Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy.

Background: The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI. Methods: We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves. Results: In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 - 0.826) and 0.852 (95% CI: 0.806 - 0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort. Conclusions: The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study.

Background: Mucin 4 (MUC4) overexpression promotes tumorigenesis and increases the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). To date, no study has reported the association between radiomics and MUC4 expression in PDAC. Thus, we aimed to explore the utility of radiomics based on multi-sequence magnetic resonance imaging (MRI) to predict the status of MUC4 expression in PDAC preoperatively. Methods: This retrospective study included 52 patients with PDAC who underwent MRI. The patients were divided into two groups based on MUC4 expression status. Two feature sets were extracted from the arterial and portal phases (PPs) of dynamic contrast-enhanced MRI (DCE-MRI). Univariate analysis, minimum redundancy maximum relevance (MRMR), and principal component analysis (PCA) were performed for the feature selection of each dataset, and features with a cumulative variance of 90% were selected to develop radiomics models. Clinical characteristics were gathered to develop a clinical model. The selected radiomics features and clinical characteristics were modeled by multivariable logistic regression. The combined model integrated radiomics features from different selected data sets and clinical characteristics. The classification metrics were applied to assess the discriminatory power of the models. Results: There were 22 PDACs with a high expression of MUC4 and 30 PDACs with a low expression of MUC4. The area under the receiver operating characteristic (ROC) curve (AUC) values of the arterial phase (AP) model, the PP model, and the combined model were 0.732 (0.591-0.872), 0.709 (0.569-0.849), and 0.861 (0.760-0.961), respectively. The AUC of the clinical model was 0.666 (0.600-0.682). The combined model that was constructed outperformed the AP, the PP, and the clinical models (P<0.05, although no statistical significance was observed in the combined model vs. AP model). Conclusions: Radiomics models based on multi-sequence MRI have the potential to predict MUC4 expression levels in PDAC.

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions.

BACKGROUND: It is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions. METHODS: This retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists' evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination. RESULTS: The areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists' evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved. CONCLUSIONS: The radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.

MR Imaging for Early Extrapancreatic Necrosis in Acute Pancreatitis.

RATIONALE AND OBJECTIVES: To study the MRI characteristics of early extrapancreatic necrosis and compare them with those of peripancreatic fluid collections in acute pancreatitis (AP). MATERIALS AND METHODS: This retrospective study enrolled 70 AP patients who had extrapancreatic collections visible on MRI within 1 week of onset. Extrapancreatic collections were divided into extrapancreatic necrosis and peripancreatic fluid collections based on follow-up MRI, CT, or pathology. The number and area of extrapancreatic collections, extrapancreatic inflammation on MRI (EPIM) score, MR severity index score and clinical characteristics were evaluated and compared between the two groups. RESULTS: Of the seventy AP patients, 32 (45.7%) had extrapancreatic necrosis, and 38 (54.3%) had peripancreatic fluid collections. The number and area of extrapancreatic collections, MR severity index score, EPIM score, and prevalence of associated hemorrhage were significantly higher in extrapancreatic necrosis patients than in those with peripancreatic fluid collections (p < 0.001). Among the single indicators, the accuracy of the area of extrapancreatic collections (AUC = 0.871) was comparable to that of the EPIM score for predicting extrapancreatic necrosis and was significantly higher than that of the other two indicators. The combination of all indicators showed the highest predictive accuracy (AUC = 0.949), and combinations of two or more indicators demonstrated significantly higher predictive accuracy for extrapancreatic necrosis than any single indicator (p < 0.05) except for the area of extrapancreatic collections (p > 0.05). CONCLUSION: The MRI characteristics have the potential to differentiate early extrapancreatic necrosis from peripancreatic fluid collections and help indicate extrapancreatic necrosis.

Magnetic Resonance Imaging-Based Radiomics Models to Predict Early Extrapancreatic Necrosis in Acute Pancreatitis.

