scholarly journals Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

2021 ◽  
Vol 12 ◽  
Author(s):  
He Miao ◽  
Song Chen ◽  
Renyu Ding
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Cecal Ligation ◽  
Research Area ◽  
Multiple Organ ◽  
Protein Characterization ◽  
Host Responses ◽  
Related Data ◽  
Proteome Changes

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

scholarly journals Recent Advances in the Molecular Mechanisms Underlying Pyroptosis in Sepsis

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Yu-Lei Gao ◽  
Jian-Hua Zhai ◽  
Yan-Fen Chai
Keyword(s):  
Molecular Mechanisms ◽  
Secondary Infection ◽  
Multiple Organ ◽  
Host Responses ◽  
Severe Condition ◽  
Caspase 1 ◽  
Recent Advances ◽  
Increased Risk ◽  
Life Threatening ◽  
Minimize Tissue Damage

Sepsis is recognized as a life-threatening organ dysfunctional disease that is caused by dysregulated host responses to infection. Up to now, sepsis still remains a dominant cause of multiple organ dysfunction syndrome (MODS) and death among severe condition patients. Pyroptosis, originally named after the Greek words “pyro” and “ptosis” in 2001, has been defined as a specific programmed cell death characterized by release of inflammatory cytokines. During sepsis, pyroptosis is required for defense against bacterial infection because appropriate pyroptosis can minimize tissue damage. Even so, pyroptosis when overactivated can result in septic shock, MODS, or increased risk of secondary infection. Proteolytic cleavage of gasdermin D (GSDMD) by caspase-1, caspase-4, caspase-5, and caspase-11 is an essential step for the execution of pyroptosis in activated innate immune cells and endothelial cells stimulated by cytosolic lipopolysaccharide (LPS). Cleaved GSDMD also triggers NACHT, LRR, and PYD domain-containing protein (NLRP) 3-mediated activation of caspase-1 via an intrinsic pathway, while the precise mechanism underlying GSDMD-induced NLRP 3 activation remains unclear. Hence, this study provides an overview of the recent advances in the molecular mechanisms underlying pyroptosis in sepsis.

scholarly journals CVCDAP: an integrated platform for molecular and clinical analysis of cancer virtual cohorts

2020 ◽  
Vol 48 (W1) ◽  
pp. W463-W471
Author(s):  
Xiaoqing Guan ◽  
Meng Cai ◽  
Yang Du ◽  
Ence Yang ◽  
Jiafu Ji ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Clinical Analysis ◽  
Therapeutic Approaches ◽  
Web Based ◽  
Related Data ◽  
Public Datasets ◽  
Level Analysis ◽  
Selection Of Patients ◽  
Selection Of

Abstract Recent large-scale multi-omics studies resulted in quick accumulation of an overwhelming amount of cancer-related data, which provides an unprecedented resource to interrogate diverse questions. While certain existing web servers are valuable and widely used, analysis and visualization functions with regard to re-investigation of these data at cohort level are not adequately addressed. Here, we present CVCDAP, a web-based platform to deliver an interactive and customizable toolbox off the shelf for cohort-level analysis of TCGA and CPTAC public datasets, as well as user uploaded datasets. CVCDAP allows flexible selection of patients sharing common molecular and/or clinical characteristics across multiple studies as a virtual cohort, and provides dozens of built-in customizable tools for seamless genomic, transcriptomic, proteomic and clinical analysis of a single virtual cohort, as well as, to compare two virtual cohorts with relevance. The flexibility and analytic competence of CVCDAP empower experimental and clinical researchers to identify new molecular mechanisms and develop potential therapeutic approaches, by building and analyzing virtual cohorts for their subject of interests. We demonstrate that CVCDAP can conveniently reproduce published findings and reveal novel insights by two applications. The CVCDAP web server is freely available at https://omics.bjcancer.org/cvcdap/.

scholarly journals Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

2020 ◽  
Vol 21 (11) ◽  
pp. 3754 ◽  
Author(s):  
Erdem Bangi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Cost Effective ◽  
Experimental Models ◽  
Cancer Data ◽  
Functional Studies ◽  
Cancer Models ◽  
Effective Manner

Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models.

Quality Control of Data in a Large-Scale Cancer Register Program

1966 ◽  
Vol 05 (02) ◽  
pp. 67-74 ◽  
Author(s):  
W. I. Lourie ◽  
W. Haenszeland
Keyword(s):  
United States ◽  
Quality Control ◽  
Cancer Patients ◽  
Large Scale ◽  
The United States ◽  
Newly Diagnosed ◽  
Related Data ◽  
Cancer Register ◽  
Independent Review ◽  
End Results

Quality control of data collected in the United States by the Cancer End Results Program utilizing punchcards prepared by participating registries in accordance with a Uniform Punchcard Code is discussed. Existing arrangements decentralize responsibility for editing and related data processing to the local registries with centralization of tabulating and statistical services in the End Results Section, National Cancer Institute. The most recent deck of punchcards represented over 600,000 cancer patients; approximately 50,000 newly diagnosed cases are added annually.Mechanical editing and inspection of punchcards and field audits are the principal tools for quality control. Mechanical editing of the punchcards includes testing for blank entries and detection of in-admissable or inconsistent codes. Highly improbable codes are subjected to special scrutiny. Field audits include the drawing of a 1-10 percent random sample of punchcards submitted by a registry; the charts are .then reabstracted and recoded by a NCI staff member and differences between the punchcard and the results of independent review are noted.

Cancer Proteomics: New Horizons and Insights into Therapeutic Applications

2020 ◽  
Vol 17 ◽  
Author(s):  
Perumal Subramaniana ◽  
Jaime Jacqueline Jayapalan ◽  
Puteri Shafinaz Abdul-Rahmanb
Keyword(s):  
Molecular Mechanisms ◽  
Rapid Development ◽  
New Horizons ◽  
Cancer Management ◽  
Proteomic Approach ◽  
Cancer Proteomics ◽  
Therapeutic Outcomes ◽  
A Genome ◽  
Highly Sensitive ◽  
Dimensional Electrophoresis

A proteome is an efficient rendition of a genome, unswervingly controlling various cancer processes. Molecular mechanisms of several cancer processes have been unraveled by proteomic approach. Thus far, numerous tumors of diverse status have been investigated by two-dimensional electrophoresis. Numerous biomarkers have been recognized and precise categorization of apparent lesions has led to the timely detection of various cancers in persons at peril. Currently used pioneering approaches and technologies in proteomics have led to highly sensitive assays of cancer biomarkers and improved the early diagnosis of various cancers. The discovery of novel and definite biomarker signatures further widened our perceptive of the disease and novel potent drugs for efficient and aimed therapeutic outcomes in persistent cancers have emerged. However, a major limitation, even today, of proteomics is resolving and quantifying the proteins of low abundance. Despite the rapid development of proteomic technologies and their applications in cancer management, annulling the shortcomings of present proteomic technologies and development of better methods are still desirable. The main objectives of this review are to discuss the developing aspects, merits and demerits of pharmacoproteomics, redox proteomics, novel approaches and therapies being used for various types of cancer based on proteome studies.

scholarly journals EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jing Zhao ◽  
Alan Blayney ◽  
Xiaorong Liu ◽  
Lauren Gandy ◽  
Weihua Jin ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Epigallocatechin Gallate ◽  
Cancer Drug ◽  
Dynamic Interactions ◽  
Cancer Drug Discovery ◽  
Intrinsically Disordered ◽  
Terminal Domain ◽  
Cancerous Cells

AbstractEpigallocatechin gallate (EGCG) from green tea can induce apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Using SPR and NMR, here we report a direct, μM interaction between EGCG and the tumor suppressor p53 (KD = 1.6 ± 1.4 μM), with the disordered N-terminal domain (NTD) identified as the major binding site (KD = 4 ± 2 μM). Large scale atomistic simulations (>100 μs), SAXS and AUC demonstrate that EGCG-NTD interaction is dynamic and EGCG causes the emergence of a subpopulation of compact bound conformations. The EGCG-p53 interaction disrupts p53 interaction with its regulatory E3 ligase MDM2 and inhibits ubiquitination of p53 by MDM2 in an in vitro ubiquitination assay, likely stabilizing p53 for anti-tumor activity. Our work provides insights into the mechanisms for EGCG’s anticancer activity and identifies p53 NTD as a target for cancer drug discovery through dynamic interactions with small molecules.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

scholarly journals A Bibliometric Insight of Genetic Factors in ASD: Emerging Trends and New Developments

2020 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Kang Wang ◽  
Weicheng Duan ◽  
Yijie Duan ◽  
Yuxin Yu ◽  
Xiuyi Chen ◽  
...  
Keyword(s):  
Genetic Factors ◽  
De Novo ◽  
Rapid Development ◽  
The United States ◽  
Research Area ◽  
Scientific Output ◽  
Autism Spectrum ◽  
De Novo Mutations ◽  
Emerging Trends ◽  
Genetic Abnormalities

Autism spectrum disorder (ASD) cases have increased rapidly in recent decades, which is associated with various genetic abnormalities. To provide a better understanding of the genetic factors in ASD, we assessed the global scientific output of the related studies. A total of 2944 studies published between 1997 and 2018 were included by systematic retrieval from the Web of Science (WoS) database, whose scientific landscapes were drawn and the tendencies and research frontiers were explored through bibliometric methods. The United States has been acting as a leading explorer of the field worldwide in recent years. The rapid development of high-throughput technologies and bioinformatics transferred the research method from the traditional classic method to a big data-based pipeline. As a consequence, the focused research area and tendency were also changed, as the contribution of de novo mutations in ASD has been a research hotspot in the past several years and probably will remain one into the near future, which is consistent with the current opinions of the major etiology of ASD. Therefore, more attention and financial support should be paid to the deciphering of the de novo mutations in ASD. Meanwhile, the effective cooperation of multi-research centers and scientists in different fields should be advocated in the next step of scientific research undertaken.

scholarly journals High-Rate Transition Metal-based Cathode Materials for Battery-Supercapacitor Hybrid Devices

2021 ◽  
Author(s):  
Cong Wang ◽  
Zehao Song ◽  
Pei Shi ◽  
Lin Lv ◽  
Houzhao Wan ◽  
...  
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Electronic Devices ◽  
High Rate ◽  
Specific Power ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Electrochemical Devices ◽  
Hybrid Devices ◽  
Scale Grid

With the rapid development of portable electronic devices, electric vehicles and large-scale grid energy storage devices, it needs to reinforce specific energy and specific power of related electrochemical devices meeting...

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

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