scholarly journals Radiolabeled Bombesin Analogs

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5766
Author(s):  
Rosalba Mansi ◽  
Berthold A. Nock ◽  
Simone U. Dalm ◽  
Martijn B. Busstra ◽  
Wytske M. van Weerden ◽  
...  
Keyword(s):  
State Of The Art ◽  
Metabolic Stability ◽  
Current Status ◽  
Breast Cancers ◽  
Clinical Testing ◽  
Molecular Tools ◽  
Peptide Analogs ◽  
Safe State ◽  
Cancer Types ◽  
Common Effort

The gastrin-releasing peptide receptor (GRPR) is expressed in high numbers in a variety of human tumors, including the frequently occurring prostate and breast cancers, and therefore provides the rationale for directing diagnostic or therapeutic radionuclides on cancer lesions after administration of anti-GRPR peptide analogs. This concept has been initially explored with analogs of the frog 14-peptide bombesin, suitably modified at the N-terminus with a number of radiometal chelates. Radiotracers that were selected for clinical testing revealed inherent problems associated with these GRPR agonists, related to low metabolic stability, unfavorable abdominal accumulation, and adverse effects. A shift toward GRPR antagonists soon followed, with safer analogs becoming available, whereby, metabolic stability and background clearance issues were gradually improved. Clinical testing of three main major antagonist types led to promising outcomes, but at the same time brought to light several limitations of this concept, partly related to the variation of GRPR expression levels across cancer types, stages, previous treatments, and other factors. Currently, these parameters are being rigorously addressed by cell biologists, chemists, nuclear medicine physicians, and other discipline practitioners in a common effort to make available more effective and safe state-of-the-art molecular tools to combat GRPR-positive tumors. In the present review, we present the background, current status, and future perspectives of this endeavor.

scholarly journals Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 742
Author(s):  
Rima Hajjo ◽  
Dima A. Sabbah ◽  
Sanaa K. Bardaweel ◽  
Alexander Tropsha
Keyword(s):  
Machine Learning ◽  
High Specificity ◽  
Cancer Biomarkers ◽  
Cancer Prognosis ◽  
Current Status ◽  
Imaging Data ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
Cancer Types ◽  
Magnetic Resonance Imaging Mri

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

scholarly journals Role of the NUDT Enzymes in Breast Cancer

2021 ◽  
Vol 22 (5) ◽  
pp. 2267
Author(s):  
Roni H. G. Wright ◽  
Miguel Beato
Keyword(s):  
Breast Cancer ◽  
Signaling Pathways ◽  
Cancer Progression ◽  
Breast Cancers ◽  
Metastatic Colonization ◽  
Hormone Receptor Positive ◽  
Aggressive Cancer ◽  
Cancer Types ◽  
Metastatic Process

Despite global research efforts, breast cancer remains the leading cause of cancer death in women worldwide. The majority of these deaths are due to metastasis occurring years after the initial treatment of the primary tumor and occurs at a higher frequency in hormone receptor-positive (Estrogen and Progesterone; HR+) breast cancers. We have previously described the role of NUDT5 (Nudix-linked to moiety X-5) in HR+ breast cancer progression, specifically with regards to the growth of breast cancer stem cells (BCSCs). BCSCs are known to be the initiators of epithelial-to-mesenchyme transition (EMT), metastatic colonization, and growth. Therefore, a greater understanding of the proteins and signaling pathways involved in the metastatic process may open the door for therapeutic opportunities. In this review, we discuss the role of NUDT5 and other members of the NUDT family of enzymes in breast and other cancer types. We highlight the use of global omics data based on our recent phosphoproteomic analysis of progestin signaling pathways in breast cancer cells and how this experimental approach provides insight into novel crosstalk mechanisms for stratification and drug discovery projects aiming to treat patients with aggressive cancer.

scholarly journals On μe-scattering at NNLO in QED

2018 ◽  
Vol 179 ◽  
pp. 01014 ◽  
Author(s):  
P. Mastrolia ◽  
M. Passera ◽  
A. Primo ◽  
U. Schubert ◽  
W. J. Torres Bobadilla
Keyword(s):  
Quantum Electrodynamics ◽  
State Of The Art ◽  
Current Status ◽  
Analytic Evaluation ◽  
Art Techniques

We report on the current status of the analytic evaluation of the two-loop corrections to the μescattering in Quantum Electrodynamics, presenting state-of-the art techniques which have been developed to address this challenging task.

scholarly journals Evaluation of Substituted Pyrazole-Based Kinase Inhibitors in One Decade (2011–2020): Current Status and Future Prospects

Molecules ◽  
2022 ◽  
Vol 27 (1) ◽  
pp. 330
Author(s):  
Mohammed I. El-Gamal ◽  
Seyed-Omar Zaraei ◽  
Moustafa M. Madkour ◽  
Hanan S. Anbar
Keyword(s):  
Kinase Inhibitors ◽  
Therapeutic Agents ◽  
Current Status ◽  
Pyrazole Derivatives ◽  
Chronological Order ◽  
Pharmacological Activities ◽  
The Past ◽  
Cancer Types ◽  
Privileged Scaffold ◽  
Almost All

Pyrazole has been recognized as a pharmacologically important privileged scaffold whose derivatives produce almost all types of pharmacological activities and have attracted much attention in the last decades. Of the various pyrazole derivatives reported as potential therapeutic agents, this article focuses on pyrazole-based kinase inhibitors. Pyrazole-possessing kinase inhibitors play a crucial role in various disease areas, especially in many cancer types such as lymphoma, breast cancer, melanoma, cervical cancer, and others in addition to inflammation and neurodegenerative disorders. In this article, we reviewed the structural and biological characteristics of the pyrazole derivatives recently reported as kinase inhibitors and classified them according to their target kinases in a chronological order. We reviewed the reports including pyrazole derivatives as kinase inhibitors published during the past decade (2011–2020).

scholarly journals Breast Cancer Survival in the Mammography Era in Brazil: A Population-Based Analysis of 2,715 Cases

2021 ◽  
Author(s):  
Juliana Fernandes ◽  
Beatriz Machado ◽  
Cassio Cardoso-Filho ◽  
Juliana Nativio ◽  
Cesar Cabello ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Survival Rates ◽  
Population Based ◽  
Breast Cancer Survival ◽  
Stage I ◽  
Current Status ◽  
Breast Cancers ◽  
Risk Of Death ◽  
Significant Difference

Abstract Background This study aims to assess breast cancer survival rates after one decade of mammography in a large urban area of Brazil. Methods It is a population-based retrospective cohort of women with breast cancer in Campinas, São Paulo, from 2010 to 2014. Age, vital status and stage were accessed through the cancer and mortality registry, and patients records. Statistics used Kaplan-Meier, log-rank and Cox's regression. Results Out of the 2,715 cases, 665 deaths (24.5%) were confirmed until early 2020. The mean age at diagnosis was 58.6 years. Women 50-69 years were 48.0%, and stage I the most frequent (25.0%). The overall mean survival was 8.4 years (8.2-8.5). The 5-year survival (5yOS) for overall, 40-49, 50-59, 60-69, 70-79 years was respectively 80.5%, 87.7%, 83.7%, 83.8% and 75.5%. The 5yOS for stages 0, I, II, III and IV was 95.2%, 92.6%, 89.4%, 71.1% and 47.1%. There was no significant difference in survival in stage I or II (p=0.058). Compared to women 50-59 years, death's risk was 2.3 times higher for women 70-79 years and 26% lower for women 40-49 years. Concerning stage I, the risk of death was 1.5, 4.1 and 8.6 times higher, and 34% lower, respectively, for stage II, III, IV and 0. Conclusions In Brazil, breast cancers are currently diagnosed in the early stages, although advanced cases persist. Survival rates may reflect improvements in screening, early detection and treatment. The results can reflect the current status of other regions or countries with similar health care conditions.

Decoupling spatial and temporal processes for clinical analyzers

1995 ◽  
Vol 41 (9) ◽  
pp. 1398-1402
Author(s):  
J Mazza ◽  
M Huber ◽  
S Frye
Keyword(s):  
Number Theory ◽  
Design Problem ◽  
State Of The Art ◽  
Spatial Arrangement ◽  
Sample Volume ◽  
Complex Task ◽  
Clinical Testing ◽  
Time And Space ◽  
Physical Processing ◽  
Temporal And Spatial

Abstract The separation of time and space in processing a sample greatly simplifies the design of automation for clinical testing. The efficient spatial arrangement of analytical units and sample manipulators has become a more complex task because of the degree of automation required on today's state-of-the-art analyzer. Minimization of sample volume and the reduction of overall analyzer size further complicate the design problem. We report the development of a proprietary method of decoupling the temporal and spatial elements required for analysis of samples. This process is based on number theory and can be used to optimize the distance between the physical processing stations while allowing these same stations to operate on samples over a substantial range of times. The technique is versatile and can also be used when it is desirable to sequentially move groups of items from location to location.

scholarly journals Role of Piezoelectric Elements in Finding the Mechanical Properties of Solid Industrial Materials

2018 ◽  
Vol 8 (10) ◽  
pp. 1737 ◽  
Author(s):  
Arshed Mohammed ◽  
Sallehuddin Haris ◽  
Mohd Nuawi
Keyword(s):  
Mechanical Properties ◽  
State Of The Art ◽  
New Technology ◽  
Material Testing ◽  
Current Status ◽  
Piezoelectric Elements ◽  
Need To Evaluate ◽  
Recent Developments ◽  
The Many

Recent developments in ultrasonic material testing have increased the need to evaluate the current status of the different applications of piezoelectric elements (PEs). This research have reviewed state-of-the-art emerging new technology and the role of PEs in tests for a number of mechanical properties, such as creep, fracture toughness, hardness, and impact toughness, among others. In this field, importance is given to the following variables, namely, (a) values of the natural frequency to PEs, (b) type and dimensions of specimens, and (c) purpose of the tests. All these variables are listed in three tables to illustrate the nature of their differences in these kinds of tests. Furthermore, recent achievements in this field are emphasized in addition to the many important studies that highlight the role of PEs.

scholarly journals Next Generation Telehealth

2011 ◽  
Vol 20 (01) ◽  
pp. 15-20
Author(s):  
S.B. Gogia ◽  
G. Hartvigsen ◽  
A.J. Maeder
Keyword(s):  
State Of The Art ◽  
Care Delivery ◽  
Models Of Care ◽  
Current Status ◽  
Human Communication ◽  
Communication Practices ◽  
Access And Quality ◽  
Political Economic ◽  
Remote Health

SummaryTelehealth has long been seen as a means of increasing access and quality of care while decreasing costs and logistical burden for remote health care delivery. Underlying technology to support Telehealth has been developed commercially. However, its widespread adoption has been hindered by numerous clinical, social, political, economic and management factors. This paper examines trends which may help to address this situation.First we consider the current status of Telehealth based on some state-of-the-art reviews. Then we present some new future modes of Telehealth services, as described by various prominent authors. From these we identify some common directional themes and fundamental issues affecting the success of future Telehealth innovations.This position paper advances a view that Telehealth in the future will be much more driven by widespread pressure from two different drivers: more ubiquitous connectivity and related technological capabilities due to greater diversity in human communication practices, and new models of care emerging from diverse widespread movements towards health services reform.The IMIA Working Group on Telehealth work agenda will address some specific items within the areas described above.

Survey of Machine Learning Techniques in Drug Discovery

2019 ◽  
Vol 20 (3) ◽  
pp. 185-193 ◽  
Author(s):  
Natalie Stephenson ◽  
Emily Shane ◽  
Jessica Chase ◽  
Jason Rowland ◽  
David Ries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
State Of The Art ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Learning Techniques ◽  
Pharmaceutical Industries ◽  
Current Stage

Background:Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

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