A participatory action research approach to strengthening health managers’ capacity at district level in Eastern Uganda

BACKGROUND: Many approaches to improving health managers’ capacity in poor countries, particularly those pursued by external agencies, employ non-participatory approaches and often seek to circumvent (rather than strengthen) weak public management structures. This limits opportunities for strengthening local health managers’ capacity, improving resource utilisation and enhancing service delivery. This study explored the contribution of a participatory action research approach to strengthening health managers’ capacity in Eastern Uganda. METHODS: This was a qualitative study that used open-ended key informant interviews, combined with review of meeting minutes and observations to collect data. Both inductive and deductive thematic analysis was undertaken. The Competing Values Framework of organisational management functions guided the deductive process of analysis and the interpretation of the findings. The framework builds on four earlier models of management and regards them as complementary rather than conflicting, and identifies four managers’ capacities (collaborate, create, compete and control) by categorising them along two axes, one contrasting flexibility versus control and the other internal versus external organisational focus. RESULTS: The findings indicate that the participatory action research approach enhanced health managers’ capacity to collaborate with others, be creative, attain goals and review progress. The enablers included expanded interaction spaces, encouragement of flexibility, empowerment of local managers, and the promotion of reflection and accountability. Tension and conflict across different management functions was apparent; for example, while there was a need to collaborate, maintaining control over processes was also needed. These tensions meant that managers needed to learn to simultaneously draw upon and use different capacities as reflected by the Competing Values Framework in order to maximise their effectiveness. CONCLUSIONS: Improved health manager capacity is essential if sustained improvements in health outcomes in lowincome countries are to be attained. The expansion of interaction spaces, encouragement of flexibility, empowerment of local managers, and the promotion of reflection and accountability were the key means by which participatory action research strengthened health managers’ capacity. The participatory approach to implementation therefore created opportunities to strengthen health managers’ capacity

Increasing vegetable intakes: rationale and systematic review of published interventions

Purpose While the health benefits of a high fruit and vegetable consumption are well known and considerable work has attempted to improve intakes, increasing evidence also recognises a distinction between fruit and vegetables, both in their impacts on health and in consumption patterns. Increasing work suggests health benefits from a high consumption specifically of vegetables, yet intakes remain low, and barriers to increasing intakes are prevalent making intervention difficult. A systematic review was undertaken to identify from the published literature all studies reporting an intervention to increase intakes of vegetables as a distinct food group. Methods Databases—PubMed, PsychInfo and Medline—were searched over all years of records until April 2015 using pre-specified terms. Results Our searches identified 77 studies, detailing 140 interventions, of which 133 (81 %) interventions were conducted in children. Interventions aimed to use or change hedonic factors, such as taste, liking and familiarity (n = 72), use or change environmental factors (n = 39), use or change cognitive factors (n = 19), or a combination of strategies (n = 10). Increased vegetable acceptance, selection and/or consumption were reported to some degree in 116 (83 %) interventions, but the majority of effects seem small and inconsistent. Conclusions Greater percent success is currently found from environmental, educational and multi-component interventions, but publication bias is likely, and long-term effects and cost-effectiveness are rarely considered. A focus on long-term benefits and sustained behaviour change is required. Certain population groups are also noticeably absent from the current list of tried interventions

Basic science of osteoarthritis

Osteoarthritis (OA) is a prevalent, disabling disorder of the joints that affects a large population worldwide and for which there is no definitive cure. This review provides critical insights into the basic knowledge on OA that may lead to innovative end efficient new therapeutic regimens. While degradation of the articular cartilage is the hallmark of OA, with altered interactions between chondrocytes and compounds of the extracellular matrix, the subchondral bone has been also described as a key component of the disease, involving specific pathomechanisms controlling its initiation and progression. The identification of such events (and thus of possible targets for therapy) has been made possible by the availability of a number of animal models that aim at reproducing the human pathology, in particular large models of high tibial osteotomy (HTO). From a therapeutic point of view, mesenchymal stem cells (MSCs) represent a promising option for the treatment of OA and may be used concomitantly with functional substitutes integrating scaffolds and drugs/growth factors in tissue engineering setups. Altogether, these advances in the fundamental and experimental knowledge on OA may allow for the generation of improved, adapted therapeutic regimens to treat human OA.(undefined

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets