Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Deficiency of Vasodilator-Stimulated Phosphoprotein (VASP) Increases Blood-Brain-Barrier Damage and Edema Formation after Ischemic Stroke in Mice

Background: Stroke-induced brain edema formation is a frequent cause of secondary infarct growth and deterioration of neurological function. The molecular mechanisms underlying edema formation after stroke are largely unknown. Vasodilator-stimulated phosphoprotein (VASP) is an important regulator of actin dynamics and stabilizes endothelial barriers through interaction with cell-cell contacts and focal adhesion sites. Hypoxia has been shown to foster vascular leakage by downregulation of VASP in vitro but the significance of VASP for regulating vascular permeability in the hypoxic brain in vivo awaits clarification. Methodology/Principal Findings: Focal cerebral ischemia was induced in Vasp2/2 mice and wild-type (WT) littermates by transient middle cerebral artery occlusion (tMCAO). Evan’s Blue tracer was applied to visualize the extent of blood-brainbarrier (BBB) damage. Brain edema formation and infarct volumes were calculated from 2,3,5-triphenyltetrazolium chloride (TTC)-stained brain slices. Both mouse groups were carefully controlled for anatomical and physiological parameters relevant for edema formation and stroke outcome. BBB damage (p,0.05) and edema volumes (1.7 mm360.5 mm3 versus 0.8 mm360.4 mm3; p,0.0001) were significantly enhanced in Vasp2/2 mice compared to controls on day 1 after tMCAO. This was accompanied by a significant increase in infarct size (56.1 mm3617.3 mm3 versus 39.3 mm3610.7 mm3, respectively; p,0.01) and a non significant trend (p.0.05) towards worse neurological outcomes. Conclusion: Our study identifies VASP as critical regulator of BBB maintenance during acute ischemic stroke. Therapeutic modulation of VASP or VASP-dependent signalling pathways could become a novel strategy to combat excessive edema formation in ischemic brain damage

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Development of a universal psycho-educational intervention to prevent common postpartum mental disorders in primiparous women: a multiple method approach

<p>Abstract</p> <p>Background</p> <p>Prevention of postnatal mental disorders in women is an important component of comprehensive health service delivery because of the substantial potential benefits for population health. However, diverse approaches to prevention of postnatal depression have had limited success, possibly because anxiety and adjustment disorders are also problematic, mental health problems are multifactorially determined, and because relationships amongst psychosocial risk factors are complex and difficult to modify. The aim of this paper is to describe the development of a novel psycho-educational intervention to prevent postnatal mental disorders in mothers of firstborn infants.</p> <p>Methods</p> <p>Data from a variety of sources were synthesised: a literature review summarised epidemiological evidence about neglected modifiable risk factors; clinical research evidence identified successful psychosocial treatments for postnatal mental health problems; consultations with clinicians, health professionals, policy makers and consumers informed the proposed program and psychological and health promotion theories underpinned the proposed mechanisms of effect. The intervention was pilot-tested with small groups of mothers and fathers and their first newborn infants.</p> <p>Results</p> <p><it>What Were We Thinking! </it>is a psycho-educational intervention, designed for universal implementation, that addresses heightened learning needs of parents of first newborns. It re-conceptualises mental health problems in mothers of infants as reflecting unmet needs for adaptations in the intimate partner relationship after the birth of a baby, and skills to promote settled infant behaviour. It addresses these two risk factors in half-day seminars, facilitated by trained maternal and child health nurses using non-psychiatric language, in groups of up to five couples and their four-week old infants in primary care. It is designed to promote confidence and reduce mental disorders by providing skills in sustainable sleep and settling strategies, and the re-negotiation of the unpaid household workload in non-confrontational ways. Materials include a Facilitators' Handbook, creatively designed worksheets for use in seminars, and a book for couples to take home for reference. A website provides an alternative means of access to the intervention.</p> <p>Conclusions</p> <p><it>What Were We Thinking! </it>is a postnatal mental health intervention which has the potential to contribute to psychologically-informed routine primary postnatal health care and prevent common mental disorders in women.</p

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation

International audienceLarge-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods