Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective:To construct a fall risk prediction model for patients with hematologic malignancies and to provide a reference for the risk assessment and accurate management of falls.Methods:The prospective study design was adopted to facilitate the selection of 510 patients with hematologic malignant in Qilu Hospital of Shandong University for investigation, and relevant data such as patient demographic characteristics, disease treatment and drugs were collected. The LASSO-Logistic regression was used to screen the risk factors of falls in patients with hematologic malignancies, to construct a nomogram risk prediction model. The receiver operating characteristic curve (ROC) and calibration curve were used to evaluate the predictive performance of the model. Bootstrap resampling were used to validate internal validation of the model.Results:Among 510 patients with hematological malignancies, there were 273 males and 237 females, aged 53.0 (41.0, 63.0) years old. A total of 6 risk factors were included in the fall risk prediction model for patients with hematological malignancies, which were disease type ( OR = 0.185, 95% CI 0.061 - 0.562), body temperature ≥38 ℃ ( OR = 2.239, 95% CI 1.128 - 4.445), pain ( OR = 15.581, 95% CI 6.592 - 36.829), anemia ( OR = 4.097, 95% CI 1.536 - 10.927), days of bone marrow suppression ( OR = 3.341, 95% CI 1.619 - 6.893), and assessment of daily self-care ability ( OR = 3.160, 95% CI 1.051 - 9.506)(all P<0.05). The ROC curve of the fall risk prediction model was 0.884 (95% CI 0.841-0.927). The optimal threshold, sensitivity, and specificity of the risk prediction model were 0.248, 87.4% and 75.6%. The internal validation C statistic was 0.873. The Calibration curve was almost coincides with the ideal curve, and the model Brier score was 0.080. Conclusions:The constructed fall risk prediction model has good predictive performance, which can efficiently and objectively quantify the risk of falls, and provide a reference for the early assessment and effective prevention of falls in patients with hematological malignancies.

Objective:To survey the prevalence and comorbidity of patients with chronic thromboembolic pulmonary hypertension (CTEPH) in Shanxi province from 2018 to 2021.Methods:The data of patients with CTEPH from 2018 to 2021 were extracted from the Health Statistics Direct Reporting System of Shanxi Provincial Health Commission; the population data of Shanxi Province was obtained from the Statistical Yearbook of Shanxi Province. The prevalence rate of CTEPH in Shanxi Province in 2018, 2019, 2020 and 2021 was calculated. The International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) was used to identity the specific Charlson comorbidity from other diagnosis on the medical record. The severity of the comorbidity scale was classified as mild (aCCI≤2 points), moderate (aCCI=3-5 points), moderate-severe (aCCI=6-8 points), and severe (aCCI≥9 points).Result:A total of 300 patients with CTEPH were identified in the whole province during the period with the mean age of(65.5±11.5)years, there were 31, 65, 83 and 121 cases in 2018, 2019, 2020 and 2021, and the corresponding prevalence rates were 0.9/10 6, 1.9/10 6, 2.4/10 6 and 3.5/10 6, respectively showing an increasing trend. The patients with CTEPH in this study involved 14 Charlson comorbidities, among which the chronic lung disease was in the highest proportion (198/300, 66.0%), followed by peripheral vascular disease (126/300, 42.0%) and chronic congestive heart failure (121/300, 40.3%). There were 16.3% (49/300) of patients with mild comorbidity, 56.3% (169/300) with moderate comorbidity, 22.3% (67/300) with moderate-severity comorbidity, and 5.0% (15/300) with severity comorbidity. Conclusions:The prevalence of CTEPH in Shanxi province from 2018 to 2021 was 0.9-3.5/10 6 showing an upward trend. The chronic lung disease, peripheral vascular disease and chronic congestive heart failure are the main comorbidities of patients with CTEPH, and mostly with moderate comorbidity.

Objective: To investigate the contamination status of Salmonella, Vibrio parahaemolyticus and Staphylococcus aureus and bacterial resistance in retail meat products in Taiyuan, Shanxi Province. Methods: In the epidemic season of diarrhea in 2017, poultry and meat product specimens were randomly collected from the farmer′s markets and supermarkets of 10 districts and counties of Taiyuan. Salmonella, Vibrio parahaemolyticus and Staphylococcus aureus were isolated form these specimens. Serotypes of Salmonella strains were analyzed. ELSIA was used to detect Staphylococcus aureus enterotoxin (A-E). Vibrio parahaemolyticus isolates were tested for the virulence genes encoding direct hemolysin (tdh) and indirect hemolysin (trh). Antibiotic resistance of the three food-borne pathogens were analyzed using microdilution methods. Results: A total of 38 food-borne pathogens were isolated from 123 poultry and livestock meat product specimens with a positive rate of 30.9%, of which mainly were Salmonella (26 strains, 21.1%), followed by Vibrio parahaemolyticus (8 strains, 6.5%) and Staphylococcus aureus (4 strains, 3.3%). The 26 strains of Salmonella belonged to 10 serotypes. The Salmonella strains isolated from pork specimens had diverse serotypes. Salmonella serovar Derby, Salmonella serovar Gold-coast and Salmonella serovar Liverpool were isolated from raw and cooked pork food for the first time in Taiyuan. All Salmonella strains isolated form chicken products were Salmonella enteritis. The enterotoxin types of the four Staphylococcus aureus strains were three E-type and one complex type (A/E). All Vibrio parahaemolyticus isolates were negative for tdh or trh gene. Ampicillin, chloramphenicol, streptomycin, sulphonamides and tetracyclines (ACSSuT) resistance was prevalent in multi-drug resistant (MDR) Salmonella strains, but there was high sensitivity to fluoroquinolones and cephalosporins. MDR Staphylococcus aureus accounted for 75%. No third-generation cephalosporin- or fluoroquinolone-resistant or MDR Vibrio parahaemolyticus strains were isolated. Conclusions There were food-borne multi-pathogenic bacteria contamination in retail raw and cooked meat products in Taiyuan. Salmonella strains had diverse serotypes and high MDR rate. It was suggested that the regulatory authorities should strengthen the management of antibiotic use in aquaculture and specialized laboratory-based monitoring of meat supply chain.

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

The accelerating growth of the public microbial genomic data imposes substantial bur-den on the research community that uses such resources. Building databases for non-redundant ref-erence sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algo-rithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demon-strate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.