Objective:To analyze the association between visceral fat area (VFA) and fatty liver based on quantitative CT (QCT) in people receiving health examination with normal body mass index (BMI).Methods:A cross-sectional study. A total of 1 305 physical examiners who underwent chest CT and QCT examination in the Department of Health Management of Henan Provincial People′s Hospital from January to December 2021 were retrospectively selected as subjects. The physical components at the central level of the lumbar two cone were measured with QCT, including subcutaneous fat area (SFA), VFA and liver fat content (LFC). And the metabolic indexes, such as blood lipids and blood glucose, were collected. The t-test and χ2 test were used to analyze the correlation between the detection rate of fatty live and LFCr and age and gender. According to level of VFA (<100 cm 2, 100-150 cm 2 and≥150 cm 2), the subjects were divided into three groups, and one-way ANOVA and χ2 test were used in comparison between groups. Multiple linear regression was used to analyze the correlation between VFA and metabolic indexes and LFC. Results:Of the 1 305 subjects, there were 634 males and 671 females. The detection rate of fatty liver in normal BMI population was 65.67%, and it was 72.71% and 59.02% respectively in men and women ( χ2=27.12, P<0.001), and the detection rate of fatty liver and LFC increased with age (both P<0.05). With the increase of VFA, the age, BMI, SFA, LFC, total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), alanine aminotransferase (ALT), blood uric acid and prevalence of fatty liver increased (all P<0.05), and the low-density lipoprotein cholesterol (HDL-C) decreased ( P<0.001). Multiple linear regression analysis showed that after adjustment for age factors, regardless of male or female, LFC was independently positively related with VFA, BMI, and ALT (male β=0.206, 0.145, 0.174, female β=0.194, 0.150, 0.184; all P<0.05). FBG was positively correlated with male independently ( β=0.134; P<0.001). The indicators related to female independently were TC, TG, and blood uric acid ( β=-0.121, 0.145, 0.141, all P<0.05) Conclusion:In the population receiving health examination with normal BMI, the VFA measured by QCT technique is closely related to fatty liver.

Objective To explore the value of machine learning(ML)models based on non-contrast CT(NCCT)radiomics features for predicting the severity of acute phase traumatic brain injury(TBI).Methods Totally 600 TBI patients were retrospectively collected as observation group,other 65 TBI patients were taken as external validation set,while 50 TBI patients were prospectively enrolled as prospective validation set.Patients in observation group were divided into high-risk subgroup(n=240)and low-risk subgroup(n=360)according to Glasgow outcome scale(GOS)at discharge.The severity of acute phase TBI in observation group was assessed by doctor A and B with the same criteria,then an artificial model was established based on clinical and NCCT data at the time of first diagnosis using logistic regression(LR)method for predicting the severity of acute phase TBI.Patients in observation group were divided into training set(n=420,including 168 in high-risk subgroup and 252 in low-risk subgroup)and test set(n=180,including 72 in high-risk subgroup and 108 in low-risk subgroup)at the ratio of 7∶3.Based on NCCT of training set,radiomics features were extracted and selected,and LR,support vector machine(SVM),random forest(RF)and K-nearest neighbor(KNN)were used to establish 4 ML models.The efficacies of the above models were validated in test set,external validation set(including 34 cases of high-risk and 31 cases of low-risk TBI)and prospective validation set(including 21 cases of high-risk and 29 cases of low-risk TBI),respectively.Results The area under the curve(AUC)of doctor A and B for evaluating the severity of acute phase TBI in observation group was 0.606 and 0.771,respectively,of artificial model was 0.824.Based on NCCT in training set,6 optimal radiomics features were selected to construct LR,SVM,RF and KNN ML models,with AUC of 0.983,0.971,0.970 and 0.984 in test set,respectively,while the AUC of artificial model was 0.708.The AUC of LR,SVM,RF,KNN ML models and artificial model in external validation set was 0.879,0.881,0.984,0.863 and 0.733,while in prospective validation set was 0.984,0.873,0.982,0.897 and 0.704,respectively.Conclusion ML models based on CT radiomics could effectively predict the severity of acute phase TBI.

Objective To explore the correlations of lumbar bone mineral density(BMD)and metabolic syndrome(MS)in adult males.Methods Data of low dose chest CT and quantitative CT of 13 490 adult males were retrospectively analyzed,and lumbar BMD were measured to judge whether MS existed and the degree of MS,and the correlations of lumbar BMD with MS or not and the degree of MS,as well as of lumbar BMD value and the related indicators of MS were assessed.Taken lumbar BMD as the dependent variable,the age,low density lipoprotein cholesterol(LDL-C),blood uric acid(BUA),hemoglobin(Hb)and MS or not were included in multiple linear regression analysis to observe the impact of MS and related indicators on lumbar BMD.Results Among 13 490 adult males,3 900 were found with MS(MS group),while 9 590 were found without MS(non-MS group).Significant difference of lumbar BMD was detected between groups(P=0.001).Lumbar BMD values were negatively correlated with MS(rs=-0.025,P=0.004)and the degree of MS(rs=-0.038,P＜0.001),whereas positively correlated with abdominal obesity,high triglyceride and low HDL-C or not(rs=0.024,0.061,0.036,all P＜0.001)but negatively correlated with hypertension and hyperglycemia or not(rs=-0.135,-0.104,both P＜0.05).After adjustment of age,lumbar BMD of adult males was negatively correlated with MS or not as well as LDL-C(both P＜0.05),but positively correlated with BUA and Hb(both P＜0.001).Conclusion Lumbar BMD was associated with MS in adult males.

Objective:To analyze the correlation between body fat distribution measured by quantitative CT (QCT) and body mass index in adults receiving physical examination.Methods:It was a cross-sectional study. From January to December 2021, 3 205 adults undergoing physical examination who met the inclusion criteria and underwent chest CT and QCT examination in the health management discipline of Henan Provincial People′s Hospital were selected as the research objects. The general data were collected; and the subcutaneous fat area, visceral fat area, total abdominal fat area, liver fat content, abdominal obesity and fatty liver detection rate were measured by QCT. According to body mass index, the subjects were divided into normal group (18.5-<24.0 kg/m 2, 1 343 cases), overweight group (24.0-<28.0 kg/m 2, 1 427 cases) and obesity group (≥28.0 kg/m 2, 435 cases). One-way analysis of variance and χ2 test were used to compare the differences of QCT indexes among the three groups. Pearson and Spearman correlation analysis were used to evaluate the correlation between QCT indexes and body mass index. Receiver operating characteristic (ROC) curve was drawn to analyze the diagnostic effect of QCT on obesity and fatty liver. Results:Subcutaneous fat area, visceral fat area, total abdominal fat area, liver fat content, abdominal obesity and fatty liver detection rate in obese group were all significantly higher than those in overweight group and normal group [males, (147.60±46.44) vs (104.33±27.68), (73.46±22.65) cm 2; (297.46±54.70) vs (229.40±53.12), (159.57±49.68) cm 2; (445.06±70.24) vs (333.73±62.91), (233.02±61.87) cm 2; 11.30% (7.90%, 15.55%) vs 8.75% (6.50%, 11.70%), 6.60% (4.80%, 8.70%); 100.0% vs 96.0%, 64.0%; 92.9% vs 86.7%, 73.3%; females, (213.96±48.61) vs (155.85±35.31), (107.24±31.01) cm 2; (185.41±43.88) vs (142.48±41.75), (96.56±36.50) cm 2; (399.37±68.07) vs (298.33±56.86), (203.80±57.53) cm 2; 9.80% (6.90%, 13.30%) vs 7.30% (5.05%, 9.80%), 5.40%(3.50%, 7.20%); 96.4% vs 74.8%, 28.9%; 87.3% vs 75.6%, 56.5%], and were all positively correlated with body mass index (males, r/ rs=0.709, 0.738, 0.831, 0.402, 0.464, 0.225; females, r/ rs=0.798, 0.695, 0.841, 0.416, 0.605, 0.276) (all P<0.001). In both male and female subjects, the detection rates of obesity based on QCT were significantly higher than those based on body mass index (male, 86.9% vs 16.6%; female, 49.3% vs 8.9%), and the detection rates of fatty liver based on QCT were significantly higher than those based on ultrasound (male, 83.6% vs 57.1%; female, 65.2% vs 27.6%) (all P<0.001). ROC curve showed that when the visceral fat area of 142 cm 2 was used as the cut-off value for the diagnosis of obesity in male subjects, the sensitivity and specificity was 100% and 15.8%, respectively; and when the cut-off value of liver fat content 5.0% was used to diagnose fatty liver, the sensitivity and specificity was 88.9% and 25.1%, respectively. When the visceral fat area of 115 cm 2 was set as the cut-off value for the diagnosis of obesity in female subjects, the sensitivity and specificity was 96.4% and 55.3%, respectively; when the liver fat content of 5.0% was set as the cut-off value for the diagnosis of fatty liver, the sensitivity and specificity was 83.7% and 43.2%, respectively. Conclusions:The indexes of abdominal fat and liver fat measured by QCT in adults receiving physical examination are all positively correlated with body mass index. The effect of QCT in the diagnosis of obesity and fatty liver are both better than body mass index and ultrasound.

Objective:To analyze the distribution of body fat with quantitative computed tomography (QCT) in people with normal body mass index (BMI).Methods:A cross-sectional study was conducted in the physical examination population who underwent chest CT and QCT examination in the Department of Health Management, Henan Provincial People′s Hospital from January to December in 2021, and 1 395 physical examination subjects who met the inclusion criteria were selected as the research subjects. The subjects were divided into five groups according to their age. The general data of the subjects were collected. The total abdominal fat area (TFA), visceral fat area (VFA), subcutaneous fat area (SFA), total abdominal muscle area (TMA) and muscle fat content (MFC) in the subjects were measured by QCT. One-way analysis of variance, Welch test and Kruskal-Wallis test were used to compare the above QCT measurement indexes between the two genders among different age groups with normal BMI. Pearson correlation analysis was used to analyze the correlation between VFA and sarcopenia indexes. Multivariate linear regression was used to analyze the relationship between VFA and linear correlation variables in the related indicators of sarcopenia.Results:There were significant differences in TFA, VFA, TMA and SMI among different age groups in subjects with normal BMI (all P<0.05). Pearson correlation analysis showed that VFA was negatively correlated with TMA in some age groups (male: 18-39 years group: r=-0.351; 40-49 years group: r=-0.278; 60-69 years group: r=-0.245; female:40-49 years group: r=-0.251; 50-59 years group: r=-0.270;≥70 years group: r=-0.391; all P<0.01); it was negatively correlated with SMI (male: 18-39 years group: r=-0.352; 40-49 years group: r=-0.340; 50-59 years group: r=-0.266; 60-69 years group: r=-0.316; female: 40-49 years group: r=-0.240; 50-59 years group: r=-0.284; all P<0.001); it was positively correlated with MFC (male: 18-39 years group: r=0.342; 40-49 years group: r=0.291; female: 50-59 years group: r=0.133; 60-69 years group: r=0.284; all P<0.05). Multivariate linear regression analysis showed that VFA was independently and negatively correlated with SMI in both men and women after adjusting for age interference factors (male B=-1.881, t=-6.025, P<0.001; female B=-0.603, t=-2.887, P=0.004), and it was independently positively correlated with MFC (male B=1.230, t=4.271, P<0.001;female B=0.893, t=3.836, P<0.001). There was an independent negative correlation between VFA and TMA in male subjects ( B=0.263, t=2.478, P=0.013). Conclusions:VFA is correlated with TMA, SMI and MFC in people with normal BMI. Regardless of gender, SMI has a negative effect on VFA, and MFC has a positive effect on VFA.

Objective Calculus Bovis(CB)is a kind of valuable traditional Chinese medicine,which has been used in clinic for a long time.It has been shown to have significant anti-stroke,anti-inflammatory and anti-tumor effects.But its mechanism for treating Prostate cancer(PCa)remains unclear.The purpose of this study was to explore the target and mechanism of its action in the treatment of prostate cancer throμgh network pharmacology and in vitro and in vivo experiments.Methods The effective compounds of Calculus Bovis were collected by TCM pharmacology database and analysis platform(TCMSP).Search for potential compound targets in TCMSP.Search the Drμgbank,GeneCards,OMIM,PharmGkb,and TTD databases for disease targets associated with prost cancer.Disease and compound targets were integrated in the STRING database to construct their interaction network(PPI)to reveal the key targets of compound treatment for prostate cancer.In order to elucidate the mechanism of Calculus Bovis in the treatment of prostate cancer,GO functional enrichment and KEGG pathway enrichment analysis were conducted using Cytoscape software.The mechanism of treating prostate cancer with Calculus Bovis was studied in vitro and in vivo.Results A total of 11 compounds with anti-prostate cancer activity were identified.Oleanolic acid,ursolic acid,ergosterol,deoxycorticosterone,methylcholine and cholverdin were potential effective components.A total of 367 targets of Calculus Bovis compounds and 2152 targets of prostate cancer were found.The core targets of Calculus Bovis in the treatment of prostate cancer included TP53,STAT3,AKT1,HSP90AA1,ESR1,SRC,JUN,RELA,CCND1,CDKN1A,EGFR,AR,etc.The biological functions of Calculus Bovis mainly involve oxidative stress response,response to steroid hormones,cell response to chemical stress,peptide-serine modification and phosphorylation,and protein serine/threonine kinase activity.Calculus Bovis treatment of prostate cancer mainly involves PI3K-AKT signaling pathway,TNF signaling pathway,MAPK signaling pathway,etc.In vitro and in vivo experiments showed that Calculus Bovis promoted apoptosis of PC3 cells of prostate cancer by inhibiting PI3K-AKT signaling pathway.Conclusion Calculus Bovis has a therapeutic effect on prostate cancer,and its function is related to inhibiting PI3K-AKT signaling pathway and promoting apoptosis of cancer cells.

Objective:To investigate the effects of personalized intermittent energy restriction (IER) diet on sleep related gut microbiome in obese patients.Methods:In this single-arm clinical trial, a total of 35 obese patients who visited Henan Provincial People′s Hospital from April to November 2018 were recruited as research subjects. They underwent a strict 32-day IER diet intervention, divided into 4 stages of 8 days each. Nutritional recipes were formulated and nutritious meals were provided to each obese patient with timed meals, including 55% carbohydrates, 15% protein, and 30% fat per meal. In stages 1, 2, 3, and 4, patients were provided with 2/3, 1/2, 1/3, and 1/4 of their previous calorie intake every other day, respectively, with meals at 8:00 and 16:00. During the remaining time, patients were allowed unrestricted eating at home. Physiological indicators (weight, body mass index, body fat percentage, waist circumference, hip circumference, fasting blood glucose, glycosylated hemoglobin, blood pressure, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein and Pittsburgh Sleep Quality Index (PSQI) scores were observed before and after the intervention. Gut microbiota changes were analyzed using metagenomic sequencing technology, and Spearman′s correlation analysis was used to assess the correlation between significantly different bacterial species and PSQI scores.Results:After the intervention, the body weight, body mass index, and PSQI scores of obese patients were all significantly lower than before intervention [(89.92±14.98) vs (97.53±15.67) kg, (31.94±3.95) vs (34.64±4.05) kg/m 2, (3.43±1.16) vs (5.42±2.27)], the abundance of gut microbiota was significantly higher after the intervention (all P<0.05). There were 45 significantly different bacterial species before and after the intervention, of which 6 bacterial species ( Enterobacter cloacae, Escherichia coli, Odoribacter splanchnicus, Oribacterium sinus, Streptococcus gordonii, and Streptococcus parasanguinis) showed significantly positive correlations with PSQI scores ( r=0.476, 0.475, 0.369, 0.391, 0.401, 0.423) (all P<0.05), and they were mainly enriched in the glutamate and tryptophan synthesis pathways. Conclusions:The personalized IER diet intervention can improve the sleep of obese patients while reducing weight, possibly mediated by changes in gut microbiota through the glutamate and tryptophan pathways.

Objective:To analyze the correlation between quantified body fat distribution measured by computed tomography (CT) and blood lipids in overweight and obese individuals undergoing physical examinations.Methods:In this retrospective cohort study, a total of 3 463 physical examination subjects who underwent chest CT combined with quantified CT examination in the Department of Health Management at Henan Provincial People′s Hospital from January to December 2021 were selected using a comprehensive sampling method. The subjects were divided into three groups: normal group (1, 424 cases), overweight group (1, 531 cases), and obese group (508 cases) based on their body mass index: 18.5 to <24.0 kg/m 2, 24.0 to <28.0 kg/m 2, and≥28.0 kg/m 2, respectively. General information, blood lipid parameters, and different body fat distributions measured by quantified CT (subcutaneous fat area, visceral fat area, total abdominal fat area, liver fat content, muscle fat content) were collected in the three groups. One-way analysis of variance was used to compare differences in body fat distribution and blood lipid parameters, and Pearson correlation analysis was performed to evaluate the correlation between body fat distribution and blood lipids. Results:In the obese group, compared to the normal and overweight groups, subcutaneous fat area, visceral fat area, total abdominal fat area, liver fat content, muscle fat content, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride levels were significantly higher [males: (152.80±49.27) vs (72.94±22.68), (103.79±28.30) cm 2; (287.95±57.55) vs (156.36±49.40), (224.67±53.10) cm 2; (440.75±72.44) vs (229.31±62.01), (328.46±62.77) cm 2; (12.92±8.61)% vs (6.82±3.31)%, (9.39±4.88)%; (9.06±9.34)% vs (4.55±5.06)%, (6.70±6.73)%; (6.52±0.94) vs (4.87±1.03), (6.27±0.96) mmol/L; (3.05±0.76) vs (2.92±0.86), (2.97±0.77) mmol/L; (2.34±1.42) vs (1.53±0.82), (1.99±1.28) mmol/L; females: (213.82±46.87) vs (104.69±30.62), (155.05±34.90) cm 2; (184.88±46.54) vs (90.67±34.09), (138.92±42.06) cm 2; (398.71±71.28) vs (195.37±55.32), (293.97±57.05) cm 2; (11.36±6.34)% vs (5.51±3.02)%, (7.98±4.77)%; (7.44±7.60)% vs (3.70±3.90)%, (5.56±5.94)%; (5.27±0.96) vs (5.04±0.86), (5.11±0.96) mmol/L; (3.26±0.84) vs (2.92±0.79), (3.01±0.74) mmol/L; (1.74±0.69) vs (1.27±0.65), (1.57±0.77) mmol/L], while high-density lipoprotein cholesterol (HDL-C) was significantly lower [males: (1.17±0.19) vs (1.38±0.28), (1.25±0.25) mmol/L; females: (1.36±0.22) vs (1.59±0.32), (1.42±0.27) mmol/L] (all P<0.001). In males, the visceral fat area and total abdominal fat area in the overweight group were positively correlated with triglycerides ( r=0.175, 0.113) and negatively correlated with HDL-C ( r=-0.125, -0.113), while liver fat content was positively correlated with total cholesterol, LDL-C, and triglycerides ( r=0.083, 0.075, 0.206) and negatively correlated with HDL-C ( r=-0.093) (all P<0.05). In the obese group, the liver fat content was positively correlated with triglycerides ( r=0.170) and negatively correlated with HDL-C ( r=-0.166) in males (both P<0.05). In females, the visceral fat area and total abdominal fat area in the overweight group were positively correlated with total cholesterol, LDL-C, and triglycerides (visceral fat area: r=0.129, 0.160, 0.348; total abdominal fat area: r=0.121, 0.130, 0.283) and negatively correlated with HDL-C ( r=-0.264, -0.173), while liver fat content was positively correlated with triglycerides ( r=0.352) and negatively correlated with HDL-C ( r=-0.195) (all P<0.05). In the obese group, the visceral fat area was positively correlated with triglycerides ( r=0.213) and negatively correlated with HDL-C ( r=-0.223) in females (both P<0.05). Conclusion:Blood lipids are correlated with body fat distribution in overweight and obese individuals undergoing physical examinations, and the degree of correlation varies between different genders and body regions, with triglycerides showing the strongest correlation with liver fat content.

Objective:To explore the effect of the location and size of region of interest (ROI) on the measurement of liver fat by means of quantitative computed tomography (QCT).Methods:A total of 98 subjects who were examined with QCT for bone mineral density examination from December 25, 2019 to January 17, 2020 were recruited continuously from the Department of Health Management of Henan Provincial People′s Hospital. The liver fat content was measured by QCT workstation. The ROI was located respectively in the left lobe, the right anterior lobe and the right posterior lobe of the liver, and it was measured independently by the A measurer and B measurer. The central position of the ROI was fixed and the diameter was increased, and it was measured by the A measurer. In this study, Friedman test was used to compare the differences of measurement results in different positions or sizes of ROI, and intra-class correlation coefficients (ICC) was used to evaluate the repeatability of inter-measurers.Results:There was a significant difference for liver fat content under different positions of ROI (χ2=62.306, P<0.001), but no difference under different seizes of ROI (χ2=1.088, P=0.581). The ICC values of the inter-measurers repeatability analysis of the A measurer and B measurer in the left lobe, right anterior lobe and right posterior lobe of the liver were 0.847, 0.917 and 0.874, all more than 0.75, and the reproducibility was good. Conclusions:When QCT technique is applied to the measurement of liver fat content, the location conditions of ROI may affect results, so it is necessary to select multiple ROI in the whole liver for measurement. The inter-measurers repeatability of QCT in different parts of the liver is good.

Objective:To provide support for the clinical application of quantitative computed tomography (QCT) in the measurement of liver fat content, this study evaluated the intra-observer and inter-observer reproducibility of liver fat content measured by QCT in a population receiving physical examinations.Methods:From April to July 2019, 291 people were consecutively selected who underwent QCT examination in the health management department of Henan Provincial People′s Hospital. There were 214 males (73.5%) and 77 females (26.5%), aged 48.7±11.0. We measured liver fat content by QCT workstation. Three observers (A, B, C) measured their liver fat content independently, then observer A performed re-testing two weeks later. The mean value of the two measurements from observer A was taken as the final result. Measurement data were described by mean±SD. Intra-observer and inter-observer reproducibility were assessed using intra-class correlation coefficients ( ICC). Results:The first measurement result for observer A was 10.46±5.55 and the second measurement for observer A was 10.66±5.59, resulting in a final value of 10.56±5.51. The measurement results of observers B and C were 10.70±5.45 and 10.86±5.77, respectively. The ICC value of liver fat content values measured by the three observers was 0.960 (95% CI: 0.951-0.967, P<0.001) and the ICC value of liver fat content values for the two measurements of observer A was 0.953 (95% CI: 0.941-0.962, P<0.001). The ICC values were>0.75, so reproducibility of results was good. Conclusions:If the measurement method is consistent, the results for liver fat content measured by a conventional CT scanner and QCT workstation will have good reproducibility between and within observers, and will also have certain clinical application prospects.