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At least 55 records · Page 3Linked to original sources
PubMed1994

Acetylcysteine as a cytoprotective antioxidant in patients with severe sepsis: potential new use for an old drug.

OBJECTIVE: To stimulate debate regarding a potential new use for acetylcysteine as a cellular antioxidant in severely septic patients with systemic inflammatory response syndrome (SIRS). DATA SOURCES: A MEDLINE review of published animal, human, and laboratory studies relating to the cytopathogenic effects of active radicals in SIRS and the protective effects of acetylcysteine and glutathione. STUDY SELECTION: Few studies were available so all studies pertinent to the objective were reviewed. DATA EXTRACTION: Clinical and basic science data from the available trials of the effects of acetylcysteine on active radical production or active radical cell injury were extrapolated to predict the effect of acetylcysteine on human sepsis. DATA SYNTHESIS: Severe sepsis is a major cause of SIRS. Much of the cellular injury associated with SIRS is mediated by active radicals produced by inflammatory cells that overwhelm endogenous antioxidants. Reduced glutathione is a crucial intracellular antioxidant that becomes depleted during SIRS. Regeneration of glutathione can be achieved by acetylcysteine, which unlike glutathione itself penetrates cells. In animal models of sepsis and lung injury, acetylcysteine mitigates the cytopathologic effects of SIRS. In humans, clinical benefit has been demonstrated in the SIRS of established fulminant hepatic failure. CONCLUSIONS: The data do not as yet lead to any firm conclusions regarding the value of acetylcysteine in the management of SIRS in severe sepsis. The animal and human studies are, however, sufficiently encouraging to warrant formal trials to test the hypothesis that acetylcysteine therapy has a cytoprotective effect in sepsis.

Acetylcysteine↗
PubMed1999

Cancer patients who prefer to die at home. Characterizations of municipalities with several or few occurrences of home deaths.

This article presents a descriptive study based on quantitative and qualitative methods. We wished to determine factors that promote or restrict home deaths. The Norwegian Central Bureau of Statistics released non-identifiable data for the time period 1990-1994 for all municipalities in Norway. Relevant health and social data from the Norwegian Social Science Data Service for the 24 municipalities, which had more than 20% or less than 10% of the cancer patients dying in their own homes, were analysed. Key persons in the home care teams were interviewed. There were few occurrences of home deaths in municipalities with a local hospital, good capacity in nursing homes or a larger percentage of one-person households. Indicators for several occurrences of home deaths were openness, good co-operation with physicians, and a stable, flexible staff. In addition, the patient had to have a strong desire to die at home. Finally, the employees had to be professionally confident and willing to go beyond the prescribed shift hours.

Choice Behavior↗
PubMed2003

A half century of longitudinal methods in social gerontology: evidence of change in the journal.

OBJECTIVES: With a focus on the use of longitudinal data, this study reviews trends in the quantitative analysis of social science data on aging during the past half century. METHODS: A content analysis was performed on 227 articles from 12 volumes that were systematically sampled from the Journal of Gerontology: Social Sciences to examine change in the type of data and quantitative methods used (1946-2000). RESULTS: Cross-sectional analysis remains the single most frequent type of study, but the publication of analyses based on longitudinal panel data increased appreciably over the five decades studied. There was little increase in the use of repeated cross-sectional analysis. DISCUSSION: Despite the widespread use of cross-sectional analysis, interest in data with more than one occasion of measurement has grown among social scientists who are reviewing for and publishing in the JOURNAL. Given the longitudinal data now available, social science research on aging should give more explicit attention to three issues: attrition, change in repeated measures of independent variables, and models to account for many waves of data.

Bibliometrics↗
PubMed2000

Demographics of the gay and lesbian population in the United States: evidence from available systematic data sources.

This work provides an overview of standard social science data sources that now allow some systematic study of the gay and lesbian population in the United States. For each data source, we consider how sexual orientation can be defined, and we note the potential sample sizes. We give special attention to the important problem of measurement error, especially the extent to which individuals recorded as gay and lesbian are indeed recorded correctly. Our concern is that because gays and lesbians constitute a relatively small fraction of the population, modest measurement problems could lead to serious errors in inference. In examining gays and lesbians in multiple data sets we also achieve a second objective: We provide a set of statistics about this population that is relevant to several current policy debates.

