Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

[Objective] To compare next generation sequencing (NGS) library construction technology between probe hybridization capture and amplicon methods, and analyze the influencing factors of HLA genotyping resolution level and its prospects in clinical applications. [Methods] A total of 207 clinical samples with known typing results and samples from the proficiency testing plan were selected. The conformity rate of HLA genotyping results, allele coverage and typing data analysis indicators were confirmed, and the effects of two library construction methods on the level of HLA genotyping discrimination were compared. [Results] The concordance rate of 207 samples with the feedback results of PT or prior well-characterized HLA genotypes was 100%. Among them, 91 samples were captured using hybridization probe capture method. Compared with the original amplicon method, the hybridization probe capture method can distinguish the alleles of DRB1 and DPB1 that cannot be determined in 13 samples. The allelic imbalance of DRB1, DPA1, and DQB1 loci in 6 samples was resolved. Three samples were found to have missed detection of alleles at the DQA1 and DQB1 loci. [Conclusion] The performance indicators of hybridization probe capture and amplicon performance confirmation meet the requirements of clinical detection of HLA genotyping, which provides an experimental method and basis for clinical application.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Aim To investigate the regulatory and an-ti-tumour effects of baicalein on mouse hepatocellular carcinoma cells and mouse macrophage co-cultures.Methods In vitro experiments,mouse hepatocellular carcinoma cell H22 and mouse macrophage RAW264.7 were randomly divided into a blank group and different concentrations of gradient administration group(5,10,20,40,80 mg·L-1),and the cell activity was detec-ted by CCK-8 assay;the two kinds of cells were co-cultured in Transwell chambers of 6-well plates for 48 h,and were randomly divided into the blank,model,and low,medium,and high baicalin groups(10,20,40 mg·L-1).Cell scratch and invasion assays,ELISA kits were used to detect TNF-α and IL-10 factor levels,and Western blot was used to determine the lev-els of CCNA2 and related proteins.The levels of TNF-α and IL-10 were detected by ELISA kits,and the ex-pression levels of CCNA2 and related proteins were de-tected by Western blot.In vivo experiments,H22 sub-cutaneous tumour model was established and randomly divided into the blank,positive,model and drug-ad-ministered groups.Mouse spleen,thymus and tumour indices were counted,and immunohistochemistry and Western blot were employed to detect the expression levels of CCNA2 and macrophage-related indexes in tumour tissues.Results Different doses of baicalein had a significant inhibitory effect on H22 and no signif-icant cytotoxicity on M0-type RAW264.7;the mor-phology of M0-type RAW264.7 cells was changed after co-culture,TNF-α was elevated and IL-10 was re-duced in the baicalein group;the results of the cell scratch assay and invasion assay found that baicalein inhibited M2-type macrophage invasion and metastasis;Arg1,p-p38/p38,p-stat3/stat3,N-cadherin,CCNA2 decreased significantly and Inos and E-cadherin in-creased significantly in the baicalein group;CCNA2,CD206 expression decreased significantly and CD86 expression increased significantly in the administered group.Conclusions Baicalein reverses M2-type mac-rophage polarisation and pro-carcinogenic functions and inhibits M2-type macrophage migration and invasion by modulating M2-type macrophage-related signalling pathways.

Objective:To establish a linkage prediction model for human leukocyte antigen (HLA) DPA1-DPB1 and validate it by using clinical data and follow-up data from unrelated allogeneic hematopoietic stem cell transplantation donors and recipients, and to explore the clinical application value of the prediction model in transplantation prognosis.Methods:This is a retrospective study. Leveraging the artificial neural network algorithm of NetMHCⅡpan and the DPA1-DPB1 haplotype linkage database of the Chinese population established in our previous research, and incorporating the amino acid FASTA data of DPA1-DPB1 of all known sequences newly published by the Latest International Immunogenetics/Human Leukocyte Antigens, 47 DPA1-DPB1 linkage models were established. Employing next-generation sequencing technology based on the hybridization capture library construction method, HLA genotyping tests for HLA-A, -B, -C, DRB1, DQB1, DQA1, DRB3/4/5, DPB1, and DPA1 (9 loci) were performed on 250 donor-recipients pairs who underwent unrelated-donor hematopoietic stem cell transplantation in the Department of Hematology of the First Affiliated Hospital of Soochow University between January 2016 and September 2021. HLA typing data and clinical information of transplant donors and recipients were retrospectively analyzed to assess and predict the impact of permissive and non-permissive linkage mismatches of DPA1-DPB1 on transplantation prognosis. The Kaplan-Meier method with the log-rank test was applied to compare the survival curves of overall survival (OS) rates between different groups. Additionally, a competing risks model was utilized to compare the cumulative incidence of grade Ⅱ-Ⅳ acute graft-versus-host disease and non-relapse mortality (NRM) across groups. The area under the receiver operating characteristic curve was employed to compare the predictive performance of the established prediction model with that of the T-cell epitope (TCE) model.Results:According to the different hydrophilic and hydrophobic properties of amino acids, the DPA1-DPB1 linkage model is categorized into types Ⅰ-Ⅳ: type I consists of 6 hydrophobic types at P1-P8 plus hydrophilic type at P9; type Ⅱ includes 17 hydrophobic types; type Ⅲ comprises 9 amphiphilic types; and type Ⅳ consists of 15 hydrophilic types. According to the prediction model, DPA1-matched and DPB1-mismatched donor-recipient cases were classed into P1-matched or P1-mismatched groups. Compared with fully matched DPA1 and DPB1 cases, P1-mismatched patients had a 2-year OS rate of 75% (12/16) versus 96.2%(25/26) (χ2=4.13, P=0.04), and a NRM rate of 4/16 versus 0 (χ2=7.05, P<0.01). However, there was no statistically significant difference in the 2-year OS and NRM rates compared to DPA1 and DPB1 cases ( P>0.05). The prediction model established in this study demonstrated a larger area under the receiver operating characteristic curve for predicting the 2-year OS rate compared with the DPB1 TCE model ( Z=0.71, P=0.48). In donor-recipient cases where both DPA1 and DPB1 were mismatched, the 2-year OS rates decreased and the NRM increased in both P1-matched and P1-mismatched cases compared with fully matched DPA1 and DPB1. Moreover, P1-mismatched patients had a worse prognosis compared to P1-matched patients. Conclusion:The DPA1-DPB1 linkage prediction model established based on high-throughput next-generation sequencing technology can be used to predict the impact of HLA-DP mismatches on OS and NRM in transplantation, and the prediction performance is superior to the TCE model.

The current status of exoskeletons was introduced in enhancing individual soldier's battlefield rescue capabilities,promoting the integrated use of battlefield rescue equipment,protecting medical personnel on the battlefield and assisting injured soldiers in rehabilitation training.The challenges of exoskeletons faced in human-machine interaction,power supply endurance,heavy overall structure,restricted movement and high cost were analyzed when applied to medical service support,and some suggestions were proposed accordingly including enhancing technology research and development,integrated application,communication and cooperation and personnel training.References were provided for the application of exoskeletons in China's medical service support.[Chinese Medical Equipment Journal,2024,45(3):71-75]