scholarly journals Pharmacogenetics in Neuroblastoma: What Can Already Be Clinically Implemented and What Is Coming Next?

2021 ◽  
Vol 22 (18) ◽  
pp. 9815
Author(s):  
Gladys G. Olivera ◽  
Andrea Urtasun ◽  
Luis Sendra ◽  
Salvador F. Aliño ◽  
Yania Yáñez ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Pediatric Cancer ◽  
Genetic Variants ◽  
Clinical Management ◽  
State Of The Art ◽  
Present Review ◽  
Level Of Evidence ◽  
Therapeutic Success ◽  
Future Directions ◽  
Review State

Pharmacogenetics is one of the cornerstones of Personalized Precision Medicine that needs to be implemented in the routine of our patients’ clinical management in order to tailor their therapies as much as possible, with the aim of maximizing efficacy and minimizing toxicity. This is of great importance, especially in pediatric cancer and even more in complex malignancies such as neuroblastoma, where the rates of therapeutic success are still below those of many other types of tumors. The studies are mainly focused on germline genetic variants and in the present review, state of the art is presented: which are the variants that have a level of evidence high enough to be implemented in the clinic, and how to distinguish them from the ones that still need validation to confirm their utility. Further aspects as relevant characteristics regarding ontogeny and future directions in the research will also be discussed.

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning

2018 ◽  
Vol 22 (03) ◽  
pp. 307-322 ◽  
Author(s):  
Leon Lenchik ◽  
Robert Boutin
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Precision Medicine ◽  
Muscle Atrophy ◽  
State Of The Art ◽  
The State ◽  
Future Directions ◽  
Common Diagnosis ◽  
The Impact

AbstractAs populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. This review focuses on the state-of-the-art imaging of sarcopenia and provides context for such imaging by discussing the epidemiology, pathophysiology, consequences, and future directions in the field of sarcopenia.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Targeting Cystathionine Beta-Synthase Misfolding in Homocystinuria by Small Ligands: State of the Art and Future Directions

2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cystathionine Beta Synthase ◽  
Small Ligands

Poly(hydroxyurethane) Adhesives and Coatings: State-of-the-Art and Future Directions

2021 ◽  
Author(s):  
Alvaro Gomez-Lopez ◽  
Satyannarayana Panchireddy ◽  
Bruno Grignard ◽  
Inigo Calvo ◽  
Christine Jerome ◽  
...  
Keyword(s):  
State Of The Art ◽  
Future Directions

scholarly journals APOE: The New Frontier in the Development of a Therapeutic Target towards Precision Medicine in Late-Onset Alzheimer’s

2021 ◽  
Vol 22 (3) ◽  
pp. 1244
Author(s):  
Anna Yang ◽  
Boris Kantor ◽  
Ornit Chiba-Falek
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Precision Medicine ◽  
Antisense Oligonucleotides ◽  
State Of The Art ◽  
Late Onset ◽  
Base Editing ◽  
Medical Need ◽  
New Frontier ◽  
Amyloid Cascade

Alzheimer’s disease (AD) has a critical unmet medical need. The consensus around the amyloid cascade hypothesis has been guiding pre-clinical and clinical research to focus mainly on targeting beta-amyloid for treating AD. Nevertheless, the vast majority of the clinical trials have repeatedly failed, prompting the urgent need to refocus on other targets and shifting the paradigm of AD drug development towards precision medicine. One such emerging target is apolipoprotein E (APOE), identified nearly 30 years ago as one of the strongest and most reproduceable genetic risk factor for late-onset Alzheimer’s disease (LOAD). An exploration of APOE as a new therapeutic culprit has produced some very encouraging results, proving that the protein holds promise in the context of LOAD therapies. Here, we review the strategies to target APOE based on state-of-the-art technologies such as antisense oligonucleotides, monoclonal antibodies, and gene/base editing. We discuss the potential of these initiatives in advancing the development of novel precision medicine therapies to LOAD.

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Enterprise search: A state of the art

2019 ◽  
Vol 36 (2) ◽  
pp. 60-69
Author(s):  
Paul H Cleverley ◽  
Simon Burnett
Keyword(s):  
Oil And Gas ◽  
Search Task ◽  
State Of The Art ◽  
Feedback Loops ◽  
Future Directions ◽  
Business Outcomes ◽  
Technological Advances ◽  
Enterprise Search ◽  
Level Model ◽  
Systems Thinking Approach

Enterprise search is changing. The explosion of information within organizations, technological advances and availability of free OpenSource machine learning libraries offer many possibilities. Eighteen informants from practice, academia, search technology vendors and large organizations (Oil and Gas, Governments, Pharmaceuticals, Aerospace and Retail) were interviewed to assess challenges and future directions. The findings confirmed the existence of the ‘Google Habitus’, technology propaganda and a need to transcend disciplines for a Systems thinking approach toward enterprise search. This encompasses information management, user search literacy, governance, learning feedback loops as well as technology. A novel four-level model for enterprise search use cases is presented, covering search as a utility, search as an answer machine, search task apps and a discovery engine. This could be used to reframe enterprise search perceptions, expanding possibilities and improving business outcomes.

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