Dexamethasone reduces food intake, weight gain and the hypothalamic 5-HT concentration and increases plasma leptin in rats

This study was conducted to define the regulatory mechanisms underlying stress-induced decreases in food intake and weight gain. Rats received a single or 4 daily injections of dexamethasone (0.1 or 1 mg/kg). Food intake and weight gain were recorded, and plasma leptin, brain contents of serotonin (5-hydroxytryptamine; 5-HT), 5-hydroxy-indole-acetic acid (5-HIAA) and the raphe expression of tryptophan hydroxylase (TPH), monoamine oxidase A (MAO-A) and 5-HT reuptake transporter (5-HTT) genes were examined. A single injection of dexamethasone did not acutely affect food intake, but cumulative food intake and weight gain were suppressed dose-dependently by daily injections of dexamethasone. Both a single and repeated injections of dexamethasone elevated plasma leptin in a dose dependent manner. 5-HT contents in the hypothalamus was decreased, but 5-HIAA increased, both by a single and repeated dexamethasone. A single injection of dexamethasone did not affect mRNA expressions of TPH, MAO-A and 5-HTT genes, but repeated dexamethasone increased them in the dorsal raphe nucleus. These results suggest that plasma leptin may play a role in dexamethasone-induced anorexia. Additionally, increased expression of MAO-A and 5-HTT genes by repeated dexamethasone appears to be implicated in decreases of the brain 5-HT contents.     

Neuropeptide Y overexpression in the preweanling Zucker (fa/fa) rat.

Hypothalamic preproNPY overexpression in the Zucker fatty (fa/fa) rat was examined. In situ hybridization was used to determine the relative level of preproNPY mRNA in the arcuate nucleus of +/+, +/fa, and fa/fa pups aged postnatal day 2 (P2), 5, 9, 12, or 25. The relative optical density (ROD) of probe hybridization in the arcuate, the area of hybridization (A), and the product of ROD x A (a measure of total arcuate preproNPY mRNA hybridization) were measured. Values were normalized to the mean +/fa value within each litter. Initial analysis showed that preproNPY mRNA hybridization (ROD x A) in fa/fa pups was significantly higher than +/fa and +/+ pups on P9, 12, and 25, and significantly higher than +/fa on P5. No significant difference between lean (+/+ and +/fa) genotypes, however, were observed at any age tested. Values from the lean genotypes were, therefore, pooled, and data were normalized to the mean value of lean animals for analysis. This analysis revealed that preproNPY mRNA hybridization in fa/fa pups was higher than lean littermates as early as P2.     

Effects of MR Parameter Changes on the Quantification of Diffusion Anisotropy and Apparent Diffusion Coefficient in Diffusion Tensor Imaging: Evaluation Using a Diffusional Anisotropic Phantom

Objective

To validate the usefulness of a diffusional anisotropic capillary array phantom and to investigate the effects of diffusion tensor imaging (DTI) parameter changes on diffusion fractional anisotropy (FA) and apparent diffusion coefficient (ADC) using the phantom.

Materials and Methods

Diffusion tensor imaging of a capillary array phantom was performed with imaging parameter changes, including voxel size, number of sensitivity encoding (SENSE) factor, echo time (TE), number of signal acquisitions, b-value, and number of diffusion gradient directions (NDGD), one-at-a-time in a stepwise-incremental fashion. We repeated the entire series of DTI scans thrice. The coefficients of variation (CoV) were evaluated for FA and ADC, and the correlation between each MR imaging parameter and the corresponding FA and ADC was evaluated using Spearman''s correlation analysis.

Results

The capillary array phantom CoVs of FA and ADC were 7.1% and 2.4%, respectively. There were significant correlations between FA and SENSE factor, TE, b-value, and NDGD, as well as significant correlations between ADC and SENSE factor, TE, and b-value.

Conclusion

A capillary array phantom enables repeated measurements of FA and ADC. Both FA and ADC can vary when certain parameters are changed during diffusion experiments. We suggest that the capillary array phantom can be used for quality control in longitudinal or multicenter clinical studies.     

复方中草药茯苓、白术和当归促进人许旺细胞的增殖

摘要： 背景和目的： 周围神经损伤修复的最佳方式是自体神经移植,然而自体神经移植造成患者的二次损伤使其在临床的应用中受到局限。雪旺细胞是周围神经组织工程研究的重要的种子细胞,需要对雪旺细胞进行体外培养、增殖和传代。由于雪旺细胞的增殖相当缓慢,培养周期长等方面限制。许多研究者转向中医药干预的方法进行探寻。令人欣慰的是：相关的实验传统复方制剂FBD能维持神经元生存和生长并促进BDNF等分泌物。本项研究将依据独特的实验模型,采用研究者选用中国药品生物制品检定所提纯的人参皂甙Rb1,加入体外培养的雪旺细胞中进行实验,观察细胞加药后的生长情况。 方法：利用SD雄性大鼠雪旺细胞株（ISC）,加入不同浓度的FBD,利用MTT比色分析法、RT-PCR, ELISA,测定法检测不同浓度FBD在不同培养时间对体外培养大鼠雪旺细胞增殖的影响。 结果：FBD在20微克/毫升的浓度对雪旺细胞增殖有明显促进作用。而50微克/毫升FBD对细胞增殖的促进作用与对照组相近。 结论 ： FBD有促进体外培养雪旺细胞快速增殖的作用,从而为促进神经损伤的再生途径提供一些新的药物使用基础研究。     

A pilot study of brain activation in children with trichotillomania during a visual-tactile symptom provocation task: A functional magnetic resonance imaging study

Objectives

In order to reveal the etiology and pathophysiology of trichotillomania (TTM), it is necessary to investigate which brain regions are involved in TTM, but limited knowledge exists regarding the neurobiology of TTM and the available functional neuroimaging studies of TTM are little. The purpose of the present study was to investigate the specific brain regions involved in the pathophysiology of TTM with symptom provocation task using functional magnetic resonance imaging (fMRI) for children and adolescents with TTM.

Methods

Pediatric subjects who met the DSM-IV TR criteria for TTM (n = 9) and age-, sex-, handedness-, IQ matched healthy controls(HC) (n = 10), ages 9 to 17 years, were recruited for two fMRI experiments; symptom provocation of Visual Only (VO) and Visual and Tactile (VT). They were scanned while viewing two alternating blocks of symptom provocation (S) and neutral (N) movies.

Results

Random effects between-group analysis revealed significant activation in left temporal cortex(including middle and superior temporal gyrus), dorsal posterior cingulate gyrus, and putamen for the contrast S > N in TTM subjects versus HC subjects during the VO session. And TTM subjects demonstrated higher activity in the precuneus and dorsal posterior cingulate gyrus to the contrast S > N during the VT session.

Conclusions

This study provided an objective whole-brain-based analysis that directed researchers to areas that were abnormal in TTM. Using the symptom provocation tasks, we found significant differences in regional brain function between pediatric TTM and HC subjects. However, in the face of modest statistical power, our preliminary findings in TTM need to be replicated in a larger sample. As the functional neuroanatomic circuits involved in TTM remain largely unexplored, future functional neuroimaging studies using other various paradigms may help investigate the neuroanatomic abnormalities of TTM.     

Local susceptibility causes diffusion alterations in patients with Alzheimer’s disease and mild cognitive impairment

Recent studies with positron emission tomography (PET) using the Pittsburgh compound B (PIB) found widespread amyloid plaque depositions in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) and even in cognitively normal (CN) subjects. The aim of this study was to investigate whether the local susceptibility gradients in brain tissue alter regional diffusion measurements using MRI in patients with AD and MCI. Two diffusion tensor (DT)-MRI data sets were acquired with alternating polarities of the external diffusion-sensitizing gradients. Three subject groups were included: 15 patients with AD, 18 patients with MCI and 16 CN. Maps of mean diffusivity (MD) and fractional anisotropy (FA) were computed separately for positive (p) and negative (n) polarities (pMD and nMD, pFA and nFA). Voxel-wise paired t-tests were performed between pMD versus nMD or between pFA versus nFA maps, separately for each subject group. We also investigated regions-of-interest (ROIs) in the brain. Based on the pair-wise comparisons, we found significant differences between pMD and nMD in all three groups. Results of ROI-based analyses showed that the non-linear behaviors of the ROI data sets were shown for all three groups. In conclusion, significant differences of MD maps between the two polarities of diffusion-sensitizing gradients were found, suggesting that the intrinsic background gradients may alter MD signals in specific regions. It can be important to take into account the effects of local gradient alterations during diffusion measurements in patients with AD, MCI and elderly controls.     

Development and Validation of a Deep Learning–Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

BACKGROUND AND PURPOSE:Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.MATERIALS AND METHODS:A deep learning–based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.RESULTS:In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758–0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668–0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.CONCLUSIONS:The deep learning–based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Alzheimer disease (AD) is the most common cause of dementia, with mild cognitive impairment (MCI) regarded as a transitional state between normal cognition and early stages of dementia.¹ Although current therapeutic and preventive options are only moderately effective, a reliable decision-making diagnostic approach is important during early stages of AD.^2,3 The guidelines of the National Institute on Aging–Alzheimer’s Association suggest that MR imaging is a supportive imaging tool in the diagnostic work-up of patients with AD and MCI.^2,3 Imaging biomarkers play an important role in the diagnosis of AD, both in the research field and in clinical practice. The identification of amyloid and the τ PET ligand provided huge advances in understanding the pathophysiologic mechanisms underlying AD and its early diagnosis, even in the preclinical or prodromal stage.^4-6 Although amyloid and τ PET are more sensitive and specific for the diagnosis of AD, they are expensive to perform, have limited availability, and require ionizing radiation, limiting their use in clinical practice. CSF amyloid and τ are also important biomarkers that could be used for AD diagnostics in the clinical research setting.^3,7-9 However, CSF AD biomarkers also have limited availability. MR imaging, however, is widely available and used in standard practice to support the diagnosis of AD and to exclude other causes of cognitive impairment, including stroke, vascular dementia, normal-pressure hydrocephalus, and inflammatory and neoplastic conditions.3D T1-weighted volumetric MR imaging is the most important MR imaging tool in the diagnosis of AD. 3D volumetry has long been used as a morphologic diagnostic tool for AD, not only as a visual assessment or manual segmentation but for semiautomatic and automatic segmentation. Examples include semiautomatic structural changes on MR imaging,¹⁰ automated hippocampal volumetry,¹¹ entorhinal cortex atrophy,¹² and changes in pineal gland volume.¹³ Although user-friendly automated segmentation algorithms were first introduced 20 years ago, evidence supporting the use of 3D volumetry in clinical practice is currently insufficient. Visual assessment requires experience, and automatic 3D volumetry requires a long acquisition time.To our knowledge, limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict AD.¹⁴ Currently available algorithms have low clinical feasibility because of the long processing time for brain segmentation, and the classification algorithm based on T1-weighted brain MR images needs to be validated in a large external dataset. The purpose of this study was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of AD using 3D T1-weighted brain MR images.