The Great Dictator

Abstract not availabl

T-cell Subsets and Antifungal Host Defenses

It has been long appreciated that protective immunity against fungal pathogens is dependent on activation of cellular adaptive immune responses represented by T lymphocytes. The T-helper (Th)1/Th2 paradigm has proven to be essential for the understanding of protective adaptive host responses. Studies that have examined the significance of regulatory T cells in fungal infection, and the recent discovery of a new T-helper subset called Th17 have provided crucial information for understanding the complementary roles played by the various T-helper lymphocytes in systemic versus mucosal antifungal host defense. This review provides an overview of the role of the various T-cell subsets during fungal infections and the reciprocal regulation between the T-cell subsets contributing to the tailored host response against fungal pathogens

Diagnosis of invasive candidiasis in the ICU

Invasive candidiasis ranges from 5 to 10 cases per 1,000 ICU admissions and represents 5% to 10% of all ICU-acquired infections, with an overall mortality comparable to that of severe sepsis/septic shock. A large majority of them are due to Candida albicans, but the proportion of strains with decreased sensitivity or resistance to fluconazole is increasingly reported. A high proportion of ICU patients become colonized, but only 5% to 30% of them develop an invasive infection. Progressive colonization and major abdominal surgery are common risk factors, but invasive candidiasis is difficult to predict and early diagnosis remains a major challenge. Indeed, blood cultures are positive in a minority of cases and often late in the course of infection. New nonculture-based laboratory techniques may contribute to early diagnosis and management of invasive candidiasis. Both serologic (mannan, antimannan, and betaglucan) and molecular (Candida-specific PCR in blood and serum) have been applied as serial screening procedures in high-risk patients. However, although reasonably sensitive and specific, these techniques are largely investigational and their clinical usefulness remains to be established. Identification of patients susceptible to benefit from empirical antifungal treatment remains challenging, but it is mandatory to avoid antifungal overuse in critically ill patients. Growing evidence suggests that monitoring the dynamic of Candida colonization in surgical patients and prediction rules based on combined risk factors may be used to identify ICU patients at high risk of invasive candidiasis susceptible to benefit from prophylaxis or preemptive antifungal treatment

Distinct roles for interleukin-12p40 and tumour necrosis factor in resistance to oral candidiasis defined by gene-targeting

Cell-mediated immunity is important for anti-Candida host defence in mucosal tissues. In this study we used cytokine-specific gene knockout mice to investigate the requirement for T helper type 1 (Th1) and Th2 cytokines in recovery from oral candidiasis. Knockout mice used in this study included interleukin-4 (IL-4), IL-10, IL-12p40, interferon-gamma (IFN-gamma), and tumour necrosis factor (TNF). The mice were challenged either orally or systemically with Candida albicans yeasts, and levels of colonization were determined. IL-12p40 knockout mice developed chronic oropharyngeal candidiasis, but were not more susceptible to systemic challenge. On the other hand, TNF knockout mice displayed increased susceptibility to both oral and systemic challenge, but only in the acute stages of infection. TNF apparently has a protective effect in the acute stages of both oral and systemic candidiasis, whereas IL-12p40 is essential for recovery from oral but not systemic candidiasis. The role of IL-12p40, and its relation to T-cell-mediated responses remain to be determined

Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI