Expression and correlation of PBRM1 and P53 in clear cell carcinoma of kidney

Prevalence of clear cell renal cell carcinoma (ccRCC) among human population though common among adults, it occurs in children and young adults as well. The prognostic value of P53 expression in ccRCC is well known. Recently, PBRM1 has also acquired attention for its prognostic and predictive value in ccRCC. Here, we investigated the expression and correlation of PBRM1 and P53 in ccRCC. Renal tissues were collected from 70 patients who have undergone radical nephrectomy for clear cell carcinoma of the kidney in our hospital and 24 healthy volunteers for the study. We used immunohistochemical approach to determine the expression of PBRM1 and P53 in clear cell carcinoma of the kidney and normal kidney tissues and to analyze the correlation between them. Clinicopathological parameters and prognosis of patients were also studied. The positive expression rate of PBRM1 in clear renal cell carcinoma tissues was significantly higher (62.86%) compared to the normal renal tissues 8.33%. Similarly, positive expression rate of P53 in clear renal cell carcinoma tissues was 40%, while it was no expression in normal renal tissues. The expression level of PBRM1 was correlated with pathological grade and clinical stage of ccRCC patients, but not with age, sex and tumor size. P53 and expression levels were independent of age, sex, tumor size, pathological grade, and clinical stage of patients with clear cell carcinoma of the kidney. The 5-year survival rate of PBRM1 positive expression patients was 40.91% significantly lower than that of PBRM1 negative expression patients (84.62%), whereas in P53 it was 50 and 61.90%, respectively. Clinical stage, pathological grade and PBRM1 were all independent risk factors affecting the prognosis of patients with clear cell carcinoma of the kidney. Overall, the results suggest that PBRM1 is positively correlated with P53 in clear cell carcinoma of kidney (r=0.781, P=0.012). PBRM1 and P53 are both highly expressed in ccRCC and play an important role in the development of the disease. PBRM1 can also be used as an independent risk factor affecting the prognosis of ccRCC patients

Expression and correlation of PBRM1 and P53 in clear cell carcinoma of kidney

717-721Prevalence of clear cell renal cell carcinoma (ccRCC) among human population though common among adults, it occurs in children and young adults as well. The prognostic value of P53 expression in ccRCC is well known. Recently, PBRM1 has also acquired attention for its prognostic and predictive value in ccRCC. Here, we investigated the expression and correlation of PBRM1 and P53 in ccRCC. Renal tissues were collected from 70 patients who have undergone radical nephrectomy for clear cell carcinoma of the kidney in our hospital and 24 healthy volunteers for the study. We used immunohistochemical approach to determine the expression of PBRM1 and P53 in clear cell carcinoma of the kidney and normal kidney tissues and to analyze the correlation between them. Clinicopathological parameters and prognosis of patients were also studied. The positive expression rate of PBRM1 in clear renal cell carcinoma tissues was significantly higher (62.86%) compared to the normal renal tissues 8.33%. Similarly, positive expression rate of P53 in clear renal cell carcinoma tissues was 40%, while it was no expression in normal renal tissues. The expression level of PBRM1 was correlated with pathological grade and clinical stage of ccRCC patients, but not with age, sex and tumor size. P53 and expression levels were independent of age, sex, tumor size, pathological grade, and clinical stage of patients with clear cell carcinoma of the kidney. The 5-year survival rate of PBRM1 positive expression patients was 40.91% significantly lower than that of PBRM1 negative expression patients (84.62%), whereas in P53 it was 50 and 61.90%, respectively. Clinical stage, pathological grade and PBRM1 were all independent risk factors affecting the prognosis of patients with clear cell carcinoma of the kidney. Overall, the results suggest that PBRM1 is positively correlated with P53 in clear cell carcinoma of kidney (r=0.781, P=0.012). PBRM1 and P53 are both highly expressed in ccRCC and play an important role in the development of the disease. PBRM1 can also be used as an independent risk factor affecting the prognosis of ccRCC patients

Identification of mutations in porcine STAT5A that contributes to the transcription of CISH

Identification of causative genes or genetic variants associated with phenotype traits benefits the genetic improvement of animals. CISH plays a role in immunity and growth, however, the upstream transcriptional factors of porcine CISH and the genetic variations in these factors remain unclear. In this study, we firstly identified the minimal core promoter of porcine CISH and confirmed the existence of STATx binding sites. Overexpression and RT-qPCR demonstrated STAT5A increased CISH transcriptional activity (P < 0.01) and mRNA expression (P < 0.01), while GATA1 inhibited CISH transcriptional activity (P < 0.01) and the following mRNA expression (P < 0.05 or P < 0.01). Then, the putative functional genetic variations of porcine STAT5A were screened and a PCR-SSCP was established for genotype g.508A>C and g.566C>T. Population genetic analysis showed the A allele frequency of g.508A>C and C allele frequency of g.566C>T was 0.61 and 0.94 in Min pigs, respectively, while these two alleles were fixed in the Landrace population. Statistical analysis showed that Min piglets with CC genotype at g.566C>T or Hap1: AC had higher 28-day body weight, 35-day body weight, and ADG than TC or Hap3: CT animals (P < 0.05, P < 0.05). Further luciferase activity assay demonstrated that the activity of g.508A>C in the C allele was lower than the A allele (P < 0.05). Collectively, the present study demonstrated that STAT5A positively regulated porcine CISH transcription, and SNP g.566C>T in the STAT5A was associated with the Min piglet growth trait

