Peanut Stunt Virus and Its Satellite RNA Trigger Changes in Phosphorylation in N. benthamiana Infected Plants at the Early Stage of the Infection

Signaling in host plants is an integral part of a successful infection by pathogenic RNA viruses. Therefore, identifying early signaling events in host plants that play an important role in establishing the infection process will help our understanding of the disease process. In this context, phosphorylation constitutes one of the most important post-translational protein modifications, regulating many cellular signaling processes. In this study, we aimed to identify the processes affected by infection with Peanut stunt virus (PSV) and its satellite RNA (satRNA) in Nicotiana benthamiana at the early stage of pathogenesis. To achieve this, we performed proteome and phosphoproteome analyses on plants treated with PSV and its satRNA. The analysis of the number of differentially phosphorylated proteins showed strong down-regulation in phosphorylation in virus-treated plants (without satRNA). Moreover, proteome analysis revealed more down-regulated proteins in PSV and satRNA-treated plants, which indicated a complex dependence between proteins and their modifications. Apart from changes in photosynthesis and carbon metabolism, which are usually observed in virus-infected plants, alterations in proteins involved in RNA synthesis, transport, and turnover were observed. As a whole, this is the first community (phospho)proteome resource upon infection of N. benthamiana with a cucumovirus and its satRNA and this resource constitutes a valuable data set for future studies.

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Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200613225404 ◽

2020 ◽

Vol 13 ◽

Author(s):

Varun Sapra ◽

M.L Saini ◽

Luxmi Verma

Keyword(s):

Neural Network ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Early Stage ◽

Second Phase ◽

Case Based Reasoning ◽

Data Set ◽

Artery Disease ◽

Severity Of The Disease ◽

Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

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PROMIS Scales for Assessment of Persistent Post-COVID Symptoms: A Cross Sectional Study

Journal of Primary Care & Community Health ◽

10.1177/21501327211030413 ◽

2021 ◽

Vol 12 ◽

pp. 215013272110304

Author(s):

Ravindra Ganesh ◽

Aditya K. Ghosh ◽

Mark A. Nyman ◽

Ivana T. Croghan ◽

Stephanie L. Grach ◽

...

Keyword(s):

Health Status ◽

Social Roles ◽

Disease Process ◽

Cross Sectional Study ◽

Large Sample Size ◽

Sectional Study ◽

Cross Sectional ◽

Data Set ◽

Patient Reported ◽

The Impact

Objective Persistent post-COVID symptoms are estimated to occur in up to 10% of patients who have had COVID-19. These lingering symptoms may persist for weeks to months after resolution of the acute illness. This study aimed to add insight into our understanding of certain post-acute conditions and clinical findings. The primary purpose was to determine the persistent post COVID impairments prevalence and characteristics by collecting post COVID illness data utilizing Patient-Reported Outcomes Measurement Information System (PROMIS®). The resulting measures were used to assess surveyed patients physical, mental, and social health status. Methods A cross-sectional study and 6-months Mayo Clinic COVID recovered registry data were used to evaluate continuing symptoms severity among the 817 positive tested patients surveyed between March and September 2020. The resulting PROMIS® data set was used to analyze patients post 30 days health status. The e-mailed questionnaires focused on fatigue, sleep, ability to participate in social roles, physical function, and pain. Results The large sample size (n = 817) represented post hospitalized and other managed outpatients. Persistent post COVID impairments prevalence and characteristics were determined to be demographically young (44 years), white (87%), and female (61%). Dysfunction as measured by the PROMIS® scales in patients recovered from acute COVID-19 was reported as significant in the following domains: ability to participate in social roles (43.2%), pain (17.8%), and fatigue (16.2%). Conclusion Patient response on the PROMIS® scales was similar to that seen in multiple other studies which used patient reported symptoms. As a result of this experience, we recommend utilizing standardized scales such as the PROMIS® to obtain comparable data across the patients’ clinical course and define the disease trajectory. This would further allow for effective comparison of data across studies to better define the disease process, risk factors, and assess the impact of future treatments.

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Multiple cellular compartments engagement in Nicotiana benthamiana-peanut stunt virus-satRNA interactions revealed by systems biology approach

Plant Cell Reports ◽

10.1007/s00299-021-02706-4 ◽

2021 ◽

Author(s):

Barbara Wrzesińska ◽

Agnieszka Zmienko ◽

Lam Dai Vu ◽

Ive De Smet ◽

Aleksandra Obrępalska-Stęplowska

Keyword(s):

Virus Infection ◽

Nicotiana Benthamiana ◽

Cellular Localization ◽

Plant Interactions ◽

Satellite Rna ◽

Functional Connections ◽

Cell Compartments ◽

Wide Range ◽

Peanut Stunt Virus ◽

Cellular Compartments

Abstract Key message PSV infection changed the abundance of host plant’s transcripts and proteins associated with various cellular compartments, including ribosomes, chloroplasts, mitochondria, the nucleus and cytosol, affecting photosynthesis, translation, transcription, and splicing. Abstract Virus infection is a process resulting in numerous molecular, cellular, and physiological changes, a wide range of which can be analyzed due to development of many high-throughput techniques. Plant RNA viruses are known to replicate in the cytoplasm; however, the roles of chloroplasts and other cellular structures in the viral replication cycle and in plant antiviral defense have been recently emphasized. Therefore, the aim of this study was to analyze the small RNAs, transcripts, proteins, and phosphoproteins affected during peanut stunt virus strain P (PSV-P)–Nicotiana benthamiana interactions with or without satellite RNA (satRNA) in the context of their cellular localization or functional connections with particular cellular compartments to elucidate the compartments most affected during pathogenesis at the early stages of infection. Moreover, the processes associated with particular cell compartments were determined. The ‘omic’ results were subjected to comparative data analyses. Transcriptomic and small RNA (sRNA)–seq data were obtained to provide new insights into PSV-P–satRNA–plant interactions, whereas previously obtained proteomic and phosphoproteomic data were used to broaden the analysis to terms associated with cellular compartments affected by virus infection. Based on the collected results, infection with PSV-P contributed to changes in the abundance of transcripts and proteins associated with various cellular compartments, including ribosomes, chloroplasts, mitochondria, the nucleus and the cytosol, and the most affected processes were photosynthesis, translation, transcription, and mRNA splicing. Furthermore, sRNA-seq and phosphoproteomic analyses indicated that kinase regulation resulted in decreases in phosphorylation levels. The kinases were associated with the membrane, cytoplasm, and nucleus components.

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Corrigendum: Effect of temperature on the pathogenesis, accumulation of viral and satellite RNAs and on plant proteome in peanut stunt virus and satellite RNA-infected plants

Frontiers in Plant Science ◽

10.3389/fpls.2016.00839 ◽

2016 ◽

Vol 7 ◽

Author(s):

Aleksandra Obrępalska-Stęplowska ◽

Jenny Renaut ◽

Sebastien Planchon ◽

Arnika Przybylska ◽

Przemysław Wieczorek ◽

...

Keyword(s):

Effect Of Temperature ◽

Satellite Rna ◽

Satellite Rnas ◽

Peanut Stunt Virus

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Racial disparities in stage-specific gastric cancer: analysis of results from the Surveillance Epidemiology and End Results (SEER) program database

Journal of Investigative Medicine ◽

10.1136/jim-2017-000413 ◽

2017 ◽

Vol 65 (6) ◽

pp. 991-998 ◽

Cited By ~ 7

Author(s):

Gang Zhang ◽

Xing Zhao ◽

Jie Li ◽

Yu Yuan ◽

Ming Wen ◽

...

Keyword(s):

Gastric Cancer ◽

Radiation Treatment ◽

Early Stage ◽

Ethnic Disparities ◽

Incidence Rates ◽

Pacific Islanders ◽

Seer Program ◽

Second Primary ◽

Data Set ◽

End Results

The incidence of gastric cancer is declining in western countries but continues to represent a serious health problem worldwide, especially in Asia and among Asian Americans. This study aimed to investigate ethnic disparities in stage-specific gastric cancer, including differences in incidence, treatment and survival. The cohort study was analyzed using the data set of patients with gastric cancer registered in the Surveillance, Epidemiology, and End Results (SEER) program from 2004 to 2013. Among 54,165 patients with gastric cancer, 38,308 were whites (70.7%), 7546 were blacks (13.9%), 494 were American Indian/Alaskan Natives (0.9%) and 7817 were Asians/Pacific Islanders (14.4%). Variables were patient demographics, disease characteristics, surgery/radiation treatment, overall survival (OS) and cause specific survival (CSS). Asians/Pacific Islanders demonstrated the highest incidence rates for gastric cancer compared with other groups and had the greatest decline in incidence during the study period (13.03 to 9.28 per 100,000/year), as well as the highest percentage of patients with American Joint Committee on Cancer (AJCC) early stage gastric cancer. There were significant differences between groups in treatment across stages I–IV (all p<0.001); Asians/Pacific Islanders had the highest rate of surgery plus radiation (45.1%). Significant differences were found in OS and CSS between groups (p<0.001); OS was highest among Asians/Pacific Islanders. Multivariate analysis revealed that age, race, grade, stage, location, and second primary cancer were valid prognostic factors for survival. Marked ethnic disparities exist in age-adjusted incidence of primary gastric cancer, with significant differences between races in age, gender, histological type, grade, AJCC stage, location, second cancer, treatment and survival.

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Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

Methods of Information in Medicine ◽

10.1160/me9050 ◽

2007 ◽

Vol 46 (03) ◽

pp. 324-331 ◽

Cited By ~ 10

Author(s):

P. Jäger ◽

S. Vogel ◽

A. Knepper ◽

T. Kraus ◽

T. Aach ◽

...

Keyword(s):

Early Stage ◽

Computation Time ◽

Hounsfield Unit ◽

Computer Assisted ◽

Clinical Test ◽

Pleural Mesothelioma ◽

Data Sets ◽

Data Set ◽

Average Computation Time ◽

Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

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An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

Frontiers in Medicine ◽

10.3389/fmed.2021.716869 ◽

2021 ◽

Vol 8 ◽

Author(s):

Cheng Guo ◽

Chenglai Dong ◽

Junjie Zhang ◽

Rui Wang ◽

Zhe Wang ◽

...

Keyword(s):

Hepatitis C Virus ◽

Hepatitis C ◽

Early Stage ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Calibration Plot ◽

Prognostic Signature ◽

Data Set ◽

Immune Infiltration ◽

Immune Related Genes

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

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Difficult Decisions in Minimally Invasive Surgery of the Thymus

Cancers ◽

10.3390/cancers13235887 ◽

2021 ◽

Vol 13 (23) ◽

pp. 5887

Author(s):

Ankit Dhamija ◽

Jahnavi Kakuturu ◽

J. W. Awori Hayanga ◽

Alper Toker

Keyword(s):

Minimally Invasive Surgery ◽

Minimally Invasive ◽

Myasthenia Gravis ◽

Invasive Surgery ◽

Early Stage ◽

Disease Process ◽

Standard Of Care ◽

Prognostic Importance

A minimally invasive resection of thymomas has been accepted as standard of care in the last decade for early stage thymomas. This is somewhat controversial in terms of higher-staged thymomas and myasthenia gravis patients due to the prognostic importance of complete resections and the indolent characteristics of the disease process. Despite concerted efforts to standardize minimally invasive approaches, there is still controversy as to the extent of excision, approach of surgery, and the platform utilized. In this article, we aim to provide our surgical perspective of thymic resection and a review of the existing literature.

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CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

10.21203/rs.3.rs-150866/v1 ◽

2021 ◽

Author(s):

Jun Du ◽

Jinguo Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Poor Prognosis ◽

Early Stage ◽

Human Tumor ◽

The Cancer Genome Atlas ◽

Data Set ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Cox Proportional Hazard Regression ◽

The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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