Long-term lithium treatment in bipolar disorder. effects on glomerular filtration rate and other metabolic parameters

.BACKGROUND: Concerns about potential adverse effects of long-term exposure to lithium as a mood-stabilizing treatment notably include altered renal function. However, the incidence of severe renal dysfunction; rate of decline over time; effects of lithium dose, serum concentration, and duration of treatment; relative effects of lithium exposure vs. aging; and contributions of sex and other factors all remain unclear. METHODS: Accordingly, we acquired data from 12 collaborating international sites and 312 bipolar disorder patients (6142 person-years, 2669 assays) treated with lithium carbonate for 8-48 (mean 18) years and aged 20-89 (mean 56) years. We evaluated changes of estimated glomerular filtration rate (eGFR) as well as serum creatinine, urea-nitrogen, and glucose concentrations, white blood cell count, and body-mass index, and tested associations of eGFR with selected factors, using standard bivariate contrasts and regression modeling. RESULTS: Overall, 29.5% of subjects experienced at least one low value of eGFR ( 55; risk of ≥2 low values was 18.1%; none experienced end-stage renal failure. eGFR declined by 0.71%/year of age and 0.92%/year of treatment, both by 19% more among women than men. Mean serum creatinine increased from 0.87 to 1.17 mg/dL, BUN from 23.7 to 33.1 mg/dL, glucose from 88 to 122 mg/dL, and BMI from 25.9 to 26.6 kg/m2. By multivariate regression, risk factors for declining eGFR ranked: longer lithium treatment, lower lithium dose, higher serum lithium concentration, older age, and medical comorbidity. Later low eGFR was also predicted by lower initial eGFR, and starting lithium at age ≥ 40 years. LIMITATIONS: Control data for age-matched subjects not exposed to lithium were lacking. CONCLUSIONS: Long-term lithium treatment was associated with gradual decline of renal functioning (eGFR) by about 30% more than that was associated with aging alone. Risk of subnormal eGFR was from 18.1% (≥2 low values) to 29.5% (≥1 low value), requiring about 30 years of exposure. Additional risk factors for low eGFR were higher serum lithium level, longer lithium treatment, lower initial eGFR, and medical comorbidity, as well as older age

Structural and doping effects in the half-metallic double perovskite A2A_2CrWO6_6

he structural, transport, magnetic and optical properties of the double perovskite $A_2$CrWO$_6$ with $A=\text{Sr, Ba, Ca}$ have been studied. By varying the alkaline earth ion on the $A$ site, the influence of steric effects on the Curie temperature $T_C$ and the saturation magnetization has been determined. A maximum $T_C=458$ K was found for Sr$_2$CrWO$_6$ having an almost undistorted perovskite structure with a tolerance factor $f\simeq 1$. For Ca$_2$CrWO$_6$ and Ba$_2$CrWO$_6$ structural changes result in a strong reduction of $T_C$. Our study strongly suggests that for the double perovskites in general an optimum $T_C$ is achieved only for $f \simeq 1$, that is, for an undistorted perovskite structure. Electron doping in Sr$_2$CrWO$_6$ by a partial substitution of Sr$^{2+}$ by La$^{3+}$ was found to reduce both $T_C$ and the saturation magnetization $M_s$. The reduction of $M_s$ could be attributed both to band structure effects and the Cr/W antisites induced by doping. Band structure calculations for Sr$_2$CrWO$_6$ predict an energy gap in the spin-up band, but a finite density of states for the spin-down band. The predictions of the band structure calculation are consistent with our optical measurements. Our experimental results support the presence of a kinetic energy driven mechanism in $A_2$CrWO$_6$, where ferromagnetism is stabilized by a hybridization of states of the nonmagnetic W-site positioned in between the high spin Cr-sites.Comment: 14 pages, 10 figure

Evaluation of the passage of Lactobacillus gasseri K7 and bifidobacteria from the stomach to intestines using a single reactor model

<p>Abstract</p> <p>Background</p> <p>Probiotic bacteria are thought to play an important role in the digestive system and therefore have to survive the passage from stomach to intestines. Recently, a novel approach to simulate the passage from stomach to intestines in a single bioreactor was developed. The advantage of this automated one reactor system was the ability to test the influence of acid, bile salts and pancreatin.</p> <p><it>Lactobacillus gasseri </it>K7 is a strain isolated from infant faeces with properties making the strain interesting for cheese production. In this study, a single reactor system was used to evaluate the survival of <it>L. gasseri </it>K7 and selected bifidobacteria from our collection through the stomach-intestine passage.</p> <p>Results</p> <p>Initial screening for acid resistance in acidified culture media showed a low tolerance of <it>Bifidobacterium dentium </it>for this condition indicating low survival in the passage. Similar results were achieved with <it>B. longum </it>subsp. <it>infantis </it>whereas <it>B. animalis </it>subsp. <it>lactis </it>had a high survival.</p> <p>These initial results were confirmed in the bioreactor model of the stomach-intestine passage. <it>B. animalis </it>subsp. <it>lactis </it>had the highest survival rate (10%) attaining approximately 5 × 10⁶cfu ml^-1compared to the other tested bifidobacteria strains which were reduced by a factor of up to 10⁶. <it>Lactobacillus gasseri </it>K7 was less resistant than <it>B. animalis </it>subsp. <it>lactis </it>but survived at cell concentrations approximately 1000 times higher than other bifidobacteria.</p> <p>Conclusion</p> <p>In this study, we were able to show that <it>L. gasseri </it>K7 had a high survival rate in the stomach-intestine passage. By comparing the results with a previous study in piglets we could confirm the reliability of our simulation. Of the tested bifidobacteria strains, only <it>B. animalis </it>subsp. <it>lactis </it>showed acceptable survival for a successful passage in the simulation system.</p

Weather Influence and Classification with Automotive Lidar Sensors

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.Comment: 8 pages, will be published in the IEEE IV 2019 Proceeding

Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs

Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. Convolutional neural networks (CNNs), in contrast, have been specifically designed for highly heterogeneous data, such as natural images, by sliding convolutional filters over different positions in an image. Here, we suggest a new CNN architecture that combines the idea of hierarchical abstraction in neural networks with a prior on the spatial homogeneity of neuroimaging data: Whereas early layers are trained globally using standard convolutional layers, we introduce for higher, more abstract layers patch individual filters (PIF). By learning filters in individual image regions (patches) without sharing weights, PIF layers can learn abstract features faster and with fewer samples. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data. We demonstrate that CNNs using PIF layers result in higher accuracies, especially in low sample size settings, and need fewer training epochs for convergence. To the best of our knowledge, this is the first study which introduces a prior on brain MRI for CNN learning