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.

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.

Purpose To measure the spatial resolution in MRI quality control testing automatically based on the American College of Radiology(ACR)phantom using the support vector machine(SVM)method,and the feasibility,accuracy and measurement speed of this method are explored.Materials and Methods Quality control tests were performed using eight MRI devices at Beijing Friendship Hospital of Capital Medical University.A retrospective study was conducted on 71 MRI quality control test images collected based on ACR phantoms between 2017 and 2019.The images were preprocessed by binarization,extraction region of interest and so on.An SVM-based classification model was constructed for analyzing the spatial resolution of dot arrays in row and column directions.The dataset was randomly split into a training set and a test set.The generalization performance of the classification model in this study was evaluated through accuracy,precision,recall and F1 score on the test set.Comparing the results of spatial resolution measurements obtained by both manual and automatic method,we demonstrated the feasibility and accuracy of the method.Additionally,the time taken for the automatic spatial resolution measurement was recorded.Results In this study,the proposed method of automatically measuring the spatial resolution of ACR phantom test images using SVM was feasible,high accuracy and short time.In classification performance test,the accuracy of the spatial resolution of the row directional latices was 95%,the precision was 100%.The accuracy of the spatial resolution of the column directional latices was 97%,the precision was 100%.Among the test cases,the results of automatic measurements matched those of manual measurements in 13 out of 14 cases.On average,automatic spatial resolution measurement took 0.158 seconds per case.Conclusion This study achieves automatic measurement of spatial resolution in MRI quality control based on the ACR phantom using SVM method.The method demonstrates high accuracy and fast measurement speeds,holding significant implications for future rapid MRI quality control stability testing.

Objective To compare the ability of single-phase,dual-phase,and triphasic models in forecasting postoperative pancreatic fistula(POPF)after pancreaticoduodenectomy(PD)using radiomics based on triphasic enhanced CT.Methods A total of 181 patients who underwent multi-phase enhanced CT prior to PD were retrospectively selected,and the collection phase included non-contrast,arterial phase(AP),and equilibrium phase(EP).3D Slicer software was utilized to segment the region of interest(ROI)for the postoperative pancreatic remnant on each phase.Radiomics feature extraction was performed using R software,followed by feature selection through least absolute shrinkage and selection operator(LASSO)regression with five-fold cross-validation to prevent model overfitting.The effective features selected were combined in a weighted linear manner to obtain a Radiomics score(Radscore).The patients were divided into training set and test set in a 7︰3 ratio.Logistic regression was employed to construct seven POPF prediction models(three single-phase,three dual-phase,and one triphasic models)based on different phase combinations.The diagnostic performance of the models was evaluated using the area under the curve(AUC)of receiver operating characteristic(ROC)curve,accuracy(ACC),sensitivity(SEN),and specificity(SPE).The DeLong test was applied to compare the differences in AUC among different models.Results After LASSO regression,24 effective features associated with POPF were selected from different phases.In the test set,the triphasic model exhibited the highest AUC and ACC(AUC=0.76,ACC=0.808).The calibration curve demonstrated the strongest agreement between the estimated probabilities and observed probabilities for the triphasic model.The decision curve analysis(DCA)curve indicated that the triphasic model had the largest threshold range with a higher net benefit.Conclusion Compared with single-phase and dual-phase models,the triphasic model based on enhanced CT provides better prediction of POPF after PD,aiding clinical decision-making and improve prognosis.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To establish a method for the rapid detection of varicella-zoster virus (VZV) by recombinase-aid amplification (RAA) combined with Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a system.Methods:Clinical samples of suspected herpes zoster in Shandong province and Shanghai from 2023 to 2024 were collected, nucleic acids of positive samples were extracted, RAA-specific primers and crRNA (CRISPR RNA, crRNA) were designed for the conserved region of VZV, and the fluorescence intensity generated by Cas12a non-specific cleavage of single-stranded fluorescent probes was used to screen highly sensitive crRNAs and optimize the concentrations of crRNA, Cas12a and ssDNA probes. The sensitivity and reproducibility of the RAA-CRISPR/Cas12a detection method were evaluated by using synthesized plasmids and clinical samples, and the specificity of the method was evaluated by using other viral nucleic acids. The method was used to detect clinical samples by using the method and quantitative real-time PCR (qPCR) method, and the detection rate and consistency of the two method were compared.Results:The highly sensitive crRNA-4 was screened from the four crRNAs designed, and a VZV detection method for RAA-CRISPR/CAS12a based on fluorescence intensity measurement was established, which could be detected at 37℃ in 45 min, and the sensitivity of the detection could reach 10 copies/μL, a minimum clinical sample with a Ct value of 38.980 can be detected. It has high specificity and no cross-reactivity with Adenovirus 7, Herpes simplex virus type I, Herpes simplex virus type II, Coxsackieviruses A16, Cytomegalovirus, Epstein-Barr virus, Measles virus, Mumps virus, Enterovirus 71, Japanese encephalitis virus genotype 5. It has good stability, and can be successfully detected in low, medium and high concentrations of viral positive plasmids with good consistency. The detection rate of the clinically positive samples was 100%, which was completely consistent with the qPCR test result.Conclusions:RAA isothermal amplification technology combined with CRISPR-CAS12a technology was used to establish an accurate method for the detection of VZV virus, which was highly sensitive, specific, and had low requirements for experimental conditions, and could be completed within 45 min, which could provide strong technical support for the early detection of VZV.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.