Adolescent↗
PubMed1996

Barriers to cancer treatment: a review of published research.

PURPOSE/OBJECTIVES: To review published research on barriers to cancer treatment to provide a foundation for subsequent research and program and policy development directed at diminishing these barriers. DATA SOURCES: Relevant literature from medical and behavioral science data bases published between 1964 and 1994. Researchers reviewed 752 abstracts; they identified 160 articles that related directly to research on barriers to cancer treatment. Of these 160 articles, researchers chose 61 for a subsequent review using criteria to evaluate the strength of the study design and sampling procedures. DATA SYNTHESIS: The major barriers consistently documented to influence whether or not patients with cancer sought or continued treatment included communication problems between patients and providers, lack of information on side effects, cost of treatment, difficulties in obtaining and maintaining insurance coverage, and absence of social support networks. Access barriers generally were greater for older women, members of minority groups, and patients of lower socioeconomic status. The vast majority of the studies were conceptual or descriptive in nature and were based on nonprobability clinic-based samples. CONCLUSIONS: The limitations of existing research point to the need for studies on barriers to cancer treatment based on analytic population-based study designs that examine the relative importance of factors derived from multivariate explanatory models. This information may be used to develop programs and policies to ameliorate treatment barriers for patients with cancer. IMPLICATIONS FOR NURSING PRACTICE: The research priorities set forth by the Oncology Nursing Society also indicate a need for this type of research because quality of life, cost containment, and outcomes assessment all are directly or indirectly affected by the timely diagnosis of cancer. Treatment barriers have the potential to significantly affect an individual's ability to seek care and ultimately to increase the cost of care associated with adverse outcomes that may result from delays in seeking treatment.

Female↗
PubMed2006

A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED.

In the social and health sciences, data are often structured hierarchically, with individuals nested within groups. Dyads constitute a special case of hierarchically structured data with variation at both the individual and dyadic level. Analyses of data from dyads pose several challenges due to the interdependence between members within dyads and issues related to small group sizes. Multilevel analytic techniques have been developed and applied to dyadic data in an attempt to resolve these issues. In this article, we describe a set of analyses for modeling individual- and dyad-level influences on binary outcomes using SAS statistical software; and we discuss the benefits and limitations of such an approach. For illustrative purposes, we apply these techniques to estimate individual-dyad-level predictors of viral hepatitis C infection among heterosexual couples in East Harlem, New York City.

Journal Article↗
PubMed2006

Statistical analysis of real-time PCR data.

BACKGROUND: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. RESULTS: In the first approach, a multiple regression analysis model was developed to derive DeltaDeltaCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the DeltaDeltaCt can be derived from analysis of effects of variables. The other two models involve calculation DeltaCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. CONCLUSION: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.

Analysis of Variance↗
PubMed2003

Students' motivations for data handling choices and behaviors: their explanations of performance.

Cries for increased accountability through additional assessment are heard throughout the educational arena. However, as demonstrated in this study, to make a valid assessment of teaching and learning effectiveness, educators must determine not only what students do, but also why they do it, as the latter significantly affects the former. This study describes and analyzes 14- to 16-year-old students' explanations for their choices and performances during science data handling tasks. The study draws heavily on case-study methods for the purpose of seeking an in-depth understanding of classroom processes in an English comprehensive school. During semistructured scheduled and impromptu interviews, students were asked to describe, explain, and justify the work they did with data during their science classes. These student explanations fall within six categories, labeled 1) implementing correct procedures, 2) following instructions, 3) earning marks, 4) doing what is easy, 5) acting automatically, and 6) working within limits. Each category is associated with distinct outcomes for learning and assessment, with some motivations resulting in inflated performances while others mean that learning was underrepresented. These findings illuminate the complexity of student academic choices and behaviors as mediated by an array of motivations, casting doubt on the current understanding of student performance.

Adolescent↗
PubMed2000

Laparoscopic resection for colorectal cancer: is it justified?

Controversy remains regarding the appropriateness of laparoscopic methods for the curative resection of colonic neoplasms. Long-term results after minimally invasive resection must be shown to be equivalent or better than those after open resection in order to justify the new technique in the setting of cancer. This article discusses adequacy of resection and short-term results, long-term outcome data, port and abdominal wound tumors, oncologic and immunologic basic science data, and the role of laparoscopy in the treatment of rectal cancers.

Anastomosis, Surgical↗
PubMed2006

Math and science motivation: A longitudinal examination of the links between choices and beliefs.

This study addresses the longitudinal associations between youths' out-of-school activities, expectancies-values, and high school course enrollment in the domains of math and science. Data were collected on 227 youth who reported on their activity participation in 5th grade, expectancies-values in 6th and 10th grade, and courses taken throughout high school. Math and science course grades at 5th and 10th grade were gathered through school record data. Results indicated youths' math and science activity participation predicted their expectancies and values, which, in turn, predicted the number of high school courses above the predictive power of grades. Although there were mean-level differences between boys and girls on some of these indicators, relations among indicators did not significantly differ by gender.

Achievement↗
PubMed1999

A generalized rank-order method for nonparametric analysis of data from exercise science: a tutorial.

Frequent violations of the assumption that data are normally distributed occur in exercise science and other life and behavioral sciences. When this assumption is violated, parametric statistical analyses may be inappropriate for data analysis. We provide a rationale for using a generalized form of nonparametric analyses based on the Puri and Sen (1985) L treated as a chi 2 approximation. If data do not meet the assumption of normality, this nonparametric approach has substantial power and is easy to use. An advantage of this generalized technique is that ranked data may be used in standard parametric statistical programs widely available on desktop and mainframe computers, for example, regression, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA) within BioMed, SAS, SPSS. Once the data are ranked and analyzed with these programs, the only adjustment required is to use a standard formula to calculate the nonparametric test statistic, L, instead of the parametric test statistic (e.g., F). Thus, rank-order nonparametric models become parallel with their parametric counterparts allowing the researcher to select between them based on characteristics of the data distribution. Examples of this approach are provided using data from exercise science for regression, ANOVA (including repeated measures) and MANOVA techniques from SPSSPC. Using these procedures, researchers can easily examine data distributions and make an appropriate decision about parametric or nonparametric analyses while continuing to use their regular statistical packages.

Analysis of Variance↗
PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗
PubMed2001

The life sciences Global Image Database (GID).

Although a vast amount of life sciences data is generated in the form of images, most scientists still store images on extremely diverse and often incompatible storage media, without any type of metadata structure, and thus with no standard facility with which to conduct searches or analyses. Here we present a solution to unlock the value of scientific images. The Global Image Database (GID) is a web-based (http://www.gwer.ch/qv/gid/gid.ht m ) structured central repository for scientific annotated images. The GID was designed to manage images from a wide spectrum of imaging domains ranging from microscopy to automated screening. The annotations in the GID define the source experiment of the images by describing who the authors of the experiment are, when the images were created, the biological origin of the experimental sample and how the sample was processed for visualization. A collection of experimental imaging protocols provides details of the sample preparation, and labeling, or visualization procedures. In addition, the entries in the GID reference these imaging protocols with the probe sequences or antibody names used in labeling experiments. The GID annotations are searchable by field or globally. The query results are first shown as image thumbnail previews, enabling quick browsing prior to original-sized annotated image retrieval. The development of the GID continues, aiming at facilitating the management and exchange of image data in the scientific community, and at creating new query tools for mining image data.

Biological Science Disciplines↗
PubMed2005

The impact of Life Science Identifier on informatics data.

Since the Life Science Identifier (LSID) data identification and access standard made its official debut in late 2004, several organizations have begun to use LSIDs to simplify the methods used to uniquely name, reference and retrieve distributed data objects and concepts. In this review, the authors build on introductory work that describes the LSID standard by documenting how five early adopters have incorporated the standard into their technology infrastructure and by outlining several common misconceptions and difficulties related to LSID use, including the impact of the byte identity requirement for LSID-identified objects and the opacity recommendation for use of the LSID syntax. The review describes several shortcomings of the LSID standard, such as the lack of a specific metadata standard, along with solutions that could be addressed in future revisions of the specification.

Computational Biology↗
PubMed2025

EDAmame: interactive exploratory data analyses with explainable models.

SUMMARY: Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. AVAILABILITY AND IMPLEMENTATION: A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.

Machine Learning↗