Comparing the Functional Independence Measure and the interRAI/MDS for use in the functional assessment of older adults: a review of the literature

<p>Abstract</p> <p>Background</p> <p>The rehabilitation of older persons is often complicated by increased frailty and medical complexity - these in turn present challenges for the development of health information systems. Objective investigation and comparison of the effectiveness of geriatric rehabilitation services requires information systems that are comprehensive, reliable, valid, and sensitive to clinically relevant changes in older persons. The Functional Independence Measure is widely used in rehabilitation settings - in Canada this is used as the central component of the National Rehabilitation Reporting System of the Canadian Institute of Health Information. An alternative system has been developed by the interRAI consortium. We conducted a literature review to compare the development and measurement properties of these two systems.</p> <p>Methods</p> <p>English language literature published between 1983 (initial development of the FIM) and 2008 was searched using Medline and CINAHL databases, and the reference lists of retrieved articles. Relevant articles were summarized and charted using the criteria proposed by Streiner. Additionally, attention was paid to the ability of the two systems to address issues particularly relevant to older rehabilitation clients, such as medical complexity, comorbidity, and responsiveness to small but clinically meaningful improvements.</p> <p>Results</p> <p>In total, 66 articles were found that met the inclusion criteria. The majority of FIM articles studied inpatient rehabilitation settings; while the majority of interRAI/MDS articles focused on nursing home settings. There is evidence supporting the reliability of both instruments. There were few articles that investigated the construct validity of the interRAI/MDS.</p> <p>Conclusion</p> <p><b>A</b>dditional psychometric research is needed on both the FIM and MDS, especially with regard to their use in different settings and with different client groups.</p

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

Testing the role of predicted gene knockouts in human anthropometric trait variation

National Heart, Lung, and Blood Institute (NHLBI) S.L. is funded by a Canadian Institutes of Health Research Banting doctoral scholarship. G.L. is funded by Genome Canada and Génome Québec; the Canada Research Chairs program; and the Montreal Heart Institute Foundation. C.M.L. is supported by Wellcome Trust (grant numbers 086596/Z/08/Z, 086596/Z/08/A); and the Li Ka Shing Foundation. N.S. is funded by National Institutes of Health (grant numbers HL088456, HL111089, HL116747). The Mount Sinai BioMe Biobank Program is supported by the Andrea and Charles Bronfman Philanthropies. GO ESP is supported by NHLBI (RC2 HL-103010 to HeartGO, RC2 HL-102923 to LungGO, RC2 HL-102924 to WHISP). The ESP exome sequencing was performed through NHLBI (RC2 HL-102925 to BroadGO, RC2 HL- 102926 to SeattleGO). EGCUT work was supported through the Estonian Genome Center of University of Tartu by the Targeted Financing from the Estonian Ministry of Science and Education (grant number SF0180142s08); the Development Fund of the University of Tartu (grant number SP1GVARENG); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN) [grant number 3.2.0304.11-0312]; and through FP7 (grant number 313010). EGCUT were further supported by the US National Institute of Health (grant number R01DK075787). A.K.M. was supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship (#7-12-MN- 02). The BioVU dataset used in the analyses described were obtained from Vanderbilt University Medical Centers BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. Genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS. Funding to pay the Open Access publication charges for this article was provided by a block grant from Research Councils UK to the University of Cambridge

CCAAT/Enhancer Binding Protein alpha uses distinct domains to prolong pituitary cells in the Growth 1 and DNA Synthesis phases of the cell cycle

BACKGROUND: A number of transcription factors coordinate differentiation by simultaneously regulating gene expression and cell proliferation. CCAAT/enhancer binding protein alpha (C/EBPα) is a basic/leucine zipper transcription factor that integrates transcription with proliferation to regulate the differentiation of tissues involved in energy balance. In the pituitary, C/EBPα regulates the transcription of a key metabolic regulator, growth hormone. RESULTS: We examined the consequences of C/EBPα expression on proliferation of the transformed, mouse GHFT1-5 pituitary progenitor cell line. In contrast to mature pituitary cells, GHFT1-5 cells do not contain C/EBPα. Ectopic expression of C/EBPα in the progenitor cells resulted in prolongation of both growth 1 (G1) and the DNA synthesis (S) phases of the cell cycle. Transcription activation domain 1 and 2 of C/EBPα were required for prolongation of G1, but not of S. Some transcriptionally inactive derivatives of C/EBPα remained competent for G1 and S phase prolongation. C/EBPα deleted of its leucine zipper dimerization functions was as effective as full-length C/EBPα in prolonging G1 and S. CONCLUSION: We found that C/EBPα utilizes mechanistically distinct activities to prolong the cell cycle in G1 and S in pituitary progenitor cells. G1 and S phase prolongation did not require that C/EBPα remained transcriptionally active or retained the ability to dimerize via the leucine zipper. G1, but not S, arrest required a domain overlapping with C/EBPα transcription activation functions 1 and 2. Separation of mechanisms governing proliferation and transcription permits C/EBPα to regulate gene expression independently of its effects on proliferation