OBJECTIVE: The aim of the study was to investigate radiomics models based on magnetic resonance imaging (MRI) for predicting early extrapancreatic necrosis (EXPN) in acute pancreatitis. METHODS: Radiomics features were extracted from T2-weighted images of extrapancreatic collections and late arterial-phase images of the pancreatic parenchyma for 135 enrolled patients (94 in the primary cohort, including 47 EXPN patients and 41 in the validation cohort, including 20 EXPN patients). The optimal features after dimension reduction were used for radiomics modeling through a support vector machine. A clinical model, the MR severity index score, and extrapancreatic inflammation on MRI were evaluated. RESULTS: Twelve optimal features from the extrapancreatic collection images and 10 from the pancreatic parenchyma images were selected for modeling. The pancreatic parenchyma-based and extrapancreatic collection-based radiomics models showed good predictive accuracy in both the training and validation cohorts. The areas under the curve of the extrapancreatic collection-based radiomics model (0.969 and 0.976) were consistent with those of the pancreatic parenchyma-based model (0.931 and 0.921) for both cohorts and better than those of the clinical model and imaging scores for both cohorts. CONCLUSIONS: The MRI-based radiomics models of both the extrapancreatic collections and the pancreatic parenchyma had excellent predictive performance for early EXPN.

A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis.

PURPOSE: To build a radiomics model of liver contrast-enhanced computed tomography (CT) to predict hepatic encephalopathy secondary to Hepatitis B related cirrhosis. MATERIALS AND METHODS: This study consisted of 304 consecutive patients with first-diagnosed hepatitis B related cirrhosis. 212 and 92 patients were randomly computer-generated into training and testing cohorts, among which 38 and 21 patients endured HE, respectively. 356 radiomics features of liver were extracted from portal venous-phase CT data, and 3 clinical features were collected from medical record. After data were standardized by Z-score, we used least absolute shrinkage and selection operator to choose useful radiomics features. Ultimately, three predictive models including a radiomics model, a clinical model and an integrated model of radiomics and clinical features were built by analysis of R-software. Predictive performance was tested by multivariable logistic regression, and evaluated by area under receiver-operating characteristic curve (AUC), and accuracy. RESULTS: 19 radiomics features of liver CT were selected. The selected radiomics features and 3 relevant clinical features were applied to develop a radiomics model, a clinical model, and an integrated model of both radiomics and clinical features. The integrated model showed better performance than the radiomics model or clinical model to predict HE (AUC = 0.94 vs. 0.91 or 0.76, and 0.87 vs. 0.86 or 0.73; accuracy = 0.93 vs. 0.89 or 0.83, and 0.83 vs. 0.84 or 0.77) in the training and testing cohorts, respectively. CONCLUSION: The integrated model of radiomics and clinical features could well predict HE secondary to hepatitis B related cirrhosis.

Magnetic resonance imaging for preoperative staging of pancreatic cancer based on the 8th edition of AJCC guidelines.

BACKGROUND: Preoperative staging of pancreatic cancer determines the choice of treatment. Magnetic resonance imaging (MRI) plays an important role in preoperative staging of pancreatic cancer. The American Joint Committee on Cancer (AJCC) TNM staging system was revised to its 8th version in 2016, there has been no report correlating the 8th edition of the AJCC TNM staging with preoperative MRI examinations and pathological findings. The purpose of our study is to determine the staging accuracy and evaluate the resectability by using MRI about pancreatic cancer compared with intraoperative or pathological findings according to the 8th edition of the AJCC TNM staging system. METHODS: One hundred thirty-two patients with a pathological diagnosis of pancreatic cancer who underwent preoperative MRI were identified. The clinical data, MRI findings and pathological findings were analyzed. Preoperative MRI staging and resectability evaluation were compared with pathological findings. The accuracy of MRI for preoperative T and N staging was evaluated, and the sensitivity, specificity and accuracy of MRI in evaluating the resectability were assessed. All the staging and resectability assessments were according to the 8th edition of the AJCC TNM staging system. RESULTS: Analysis showed that the accuracy of MRI for evaluation of the T and N stages was 82.6% (109/132) and 74.2% (98/132), respectively. The sensitivity and specificity of MRI in assessing the resectability were 94.2% and 71.4%, respectively. Integrating the 8th edition of the AJCC TNM stage, no significant differences were identified between the preoperative MRI and pathological results for the staging of pancreatic cancer (P=0.805). CONCLUSIONS: MRI is highly accurate for T staging and moderately accurate for N staging. MRI provides important preoperative evaluation of the stage and resectability of pancreatic cancer based on the 8th edition of the AJCC TNM staging system.

Assessment of earthworm activity on Cu, Cd, Pb and Zn bioavailability in contaminated soils using biota to soil accumulation factor and DTPA extraction.

The study aims to investigate effect of earthworm activity on metal bioavailability in soils using their BSAF-metals. Based on a microcosmic laboratory experiment, epigeic species Amynthas corticis (A. corticis) and endogeic species Amynthas robustus (A. robustus) were cultured in two types of soils contaminated by Cd, Zn, Pb and Cu for 120 days. Earthworm characteristics (i.e. numbers, biomass and BSAF), soil properties (i.e. pH, organic C and N contents along with their components such as mineralization and microbial masses) and DTPA extracted metals in soil were determined. After the incubation, the biomass and survival numbers of both earthworm species decreased significantly (P < 0.05). The accumulation of Cd, Zn and Pb in earthworm tissues and BSAF-metals were earthworm species dependent. According to two-way ANOVA, BSAF-Pb clearly showed the effect of different species of earthworms while BSAF-Cu indicated an interactive effect of earthworms and soil type. Earthworms changed soil properties significantly, especially for mineralized C (Cmin), dissolved N (Ndis) and pH (P < 0.05). Earthworm activity increase DTPA extracted Zn and Cu, and the effect of A. robustus were stronger than for A. corticis. Redundancy analysis (RDA) showed that BSAF-Cu and BSAF-Pb contributed for respectively 51.9% and 51.7% of soil properties and DTPA metal changes, indicating that the effects of BSAF-Cu and BSAF-Pb on soil properties and on metal bioavailability in soil were similar. BSAF-Cu, indicating the interactive effect of earthworms and soil, accounted for 38.5% and 45.1% of soil properties and soil metal bioavailability changes. BSAF-Pb, representing the effect of earthworm species, accounted for 13.3% and 6.6% of soil property and soil metal bioavailability variations. Stepwise regression indicated that earthworm might change soil properties through their activities and interactions with soil, and hence increase heavy metal bioavailability. It suggested that BSAF is an important indicator for evaluating the effect of earthworm activity on soil metal bioavailability and designing remediation strategies.

Extrapancreatic Inflammation on Magnetic Resonance Imaging for the Early Prediction of Acute Pancreatitis Severity.

OBJECTIVES: This work aimed to study the early predictive value of extrapancreatic inflammation on magnetic resonance imaging (EPIM) for acute pancreatitis (AP) severity. METHODS: The EPIM score, magnetic resonance severity index, Acute Physiology and Chronic Health Evaluation (APACHE II) score, bedside index of severity in AP, and high-sensitivity C-reactive protein levels were evaluated for 337 AP patients. The extrapancreatic inflammation on computed tomography (EPIC) was also assessed for 86 patients undergoing computed tomography. The predictive values of these scores for severe AP and organ failure were evaluated using receiver operating characteristic curve analyses. RESULTS: Of the 337 AP patients, 55 (16.3%) had organ failure and 17 (5.0%) had severe AP. The EPIM showed a strong correlation with the EPIC (r = 0.794, P < 0.001) and had a higher correlation with the APACHE II and hospital stay compared with the EPIC. The accuracy of the EPIM in predicting severe AP and organ failure (areas under the curve, 0.844 and 0.817) was consistent with that of the APACHE II and bedside index of severity in AP, and higher than that of the magnetic resonance severity index. CONCLUSION: The EPIM is more helpful in assessing AP severity than the EPIC and can indicate the occurrence of severe AP and organ failure early.

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis.

OBJECTIVES: To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack. METHODS: We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics. RESULTS: The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05). CONCLUSIONS: The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions. KEY POINTS: • The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking. • The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis. • As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

Interleukin-27, a novel cytokine induced by ischemia-reperfusion injury in rat hearts, mediates cardioprotective effects via the gp130/STAT3 pathway.

Patients with coronary artery disease show high serum levels of interleukin (IL)-27, a novel member of the IL-6 family. However, the function of IL-27 in hearts suffering ischemia/reperfusion (IR) injury is unclear. Here, we showed increased expression of mRNA for the IL-27 subunits, EBI3 and p28, in rat hearts after 40 min of coronary ligation and release for 7 days. This increase was associated with a peak in the release of the cardiac enzyme, creatine kinase-MB, on day 2 post-release. Moreover, levels of IL-27 receptor subunit gp130 mRNA, but not those of subunit WSX-1 mRNA, decreased in post-ischemic hearts. These results suggest that increased IL-27 production may compensate for receptor downregulation during myocardial recovery. Lactate dehydrogenase release and crystal violet staining revealed that IL-27 or IL-6 significantly attenuated severe hypoxia (SH, 2 % O2)-induced cell damage in H9c2 cardiomyoblasts and primary rat neonatal cardiomyocytes. Incubating cardiomyocytes with IL-27 or IL-6 resulted in time-dependent activation of signal transducers and activators of transcription 3 (STAT3). Interestingly, IL-27-induced STAT3 activation was attenuated by pre-treatment with a gp130-neutralizing antibody. Blocking gp130 also reduced the cytoprotective effects of IL-27 or IL-6. Moreover, IL-27-mediated protection against SH was blocked by stattic, a small-molecule inhibitor of STAT3. IL-27 markedly improved post-ischemic recovery and reduced tissue damage in isolated perfused hearts when administered 5 min before reperfusion. These results indicate that IL-27 protects the myocardium against IR injury and facilitates the recovery of damaged cardiomyocytes via the gp130/STAT3 pathway.

Prediction of axillary lymph node metastases in breast cancer patients based on pathologic information of the primary tumor.

BACKGROUND: Axillary lymph nodes (ALN) are the most commonly involved site of disease in breast cancer that has spread outside the primary lesion. Although sentinel node biopsy is a reliable way to manage ALN, there are still no good methods of predicting ALN status before surgery. Since morbidity in breast cancer surgery is predominantly related to ALN dissection, predictive models for lymph node involvement may provide a way to alert the surgeon in subgroups of patients. MATERIAL AND METHODS: A total of 1325 invasive breast cancer patients were analyzed using tumor biological parameters that included age, tumor size, grade, estrogen receptor, progesterone receptor, lymphovascular invasion, and HER2, to test their ability to predict ALN involvement. A support vector machine (SVM) was used as a classification model. The SVM is a machine-learning system developed using statistical learning theories to classify data points into 2 classes. Notably, SVM models have been applied in bioinformatics. RESULTS: The SVM model correctly predicted ALN metastases in 74.7% of patients using tumor biological parameters. The predictive ability of luminal A, luminal B, triple negative, and HER2 subtypes using subgroup analysis showed no difference, and this predictive performance was inferior, with only 60% accuracy. CONCLUSIONS: With an SVM model based on clinical pathologic parameters obtained in the primary tumor, it is possible to predict ALN status in order to alert the surgeon about breast cancer counseling and in decision-making for ALN management.

Interleukin-27 and interleukin-12 augment activation of distinct cord blood natural killer cells responses via STAT3 pathways.