Magnetic resonance image reconstruction using similarities learnt from multi-modal images

Compressed sensing has shown great potential to speed up magnetic resonance imaging (MRI) assuming the image is sparse and compressible in a transform domain. Conventional methods typically use a pre-defined patch-based nonlocal operator (PANO) to model the sparsity between image patches. The linearity of PANO allows us to establish a general formulation to reconstruct magnetic resonance image from undersampled data and provides feasibility to incorporate prior information learnt from guide images. To demonstrate the feasibility and performance of PANO, learning similarities from multi-modal images are presented to significantly improve the reconstructed images over conventional redundant wavelets in terms of visual quality and reconstruction errors

Safety and efficacy of endovascular recanalization for symptomatic non-acute atherosclerotic intracranial large artery occlusion

Background and objectiveThe optimal treatment for patients with symptomatic non-acute atherosclerotic intracranial large artery occlusion (ILAO) despite medical treatment is not well established. We aimed to assess the safety, efficacy, and feasibility of angioplasty and stenting for these patients.MethodsA total of 251 consecutive patients with symptomatic non-acute atherosclerotic ILAO treated with interventional recanalization were retrospectively collected in our center from March 2015 to August 2021. The rate of successful recanalization, perioperative complications, and follow-up outcomes were evaluated.ResultsSuccessful recanalization was achieved in 88.4% (222/251) of the patients. A total of 24 (24/251, 9.6%) symptomatic complications occurred among 251 procedures. In the 193 patients with clinical follow-up during 19.0 ± 14.7 months, 11 (11/193, 5.7%) patients developed ischemic stroke and four (4/193, 2.1%) patients developed transient ischemic attack (TIA). In the 106 patients with vascular imaging follow-up during 6.8 ± 6.6 months, seven (7/106, 6.6%) patients had restenosis and 10 (10/106, 9.4%) patients had reocclusion.ConclusionThis study suggests that interventional recanalization may be a feasible, basically safe, and an effective alternative in carefully selected patients with symptomatic non-acute atherosclerotic ILAO who have failed medical management

A Probabilistic Privacy Preserving Strategy for Word-of-Mouth Social Networks

An online social network (OSN) is a platform that makes people communicate with friends, share messages, accelerate business, and enhance teamwork. In the OSN, privacy issues are increasingly concerned, especially in private message leaks in word-of-mouth. A user’s privacy may be leaked out by acquaintances without user’s consent. In this paper, an integrated system is designed to prevent this illegal privacy leak. In particular, we only use the method of space vector model to determine whether the user’s private message is really leaked. Canary traps techniques are used to detect leakers. Then, we define a trust degree mechanism to evaluate trustworthiness of a communicator dynamically. Finally, we set up a new message publishing system to determine who can obtain the message of publisher. Secrecy performance analysis is provided to verify the effectiveness of the proposed message publishing system. Accordingly, a user in social networks can check whether other users are trustworthy before sending their private messages

Study on Dynamic Evaluation of Sci-tech Journals Based on Time Series Model

As science and technology continue to advance, sci-tech journals are developing rapidly, and the quality of these journals affects the development and progress of particular subjects. Whether sci-tech journals can be evaluated and predicted comprehensively and dynamically from multiple angles based on the current qualitative and quantitative evaluations of sci-tech journals is related to a rational adjustment of journal resource allocation and development planning. In this study, we propose a time series analysis task for the comprehensive and dynamic evaluation of sci-tech journals, construct a multivariate short-time multi-series time series dataset that contains 18 journal evaluation metrics, and build models based on machine learning and deep learning methods commonly used in the field of time series analysis to carry out training and testing experiments on the dataset. We compare and analyze the experimental results to confirm the generalizability of these methods for the comprehensive dynamic evaluation of journals and find the LSTM model built on our dataset produced the best performance (MSE: 0.00037, MAE: 0.01238, accuracy based on 80% confidence: 72.442%), laying the foundation for subsequent research on this task. In addition, the dataset constructed in this study can support research on the co-analysis of multiple short time series in the field of time series analysis