Challenges in multidisciplinary cancer care among general surgeons in Canada

<p>Abstract</p> <p>Background</p> <p>While many factors can influence the way that cancer care is delivered, including the way that evidence is packaged and disseminated, little research has evaluated how health care professionals who manage cancer patients seek and use this information to identify whether and how this could be supported. Through interviews we identified that general surgeons experience challenges in coordinating care for complex cancer patients whose management is not easily addressed by guidelines, and conducted a population-based survey of general surgeon information needs and information seeking practices to extend these findings.</p> <p>Methods</p> <p>General surgeons with privileges at acute care hospitals in Ontario, Canada were mailed a questionnaire to solicit information needs (task, importance), information seeking (source, frequency of and reasons for use), key challenges and suggested solutions. Non-responders received up to three reminder packages. Significant differences among sub-groups (age, setting) were examined statistically (Kruskal Wallis, Mann Whitney, Chi Square). Standard qualitative methods were used to thematically analyze open-ended responses.</p> <p>Results</p> <p>The response rate was 44.2% (170/385) representing all 14 health regions. System resource constraints (60.4%), comorbidities (56.4%) and physiologic factors (51.8%) were top-ranked issues creating information needs. Local surgical colleagues (84.6%), other local colleagues (82.2%) and the Internet (81.1%) were top-ranked sources of information, primarily due to familiarity and speed of access. No resources were considered to be highly applicable to patient care. Challenges were related to limitations in diagnostics and staging, operative resources, and systems to support multidisciplinary care, together accounting for 76.0% of all reported issues. Findings did not differ significantly by surgeon age or setting of care.</p> <p>Conclusion</p> <p>General surgeons appear to use a wide range of information resources but they may not address the complex needs of many cancer patients. Decision-making is challenged by informational and logistical issues related to the coordination of multidisciplinary care. This suggests that limitations in system capacity may, in part, contribute to variable guideline compliance. Further research is required to evaluate the appropriateness of information seeking, and both concurrent and consecutive mechanisms by which to achieve multidisciplinary care.</p

Hung Out to Dry: Choice of Priority Ecoregions for Conserving Threatened Neotropical Anurans Depends on Life-History Traits

Background: In the Neotropics, nearly 35 % of amphibian species are threatened by habitat loss, habitat fragmentation, and habitat split; anuran species with different developmental modes respond to habitat disturbance in different ways. This entails broad-scale strategies for conserving biodiversity and advocates for the identification of high conservation-value regions that are significant in a global or continental context and that could underpin more detailed conservation assessments towards such areas. Methodology/Principal Findings: We identified key ecoregion sets for anuran conservation using an algorithm that favors complementarity (beta-diversity) among ecoregions. Using the WWF’s Wildfinder database, which encompasses 700 threatened anuran species in 119 Neotropical ecoregions, we separated species into those with aquatic larvae (AL) or terrestrial development (TD), as this life-history trait affects their response to habitat disturbance. The conservation target of 100 % of species representation was attained with a set of 66 ecoregions. Among these, 30 were classified as priority both for species with AL and TD, 26 were priority exclusively for species with AL, and 10 for species with TD only. Priority ecoregions for both developmental modes are concentrated in the Andes and in Mesoamerica. Ecoregions important for conserving species with AL are widely distributed across the Neotropics. When anuran life histories were ignored, species with AL were always underrepresented in priority sets

SPARC: a matricellular regulator of tumorigenesis

Although many clinical studies have found a correlation of SPARC expression with malignant progression and patient survival, the mechanisms for SPARC function in tumorigenesis and metastasis remain elusive. The activity of SPARC is context- and cell-type-dependent, which is highlighted by the fact that SPARC has shown seemingly contradictory effects on tumor progression in both clinical correlative studies and in animal models. The capacity of SPARC to dictate tumorigenic phenotype has been attributed to its effects on the bioavailability and signaling of integrins and growth factors/chemokines. These molecular pathways contribute to many physiological events affecting malignant progression, including extracellular matrix remodeling, angiogenesis, immune modulation and metastasis. Given that SPARC is credited with such varied activities, this review presents a comprehensive account of the divergent effects of SPARC in human cancers and mouse models, as well as a description of the potential mechanisms by which SPARC mediates these effects. We aim to provide insight into how a matricellular protein such as SPARC might generate paradoxical, yet relevant, tumor outcomes in order to unify an apparently incongruent collection of scientific literature

Diffuse glioma growth: a guerilla war

In contrast to almost all other brain tumors, diffuse gliomas infiltrate extensively in the neuropil. This growth pattern is a major factor in therapeutic failure. Diffuse infiltrative glioma cells show some similarities with guerilla warriors. Histopathologically, the tumor cells tend to invade individually or in small groups in between the dense network of neuronal and glial cell processes. Meanwhile, in large areas of diffuse gliomas the tumor cells abuse pre-existent “supply lines” for oxygen and nutrients rather than constructing their own. Radiological visualization of the invasive front of diffuse gliomas is difficult. Although the knowledge about migration of (tumor)cells is rapidly increasing, the exact molecular mechanisms underlying infiltration of glioma cells in the neuropil have not yet been elucidated. As the efficacy of conventional methods to fight diffuse infiltrative glioma cells is limited, a more targeted (“search & destroy”) tactic may be needed for these tumors. Hopefully, the study of original human glioma tissue and of genotypically and phenotypically relevant glioma models will soon provide information about the Achilles heel of diffuse infiltrative glioma cells that can be used for more effective therapeutic strategies