Objective To investigate the image quality and radiation dose of automatic spectral imaging mode selection and adaptive statistical iterative reconstruction (ASIR) at abdominal CT with low contrast agent dose. Methods One hundred cases with the arterial-phase (AP) and portal venous phase (PVP) contrast-enhanced abdominal CT scanning were analyzed prospectively. Patients were randomly assigned to the study group and control group (n=50 each). In the study group, automatic spectral imaging mode selection and contrast agent dose of 300 mg/kg were used and spectral monochromatic images(40 to 60 keV) were reconstructed using either filtered back-projection (FBP) (group A) or ASIR (group B). In the control group, the fixed tube potential of 120 kVp and contrast agent dose of 450 mg/kg were used with images reconstructed using FBP (group C). Quantitative parameters (image noise and contrast-to-noise ratio of the liver, pancreas, aorta and portal vein) and qualitative visual parameters (overall image quality as graded on a 5-point scale) were compared among the groups by using One-way ANOVA or Kruskal-Wallis H test. Two sample t tests were used compare the radiation dose difference. Results There had no significant difference in CTDIvol[both (12±5) mGy] and DLP[(364±142) mGy·cm versus (377±131) mGy·cm] between the study group and control group(t=-0.408 and-0.428,P>0.05). During the AP and PVP, at the energy level of 40 keV, group B showed higher CNRs than group A and group C, lower image noise[ (29±6) HU in AP, (24±6) HU in PVP] than group A[(43±11) HU, (44±10) HU] but higher image noise than group C[ (18± 4) HU, (18±4) HU], lower overall image quality scores[(3.0±0.2) point, (2.9±0.3) point] than group C[(3.6± 0.4) point , (3.6±0.5) point] but similar scores to group A[(2.9±0.4) point,(2.8±0.4)point]. At the energy level of 50 keV, group B showed higher CNRs than group A but higher than or similar CNRs to group C, lower image noise[ (20±5) HU, (20±4) HU] than group A[(31±8) HU, (31±7) HU] but similar image noise to group C, higher overall image quality scores[(3.6±0.4) point, (3.5±0.4) point]than group A[(3.3±0.3) point,(3.3±0.3) point] but similar scores to group C. At the energy level of 60 keV, group B showed lower image noise[(14±4) HU, (14±3) HU], higher CNRs and overall image quality scores[(3.9±0.4) point,(3.9±0.3) point] than group A[(19 ± 5) and (20 ± 5)HU in image noise, (3.7 ± 0.4) and (3.7 ± 0.3) point in overall image quality scores ]and group C. Except for monochromatic images at 40 keV, the overall image quality scores in group B were all greater than 3 point and met the clinical diagnostic level. Conclusions The radiation dose of CT spectral imaging and conventional 120 kVp CT scan is equivalent with the use of automatic spectral imaging mode selection. By combining ASIR technique, monochromatic images at 50 and 60 keV can improve CNR and reduce contrast agent dose while maintain or improve overall image quality.

Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ²=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ²=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ²=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ²=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.

Objective:To investigate the predictive model construction of anastomotic thickening character after radical surgery of esophageal cancer based on computed tomogralphy(CT) radiomics and its application value.Methods:The retrospective cohort study was conducted. The clinicopathological data of 202 patients with esophageal squamous cell carcinoma (ESCC) who were admitted to The First Affiliated Hospital of Zhengzhou University from January 2013 to June 2021 were collected. There were 147 males and 55 females, aged (63±8) years. Based on random number table, 202 patients were assigned into training dataset and validation dataset at a ratio of 7:3, including 141 cases and 61 cases respectively. Patients underwent radical resection of ESCC and enhanced CT examination. Observation indicators: (1) influencing factor analysis of malignant anas-tomotic thickening; (2) construction and evaluation of predictive model; (3) performance comparison of 3 predictive models. The normality of continuous variables was tested by Kolmogorov-Smirnov method. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was analyzed using the Mann-Whintney U test. Count data were represented as absolute numbers, and comparison between groups was analyzed using the chi-square test or Fisher's exact probability. The consistency between subjective CT features by two doctors and measured CT numeric variables was analyzed by Kappa test and intraclass correlation coefficient (ICC), with Kappa >0.6 and ICC >0.6 as good consistency. Univariate analysis was conducted by corresponding statistic methods. Multivariate analysis was conducted by Logistics stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and area under curve (AUC), Delong test, decision curve were used to evaluate the diagnostic efficiency and clinical applicability of model. Results:(1) Influencing factor analysis of malignant anastomotic thickening. Of the 202 ESCC patients, 97 cases had malignant anastomotic thickening and 105 cases had inflammatory anastomotic thickening. The consistency between subjective CT features by two doctors and measured CT numeric variables showed Kappa and ICC values >0.6. Results of multivariate analysis showed that the maximum thickness of anastomosis and CT enhancement pattern were independent influencing factors for malignant anastomotic thickening[ hazard ratio=1.46, 3.09, 95% confidence interval ( CI) as 1.26-1.71,1.18-8.12, P<0.05]. (2) Construction and evaluation of predictive model. ① Clinical predictive model. The maximum thickness of anasto-mosis and CT enhancement pattern were used to construct a clinical predictive model. ROC curve of the clinical predictive model showed an AUC, accuracy, sensitivity, specificity as 0.86 (95% CI as 0.80-0.92),0.77, 0.77, 0.80 for the training dataset, and 0.78 (95% CI as 0.65-0.89), 0.77, 0.77, 0.80 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=1.22, P>0.05). ② Radiomics predictive model. A total of 854 radiomics features were extracted and 2 radiomics features (wavelet-LL_first order_ Maximum and original_shape_VoxelVolume) were finally screened out to construct a radiomics predictive model. ROC curve of the radiomics predictive model showed an AUC, accuracy, sensitivity, specificity as 0.87 (95% CI as 0.81-0.93), 0.80, 0.75, 0.86 for the training dataset, and 0.73 (95% CI as 0.63-0.83), 0.80, 0.76, 0.94 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=-0.25, P>0.05). ③ Combined predictive model. Results of multivariate analysis and radiomics features were used to construct a combined predictive model. ROC curve of the combined predictive model showed an AUC, accuracy, sensitivity, specificity as 0.93 (95% CI as 0.89-0.97),0.84, 0.90, 0.84 for the training dataset, and 0.79 (95% CI as 0.70-0.88), 0.89, 0.86, 0.91 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=0.22, P>0.05). (3) Performance comparison of 3 predictive models. Results of Hosmer-Lemeshow goodness-of-fit test showed that the clinical predictive model, radiomics predictive model and combined predictive model had a good fitting degree ( χ2=4.88, 7.95, 4.85, P>0.05). Delong test showed a significant difference in AUC between the combined predictive model and clinical predictive model, also between the combined predictive model and radiomics predictive model ( Z=2.88, 2.51, P<0.05 ). There was no significant difference in AUC between the clinical predictive model and radiomics predictive model ( Z=-0.32, P>0.05). The calibration curve showed a good predictive performance in the combined predictive model. The decision curve showed a higher distinguishing performance for anastomotic thickening character in the combined predictive model than in the clinical predictive model or radiomics predictive model. Conclusions:The maximum thickness of anastomosis and CT enhancement pattern are independent influencing factors for malignant anastomotic thickening. Radiomics predictive model can distinguish the benign from malignant thickening of anastomosis. Combined predictive model has the best diagnostic efficacy.

Objective:To investigate the differences of white matter diffusion properties between vulnerable and resistant individuals to continuous attention after sleep deprivation.Methods:According to the psychomotor vigilance test performance before and after sleep deprivation, the participants were divided into the vulnerable group( n=24) and resistant group( n=25). All participants underwent diffusion tensor imaging (DTI) scans.Tract based spatial statistics(TBSS) was used to compare fractional anisotropy(FA), mean diffusivity(MD), axial diffusivity(AD), radial diffusivity(RD) maps between the two groups.Spearman correlation analysis was conducted by SPSS 24.0 to investigate the relationships between the altered DTI metrics and PVT task performance. Results:(1) Compared with resistant group, FA value of vulnerable group decreased in the body of corpus callosum(x, y, z=-8, 9, 25, t=-7.855), right superior longitudinal fasciculus(x, y, z=-39, -7, 26, t=-6.252), bilateral anterior limb of internal capsule(x, y, z=-13, 8, 13, t=-5.235; x, y, z=12, 8, 3, t=-5.024) and right posterior thalamic radiation(x, y, z=-26, -56, 17, t=-5.469)(TFCE corrected, P<0.05, cluster size≥50 voxel). (2) Compared with resistant group, MD value of vulnerable group increased in the body of corpus callosum(x, y, z=-3, -6, 26, t=7.613), right superior longitudinal fasciculus(x, y, z=-31, -19, 38, t=5.314), bilateral anterior limb of internal capsule(x, y, z=-16, 7, 8, t=6.898; x, y, z=15, 5, 7, t=6.652), splenium of corpus callosum(x, y, z=27, -53, 17, t=6.541), and AD value increased in the right superior longitudinal fasciculus(x, y, z=-33, -19, 39, t=4.892), splenium of corpus callosum(x, y, z=-22, -49, 21, t=5.450), genu of corpus callosum(x, y, z=4, 26, 0, t=4.332), as well as RD value increased in the right superior corona radiata(x, y, z=-17, 1, 33, t=7.558), body of corpus callosum(x, y, z=4, -8, 26, t=6.699), right anterior limb of internal capsule(x, y, z=-12, 7, 3, t=5.212) (TFCE corrected, P<0.05, cluster size≥50 voxel). (3) Correlational analysis revealed that the negative correlations were found between PVT task performance and the FA value in the right superior longitudinal fasciculus( r=-0.492, P<0.001), right anterior limb of internal capsule( r=-0.510, P<0.001), right posterior thalamic radiation( r=-0.502, P<0.001) and body of corpus callosum( r=-0.464, P<0.001). The positive correlations were found between PVT task performance and the MD value in the body of corpus callosum( r=0.500, P<0.001), right superior longitudinal fasciculus( r=0.499, P<0.001), splenium of corpus callosum( r=0.462, P<0.001), right anterior limb of internal capsule( r=0.471, P<0.001), and AD value in right superior longitudinal fasciculus( r=0.643, P<0.001), as well as RD value in right superior corona radiate( r=0.498, P<0.001) (Bonferroni corrected, P<0.003). Conclusion:Differences in the microstructural characteristics of white matter fiber tracts in specific brain regions may constitute the potential neuropathological basis for the phenotypes of vulnerable and resistant individuals to continuous attention after sleep deprivation.

Chicoric acid， a hydroxycinnamic acid with the molecular formula C₂₂H₁₈O₁₂， is an important active ingredient in Taraxacum mongolicum， Echinacea purpurea， Cichorium intybus and other natural plants， and it helps plants protect themselves from insects and infection from viruses， bacteria， fungi， and nematodes. Modern pharmacological research suggests that chicoric acid has significant bioactivities such as anti-inflammatory， antioxidant， immune-regulating， antibacterial， antiviral and anti-tumor properties. The first reported bioactivity of chicoric acid is its inhibitory effect on human immunodeficiency virus （HIV）. With the development and application of molecular biology and related technologies， the inhibitory activities of chicoric acid on other viruses as well as its mechanism of action have been frequently reported. Another study indicates that chicoric acid has significant inhibitory effects on different pathogenic bacteria. This paper summarized the research progress on the antiviral and antibacterial effects of chicoric acid through a comprehensive review of relevant literature in China and abroad in the past 20 years. Studies have shown that chicoric acid has significant inhibitory activities against various viruses such as HIV， hepatitis B virus （HBV）， respiratory syncytial virus （RSV）， and herpes simplex virus （HSV）， as well as different pathogenic bacteria such as Staphylococcus aureus， Yersinia enterocolitica and Mycobacterium tuberculosis. This study is expected to provide references for in-depth research on chicoric acid against pathogenic microorganisms and antiviral and antibacterial study of traditional Chinese medicine.

OBJECTIVETo investigate the potential immunomodulatory properties of fetal bone marrow derived mesenchymal stem cells (FBM- MSCs).

METHODSMononuclear cells from the bone marrow of second trimester (14-22 wks) fetus were isolated and cultured for the derivation of MSCs. The derived FBM-MSC cells were characterized via morphology, immunophenotyping and the adipogenic and osteogenic differentiation assays. The immunomodulatory properties of FBM-MSC on lymphocytes were evaluated through the co- culture assay with PHA activated adult peripheral blood mononuclear cells (PBMCs).

RESULTSDerived FBM-MSCs were CD29⁺, CD44⁺, CD49e⁺, CD73⁺, CD90⁺, CD105⁺ and CD31⁻ , CD34⁻ , CD45⁻ , HLA-DR⁻ and can be differentiated into adipocytes and osteocytes. When co-cultured with PHA-activated PBMCs, FBM-MSCs inhibited the proliferation of lymphocytes up to 96% and down-regulated the secretion of inflammatory cytokines such as IFN-γ and TNF-α up to 90.9% and 58.4% respectively. When compared with FBM-MSCs cultured alone, the expression of MSCs derived immunomodulatory cytokines, such as IDO, TSG-6 and TGF-β, was up-regulated significantly in the co-culture system.

CONCLUSIONMSC derived from fetal bone marrow demonstrated immunosuppressive effects on adult PBMCs in vitro. MSC-derived cytokines like IDO, TSG-6 and TGF-β may be critical for FBM-MSCs mediated immunosuppressive function.

Adult ; Bone Marrow ; Bone Marrow Cells ; cytology ; immunology ; Cell Differentiation ; Cell Proliferation ; Cells, Cultured ; Coculture Techniques ; Cytokines ; Hematopoietic Stem Cells ; Humans ; Immune Tolerance ; Immunophenotyping ; In Vitro Techniques ; Leukocytes, Mononuclear ; Lymphocytes ; Mesenchymal Stromal Cells ; cytology ; immunology ; Osteogenesis

Objective: To systematically review the quality and reporting quality of colorectal cancer screening guidelines, and to provide reference for the update of colorectal cancer screening guidelines and colorectal cancer screening in China. Methods: "Colorectal cancer", "colorectal tumor", "screening", "screening", "guide", "consensus", "Colorectal cancer", "Colorectal neoplasms", "Screening", "Early Detection of Cancer", "Guideline" and "recommendation" were used as search keywords. The literature retrieval for all the Chinese and English guidelines published before April 2018 was conducted by using PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data, China Biology Medicine disc (CBMdisc), Cochrane Library, Guideline International Network, China Guidelines Clearinghouse (CGC) and the official website of the US Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), International Agency for Research on Cancer (IARC), Australia Cancer Council (ACC) and Association of Coloproctology of Great Britain & Ireland (ACPGBI). The inclusion criteria were independent guidance documents for colorectal cancer screening. The language is limited to Chinese and English. The exclusion criteria were literature on interpretation, evaluation, introduction, etc., as well as the translated version of the guide and old guides. The quality and reporting norms of colorectal cancer screening guidelines were compared and evaluated using the European Guideline Research and Assessment Tool (AGREE Ⅱ) and the Practice Guideline Reporting Standard (RIGHT). Results: A total of 15 guides were included. The results of the AGREE Ⅱ quality evaluation showed that the overall quality of 15 guides was high. Among them, there were 9 guides with an overall score of 50 or more, 10 with a recommendation level of "A", and 2 with a rating of "B". There were 3 guides for "C"; each guide scores higher in scope and purpose, and clarity, and scores vary greatly in the areas of participants, rigor, applicability, and independence. The results of the RIGHT evaluation showed that 15 guides were insufficient in six areas except for background information, evidence, recommendations, reviews and quality assurance, funding and conflict of interest statements and management, and other aspects. Conclusion The overall quality of included guidelines for colorectal cancer screening is high, but the normative nature needs to be strengthened.