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Biomedical subjects

Jennifer Hall

Publications and source records attributed to Jennifer Hall.

At least 19 recordsLinked to original sources
PubMed2006

Spirituality at the beginning of life.

AIM: The aim of this paper was to explore the issues surrounding the spirit of the unborn child. BACKGROUND: Pregnancy and birth have been recognised to have a spiritual nature by women and health professionals caring for them. Midwives and nurses are expected to have a holistic approach to care. I suggest that for care to be truly holistic exploration is required of the spiritual nature of the unborn fetus. METHODS: Historical, philosophical and religious views of the spirit of the fetus, are explored as well as those of women. Investigation was made of views of the timing of 'ensoulment'. RESULTS: The review demonstrates the value women place on the sacredness of pregnancy and birth, and that the spiritual nature of the unborn should be recognised. CONCLUSION: This paper shows that the views and values women have of pregnancy and birth and the powerful, spiritual relationship they have with the unborn, indicates that further discussion and research needs to be carried out in this area. RELEVANCE TO CLINICAL PRACTICE: It is recommended that all who work with women who are pregnant should recognise the spiritual nature of the unborn when carrying out care.

Attitude to Health↗
PubMed2005

A comparative study of discriminating human heart failure etiology using gene expression profiles.

BACKGROUND: Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clinically with many similar decreases in ventricular function, emerging work suggests that they are distinct diseases with different responses to therapy. The ability to distinguish between ischemic and non-ischemic heart failure may be essential to guide appropriate therapy and determine prognosis for successful treatment. In this paper we consider discriminating the etiologies of heart failure using gene expression libraries from two separate institutions. RESULTS: We apply five new statistical methods, including partial least squares, penalized partial least squares, LASSO, nearest shrunken centroids and random forest, to two real datasets and compare their performance for multiclass classification. It is found that the five statistical methods perform similarly on each of the two datasets: it is difficult to correctly distinguish the etiologies of heart failure in one dataset whereas it is easy for the other one. In a simulation study, it is confirmed that the five methods tend to have close performance, though the random forest seems to have a slight edge. CONCLUSIONS: For some gene expression data, several recently developed discriminant methods may perform similarly. More importantly, one must remain cautious when assessing the discriminating performance using gene expression profiles based on a small dataset; our analysis suggests the importance of utilizing multiple or larger datasets.

Data Interpretation, Statistical↗
PubMed2005

Borrowing information from relevant microarray studies for sample classification using weighted partial least squares.

With an increasing number of publicly available microarray datasets, it becomes attractive to borrow information from other relevant studies to have more reliable and powerful analysis of a given dataset. We do not assume that subjects in the current study and other relevant studies are drawn from the same population as assumed by meta-analysis. In particular, the set of parameters in the current study may be different from that of the other studies. We consider sample classification based on gene expression profiles in this context. We propose two new methods, a weighted partial least squares (WPLS) method and a weighted penalized partial least squares (WPPLS) method, to build a classifier by a combined use of multiple datasets. The methods can weight the individual datasets depending on their relevance to the current study. A more standard approach is first to build a classifier using each of the individual datasets, then to combine the outputs of the multiple classifiers using a weighted voting. Using two quite different datasets on human heart failure, we show first that WPLS/WPPLS, by borrowing information from the other dataset, can improve the performance of PLS/PPLS built on only a single dataset. Second, WPLS/WPPLS performs better than the standard approach of combining multiple classifiers. Third, WPPLS can improve over WPLS, just as PPLS does over PLS for a single dataset.

Gene Expression Profiling↗
PubMed2005

Protocols.

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Adult↗
PubMed2004

Modeling the relationship between LVAD support time and gene expression changes in the human heart by penalized partial least squares.

MOTIVATION: Heart failure affects more than 20 million people in the world. Heart transplantation is the most effective therapy, but the number of eligible patients far outweighs the number of available donor hearts. The left mechanical ventricular assist device (LVAD) has been developed as a successful substitution therapy that aids the failing ventricle while a patient is waiting for the donor heart. We obtained genomics data from paired human heart samples harvested at the time of LVAD implant and explant. The heart failure patients in our study were supported by the LVAD for various periods of time. The goal of this study is to model the relationship between the time of LVAD support and gene expression changes. RESULTS: To serve the purpose, we propose a novel penalized partial least squares (PPLS) method to build a regression model. Compared with partial least squares and Breiman's random forest method, PPLS gives the best prediction results for the LVAD data.

Adaptation, Physiological↗
PubMed2004

Hands of life.

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Adult↗