Calcineurin regulates innate antifungal immunity in neutrophils

Patients taking immunosuppressive drugs, like cyclosporine A (CsA), that inhibit calcineurin are highly susceptible to disseminated fungal infections, although it is unclear how these drugs suppress resistance to these opportunistic pathogens. We show that in a mouse model of disseminated Candida albicans infection, CsA-induced susceptibility to fungal infection maps to the innate immune system. To further define the cell types targeted by CsA, we generated mice with a conditional deletion of calcineurin B (CnB) in neutrophils. These mice displayed markedly decreased resistance to infection with C. albicans, and both CnB-deficient and CsA-treated neutrophils showed a defect in the ex vivo killing of C. albicans. In response to the fungal-derived pathogen-associated molecular pattern zymosan, neutrophils lacking CnB displayed impaired up-regulation of genes (IL-10, Cox2, Egr1, and Egr2) regulated by nuclear factor of activated T cells, the best characterized CnB substrate. This activity was Myd88 independent and was reproduced by stimulation with the β(1,3) glucan curdlan, indicating that dectin-1, rather than toll-like receptors, is the upstream activator of calcineurin. Our results suggest that disseminated fungal infections seen in CsA-treated patients are not just a general consequence of systemic suppression of adaptive immunity but are, rather, a result of the specific blockade of evolutionarily conserved innate pathways for fungal resistance

Spectroscopy and laser emission from materials doped with rare-earths

This thesis investigated the complete spectroscopic analysis for the development of solid-state laser sources in the infra-red (IR) region. For this purpose YAG (Yttrium aluminium garnet) ceramics doped with Yb, Tm and Ho ions were utilized

A big data approach to the development of mixed‐effects models for seizure count data

ObjectiveOur objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.MethodsUsing data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies.ResultsFor both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day.SignificanceThe optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications