1.99–4.23), 4.59 (2.88–7.32) for males and 1.82 (1.20–2.75), 2.46 (1.47–4.11) for females) and elevated blood pressure (BP) (3.26 (2.35–4.51), 4.55 (2.92–7.09) for males and 1.59 (1.07–2.34), 3.98 (2.27–6.17) for females). Obesity females showed a higher likelihood for hyperglycemia (2.19 (1.24–3.84)) than non-overweight females. In both sexes, the predictive effect of PBF on dyslipidemia and elevated BP in adolescents was better than that in children. For hyperglycemia, the predictive effect of PBF was better in male adolescents and female children. There was no risk difference for cardiometabolic abnormalities attributable to BMI-based obesity categories. Conclusions::PBF but not BMI was associated with CMR. Overweight and obesity categories based on PBF had an increased risk for cardiometabolic abnormalities in children and adolescents.

ObjectiveTo determine the situation and challenges of innovation platforms in China， and to explore the construction strategy of Shanghai Nutrition Innovation Platform， which is suitable for Shanghai and may achieve the research and transformation of nutrition innovation and population health， so as to coordinate， unite and gather the superior resources of all parties and promote nutrition innovation. MethodsConstruction scheme and operational mechanism of Shanghai Nutrition Innovation Platform were explored by literature review， expert consultation and questionnaire. ResultsThere were various forms of innovation platforms in China. However， challenges were identified， such as decentralizing force， resource rearrangement and insufficient sharing effect. Shanghai Nutrition Innovation Platform adopted a modular organizational structure， which was divided into central group， node group， and subject group. Shanghai Center for Disease Control and Prevention， as the central organization， is responsible for the platform operation management. The expert database as an academic committee selected key organizations from nutrition-related universities， research institutes， academic associations， centers for disease control and prevention， hospitals and the industry. Based on the opening of its own innovation resources， the platform made effective use of external innovation resources and formed a closely integrated nutrition innovation network of multiple disciplines. ConclusionThis study promotes the construction of innovation platform model of cooperation， co-construction and resource sharing， and provides reference for the construction of innovation platform in China.

Objective:To explore the correlation of damage-associated molecular pattern molecules(DAMPs) serum S100, C-reactive protein (CRP), serum amyloid A (SAA) and uric acid (UA) with age and body mass index (BMI) to provide direction for further study of metabolic inflammation and inflammaging.Methods:The observational study method was used,and three hundred and sixty-six healthy people (131 males and 235 females) were selected from the physical examination center of the Second People′s Hospital of Hunan Province from May to October 2020. They were divided into three age groups according to the age interval of 20 years, including 156 (53 males and 103 females) aged 20-40 years, 110 (36 males and 74 females) aged 41-60 years, and 100 (42 males and 58 females) aged 61-80 years. Kruskal Wallis H test was used to compare the differences of serum S100, CRP, SAA and UA levels among different age groups. According to the Health Industry Standards of the People′s Republic of China-Weight Determination for Adults, the boundary is BMI =24 kg/m 2. The healthy people were divided into non overweight (BMI<24 kg/m 2) and overweight (BMI ≥ 24 kg/m 2) two groups. The 1∶1 propensity score was used to match the age and gender. There were 96 non overweight subjects [43 males, 53 females, age 52 (35, 66) years], 96 overweight subjects [44 males, 52 females, age 52 (36, 64) years]. The serum levels of S100, CRP, SAA and UA in different BMI groups were compared by Mann-Whitney U test. Results:The median serum UA concentrations in males and females were 356 and 277 μmol/L, and the levels of serum UA of male was significantly higher than that of female ( Z=-10.428, P<0.001); the median serum SAA concentrations in males and females were 3.1 mg/L and 4.4 mg/L, while the serum SAA level of female was significantly higher than that of male ( Z=3.652, P<0.001); for 20-40, 41-60, and 61-80 years old group, the median concentration of serum S100 was 0.058, 0.057, 0.070 μg/L, and the median concentration of serum CRP was 0.32, 0.58, 0.93 mg/L; the median serum SAA concentrations were 3.2, 4.0, 5.2 mg/L; serum uric acid concentrations were (301.8±61.5), (298.6±69.8), (329.0±77.8) μmol/L. The levels of serum S100, CRP, SAA, UA in 61-80 years group were significantly higher than those of 20-40 years group ( H=-2.749, H=-6.731, H=-5.033, H=-2.521, P=0.018, P<0.001, P<0.001, P=0.035) and 41-60 years old group ( H=-2.719, H=-2.539, H=-2.540, H=-2.486, P=0.020, P=0.033, P=0.033, P=0.039).The levels of serum CRP of 41-60 years group was significantly higher than that of 20-40 years group ( H=-4.108, P<0.001). There was no significant difference in levels of serum S100, SAA and UA between 20-40 years group and 41-60 years group ( H=0.189, H=-2.360, H=-0.165, P=1.000, P=0.055, P=1.000); the levels of serum CRP and SAA were positively correlated with age ( r s =0.342, r s =0.301, P<0.001, P<0.001); for overweight, non-overweight group, the median concentrations of serum S100 were 0.065 μg/L, 0.059 μg/L, the median concentrations of serum CRP were 0.92 mg/L, 0.47 mg/L, the median concentrations of serum SAA were 5.0 mg/L, 4.1 mg/L, the median concentrations of serum UA were 339.5 μmol/L, 301.5 μmol/L, the levels of CRP, SAA and UA in the overweight group were higher than those in the non-overweight group ( Z=4.278, Z=2.025, Z=3.787, P<0.001, P=0.043, P<0.001); the levels of S100 in the overweight group was higher than those in the non-overweight group, but there was no significant difference in S100 between the two groups ( Z=0.862, P=0.388); the levels of Serum CRP and UA were positively correlated with BMI ( r s =0.348, r s =0.264, P<0.001, P=0.009). Conclusions:With the increase of age, the serum S100, CRP, SAA and UA levels of healthy people may be on the rise, especially the serum CRP and SAA levels are positively correlated with age; the serum S100, CRP, SAA and UA levels of overweight people may be higher than those of non-overweight people, especially the serum CRP, UA levels are positively correlated with BMI.

