Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors.

Background: Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. Methods: We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Results: Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. Conclusions: The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

Visualization of the relationship between macrophage and wound healing from the perspective of bibliometric analysis.

Macrophages play a crucial role in aiding all phases of the wound-healing process and has garnered increasing attention recently. Although a substantial body of related studies has been published, there remains a lack of comprehensive bibliometric analysis. In this study, we collected 4296 papers from the Web of Science Core Collection database. Three tools including CiteSpace, VOSviewer and one online analytical platform were employed to conduct bibliometric analysis and data visualization. Our results revealed that the annual number of publications related to macrophage and wound healing has increased exponentially with the year. The United States and China stand as the primary driving forces within this field, collectively constituting 58.2% of the total publication output. The application of biomaterials was one of the most concerned research areas in this field. According to references analysis, the current research focus has shifted to diabetic wound healing and regulating macrophage polarization. Based on the keywords analysis, we identified the following research frontiers in the future: exosomes and other extracellular vesicles; bio-derived materials and drug delivery methods such as nanoparticles, scaffolds and hydrogels; immunomodulation and macrophage polarization in the M2-state; chronic wounds, particularly those associated with diabetes; antimicrobial peptides; and antioxidant. Additionally, TNF, IL-6, IL-10, TGF-ß1 and VEGF ranked as the five genes that have garnered the most research attention in the intersection of macrophage and wound healing. All in all, our findings offered researchers a holistic view of the ongoing progress in the field of macrophages and wound healing, serving as a valuable reference for scholars and policymakers in this domain.

The AEG-1-USP10-PARP1 axis confers radioresistance in esophageal squamous cell carcinoma via facilitating homologous recombination-dependent DNA damage repair.

Radiotherapy is the standard adjuvant treatment for esophageal squamous cell carcinoma (ESCC), yet radioresistance remains a major obstacle leading to treatment failure and unfavorable prognosis. Previous reports have demonstrated the involvement of astrocyte elevated gene-1 (AEG-1) in tumorigenesis and progression of multiple malignancies. Nevertheless, the precise role of AEG-1 in the radioresistance of ESCC remains elusive. Here, we unveiled a strong correlation between aberrant AEG-1 gene overexpression and malignant progression as well as adverse prognosis in ESCC patients. Moreover, both in vitro and in vivo investigations revealed that AEG-1 significantly alleviated irradiation-induced DNA damage and enhanced radiation resistance in ESCC cells. Mechanistically, AEG-1 recruited the deubiquitinase USP10 to remove the K48-linked polyubiquitin chains at the Lys425 of PARP1, thus preventing its proteasomal degradation. This orchestrated process facilitated homologous recombination-mediated DNA double-strand breaks (DSBs) repair, culminating in mitigated DNA damage and acquired radioresistance in ESCC cells. Notably, PARP1 overexpression reversed the radiosensitizing effect caused by AEG-1 deficiency. Collectively, these findings shed new light on the mechanism of ESCC radioresistance, providing potential therapeutic targets to enhance the efficacy of radiotherapy in ESCC.

PFKFB3-driven vascular smooth muscle cell glycolysis promotes vascular calcification via the altered FoxO3 and lactate production.

