Prediction Modelling for Gastroesophageal Variceal Bleeding in Patients With Chronic Hepatitis B Using Four-dimensional Flow MRI.

Background/Aims: In this study, we aim to develop a model for predicting gastroesophageal varices (GEV) bleeding in patients with chronic hepatitis B (CHB) by utilizing hemodynamic parameters obtained through four-dimensional flow MRI (4D flow MRI). Methods: This study conducted a prospective enrollment of CHB patients suspected of GEV from October 2021 to May 2022. The severity of varices and bleeding risk were evaluated using clinical findings and upper gastrointestinal endoscopy, and patients were classified into high-risk and non-high-risk groups. The study utilized serological examination, ultrasonographic examination, and 4D flow MRI. Relevant parameters were selected through univariate and multivariate analyses, and a prediction model was established using binary logistic regression analysis. The model was combined with the Baveno Ⅵ/Ⅶ and Expanded Baveno Ⅵ/Ⅶ criteria to evaluate diagnostic efficacy and the risk of avoiding endoscopic examination. Results: A total of 40 CHB patients were enrolled and categorized into the high-risk group (n = 15) and the non-high-risk group (n = 25). The spleen diameter and regurgitant fraction (R%) were independent predictors of variceal bleeding and a predictive model was established. The combination of this prediction model and the Baveno Ⅵ/Ⅶ criteria achieved high diagnostic efficiency, enabling 45.00% (18/40) of patients to be exempted from the unnecessary endoscopic procedure and the high-risk misclassification rate (0%) was less than 5%. Conclusion: The prediction model generated by 4D flow MRI has the potential to assess the likelihood of varices and can be supplemented by the Baveno VI/VII criteria to improve diagnostic accuracy in CHB patients.

Maternal third hand exposure to e-cigarette vapor alters lung and bone marrow immune cell responses in offspring in the absence or presence of flu infection.

There is increasing evidence that third hand exposure to e-cigarette vapor (e-vapor) can have detrimental effects on the lungs. However, whether maternal exposure during pregnancy results in harmful changes to the offspring is unknown. Using two different e-cigarette settings (low versus high power), BALB/c mice were subjected to third hand e-vapor (e-vapor deposited onto towels, towels changed daily) in the absence or presence of nicotine, before, during, and after pregnancy. Male adult offspring were then infected with mouse-adapted influenza A virus (A/PR/8/34 H1N1) and lung and bone marrow immune cell responses assessed 7 days post infection. Maternal third hand exposure to low power (MLP) or high power (MHP) e-vapor with nicotine (MLP+NIC and MHP+NIC, respectively) increased the percentage of lung immune cells and neutrophils in the bone marrow. Interestingly, Flu-infected offspring from MLP+NIC and MHP+NIC groups had lower percentages of lung alveolar macrophages, and more pronounced increases in neutrophils in the bone marrow, when compared to offspring from MSham Flu controls. Flu infection also decreased the percentage of lung CD4+ T cells and increased the percentage of lung CD8+ T cells, irrespective of maternal exposure (MLP-/+NIC and MHP-/+NIC). Significantly, both MLP+NIC and MHP+NIC resulted in blunted activation of lung CD4+ T cells, but only MLP+NIC caused blunted activation of lung CD8+ T cells. Together, we show for the first time that maternal third hand exposure to e-vapor results in significant, long-lived effects on lung and bone marrow immune cell responses in offspring at baseline and in response to Flu infection.

Cl--templated coordination polymers constructed from dragonfly-like Ln4 clusters exhibiting excellent magnetothermal properties.

Lanthanide clusters with high magnetic entropy and an exquisite structure have always been a hot research direction. Herein, two delicately structured lanthanide clusters were synthesized using isonicotinic acid (HIN) as a ligand by the solvothermal method, namely {[Gd4(IN)4(µ3-OH)4(OAc)2(H2O)6]·Cl2·7H2O}n (1) and {[Dy4(IN)4(µ3-OH)4(OAc)2(H2O)6]·Cl2·8H2O}n (2). Structural analysis indicates that the two compounds exhibit comparable structures, and both have the same cubane-like Ln4 metal skeleton and form an interesting dragonfly molecular configuration by connecting to HIN. Moreover, {Ln4} serves as the fundamental structural unit for the construction of a 1D channel of 1, with a diameter of approximately 10.08 Å, which is connected to each other by extra {Ln4} units to form an exquisite porous 3D structure of 1. Cl- exists as an anion-template in its 3D structure, balancing the charge and maintaining its structural stability. Magnetic measurements indicate that 1 exhibits a rather satisfactory magnetic entropy change, reaching a maximum value of -ΔSmaxm = 31.59 J kg-1 K-1 at 2 K for ΔH = 7 T.

Chronic maternal exposure to low-dose PM2.5 impacts cognitive outcomes in a sex-dependent manner.

There is no safe level of air pollution for human health. Traffic-related particulate matter (PM2.5) is a major in-utero toxin, mechanisms of action of which are not fully understood. BALB/c dams were exposed to an Australian level of traffic PM2.5 (5 µg/mouse/day, intranasal, 6 weeks before mating, during gestation and lactation). Male offspring had reduced memory in adulthood, whereas memory was normal in female littermates, similar to human responses. Maternal PM2.5 exposure resulted in oxidative stress and abnormal mitochondria in male, but not female, brains. RNA-sequencing analysis showed unique sex-related changes in newborn brains. Two X-chromosome-linked histone lysine demethylases, Kdm6a and Kdm5c, demonstrated higher expression in female compared to male littermates, in addition to upregulated genes with known functions to support mitochondrial function, synapse growth and maturation, cognitive function, and neuroprotection. No significant changes in Kdm6a and Kdm5c were found in male littermates, nor other genes, albeit significantly impaired memory function after birth. In primary foetal cortical neurons, PM2.5 exposure suppressed neuron and synaptic numbers and induced oxidative stress, which was prevented by upregulation of Kdm6a or Kdm5c. Therefore, timely epigenetic adaptation by histone demethylation to open DNA for translation before birth may be the key to protecting females against prenatal PM2.5 exposure-induced neurological disorders, which fail to occur in males associated with their poor cognitive outcomes.

Neo-5,10-seco-clerodane diterpenoids from Schnabelia terniflora.

Three new neo-5,10-seco-clerodane diterpenoids (1-3), four previously undescribed ethoxy/methoxy acetal analogues (4-7), one new etherified labdane diterpenoid (8), and seven known diterpenoids (9-15) were isolated from the whole plant of Schnabelia terniflora. Their structures were established on the basis of extensive spectroscopic analysis, single-crystal X-ray diffraction data, calculated electronic circular dichroism (ECD), and Mo2(OAc)4-induced circular dichroism. Compounds 2 and 3 represent the first examples of neo-5,10-seco-clerodane diterpenoids containing a 1H-pyrrole-2,5-dione and a pyrrolidine-2,5-dione moiety, respectively. A plausible biosynthetic pathway for 1-3 is proposed. All diterpenoids were evaluated for their cytotoxic activity against non-small-cell lung cancer lines (A549 and H460) and gastric cancer lines (HGC27 and AGS). Among them, 2 and 14 showed moderate cytotoxicity against four cell lines.

Clear Cell Renal Cell Carcinoma: Characterizing the Phenotype of Von Hippel-Lindau Mutation Using MRI.

BACKGROUND: The von Hippel-Lindau (VHL) mutation is an important alteration in clear cell renal cell carcinoma (ccRCC); however, its imaging phenotype remains unclear. PURPOSE: To investigate whether MRI features can reflect the VHL mutation status. STUDY TYPE: Retrospective. FIELD STRENGTH/SEQUENCE: 3 T/fast spin echo T2-weighted, spin-echo echo planar diffusion-weighted, gradient recalled echo T1-weighted, gradient recalled echo chemical-shift T1-weighted, and contrast-enhanced gradient recalled echo T1-weighted sequences. POPULATION: One hundred five patients with ccRCC who underwent preoperative contrast-enhanced MRI and subsequent genomic sequencing: 59 consecutive patients from our institution (38 [64.41%] with VHL mutations) formed a training cohort, and 46 from The Cancer Genome Atlas (TCGA) database (24 [52.17%] with VHL mutations) formed an independent test cohort. ASSESSMENT: Two radiologists, with 23 and 33 years of experience respectively, jointly evaluated the semantic MRI features of the primary lesion in ccRCCs to propose potential features related to VHL mutations in both cohorts. Three additional readers, with 5, 7, and 10 years of experience respectively, independently reviewed all lesions to assess the interobserver agreement of MRI features. A VHL mutational likelihood score (VHL-MULIS) system was constructed using the training cohort and validated using the independent test cohort. STATISTICAL TESTS: Fisher's test or chi-square test, t-test or Mann-Whitney U test, logistic regression, Cohen's kappa (κ), area under the receiver operating characteristic curve (AUC). A two-sided P value <0.05 was considered statistically significant. RESULTS: In both the local and public cohorts, T2-weighted signal intensity and presence of microscopic fat from primary lesions were significantly associated with VHL mutation status. The VHL-MULIS incorporated maximum diameter, T2-weighted signal intensity, and presence of microscopic fat in the training cohort and demonstrated promising diagnostic ability (AUC, 0.82; sensitivity, 0.79; specificity, 0.82) and substantial interobserver agreement (κ, 0.787) in the test cohort. DATA CONCLUSION: The VHL mutation exhibited a distinct MRI phenotype. Integrating multiple semantic MRI features has potential to reflect the mutation status in patients with ccRCC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Predicting miRNA-disease Associations Based on Spectral Graph Transformer with Dynamic Attention and Regularization.

Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs and diseases. However, these methods often face challenges in terms of overall effectiveness and are sensitive to node positioning. To address these issues, the researchers introduce DARSFormer, an advanced deep learning model that integrates dynamic attention mechanisms with a spectral graph Transformer effectively. In the DARSFormer model, a miRNA-disease heterogeneous network is constructed initially. This network undergoes spectral decomposition into eigenvalues and eigenvectors, with the eigenvalue scalars being mapped into a vector space subsequently. An orthogonal graph neural network is employed to refine the parameter matrix. The enhanced features are then input into a graph Transformer, which utilizes a dynamic attention mechanism to amalgamate features by aggregating the enhanced neighbor features of miRNA and disease nodes. A projection layer is subsequently utilized to derive the association scores between miRNAs and diseases. The performance of DARSFormer in predicting miRNA-disease associations is exemplary. It achieves an AUC of 94.18% in a five-fold cross-validation on the HMDD v2.0 database. Similarly, on HMDD v3.2, it records an AUC of 95.27%. Case studies involving colorectal, esophageal, and prostate tumors confirm 27, 28, and 26 of the top 30 associated miRNAs against the dbDEMC and miR2Disease databases, respectively. The code and data for DARSFormer are accessible at https://github.com/baibaibaialone/DARSFormer.

An NMPC-Based Integrated Longitudinal and Lateral Vehicle Stability Control Based on the Double-Layer Torque Distribution.

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro-mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.

Critical behaviors of van der Waals itinerant ferromagnet Fe3.8GaTe2.

The critical behavior of the van der Waals ferromagnet Fe3.8GaTe2was systematically studied through measurements of isothermal magnetization, with the magnetic field applied along thec-axis. Fe3.8GaTe2undergoes a non-continuous paramagnetic to ferromagnetic phase transition at the Curie temperatureTc∼ 355 K. A comprehensive analysis of isotherms aroundTcutilizing the modified Arrott diagram, the Kouvel-Fisher method, the Widom scaling law, and the critical isotherm analysis yielded the critical exponent ofß= 0.411,γ= 1.246, andδ= 3.99. These critical exponents are found to be self-consistent and align well with the scaling equation at high magnetic fields, underscoring the reliability and intrinsic nature of these parameters. However, the low-field data deviates from the scaling relation, exhibiting a vertical trend whenT

Dual-learning Multi-hop Nonnegative Matrix Factorization for community detection.

As an important branch of network science, community detection has garnered significant attention. Among various community detection methods, nonnegative matrix factorization (NMF)-based community detection approaches have become a popular research topic. However, most NMF-based methods overlook the network's multi-hop information, let alone the community detection results specific to each hop of the network. In this paper, we propose Dual-learning Multi-hop NMF (DL-MHNMF), a method that considers not only the multi-hop connectivity between two nodes but also factors in the shared results across multiple hops and the impact of differences in the specific results at each hop on the shared outcomes. An efficient iterative optimization algorithm with guaranteed theoretical convergence is proposed for solving DL-MHNMF. Methodologically, by iteratively removing the specific results during the optimization process of DL-MHNMF, we achieve enhanced detection accuracy, which is also verified by subsequent experiments. Specifically, we compare fourteen algorithms on eleven publicly available datasets, and experimental results show that our algorithm outperforms most state-of-the-art methods. The source code is availiable at https://github.com/bx20000827/DL-MHNMF.git.

A novel BSL-2 Lassa virus reverse genetics system modelling the complete viral life cycle.

Lassa virus (LASV), a risk-group 4 pathogen, must be handled in biosafety level-4 (BSL-4) conditions, thereby limiting its research and antiviral development. Here, we developed a novel LASV reverse genetics system which, to our knowledge, is the first to study the complete LASV life cycle under BSL-2 conditions. Viral particles can be produced efficiently when LASV minigenomic RNA harbouring minimal viral cis-elements and reporter genes is transfected into a helper cell line stably expressing viral NP, GP, Z and L proteins. The resulting defective virions, named LASVmg, can propagate only in the helper cell line, providing a BSL-2 model to study the complete LASV life cycle. Using this model, we found that a previously reported cellular receptor α-dystroglycan is dispensable for LASVmg infection. Furthermore, we showed that ribavirin can inhibit LASVmg infection by inducing viral mutations. This new BSL-2 system should facilitate studying the LASV life cycle and screening antivirals.

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study.

BACKGROUND: Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension. PURPOSE: To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs. STUDY TYPE: Retrospective. POPULATION: 797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses. FIELD STRENGTH/SEQUENCES: 3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging. ASSESSMENT: Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions. STATISTICAL TESTS: Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05. RESULTS: The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993). CONCLUSION: The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Differentiating mixed epithelial and stromal tumor family from predominantly cystic renal cell carcinoma using magnetic resonance imaging-based Bosniak classification system version 2019.

PURPOSE: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019). MATERIALS AND METHODS: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T2-weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis. RESULTS: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T2WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%. CONCLUSION: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference.

Reinforced GNNs for Multiple Instance Learning.

Multiple instance learning (MIL) trains models from bags of instances, where each bag contains multiple instances, and only bag-level labels are available for supervision. The application of graph neural networks (GNNs) in capturing intrabag topology effectively improves MIL. Existing GNNs usually require filtering low-confidence edges among instances and adapting graph neural architectures to new bag structures. However, such asynchronous adjustments to structure and architecture are tedious and ignore their correlations. To tackle these issues, we propose a reinforced GNN framework for MIL (RGMIL), pioneering the exploitation of multiagent deep reinforcement learning (MADRL) in MIL tasks. MADRL enables the flexible definition or extension of factors that influence bag graphs or GNNs and provides synchronous control over them. Moreover, MADRL explores structure-to-architecture correlations while automating adjustments. Experimental results on multiple MIL datasets demonstrate that RGMIL achieves the best performance with excellent explainability. The code and data are available at https://github.com/RingBDStack/RGMIL.

Whole-transcriptome profiling and identification of cold tolerance-related ceRNA networks in japonica rice varieties.

Introduction: Low-temperature stress negatively impacts rice yield, posing a significant risk to food security. While previous studies have explored the physiological and linear gene expression alterations in rice under low-temperature conditions, the changes in competing endogenous RNA (ceRNA) networks remain largely unexamined. Methods: We conducted RNA sequencing on two japonica rice varieties with differing cold-tolerance capabilities to establish ceRNA networks. This enabled us to investigate the transcriptional regulatory network and molecular mechanisms that rice employs in response to low-temperature stress. Results: We identified 364 differentially expressed circular RNAs (circRNAs), 224 differentially expressed microRNAs (miRNAs), and 12,183 differentially expressed messenger RNAs (mRNAs). WRKY family was the most prominent transcription factor family involved in cold tolerance. Based on the expression patterns and targeted relationships of these differentially expressed RNAs, we discerned five potential ceRNA networks related to low-temperature stress in rice: osa-miR166j-5p from the miR166 family was associated with cold tolerance; osa-miR528-3p and osa-miR156j-3p were linked to stress response; and osa-miR156j-3p was involved in the antioxidant system. In addition, Os03g0152000 in the antioxidant system, as well as Os12g0491800 and Os05g0381400, correlated with the corresponding stress response and circRNAs in the network. A gene sequence difference analysis and phenotypic validation of Os11g0685700 (OsWRKY61) within the WRKY family suggested its potential role in regulating cold tolerance in rice. Discussion and conclusion: We identified Os11g0685700 (OsWRKY61) as a promising candidate gene for enhancing cold tolerance in japonica rice. The candidate miRNAs, mRNAs, and circRNAs uncovered in this study are valuable targets for researchers and breeders. Our findings will facilitate the development of cold-tolerant rice varieties from multiple angles and provide critical directions for future research into the functions of cold-tolerance-related miRNAs, mRNAs, and circRNAs in rice.

