Svenningsenmurphy1525

Patients with diabetes have a higher incidence of infections with Candida albicans, Staphylococcus aureus and Mycobacterium tuberculosis, yet factors contributing to this increased risk are largely unknown. We hypothesize that altered innate and adaptive immune responses during diabetes contribute to an increased susceptibility to infections.

We studied cytokine responses to ex vivo pathogenic stimulations in a cohort with type 1 diabetes (n = 243) and non-diabetic healthy control subjects (n = 56) using isolated peripheral blood mononuclear cells (PBMCs). Clinical phenotypical data including BMI, duration of diabetes, and HbA

levels were collected and related to the cytokine production capacity.

Adjusted for age, sex and BMI, the presence of diabetes was associated with significantly lower IL-1β, IL-6, TNF-α, and IL-17 production upon ex vivo stimulation of PBMCs with C. albicans and S. aureus (all, p < 0.05). In response to stimulation with M. tuberculosis only IL-17 (p < 0.001) was lower in patients with diabetes. Patients with the shortest diabetes duration had a significant lower IL-1β, IL-6 and TNF-α production (all, p < 0.01) after M. tuberculosis stimulation. Older patients had a significant lower IFN-γ (p < 0.05) production after stimulation with all three pathogens. HbA

levels and BMI had no significant impact on cytokine production.

PBMCs of patients with type 1 diabetes demonstrate significantly lower cytokine production in response to stimulation with several pathogens, which likely explain, at least in part, the increased susceptibility for these infections.

PBMCs of patients with type 1 diabetes demonstrate significantly lower cytokine production in response to stimulation with several pathogens, which likely explain, at least in part, the increased susceptibility for these infections.

Fractional anisotropy (FA) and mean diffusivity (MD) are measures derived from diffusion-weighted imaging that represent the integrity of the corticospinal tract (CST) after stroke. Some studies of the motor system after stroke extract FA and MD from native space while others extract from standard space making comparison across studies challenging.

The purpose was to compare CST integrity measures extracted from standard versus native space in individuals with chronic stroke. Twenty-four individuals with stroke underwent diffusion-weighted imaging and motor impairment assessment. The spatial location of the CST was identified using four commonly utilized approaches; therefore, our results are applicable to a variety of approaches.

FA extracted from standard space (FA

) was significantly different from FA extracted from native space (FA

) for all four approaches; FA

was greater than FA

for three approaches. read more The relationship between ipsilesional CST FA and UE FM was significant for all approaches ane.

Many electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need.

Ten-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recos EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.

These findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.

Generally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T

*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches.

We derived the standard deviation to the mean-square ratio of the true T

* signal from the multi-echo EPI (ME-EPI) dataset. For the true T

*-variation-based value, we removed the S

(initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset.

The activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S

component removed.

Conventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied.

We demonstrated white noise-removed true T

*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.

We demonstrated white noise-removed true T2*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.