The value of mastectomy flap fixation in reducing fluid drainage and seroma formation in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Prolonged and excessive drainage of serous fluid and seroma formation constitute the most common complications after mastectomy for breast carcinoma. Seroma formation delays wound healing, increases susceptibility to infection, skin flap necrosis, persistent pain and prolongs convalescence. For this, several techniques have been investigated to improve primary healing and minimize seroma formation.</p> <p>Materials and methods</p> <p>Between June 2009 and July 2010 forty patients with breast carcinoma, scheduled for modified radical mastectomy, were randomly divided into 2 groups, the study group (20) and the control group (20). In the study group; the mastectomy flaps were fixed to the underlying muscles in raws, at various parts of the flap and at the wound edge using fine absorbable sutures. In the control group; the wound was closed in the conventional method at the edges. Closed suction drains were used in both groups. Patients, tumor characteristics and operative related factors were recorded. The amount and color of drained fluid were recorded daily. The drains were removed when the amount become less than 50 cc. The total amount and duration of drained fluid and the formation of seroma were recorded and the results were compared between the two groups.</p> <p>Results</p> <p>In the flap fixation group, the drain was removed in significantly shorter time compared to the control group (p < 0.001). Also, the total amount of fluid drained was significantly lower in the flap fixation group (p < 0.001). The flap fixation group showed a significantly lower frequency of seroma formation compared to the control group, both clinically (p = 0.028) and ultrasonographically (p = 0.047).</p> <p>Conclusions</p> <p>The mastectomy flap fixation technique is a valuable procedure that significantly decreases the incidence of seroma formation, and reduces the duration and amount of drained fluid. However, it should be tried on a much wider scale to prove its validity.</p

Halophyte–Endophyte Interactions: Linking Microbiome Community Distribution and Functionality to Salinity

Many plants are unable to adapt to rapid environmental changes (e.g., salinity, drought, or limited nutrients) and may acquire assistance from microbes that have the capacity to increase tolerance of host-plants in stress conditions. By having the right microbes, the plants are more resilient! Such microbes include endophytes that inhabit inner tissues of the plant without causing symptoms of disease in their host. However, this plant–endophytic association exists only when chemical equilibrium is maintained between both, therefore making this mutualistic interaction even more unique. Therefore it is interesting to decode the endophytic community composition in halophytes specifically in the most salt-tolerant halophyte species Salicornia europaea, and further determine the factors that could affect this association. Moreover, understanding the endophytes potential plant growth-promoting activities in association with host (S. europaea) and non-host plant (non-halophytes) are the focus of this chapter

IMP 2.0: A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks

IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated highthroughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.ed

IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks

IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu

Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes - about 65 genes out of an estimated several hundred - are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu