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/Significance To analyze the risks of near-source cyber-attacks faced by hospitals,and to propose counter-measures.Method/Process Combined with practical work experience,the risk analysis of hospital network architecture,on-site physi-cal environment,personnel behavior and other aspects is carried out from the perspective of near-source cyber-attacker.Then,from the perspective of defender and in combination with regulatory requirements and technical practices,countermeasures and suggestions are proposed.Result/Conclusion 5 main risks are identified,including wireless LAN cracking,exposed wired network sockets,improper configuration of self-service machines,poisoning & phishing,and sensitive information leakage.5 preventive suggestions are put for-ward,including strengthening Wi-Fi management,full coverage of network terminal access,multi-department collaboration in self-service device management,disabling mobile storage media on Intranet terminals,and updating cyber-security education.

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015-2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ⩾15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management + 17.97%xcompliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.
Humans ; China/epidemiology* ; Cross Infection/epidemiology* ; Intensive Care Units/statistics & numerical data* ; Quality Control ; Quality Indicators, Health Care/statistics & numerical data* ; Sepsis/therapy* ; East Asian People/statistics & numerical data*

The achievements of the national health informatization of China have been remarkable while still facing various challenges,including infrastructure,overall coordination,technical specifications,network security,and public health risks.By conducting a comparative study of the information management of the top 5 best hospitals in the world in 2021,it identifies that for the future of hospital information construction,there is a need for deepening the application of core scenarios such as electronic medical records,mobile medical care,and telemedicine.Further-more,there is a need to expand technology development at the terminal layer,network layer and platform layer.The key to accelerating the construction of national health information is closely integrating the application require-ments of hospital information management with the development trend of intelligent technology.

Objective:Hybrid attention U-net (HA-U-net) neural network was designed based on U-net for automatic delineation of craniospinal clinical target volume (CTV) and the segmentation results were compared with those of U-net automatic segmentation model.Methods:The data of 110 craniospinal patients were reviewed, Among them, 80 cases were selected for the training set, 10 cases for the validation set and 20 cases for the test set. HA-U-net took U-net as the basic network architecture, double attention module was added at the input of U-net network, and attention gate module was combined in skip-connection to establish the craniospinal automatic delineation network model. The evaluation parameters included Dice similarity coefficient (DSC), Hausdorff distance (HD) and precision.Results:The DSC, HD and precision of HA-U-net network were 0.901±0.041, 2.77±0.29 mm and 0.903±0.038, respectively, which were better than those of U-net (all P<0.05). Conclusion:The results show that HA-U-net convolutional neural network can effectively improve the accuracy of automatic segmentation of craniospinal CTV, and help doctors to improve the work efficiency and the consistent delineation of CTV.

Objective:To evaluate the clinical efficacy and prognosis of robotic-assisted thoracoscopic surgery (RATS )compared with traditional thoracoscopic surgery (VATS) in the treatment of thymoma.Methods:The clinical data of 128 patients with thymoma who underwent surgery in our hospital from January 2006 to November 2019 were retrospectively analyzed, There were 83 males and 45 females. The age ranged from 23 to 76 years old, with an average of (45.89±13.84) years old. The patients were divided into RATS group (58 cases) and VATS group (70 cases). Cox proportional risk model was used to analyze the factors affecting the postoperative hospital stay. Results:Compared with VATS group, RATS group patients had longer operation time[(128.61±32.13)min vs. (96.42±45.37)min, P=0.036], less intraoperative blood loss[(35.25±5.62)ml vs. (58.36±3.65)ml, P=0.016], less blood transfusion (1.72% vs. 7.14%, P=0.029), and less postoperative complications (17.2% vs. 22.9%, P=0.039). The average total hospitalization cost was higher [(56 721.18±98 457.24) yuan vs. (25 135.68±12 403.29) yuan, P<0.001], and the average postoperative hospitalization time was shorter[(4.15±1.51) days vs. (6.65±2.74)days, P<0.001], all with statistically significant differences. However, there was no statistical differences in conversion to thoracotomy, intraoperative complication, the surgical margin was positive, postoperative infectionpostoperative drainage amount, postoperative drainage time, expenses for medicine and anesthetic fee( P>0.05). Multiple linear regression models showed that different groups ( P=0.013), age ( P=0.025), combined with myasthenia gr avis( P=0.047), combined with underlying disease( P=0.016), intraoperative blood loss( P=0.034), conversion to thoracotomy ( P=0.024), postoperative infection( P=0.008), postoperative complications( P=0.026) and postoperative drainage time ( P=0.031) affected postoperative hospital stay. Conclusion:Robot-assisted thymectomy is a safe and effective method for the treatment of thymomas. RATS recover faster after surgery with fewer complications and shorter hospital stays than RATS after thoracoscopic surgery, but more large, high-quality studies are needed to evaluate the effectiveness of RATS.