Objective:To explore the correlation of damage-associated molecular pattern molecules(DAMPs) serum S100, C-reactive protein (CRP), serum amyloid A (SAA) and uric acid (UA) with age and body mass index (BMI) to provide direction for further study of metabolic inflammation and inflammaging.Methods:The observational study method was used,and three hundred and sixty-six healthy people (131 males and 235 females) were selected from the physical examination center of the Second People′s Hospital of Hunan Province from May to October 2020. They were divided into three age groups according to the age interval of 20 years, including 156 (53 males and 103 females) aged 20-40 years, 110 (36 males and 74 females) aged 41-60 years, and 100 (42 males and 58 females) aged 61-80 years. Kruskal Wallis H test was used to compare the differences of serum S100, CRP, SAA and UA levels among different age groups. According to the Health Industry Standards of the People′s Republic of China-Weight Determination for Adults, the boundary is BMI =24 kg/m 2. The healthy people were divided into non overweight (BMI<24 kg/m 2) and overweight (BMI ≥ 24 kg/m 2) two groups. The 1∶1 propensity score was used to match the age and gender. There were 96 non overweight subjects [43 males, 53 females, age 52 (35, 66) years], 96 overweight subjects [44 males, 52 females, age 52 (36, 64) years]. The serum levels of S100, CRP, SAA and UA in different BMI groups were compared by Mann-Whitney U test. Results:The median serum UA concentrations in males and females were 356 and 277 μmol/L, and the levels of serum UA of male was significantly higher than that of female ( Z=-10.428, P<0.001); the median serum SAA concentrations in males and females were 3.1 mg/L and 4.4 mg/L, while the serum SAA level of female was significantly higher than that of male ( Z=3.652, P<0.001); for 20-40, 41-60, and 61-80 years old group, the median concentration of serum S100 was 0.058, 0.057, 0.070 μg/L, and the median concentration of serum CRP was 0.32, 0.58, 0.93 mg/L; the median serum SAA concentrations were 3.2, 4.0, 5.2 mg/L; serum uric acid concentrations were (301.8±61.5), (298.6±69.8), (329.0±77.8) μmol/L. The levels of serum S100, CRP, SAA, UA in 61-80 years group were significantly higher than those of 20-40 years group ( H=-2.749, H=-6.731, H=-5.033, H=-2.521, P=0.018, P<0.001, P<0.001, P=0.035) and 41-60 years old group ( H=-2.719, H=-2.539, H=-2.540, H=-2.486, P=0.020, P=0.033, P=0.033, P=0.039).The levels of serum CRP of 41-60 years group was significantly higher than that of 20-40 years group ( H=-4.108, P<0.001). There was no significant difference in levels of serum S100, SAA and UA between 20-40 years group and 41-60 years group ( H=0.189, H=-2.360, H=-0.165, P=1.000, P=0.055, P=1.000); the levels of serum CRP and SAA were positively correlated with age ( r s =0.342, r s =0.301, P<0.001, P<0.001); for overweight, non-overweight group, the median concentrations of serum S100 were 0.065 μg/L, 0.059 μg/L, the median concentrations of serum CRP were 0.92 mg/L, 0.47 mg/L, the median concentrations of serum SAA were 5.0 mg/L, 4.1 mg/L, the median concentrations of serum UA were 339.5 μmol/L, 301.5 μmol/L, the levels of CRP, SAA and UA in the overweight group were higher than those in the non-overweight group ( Z=4.278, Z=2.025, Z=3.787, P<0.001, P=0.043, P<0.001); the levels of S100 in the overweight group was higher than those in the non-overweight group, but there was no significant difference in S100 between the two groups ( Z=0.862, P=0.388); the levels of Serum CRP and UA were positively correlated with BMI ( r s =0.348, r s =0.264, P<0.001, P=0.009). Conclusions:With the increase of age, the serum S100, CRP, SAA and UA levels of healthy people may be on the rise, especially the serum CRP and SAA levels are positively correlated with age; the serum S100, CRP, SAA and UA levels of overweight people may be higher than those of non-overweight people, especially the serum CRP, UA levels are positively correlated with BMI.