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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.
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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.
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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.
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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 temperatureTcâ¼ 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
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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.
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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.
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Virus Lassa , Genética Inversa , Virus Lassa/genética , Virus Lassa/fisiología , Genética Inversa/métodos , Humanos , Animales , Antivirales/farmacología , Chlorocebus aethiops , Línea Celular , Replicación Viral , Fiebre de Lassa/virología , Ribavirina/farmacología , Células Vero , Contención de Riesgos Biológicos , Genoma Viral , Virión/genética , Virión/metabolismoRESUMEN
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.
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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.
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Algoritmos , Redes Neurales de la Computación , Aprendizaje Automático , HumanosRESUMEN
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.
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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.
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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.
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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.
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Carcinoma de Células Renales , Neoplasias Renales , Humanos , Persona de Mediana Edad , Masculino , Femenino , Neoplasias Renales/diagnóstico por imagen , Neoplasias Renales/patología , Neoplasias Renales/cirugía , Anciano , Carcinoma de Células Renales/diagnóstico por imagen , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/cirugía , Estudios Prospectivos , Imagen de Difusión por Resonancia Magnética/métodos , Imagen por Resonancia Magnética/métodos , Cuidados Preoperatorios/métodos , Trombosis de la Vena/diagnóstico por imagen , Trombosis de la Vena/patología , Valor Predictivo de las Pruebas , Desdiferenciación CelularRESUMEN
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.
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Chiral phosphonate esters have been widely applied in the fields of organic chemistry, medicine, and photoelectric materials. However, it requires the challenging enantioselective synthesis of cyclic phosphonate esters with the desired chiral configuration. The two epimers of 2-oxo-2H-1,3,2-dioxaphosphorinane derivatives should have different reactivities in Pd-catalyzed coupling reactions, which could lead to an effective methodology for the asymmetric synthesis of 2-oxo-2-aryl-1,3,2-dioxaphosphorinane derivatives. A thorough investigation of the coupling reactions both computationally and experimentally led to the stereoselective synthesis of chiral cyclic phosphonate esters. The axial isomer of products can be obtained with both high diastereoselectivity and good chemical yields from the mixture of 2-oxo-2H-1,3,2-dioxaphosphorinane derivatives under kinetically controlled conditions.
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Low-temperature chilling is a major abiotic stress leading to reduced rice yield and is a significant environmental threat to food security. Low-temperature chilling studies have focused on physiological changes or coding genes. However, the competitive endogenous RNA mechanism in rice at low temperatures has not been reported. Therefore, in this study, antioxidant physiological indices were combined with whole-transcriptome data through weighted correlation network analysis, which found that the gene modules had the highest correlation with the key antioxidant enzymes superoxide dismutase and peroxidase. The hub genes of the superoxide dismutase-related module included the UDP-glucosyltransferase family protein, sesquiterpene synthase and indole-3-glycerophosphatase gene. The hub genes of the peroxidase-related module included the WRKY transcription factor, abscisic acid signal transduction pathway-related gene plasma membrane hydrogen-ATPase and receptor-like kinase. Therefore, we selected the modular hub genes and significantly enriched the metabolic pathway genes to construct the key competitive endogenous RNA networks, resulting in three competitive endogenous RNA networks of seven long non-coding RNAs regulating three co-expressed messenger RNAs via four microRNAs. Finally, the negative regulatory function of the WRKY transcription factor OsWRKY61 was determined via subcellular localization and validation of the physiological indices in the mutant.
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MicroARNs , Oryza , ARN Largo no Codificante , Oryza/metabolismo , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Antioxidantes , Perfilación de la Expresión Génica , MicroARNs/genética , MicroARNs/metabolismo , Redes Reguladoras de Genes , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , Peroxidasas/genética , Superóxido Dismutasa/genéticaRESUMEN
RATIONALE AND OBJECTIVE: Accurate differentiation between benign and malignant cystic renal masses (CRMs) is challenging in clinical practice. This study aimed to develop MRI-based machine learning models for differentiating between benign and malignant CRMs and compare the best-performing model with the Bosniak classification, version 2019 (BC, version 2019). METHODS: Between 2009 and 2021, consecutive surgery-proven CRM patients with renal MRI were enrolled in this multicenter study. Models were constructed to differentiate between benign and malignant CRMs using logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms, respectively. Meanwhile, two radiologists classified CRMs into I-IV categories according to the BC, version 2019 in consensus in the test set. A subgroup analysis was conducted to investigate the performance of the best-performing model in complicated CRMs (II-IV lesions in the test set). The performances of models and BC, version 2019 were evaluated using the area under the receiver operating characteristic curve (AUC). Performance was statistically compared between the best-performing model and the BC, version 2019. RESULTS: 278 and 48 patients were assigned to the training and test sets, respectively. In the test set, the AUC and accuracy of the LR model, the RF model, the SVM model, and the BC, version 2019 were 0.884 and 75.0%, 0.907 and 83.3%, 0.814 and 72.9%, and 0.893 and 81.2%, respectively. Neither the AUC nor the accuracy of the RF model that performed best were significantly different from the BC, version 2019 (P = 0.780, P = 0.065). The RF model achieved an AUC and accuracy of 0.880 and 81.0% in complicated CRMs. CONCLUSIONS: The MRI-based RF model can accurately differentiate between benign and malignant CRMs with comparable performance to the BC, version 2019, and has good performance in complicated CRMs, which may facilitate treatment decision-making and is less affected by interobserver disagreements.
