scholarly journals Do MicroRNAs Modulate Visceral Pain?

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhuo-Ying Tao ◽  
Yang Xue ◽  
Jin-Feng Li ◽  
Richard J. Traub ◽  
Dong-Yuan Cao
Keyword(s):  
Significant Role ◽  
Strong Evidence ◽  
Recent Progress ◽  
Target Genes ◽  
Visceral Pain ◽  
Therapeutic Targets ◽  
The Past ◽  
Complex Interactions ◽  
Pathological Conditions ◽  
Posttranscriptional Level

Visceral pain, a common characteristic of multiple diseases relative to viscera, impacts millions of people worldwide. Although hundreds of studies have explored mechanisms underlying visceral pain, it is still poorly managed. Over the past decade, strong evidence emerged suggesting that microRNAs (miRNAs) play a significant role in visceral nociception through altering neurotransmitters, receptors and other genes at the posttranscriptional level. Under pathological conditions, one kind of miRNA may have several target mRNAs and several kinds of miRNAs may act on one target, suggesting complex interactions and mechanisms between miRNAs and target genes lead to pathological states. In this review we report on recent progress in examining miRNAs responsible for visceral sensitization and provide miRNA-based therapeutic targets for the management of visceral pain.

scholarly journals Biobanks—A Platform for Scientific and Biomedical Research

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 485
Author(s):  
Kristina Malsagova ◽  
Artur Kopylov ◽  
Alexander Stepanov ◽  
Tatyana Butkova ◽  
Alexandra Sinitsyna ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Biological Samples ◽  
Biological Material ◽  
Therapeutic Targets ◽  
Healthy People ◽  
Biomedical Science ◽  
The Past ◽  
Pathological Conditions ◽  
Basic Understanding ◽  
New Therapeutic Targets

The development of biomedical science requires the creation of biological material collections that allow for the search and discovery of biomarkers for pathological conditions, the identification of new therapeutic targets, and the validation of these findings in samples from patients and healthy people. Over the past decades, the importance and need for biobanks have increased considerably. Large national and international biorepositories have replaced small collections of biological samples. The aim of this work is to provide a basic understanding of biobanks and an overview of how biobanks have become essential structures in modern biomedical research.

MicroRNAs and their role in hematological malignant diseases

2012 ◽  
Vol 153 (52) ◽  
pp. 2051-2059 ◽  
Author(s):  
Zsuzsanna Gaál ◽  
Éva Oláh
Keyword(s):  
Target Genes ◽  
Lymphoblastic Leukemia ◽  
Cancer Tissue ◽  
Altered Expression ◽  
Diagnosis And Prognosis ◽  
Oncologic Patients ◽  
Different Types ◽  
Non Coding Rnas ◽  
Success Of Treatment ◽  
Posttranscriptional Level

MicroRNAs are a class of small non-coding RNAs regulating gene expression at posttranscriptional level. Their target genes include numerous regulators of cell cycle, cell proliferation as well as apoptosis. Therefore, they are implicated in the initiation and progression of cancer, tissue invasion and metastasis formation as well. MicroRNA profiles supply much information about both the origin and the differentiation state of tumours. MicroRNAs also have a key role during haemopoiesis. An altered expression level of those have often been observed in different types of leukemia. There are successful attempts to apply microRNAs in the diagnosis and prognosis of acute lymphoblastic leukemia and acute myeloid leukemia. Measurement of the expression levels may help to predict the success of treatment with different kinds of chemotherapeutic drugs. MicroRNAs are also regarded as promising therapeutic targets, and can contribute to a more personalized therapeutic approach in haemato-oncologic patients. Orv. Hetil., 2012, 153, 2051–2059.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Nucleosides and Nucleoside Analogues as Emerging Antiviral Drugs

2020 ◽  
Vol 17 ◽  
Author(s):  
Mohamed Ibrahim Elzagheid
Keyword(s):  
Significant Role ◽  
Mode Of Action ◽  
Crucial Role ◽  
Viral Infections ◽  
Antiviral Drugs ◽  
Review Article ◽  
Nucleoside Analogues ◽  
The Past ◽  
Nucleoside Drugs

: Nucleosides and their analogues have been in use for many years and have become essential for treating patients with viral infections. Many additional nucleoside drugs have been approved over the past decades. This strongly demonstrates how important these compounds are and the crucial role they play. Given that a significant amount of research and literature has been documented regarding nucleoside analogues, this review article mainly focuses the discussion on nucleosides and nucleoside analogous that have proven to play significant role or be emerging in the treatment of known viral infections. This covers the names, structures, applications, toxicity, and mode of action of relevant nucleoside analogues.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals Template-Free Self-Assembly of Two-Dimensional Polymers into Nano/Microstructured Materials

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3310
Author(s):  
Shengda Liu ◽  
Jiayun Xu ◽  
Xiumei Li ◽  
Tengfei Yan ◽  
Shuangjiang Yu ◽  
...  
Keyword(s):  
Self Assembly ◽  
Recent Progress ◽  
Two Dimensional ◽  
Subsequent Removal ◽  
The Past ◽  
2D Polymers ◽  
Template Free ◽  
Self Assembled ◽  
The Relationship ◽  
Polymer Tubes

In the past few decades, enormous efforts have been made to synthesize covalent polymer nano/microstructured materials with specific morphologies, due to the relationship between their structures and functions. Up to now, the formation of most of these structures often requires either templates or preorganization in order to construct a specific structure before, and then the subsequent removal of previous templates to form a desired structure, on account of the lack of “self-error-correcting” properties of reversible interactions in polymers. The above processes are time-consuming and tedious. A template-free, self-assembled strategy as a “bottom-up” route to fabricate well-defined nano/microstructures remains a challenge. Herein, we introduce the recent progress in template-free, self-assembled nano/microstructures formed by covalent two-dimensional (2D) polymers, such as polymer capsules, polymer films, polymer tubes and polymer rings.

scholarly journals Through what mechanisms do protected areas affect environmental and social outcomes?

2015 ◽  
Vol 370 (1681) ◽  
pp. 20140267 ◽  
Author(s):  
Paul J. Ferraro ◽  
Merlin M. Hanauer
Keyword(s):  
Experimental Data ◽  
Protected Areas ◽  
Empirical Evidence ◽  
Strong Evidence ◽  
Protected Area ◽  
Evidence Base ◽  
Social Outcomes ◽  
Causal Effects ◽  
The Past ◽  
Made In

To develop effective protected area policies, scholars and practitioners must better understand the mechanisms through which protected areas affect social and environmental outcomes. With strong evidence about mechanisms, the key elements of success can be strengthened, and the key elements of failure can be eliminated or repaired. Unfortunately, empirical evidence about these mechanisms is limited, and little guidance for quantifying them exists. This essay assesses what mechanisms have been hypothesized, what empirical evidence exists for their relative contributions and what advances have been made in the past decade for estimating mechanism causal effects from non-experimental data. The essay concludes with a proposed agenda for building an evidence base about protected area mechanisms.

Soul of Classicality in Western Architectural Design

2014 ◽  
Vol 638-640 ◽  
pp. 2253-2256
Author(s):  
Cong Ru Liu ◽  
Ming Sen Lin ◽  
Qing Li
Keyword(s):  
Significant Role ◽  
Architectural Design ◽  
Ancient Greece ◽  
Architecture Design ◽  
Ancient Rome ◽  
The Past ◽  
Renaissance Period

The classicality of the western architecture establishes its foundation at the beginning of the ancient Greece, is flourished in the ancient Rome and revitalized in the renaissance period, extends to the classicism and the classical revival, and finally is overthrown by the postmodernism. By going through development and prosperity in the past thousands of years, the classical spirit has always played a greatly significant role in the field of western architecture design.

scholarly journals Tackling resistance: emerging antimalarials and new parasite targets in the era of elimination

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1170 ◽  
Author(s):  
Emily S. Mathews ◽  
Audrey R. Odom John
Keyword(s):  
Drug Resistance ◽  
Malaria Elimination ◽  
Large Scale ◽  
Therapeutic Targets ◽  
Control Measures ◽  
Multi Drug Resistance ◽  
Development Projects ◽  
Combination Therapies ◽  
The Past ◽  
Antimalarial Compounds

Malaria remains a significant contributor to global human mortality, and roughly half the world’s population is at risk for infection with Plasmodium spp. parasites. Aggressive control measures have reduced the global prevalence of malaria significantly over the past decade. However, resistance to available antimalarials continues to spread, including resistance to the widely used artemisinin-based combination therapies. Novel antimalarial compounds and therapeutic targets are greatly needed. This review will briefly discuss several promising current antimalarial development projects, including artefenomel, ferroquine, cipargamin, SJ733, KAF156, MMV048, and tafenoquine. In addition, we describe recent large-scale genetic and resistance screens that have been instrumental in target discovery. Finally, we highlight new antimalarial targets, which include essential transporters and proteases. These emerging antimalarial compounds and therapeutic targets have the potential to overcome multi-drug resistance in ongoing efforts toward malaria elimination.

scholarly journals In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

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