A link between increased glycolysis and vascular calcification has recently been reported, but it remains unclear how increased glycolysis contributes to vascular calcification. We therefore investigated the role of PFKFB3, a critical enzyme of glycolysis, in vascular calcification. We found that PFKFB3 expression was upregulated in calcified mouse VSMCs and arteries. We showed that expression of miR-26a-5p and miR-26b-5p in calcified mouse arteries was significantly decreased, and a negative correlation between Pfkfb3 mRNA expression and miR-26a-5p or miR-26b-5p was seen in these samples. Overexpression of miR-26a/b-5p significantly inhibited PFKFB3 expression in VSMCs. Intriguingly, pharmacological inhibition of PFKFB3 using PFK15 or knockdown of PFKFB3 ameliorated vascular calcification in vD3 -overloaded mice in vivo or attenuated high phosphate (Pi)-induced VSMC calcification in vitro. Consistently, knockdown of PFKFB3 significantly reduced glycolysis and osteogenic transdifferentiation of VSMCs, whereas overexpression of PFKFB3 in VSMCs induced the opposite effects. RNA-seq analysis and subsequent experiments revealed that silencing of PFKFB3 inhibited FoxO3 expression in VSMCs. Silencing of FoxO3 phenocopied the effects of PFKFB3 depletion on Ocn and Opg expression but not Alpl in VSMCs. Pyruvate or lactate supplementation, the product of glycolysis, reversed the PFKFB3 depletion-mediated effects on ALP activity and OPG protein expression in VSMCs. Our results reveal that blockade of PFKFB3-mediated glycolysis inhibits vascular calcification in vitro and in vivo. Mechanistically, we show that FoxO3 and lactate production are involved in PFKFB3-driven osteogenic transdifferentiation of VSMCs. PFKFB3 may be a promising therapeutic target for the treatment of vascular calcification.

An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study.

Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.

Radiation induces IRAK1 expression to promote radioresistance by suppressing autophagic cell death via decreasing the ubiquitination of PRDX1 in glioma cells.

Radiotherapy is the standard adjuvant treatment for glioma patients; however, the efficacy is limited by radioresistance. The function of Interleukin-1 receptor associated kinase 1 (IRAK1) in tumorigenesis and radioresistance remains to be elucidated. IRAK1 expression and its correlation with prognosis were analyzed in glioma tissues. We found that glioma patients with overexpressed IRAK1 show a poor prognosis. Notably, ionizing radiation (IR) remarkably induces IRAK1 expression, which was decreased by STING antagonist H-151 treatment. JASPAR prediction, ChIP assays, and dual luciferase reporter assays indicated that transcription factor FOXA2, suppressed by STING inhibition, directly binds to the IRAK1 promoter region and activates its transcription. IRAK1 knockdown inhibits malignancy and enhances the radiosensitivity of glioma in vitro and in vivo. To explore the potential IRAK1 interacting targets mediating the radioresistance of glioma cells, IP/Co-IP, LC-MS/MS, GST pull-down, and ubiquitination analyses were conducted. Mechanistically, IRAK1 bound to PRDX1, a major member of antioxidant enzymes, and further prevents ubiquitination and degradation of PRDX1 mediated by E3 ubiquitin ligase HECTD3; Both the DOC and HECT domains of HECTD3 directly interacted with PRDX1 protein. Overexpression of PRDX1 reverses the radiotherapy sensitization effect of IRAK1 depletion by diminishing autophagic cell death. These results suggest the IRAK1-PRDX1 axis provides a potential therapeutic target for glioma patients.

SETD2 regulates chromatin accessibility and transcription to suppress lung tumorigenesis.

SETD2, a H3K36 trimethyltransferase, is the most frequently mutated epigenetic modifier in lung adenocarcinoma, with a mutation frequency of approximately 9%. However, how SETD2 loss of function promotes tumorigenesis remains unclear. Using conditional Setd2-KO mice, we demonstrated that Setd2 deficiency accelerated the initiation of KrasG12D-driven lung tumorigenesis, increased tumor burden, and significantly reduced mouse survival. An integrated chromatin accessibility and transcriptome analysis revealed a potentially novel tumor suppressor model of SETD2 in which SETD2 loss activates intronic enhancers to drive oncogenic transcriptional output, including the KRAS transcriptional signature and PRC2-repressed targets, through regulation of chromatin accessibility and histone chaperone recruitment. Importantly, SETD2 loss sensitized KRAS-mutant lung cancer to inhibition of histone chaperones, the FACT complex, or transcriptional elongation both in vitro and in vivo. Overall, our studies not only provide insight into how SETD2 loss shapes the epigenetic and transcriptional landscape to promote tumorigenesis, but they also identify potential therapeutic strategies for SETD2 mutant cancers.

Molecular mechanisms of neuroendocrine differentiation in prostate cancer progression.

