Benefits of low-frequency attenuation of baha® in single-sided sensorineural deafness

To investigate the effect of low-frequency attenuation of Bone-Anchored Hearing Aids (Bahas) in users with single-sided sensorineural deafness (SSD). The underlying notion is that low-frequency sounds up to approximately 1500 Hz reach the contralateral ear without significant attenuation and that Bahas tend to show more distortion at lower frequencies. Furthermore, to transmit low frequencies, higher moving masses are needed when compared with high frequencies.

Focal cystic high-attenuation lesions: characterization in renal phantom by using photon-counting spectral CT--improved differentiation of lesion composition

To evaluate the capability of spectral computed tomography (CT) to improve the characterization of cystic high-attenuation lesions in a renal phantom and to test the hypothesis that spectral CT will improve the differentiation of cystic renal lesions with high protein content and those that have undergone hemorrhage or malignant contrast-enhancing transformation.

Attenuation of cerebrospinal fluid inflammation by the nonbacteriolytic antibiotic daptomycin versus that by ceftriaxone in experimental pneumococcal meningitis

Antibiotic-induced bacteriolysis exacerbates inflammation and brain damage in bacterial meningitis. Here the quality and temporal kinetics of cerebrospinal fluid (CSF) inflammation were assessed in an infant rat pneumococcal meningitis model for the nonbacteriolytic antibiotic daptomycin versus ceftriaxone. Daptomycin led to lower CSF concentrations of interleukin 1beta (IL-1beta), IL-10, IL-18, monocyte chemoattractant protein 1 (MCP-1), and macrophage inflammatory protein 1 alpha (MIP-1alpha) (P < 0.05). In experimental pneumococcal meningitis, daptomycin treatment resulted in more rapid bacterial killing, lower CSF inflammation, and less brain damage than ceftriaxone treatment.

Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20\% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning.

Peroxisome proliferating activating receptor gamma-independent attenuation of interleukin 6 and interleukin 8 secretion from primary endometrial stromal cells by thiazolidinediones

To assess the effect of thiazolidinediones on the regulation of inflammatory cytokines related to endometriosis in endometrial tissue and determine whether these effects occur via activation of the peroxisome proliferating activating receptor gamma (PPAR)-γ.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.