BACKGROUND/PURPOSE: Umbilical cord blood is rich in primitive natural killer (NK) cells, which are activated by interleukin (IL)-12. It was previously reported that a novel IL-12 family cytokine, IL-27 comprised of EBI3 and p28, was elevated in maternal serum during normal pregnancy. Thus, we compared the immune regulatory functions of IL-27 and IL-12 on mononuclear cells derived from cord blood and adult peripheral blood. METHODS: After stimulation with IL-27, IL-12, and IL-27 combined with IL-12, the cytotoxicity against BJAB lymphoma cells by blood mononuclear cells was performed. Then immunofluorescence staining, reverse transcriptase-polymerase chain reaction and Western blotting were used to detect the effects of IL-27 and IL-12 in isolated NK cells. RESULTS: IL-27, IL-12, and IL-27 combined with IL-12 enhanced the cytotoxicity of adult peripheral blood cells and cord blood cells, but the proliferation of distinct subpopulations of cells was not evident. Similar results were also obtained with purified cord blood NK cells. Interestingly, distinct from IL-12, IL-27 could induce aggregation and morphological changes of umbilical cord blood cells. Finally, IL-27 combined with IL-12 could stimulate increased IL-27 receptor (gp130 and WSX-1) transcripts in purified cord blood NK cells. However, the activation of signal transducer and activator of transcription 3 (STAT3) in NK cells was only detected in the presence of IL-27, but not IL-12 alone. CONCLUSION: From previous results, we summarize our current understanding of the augmentation of distinct regulation of NK cells by IL-27 and IL-12.

MCRS2 represses the transactivation activities of Nrf1.

BACKGROUND: Nrf1 [p45 nuclear factor-erythroid 2 (p45 NF-E2)-related factor 1], a member of the CNC-bZIP (CNC basic region leucine zipper) family, is known to be a transcriptional activator by dimerization with distinct partners, such as Maf, FosB, c-Jun, JunD, etc. The transcriptional roles of CNC-bZIP family are demonstrated to be involved in globin gene expression as well as the antioxidant response. For example, CNC-bZIP factors can regulate the expression of detoxification proteins through AREs, such as expression of human gamma-glutamylcysteine synthetases (GCS), glutathione S-transferases (GST), UDP-glucuronosyl transferase (UDP-GT), NADP (H) quinone oxidoreductase (NQOs), etc. To further explore other factor(s) in cells related to the function of Nrf1, we performed a yeast two-hybrid screening assay to identify any Nrf1-interacting proteins. In this study, we isolated a cDNA encoding residues 126-475 of MCRS2 from the HeLa cell cDNA library. Some functions of MCRS1 and its splice variant-MSP58 and MCRS2 have been previously identified, such as transforming, nucleolar sequestration, ribosomal gene regulation, telomerase inhibition activities, etc. Here, we demonstrated MCRS2 can function as a repressor on the Nrf1-mediated transactivation using both in vitro and in vivo systems. RESULTS: To find other proteins interacting with the CNC bZIP domain of Nrf1, the CNC-bZIP region of Nrf1 was used as a bait in a yeast two-hybrid screening assay. MCRS2, a splicing variant of p78/MCRS1, was isolated as the Nrf1-interacting partner from the screenings. The interaction between Nrf1 and MCRS2 was confirmed in vitro by GST pull-down assays and in vivo by co-immunoprecipitation. Further, the Nrf1-MCRS2 interaction domains were mapped to the residues 354-447 of Nrf1 as well as the residues 314-475 of MCRS2 respectively, by yeast two-hybrid and GST pull-down assays. By immunofluorescence, MCRS2-FLAG was shown to colocalize with HA-Nrf1 in the nucleus and didn't result in the redistribution of Nrf1. This suggested the existence of Nrf1-MCRS2 complex in vivo. To further confirm the biological function, a reporter driven by CNC-bZIP protein binding sites was also shown to be repressed by MCRS2 in a transient transfection assay. An artificial reporter gene activated by LexA-Nrf1 was also specifically repressed by MCRS2. CONCLUSION: From the results, we showed MCRS2, a new Nrf1-interacting protein, has a repression effect on Nrf1-mediated transcriptional activation. This was the first ever identified repressor protein related to Nrf1 transactivation.