Analyzing the photoassociation spectrum of ultracold 85Rb 133Cs molecule in (3)3Σ+ state.

We establish a theoretical model to analyze the photoassociative spectroscopy of 85Rb 133Cs molecules in the (3)3Σ+ state. The vibrational energy, spin-spin coupling constant, and hyperfine interaction constant of the (3)3Σ+ state are determined based on nine observed vibrational levels. Consequently, the Rydberg-Klein-Rees potential energy curve of the (3)3Σ+ state is obtained and compared with the ab initial potential energy curve. Our model can be adopted to analyze the photoassociative spectroscopy of other heteronuclear alkali-metal diatomic molecules in the (3)3Σ+ state.

Renal cell carcinoma and venous tumor thrombus: predicting sarcomatoid dedifferentiation through preoperative IVIM-based MR imaging.

PURPOSE: To evaluate the value of preoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and conventional MRI indicators in identifying sarcomatoid dedifferentiation in renal cell carcinoma (RCC) and tumor thrombus. METHODS: From September 2016 to April 2023, consecutive patients with RCC and tumor thrombus who received routine MRI examination and IVIM-DWI before radical resection were enrolled prospectively. Kaplan-Meier method with log-rank test was used to calculate and compare the survival probability. The preoperative imaging features were analyzed. Univariate and multivariable logistic regression analyses were employed to identify independent predictors of sarcomatoid dedifferentiation. The predictive ability was evaluated by receiver operating characteristic (ROC) curves. RESULTS: Twenty-two patients (15.3%) of the 144 patients in the training set (median age, 58.0 years [IQR, 52.0-65.0 years]; 108 men) and 11 patients (22.4%) of the 49 patients in the test set (median age, 58.0 years [IQR, 53.0-63.0 years]; 38 men) had sarcomatoid dedifferentiated tumors. Patients with sarcomatoid-differentiated tumors had poor progress-free survival in the training set and test set (P < 0.001 and P = 0.007). f value (P = 0.011), mN stage (P = 0.007), and necrosis (P = 0.041) were independent predictors for predicting sarcomatoid dedifferentiation in the training set. The model combining conventional MRI features and f value had AUCs of 0.832 (95% CI 0.755-0.909) and 0.825 (95% CI 0.702-0.948) in predicting sarcomatoid dedifferentiation in the training set and test set. CONCLUSION: It is feasible to preoperatively identify sarcomatoid dedifferentiation based on IVIM-DWI and conventional MR imaging indicators.

4D Flow MRI of Portal Vein Hemodynamics in Healthy Volunteers and Patients with Chronic Liver Disease.

AIM: To identify age-matched healthy volunteers, non-cirrhotic chronic liver disease (CLD) and cirrhotic patients based on portal hemodynamic parameters using 4D flow MRI. METHODS: A total of 10 age-matched healthy volunteers and 69 CLD patients were enrolled and underwent 4D flow MRI prospectively. 4D flow MR images were processed by an MD in biomedical engineering working on the GTFlow platform. Portal hemodynamic parameters include net flow (mL/cycle), flow volume per second through the lumen (mL/sec), average flow velocity (cm/sec), and maximum flow velocity (cm/sec). The difference in portal hemodynamic parameters of 4D flow MRI was compared among healthy volunteers, non-cirrhotic CLD patients and patients with cirrhosis by one-way ANOVA or Kruskal-Wallis nonparametric test and post hoc tests. RESULTS: 10 CLD patients without cirrhosis and 56 patients with cirrhosis were eventually included, along with 10 healthy volunteers who were divided into three groups. 3 patients with cirrhosis whose image quality did not meet the requirements were excluded. There were no significant differences in portal hemodynamic parameters among the three groups except portal average velocity (P > 0.05). There was no statistical difference in all portal hemodynamic parameters of 4D flow MRI between healthy volunteers and patients with cirrhosis (P > 0.05). There were significant differences in portal average velocity between non-cirrhotic CLD patients, healthy volunteers and patients with cirrhosis, respectively (11.44±3.93 vs 8.10±2.66, P=0.013; 11.44±3.93 vs 8.60±2.22, P=0.007). CONCLUSION: Portal average velocity obtained by 4D flow MRI can be an auxiliary means to identify cirrhosis in patients with CLD.