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Dietary exposure to methylmercury (MeHg) causes irreversible damage to human cognition and is mitigated by photolysis and microbial demethylation of MeHg. Rice (Oryza sativa L.) has been identified as a major dietary source of MeHg. However, it remains unknown what drives the process within plants for MeHg to make its way from soils to rice and the subsequent human dietary exposure to Hg. Here we report a hidden pathway of MeHg demethylation independent of light and microorganisms in rice plants. This natural pathway is driven by reactive oxygen species generated in vivo, rapidly transforming MeHg to inorganic Hg and then eliminating Hg from plants as gaseous Hg°. MeHg concentrations in rice grains would increase by 2.4- to 4.7-fold without this pathway, which equates to intelligence quotient losses of 0.01-0.51 points per newborn in major rice-consuming countries, corresponding to annual economic losses of US$30.7-84.2 billion globally. This discovered pathway effectively removes Hg from human food webs, playing an important role in exposure mitigation and global Hg cycling.
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Mercurio , Compuestos de Metilmercurio , Oryza , Recién Nacido , Humanos , Mercurio/metabolismo , Oryza/metabolismo , Cadena Alimentaria , Compuestos de Metilmercurio/metabolismo , DesmetilaciónRESUMEN
PURPOSE: To determine the optimal cut-off value of Ki-67 for predicting the survival of patients with clear cell renal cell carcinoma (ccRCC) and tumor thrombus and to explore the correlation between Ki-67 expression and pathological features. PATIENTS AND METHODS: We retrospectively analyzed Ki-67 immunohistochemical staining of ccRCC and tumor thrombus resected from February 2006 to February 2022. The survival rate was evaluated using the Kaplan-Meier method. The optimal cut-off value of the Ki-67 expression for predicting survival was determined by the minimum P-value method. Clinicopathological data were compared based on Ki-67 status (low versus high expression). Univariate and multivariate Cox regression analysis was used to explore independent predictors. RESULTS: A total of 202 patients (median age, 58 years [IQR, 52-65 years], 147 men) with ccRCC and tumor thrombus were included in the study. The optimal cut-off value of Ki-67 for predicting survival was 30%. 159 (78.7%) and 43 (21.3%) patients were included in the low-expression and high-expression groups. Patients with Ki-67 high expression had significantly worse recurrence-free survival (P < 0.001) and cancer-specific survival (P < 0.001). Ki-67 high expression was associated with adverse pathological features, including tumor necrosis, ISUP nuclear grade, sarcomatoid differentiation, perirenal fat invasion, renal pelvis invasion, and inferior vena cava wall invasion (all P < 0.050). Ki-67 expression ≥ 30% (Pâ¯=â¯0.016), tumor side (Pâ¯=â¯0.003), diabetes (Pâ¯=â¯0.040), blood loss (Pâ¯=â¯0.016), inferior vena cava wall invasion (Pâ¯=â¯0.016), and sarcomatoid differentiation (Pâ¯=â¯0.014) were independent predictors of cancer-specific survival. CONCLUSION: The optimal cut-off level of Ki-67 in predicting the prognosis of ccRCC and tumor thrombus was 30%. The high expression of Ki-67 was associated with the aggressive pathological phenotype and poor prognosis.
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Carcinoma de Células Renales , Carcinoma , Neoplasias Renales , Trombosis , Masculino , Humanos , Persona de Mediana Edad , Carcinoma de Células Renales/patología , Neoplasias Renales/patología , Antígeno Ki-67 , Estudios Retrospectivos , Vena Cava Inferior/patología , Trombosis/cirugía , Pronóstico , Procesos Neoplásicos , Carcinoma/patología , Proliferación Celular , Nefrectomía/métodosRESUMEN
BACKGROUND: Identification of non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus (T2DM) may help tailor treatment. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising tool to evaluate renal function but its potential role in the clinical differentiation between diabetic nephropathy (DN) and NDRD remains unclear. PURPOSE: To investigate the added role of IVIM-DWI in the differential diagnosis between DN and NDRD in patients with T2DM. STUDY TYPE: Prospective. POPULATION: Sixty-three patients with T2DM (ages: 22-69 years, 17 females) confirmed by renal biopsy divided into two subgroups (28 DN and 35 NDRD). FIELD STRENGTH/SEQUENCE: 3 T/ T2 weighted imaging (T2WI), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). ASSESSMENT: The parameters derived from IVIM-DWI (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion fraction [f]) were calculated for the cortex and medulla, respectively. The clinical indexes related to renal function (eg cystatin C, etc.) and diabetes (eg diabetic retinopathy [DR], fasting blood glucose, etc.) were measured and calculated within 1 week before MRI scanning. The clinical model based on clinical indexes and the IVIM-based model based on IVIM parameters and clinical indexes were established and evaluated, respectively. STATISTICAL TESTS: Student's t-test; Mann-Whitney U test; Fisher's exact test; Chi-squared test; Intraclass correlation coefficient; Receiver operating characteristic analysis; Hosmer-Lemeshow test; DeLong's test. P < 0.05 was considered statistically significant. RESULTS: The cortex D*, DR, and cystatin C values were identified as independent predictors of NDRD in multivariable analysis. The IVIM-based model, comprising DR, cystatin C, and cortex D*, significantly outperformed the clinical model containing only DR, and cystatin C (AUC = 0.934, 0.845, respectively). DATA CONCLUSION: The IVIM parameters, especially the renal cortex D* value, might serve as novel indicators in the differential diagnosis between DN and NDRD in patients with T2DM. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.