Modeling strategy to identify patients with primary immunodeficiency utilizing risk management and outcome measurement.

This study seeks to generate analytic insights into risk management and probability of an identifiable primary immunodeficiency defect. The Jeffrey Modell Centers Network database, Jeffrey Modell Foundation's 10 Warning Signs, the 4 Stages of Testing Algorithm, physician-reported clinical outcomes, programs of physician education and public awareness, the SPIRIT® Analyzer, and newborn screening, taken together, generates P values of less than 0.05%. This indicates that the data results do not occur by chance, and that there is a better than 95% probability that the data are valid. The objectives are to improve patients' quality of life, while generating significant reduction of costs. The advances of the world's experts aligned with these JMF programs can generate analytic insights as to risk management and probability of an identifiable primary immunodeficiency defect. This strategy reduces the uncertainties related to primary immunodeficiency risks, as we can screen, test, identify, and treat undiagnosed patients. We can also address regional differences and prevalence, age, gender, treatment modalities, and sites of care, as well as economic benefits. These tools support high net benefits, substantial financial savings, and significant reduction of costs. All stakeholders, including patients, clinicians, pharmaceutical companies, third party payers, and government healthcare agencies, must address the earliest possible precise diagnosis, appropriate intervention and treatment, as well as stringent control of healthcare costs through risk assessment and outcome measurement. An affected patient is entitled to nothing less, and stakeholders are responsible to utilize tools currently available. Implementation offers a significant challenge to the entire primary immunodeficiency community.

Linezolid effects on bacterial toxin production and host immune response: review of the evidence.

BACKGROUND: Linezolid is active against a broad range of gram-positive pathogens and has the potential to also affect production of bacterial toxins and host immune function. OBJECTIVE: To assess the evidence for direct effects of linezolid on bacterial toxin synthesis and modulation of host immune responses. METHODS: Literature searches were performed of the PubMed and OVID databases. Reviews and non-English language articles were excluded. Articles with information on the effect of linezolid on bacterial toxin synthesis and immune responses were selected for further review, and data were summarized. RESULTS: Substantial in vitro evidence supports effects of linezolid on bacterial toxin production; however, the strength of the evidence and the nature of the effects are mixed. In the case of Staphylococcus aureus, repeated observations support the inhibition of production of certain staphylococcal toxins (Panton-Valentine leukocidin, protein A, and α- and ß-hemolysin) by linezolid, whereas only solitary reports indicate inhibition (toxic shock syndrome toxin-1, coagulase, autolysins, and enterotoxins A and B) or stimulation (phenol-soluble modulins) of toxin production by linezolid. In the case of Streptococcus pyogenes, there are solitary reports of linezolid inhibition (protein M, deoxyribonuclease, and streptococcal pyrogenic exotoxins A, B, and F) or stimulation (immunogenic secreted protein 2 and streptococcal inhibitor of complement-mediated lysis) of toxin production, whereas published evidence for effects on streptolysin O production is conflicting. In vitro data are limited, but suggest that linezolid might also have indirect effects on host cytokine expression through inhibition of bacterial production of toxins. In vivo data from preclinical animal studies and a single clinical study in humans are limited and equivocal insofar as a potential role for linezolid in modulating the host inflammatory response; this is due in part to the difficulty in isolating antimicrobial effects and toxin synthesis inhibitory effects of linezolid from any secondary effects on host inflammatory response. CONCLUSIONS: Available evidence supports the possibility that linezolid can inhibit, and in some cases stimulate, toxin production in clinically relevant pathogens. However, more research will be needed to determine the potential clinical relevance of those findings for linezolid.

CXCR 3 activation promotes lymphocyte transendothelial migration across human hepatic endothelium under fluid flow.

T cells infiltrating the inflamed liver express high levels of CXCR 3 and show enhanced migration to CXCR 3 ligands in chemotactic assays. Moreover, CXCR 3 ligands are up-regulated on hepatic endothelium at sites of T-cell infiltration in chronic hepatitis, and their presence correlates with outcome of inflammatory liver disease. We used a flow-based adhesion assay with human hepatic endothelium to investigate the function of CXCR 3 on lymphocyte adhesion to and transmigration through hepatic endothelium under physiological conditions of blood flow. To more accurately model the function of in vivo activated CXCR 3(high) lymphocytes, we isolated T cells from human liver tissue and studied their behavior in flow-based adhesion assays. We demonstrate that CXCR 3 not only promoted the adhesion of effector T cells to endothelium from flow but also drove transendothelial migration. Moreover, these responses could be stimulated either by endogenous CXCR 3 ligands secreted by the endothelium or by exogenous CXCR 3 ligands derived from other cell types and presented by the endothelium. This study thus demonstrates that activation of CXCR 3 promotes lymphocyte adhesion and transendothelial migration under flow and that human hepatic endothelium can present functionally active chemokines secreted by other cell types within the liver.