BACKGROUND: Rapid evolution of the therapeutic management of prostate cancer, especially in in second-generation androgen inhibitors, has increased the opportunity of transformation from prostate cancer (PCa) to neuroendocrine prostate cancer (NEPC). NEPC still lacks effective diagnostic and therapeutic interventions. Researches into the molecular characteristics of neuroendocrine differentiation is undoubtedly crucial to the discovery of new target genes for accurate diagnostic and therapeutic targets. PURPOSE: In this review, we focus on the relevant genes and molecular mechanisms that have contributed to the transformation in the progression of PCa and discuss the potential targeted molecule that might improve diagnostic accuracy and therapeutic effectiveness. METHODS: The relevant literatures from PubMed have been reviewed for this article. CONCLUSION: Several molecular characteristics influence the progression of neuroendocrine differentiation of prostate cancer which will provide a novel sight for accurate diagnosis and target therapeutic intervention for patients with NEPC.

RFC2: a prognosis biomarker correlated with the immune signature in diffuse lower-grade gliomas.

Diffuse lower-grade gliomas (LGG) represent the highly heterogeneous and infiltrative neoplasms in the central nervous system (CNS). Replication factor C 2 (RFC2) is a subunit of the RFC complex that modulates DNA replication and repair. However, the prognosis value of RFC2 and its association with the immune signature of tumor microenvironment (TME) in LGG remains unknown. Based on Oncomine, TCGA, GTEx, TIMER, GEPIA, and HPA databases, we evaluated RFC2 expression levels and its clinical prognostic value in LGG and other cancers. Then we analyzed the correlations between RFC2 expression and tumor mutation burden (TMB), tumor microsatellite instability (MSI), and mismatch repair (MMR) genes across cancers. And CIBERSORT and ESTIMATE algorithms were conducted to estimate the association of RFC2 with immune cell infiltration of LGG. Additionally, we performed the functional enrichment analyses of RFC2 in LGG. Then functional experiments were employed to further validate the oncogenic role of RFC2 in LGG. Our results showed that RFC2 was widely highly expressed in most types of cancer. And its expression was closely related to the clinicopathological features and prognosis in LGG and other cancer types. RFC2 levels were also correlated with TMB and MSI across various cancers. Furthermore, RFC2 was positively associated with the infiltration levels of immune cells and immune checkpoint genes in LGG. Additionally, in vitro experiments revealed that RFC2 played an oncogenic role in LGG progression. In conclusion, our findings revealed that RFC2 could serve as a reliable biomarker to predict the prognosis and immune signature for LGG.

SETD2 loss perturbs the kidney cancer epigenetic landscape to promote metastasis and engenders actionable dependencies on histone chaperone complexes.

SETD2 is a histone H3 lysine 36 (H3K36) trimethyltransferase that is mutated with high prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2 loss promotes metastasis remains unclear. In this study, we used a SETD2-mutant (SETD2MT) metastatic ccRCC human-derived cell line and xenograft models and showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice in a matrix metalloproteinase 1 (MMP1)-dependent manner. An integrated multiomics analysis using assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation-sequencing (ChIP-seq) and RNA sequencing (RNA-seq) established a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcriptional output through regulation of chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through the targeting of specific histone chaperone complexes, including ASF1A/ASF1B and SPT16. Overall, SETD2 loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.

Targeting Aurora B kinase prevents and overcomes resistance to EGFR inhibitors in lung cancer by enhancing BIM- and PUMA-mediated apoptosis.

The clinical success of EGFR inhibitors in EGFR-mutant lung cancer is limited by the eventual development of acquired resistance. We hypothesize that enhancing apoptosis through combination therapies can eradicate cancer cells and reduce the emergence of drug-tolerant persisters. Through high-throughput screening of a custom library of ∼1,000 compounds, we discover Aurora B kinase inhibitors as potent enhancers of osimertinib-induced apoptosis. Mechanistically, Aurora B inhibition stabilizes BIM through reduced Ser87 phosphorylation, and transactivates PUMA through FOXO1/3. Importantly, osimertinib resistance caused by epithelial-mesenchymal transition (EMT) activates the ATR-CHK1-Aurora B signaling cascade and thereby engenders hypersensitivity to respective kinase inhibitors by activating BIM-mediated mitotic catastrophe. Combined inhibition of EGFR and Aurora B not only efficiently eliminates cancer cells but also overcomes resistance beyond EMT.

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. METHODS: In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. FINDINGS: From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. INTERPRETATION: This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. FUNDING: Ministry of Health, Singapore.

Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening.

Systematic screening for diabetic retinopathy (DR) has been widely recommended for early detection in patients with diabetes to address preventable vision loss. However, substantial manpower and financial resources are required to deploy opportunistic screening and transition to systematic DR screening programs. The advent of artificial intelligence (AI) technologies may improve access and reduce the financial burden for DR screening while maintaining comparable or enhanced clinical effectiveness. To deploy an AI-based DR screening program in a real-world setting, it is imperative that health economic assessment (HEA) and patient safety analyses are conducted to guide appropriate allocation of resources and design safe, reliable systems. Few studies published to date include these considerations when integrating AI-based solutions into DR screening programs. In this article, we provide an overview of the current state-of-the-art of AI technology (focusing on deep learning systems), followed by an appraisal of existing literature on the applications of AI in ophthalmology. We also discuss practical considerations that drive the development of a successful DR screening program, such as the implications of false-positive or false-negative results and image gradeability. Finally, we examine different plausible methods for HEA and safety analyses that can be used to assess concerns regarding AI-based screening.

Phytophthora Effectors Modulate Genome-wide Alternative Splicing of Host mRNAs to Reprogram Plant Immunity.

Alternative splicing (AS) of pre-mRNAs increases transcriptome and proteome diversity, regulates gene expression through multiple mechanisms, and plays important roles in plant development and stress responses. However, the prevalence of genome-wide plant AS changes during infection and the mechanisms by which pathogens modulate AS remain poorly understood. Here, we examined the global AS changes in tomato leaves infected with Phytophthora infestans, the infamous Irish famine pathogen. We show that more than 2000 genes exhibiting significant changes in AS are not differentially expressed, indicating that AS is a distinct layer of transcriptome reprogramming during plant-pathogen interactions. Furthermore, our results show that P. infestans subverts host immunity by repressing the AS of positive regulators of plant immunity and promoting the AS of susceptibility factors. To study the underlying mechanism, we established a luminescence-based AS reporter system in Nicotiana benthamiana to screen pathogen effectors modulating plant AS. We identified nine splicing regulatory effectors (SREs) from 87 P. infestans effectors. Further studies revealed that SRE3 physically binds U1-70K to manipulate the plant AS machinery and subsequently modulates AS-mediated plant immunity. Our study not only unveils genome-wide plant AS reprogramming during infection but also establishes a novel AS screening tool to identify SREs from a wide range of plant pathogens, providing opportunities to understand the splicing regulatory mechanisms through which pathogens subvert plant immunity.

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

BACKGROUND: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country. METHODS: We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. FINDINGS: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969-0·978), with corresponding sensitivity of 92·25% (90·10-94·12) and specificity of 89·04% (87·85-90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15-99·68) and diabetic macular oedema sensitivity was 97·19% (96·61-97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy. INTERPRETATION: An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. FUNDING: National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Targeting dual specificity protein kinase TTK attenuates tumorigenesis of glioblastoma.

Accumulating evidence has proved that glioma stem-like cells (GSCs) are responsible for tumorigenesis, treatment resistance, and subsequent tumor recurrence in glioblastoma (GBM). In this study, we identified dual specificity protein kinase TTK (TTK) as the most up-regulated and differentially expressed kinase encoding genes in GSCs. Functionally, TTK was essential for in vitro clonogenicity and in vivo tumor propagation in GSCs. Clinically, TTK expression was highly enriched in GBM, moreover, was inversely correlated with a poor prognosis in GBM patients. Mechanistically, mitochondrial fission regulator 2 (MTFR2) was identified as one of the most correlated genes to TTK and transcriptionally regulated TTK expression via activation of TTK promoter. Collectively, MTFR2-dependent regulation of TTK plays a key role in maintaining GSCs in GBM and is a potential novel druggable target for GBM.

Serine/Threonine Kinase CHEK1-Dependent Transcriptional Regulation of RAD54L Promotes Proliferation and Radio Resistance in Glioblastoma.

Accumulating evidence indicates that Checkpoint kinase 1 (CHEK1) plays an essential role in tumor cells and that it could induce cell proliferation and could be related to prognosis in multiple types of cancer. However, the biological role and molecular mechanism of CHEK1 in GBM still remain unclear. In this study, we identified that CHEK1 expression was enriched in glioblastoma (GBM) tumors and was functionally required for tumor proliferation and that its expression was associated to poor prognosis in GBM patients. Mechanically, CHEK1 induced radio resistance in GBM cells, and CHEK1 knockdown increased cell apoptosis when combined with radiotherapy via regulation of the DNA repair/recombination protein 54L (RAD54L) expression. Therapeutically, we found that CHEK1 inhibitor attenuated tumor growth both in vitro and in vivo. Collectively, CHEK1 promotes proliferation, induces radio resistance in GBM, and could become a potential therapeutic target for GBM.

Assessment of Liver Fibrosis Using Real-time Shear-wave Elastography for Patients with Hepatitis B e Antigen-negative Chronic Hepatitis B and Alanine Transaminase <2 Times the Upper Limit of Normal.

BACKGROUND: We assessed liver fibrosis using real-time shear-wave elastography (SWE) combined with liver biopsy (LB) for patients with hepatitis B e antigen (HBeAg)-negative chronic hepatitis B (CHB) and alanine transaminase < 2 times the upper limit of normal and hepatitis B virus DNA < 2000 IU/ml. METHODS: A total of 107 patients met the inclusion criteria. Real- ime SWE and ultrasoundassisted liver biopsies were consecutively performed. Fibrosis was staged according to the METAVIR scoring system. Analyses of receiver operating characteristic curve were performed to calculate the optimal area under the receiver operating characteristic curve for F0-F1 versus F2-F4, F0-F2 versus F3-F4, and F0-F3 versus F4 for real-time SWE. RESULTS: The most concurrent liver fibrosis degrees were between F1 and F2 for these HBeAg-negative CHB patients. Liver stiffness increased in parallel with the degree of liver fibrosis using SWE measurements. The area under the receiver operating characteristic curves was 0.881 (95% confidence interval [CI]: 0.704-1.000) for SWE (p = 0.004); 0.912 (95% CI: 0.836-0.987) for SWE (p = 0.000); 0.981 (95% CI: 0.956-1.000) for SWE (p = 0.000); 0.974 (95% CI: 0.936-1.000) for SWE (p = 0.000) when comparing F0 versus F1-F4, F0-F1 versus F2-F4, F0-F2 versus F3-F4, and F0-F3 versus F4, respectively. CONCLUSIONS: SWE has the advantage of providing an image of liver stiffness in real-time. As an alternative to LB, the development of all these noninvasive methods for dynamic evaluation of liver fibrosis will decrease the need for LB, making clinical care safer and more convenient for patients with liver diseases.

The SWI/SNF Protein PBRM1 Restrains VHL-Loss-Driven Clear Cell Renal Cell Carcinoma.

PBRM1 is the second most commonly mutated gene after VHL in clear cell renal cell carcinoma (ccRCC). However, the biological consequences of PBRM1 mutations for kidney tumorigenesis are unknown. Here, we find that kidney-specific deletion of Vhl and Pbrm1, but not either gene alone, results in bilateral, multifocal, transplantable clear cell kidney cancers. PBRM1 loss amplified the transcriptional outputs of HIF1 and STAT3 incurred by Vhl deficiency. Analysis of mouse and human ccRCC revealed convergence on mTOR activation, representing the third driver event after genetic inactivation of VHL and PBRM1. Our study reports a physiological preclinical ccRCC mouse model that recapitulates somatic mutations in human ccRCC and provides mechanistic and therapeutic insights into PBRM1 mutated subtypes of human ccRCC.