Next Issue
Previous Issue

Table of Contents

J. Pers. Med., Volume 3, Issue 2 (June 2013), Pages 70-123

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle Machine Learning Techniques for Arterial Pressure Waveform Analysis
J. Pers. Med. 2013, 3(2), 82-101; doi:10.3390/jpm3020082
Received: 1 March 2013 / Revised: 18 April 2013 / Accepted: 25 April 2013 / Published: 2 May 2013
Cited by 2 | PDF Full-text (963 KB) | HTML Full-text | XML Full-text
Abstract
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work [...] Read more.
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation. Full article
Open AccessArticle Motivations and Barriers to Sharing Biological Samples: A Case Study
J. Pers. Med. 2013, 3(2), 102-110; doi:10.3390/jpm3020102
Received: 2 May 2013 / Revised: 22 May 2013 / Accepted: 27 May 2013 / Published: 6 June 2013
Cited by 1 | PDF Full-text (311 KB) | HTML Full-text | XML Full-text
Abstract
One of the most significant impediments to the current goals of genomic research is the limited availability of high quality biological samples. Despite efforts to increase both the quality and quantity of samples collected, access to such samples remains limited. This may [...] Read more.
One of the most significant impediments to the current goals of genomic research is the limited availability of high quality biological samples. Despite efforts to increase both the quality and quantity of samples collected, access to such samples remains limited. This may be due, at least in part, to a general reluctance of biobanking professionals, clinicians, and researchers to share biological specimens with others. Ethnographic methods were used in a biobank setting to explore professionals’ perspectives toward and practices of sharing samples. Several motivations and barriers to sharing that may influence research practice were identified. Contrary to existing literature that suggests that professionals are unlikely to share samples with one another, the participants of this study were open to and supportive of sharing samples for research. However, clear communication and effective infrastructure are needed to support the distribution of biobank materials. Full article
Open AccessArticle Physician Awareness and Utilization of Food and Drug Administration (FDA)-Approved Labeling for Pharmacogenomic Testing Information
J. Pers. Med. 2013, 3(2), 111-123; doi:10.3390/jpm3020111
Received: 17 April 2013 / Revised: 24 May 2013 / Accepted: 4 June 2013 / Published: 10 June 2013
Cited by 2 | PDF Full-text (427 KB) | HTML Full-text | XML Full-text
Abstract
We surveyed 10,303 United States physicians on where they obtain pharmacogenomic testing information. Thirty-nine percent indicated that they obtained this from drug labeling. Factors positively associated with this response included older age, postgraduate instruction, using other information sources, regulatory approval/ recommendation of [...] Read more.
We surveyed 10,303 United States physicians on where they obtain pharmacogenomic testing information. Thirty-nine percent indicated that they obtained this from drug labeling. Factors positively associated with this response included older age, postgraduate instruction, using other information sources, regulatory approval/ recommendation of testing, reliance on labeling for information, and perception that patients have benefited from testing. Physicians use pharmacogenomic testing information from drug labeling, highlighting the importance of labeling information that is conducive to practice application. Full article

Other

Jump to: Research

Open AccessMeeting Report Personalized Cancer Care Conference
J. Pers. Med. 2013, 3(2), 70-81; doi:10.3390/jpm3020070
Received: 19 February 2013 / Revised: 30 March 2013 / Accepted: 23 April 2013 / Published: 29 April 2013
Cited by 1 | PDF Full-text (338 KB) | HTML Full-text | XML Full-text
Abstract
The Oslo University Hospital (Norway), the K.G. Jebsen Centre for Breast Cancer Research (Norway), The Radiumhospital Foundation (Norway) and the Fritz-Bender-Foundation (Germany) designed under the conference chairmen (E. Mihich, K.S. Zänker, A.L. Borresen-Dale) and advisory committee (A. Borg, Z. Szallasi, O. Kallioniemi, [...] Read more.
The Oslo University Hospital (Norway), the K.G. Jebsen Centre for Breast Cancer Research (Norway), The Radiumhospital Foundation (Norway) and the Fritz-Bender-Foundation (Germany) designed under the conference chairmen (E. Mihich, K.S. Zänker, A.L. Borresen-Dale) and advisory committee (A. Borg, Z. Szallasi, O. Kallioniemi, H.P. Huber) a program at the cutting edge of “PERSONALIZED CANCER CARE: Risk prediction, early diagnosis, progression and therapy resistance.” The conference was held in Oslo from September 7 to 9, 2012 and the science-based presentations concerned six scientific areas: (1) Genetic profiling of patients, prediction of risk, late side effects; (2) Molecular profiling of tumors and metastases; (3) Tumor-host microenvironment interaction and metabolism; (4) Targeted therapy; (5) Translation and (6) Informed consent, ethical challenges and communication. Two satellite workshops on (i) Ion Ampliseq—a novel tool for large scale mutation detection; and (ii) Multiplex RNA ISH and tissue homogenate assays for cancer biomarker validation were additionally organized. The report concludes that individual risk prediction in carcinogenesis and/or metastatogenesis based on polygenic profiling may be useful for intervention strategies for health care and therapy planning in the future. To detect distinct and overlapping DNA sequence alterations in tumor samples and adjacent normal tissues, including point mutations, small insertions or deletions, copy number changes and chromosomal rearrangements will eventually make it possible to design personalized management plans for individualized patients. However, large individualized datasets need a new approach in bio-information technology to reduce this enormous data dimensionally to simply working hypotheses about health and disease for each individual. Full article

Journal Contact

MDPI AG
JPM Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
jpm@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to JPM
Back to Top