Ecological evaluation of heavy metal pollution in the soil of Pb-Zn mines

Ecotoxicology - Soil heavy metal pollution evaluations are a necessary measure for mine ecological control projects. In this study, the heavy metals Pb, Zn and Cd were studied in mining areas,...     

老年人生命晚期获知疾病信息意向及影响因素CSCD

目的了解老年人生命晚期获知疾病相关信息意向及影响因素。方法2016年10月至2017年6月,采用生命晚期疾病信息意向问卷,利用方便抽样法对福州市中心城区7所养老机构及15个社区的414例年龄≥60岁的老年人进行横断面调查,采用单因素分析、多元线性回归与有序多分类logistic回归分析老年人对疾病相关信息的需求水平、获知程度意向及其影响因素。结果414例老年人疾病相关信息需求得分为(17.1±4.9)分;48.8%(202/414)希望详尽知晓,30.7%(127/414)希望选择性了解,20.5%(85/414)不想知道任何信息;多元线性回归分析显示,年龄、文化程度、是否接受/见过其他生命维持治疗(LSTs)是影响老年人疾病相关信息需求水平的主要因素(标准化回归系数分别为-0.141、0.116、0.115,均P<0.05);有序多分类logistic分析显示,年龄(以60~69岁为参照,70~79岁:OR=0.544,95%CI:0.310~0.957;80~89岁:OR=0.526,95%CI:0.289~0.956)、文化程度(以小学及以下为参照,大专及以上:OR=2.166,95%CI:1.093~4.290)、主要生活费来源(以其他补贴为参照,家人支持:OR=7.303,95%CI:1.157~46.108;退休金:OR=9.288,95%CI:1.502~57.415;公积金/储蓄:OR=15.676,95%CI:2.122~115.793)、是否接受/见过其他LSTs(以是为参照,OR=1.985,95%CI:1.150~3.425)是影响老年人疾病相关信息获知程度意向的主要因素。结论老年人生命晚期获知疾病相关信息的意向程度较高,年龄、文化程度、主要生活费来源、是否接受/见过其他生命维持治疗等是其主要影响因素。     

Specific alterations in plasma proteins during depressed,manic, and euthymic states of bipolar disorder

Bipolar disorder (BD) is a common psychiatric mood disorder affecting more than 1-2% of the general population of different European countries. Unfortunately, there is no objective laboratory-based test to aid BD diagnosis or monitor its progression, and little is known about the molecular basis of BD. Here, we performed a comparative proteomic study to identify differentially expressed plasma proteins in various BD mood states (depressed BD, manic BD, and euthymic BD) relative to healthy controls. A total of 10 euthymic BD, 20 depressed BD, 15 manic BD, and 20 demographically matched healthy control subjects were recruited. Seven high-abundance proteins were immunodepleted in plasma samples from the 4 experimental groups, which were then subjected to proteome-wide expression profiling by two-dimensional electrophoresis and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight tandem mass spectrometry. Proteomic results were validated by immunoblotting and bioinformatically analyzed using MetaCore. From a total of 32 proteins identified with 1.5-fold changes in expression compared with healthy controls, 16 proteins were perturbed in BD independent of mood state, while 16 proteins were specifically associated with particular BD mood states. Two mood-independent differential proteins, apolipoprotein (Apo) A1 and Apo L1, suggest that BD pathophysiology may be associated with early perturbations in lipid metabolism. Moreover, down-regulation of one mood-dependent protein, carbonic anhydrase 1 (CA-1), suggests it may be involved in the pathophysiology of depressive episodes in BD. Thus, BD pathophysiology may be associated with early perturbations in lipid metabolism that are independent of mood state, while CA-1 may be involved in the pathophysiology of depressive episodes.     

Population pharmacokinetics of tanezumab in phase 3 clinical trials for osteoarthritis pain

AimsThe aims were to 1) develop the pharmacokinetics model to describe and predict observed tanezumab concentrations over time, 2) test possible covariate parameter relationships that could influence clearance and distribution and 3) assess the impact of fixed dosing vs. a dosing regimen adjusted by body weight.MethodsIndividual concentration–time data were determined from 1608 patients in four phase 3 studies conducted to assess efficacy and safety of intravenous tanezumab. Patients received two or three intravenous doses (2.5, 5 or 10 mg) every 8 weeks. Blood samples for assessment of tanezumab PK were collected at baseline, 1 h post‐dose and at weeks 4, 8, 16 and 24 (or early termination) in all studies. Blood samples were collected at week 32 in two studies. Plasma samples were analyzed using a sensitive, specific, validated enzyme‐linked immunosorbent assay.ResultsA two compartment model with parallel linear and non‐linear elimination processes adequately described the data. Population estimates for clearance (CL), central volume (V ₁), peripheral volume (V ₂), inter‐compartmental clearance, maximum elimination capacity (VM) and concentration at half‐maximum elimination capacity were 0.135 l day^–1, 2.71 l, 1.98 l, 0.371 l day^–1, 8.03 μg day^–1 and 27.7 ng ml^–1, respectively. Inter‐individual variability (IIV) was included on CL, V ₁, V ₂ and VM. A mixture model accounted for the distribution of residual error. While gender, dose and creatinine clearance were significant covariates, only body weight as a covariate of CL, V ₁ and V ₂ significantly reduced IIV.ConclusionsThe small increase in variability associated with fixed dosing is consistent with other monoclonal antibodies and does not change risk : benefit.     

Severe lumbar spinal stenosis combined with Guillain-Barré syndrome: A case report

BACKGROUNDGuillain-Barré syndrome (GBS) is a rare disorder that typically presents with ascending weakness, pain, paraesthesias, and numbness, which mimic the findings in lumbar spinal stenosis. Here, we report a case of severe lumbar spinal stenosis combined with GBS.CASE SUMMARYA 70-year-old man with a history of lumbar spinal stenosis presented to our emergency department with severe lower back pain and lower extremity numbness. Magnetic resonance imaging confirmed the diagnosis of severe lumbar spinal stenosis. However, his symptoms did not improve postoperatively and he developed dysphagia and upper extremity numbness. An electromyogram was performed. Based on his symptoms, physical examination, and electromyogram, he was diagnosed with GBS. After 5 d of intravenous immunoglobulin (0.4 g/kg/d for 5 d) therapy, he gained 4/5 of strength in his upper and lower extremities and denied paraesthesias. He had regained 5/5 of strength in his extremities when he was discharged and had no symptoms during follow-up.CONCLUSIONGBS should be considered in the differential diagnosis of spinal disorder, even though magnetic resonance imaging shows severe lumbar spinal stenosis. This case highlights the importance of a careful diagnosis when a patient has a history of a disease and comes to the hospital with the same or similar symptoms.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

层面递进法激光腔内剜除高危前列腺增生的治疗体会

目的探讨1 470 nm激光剜除治疗高危前列腺增生的手术技巧及临床效果。 方法回顾分析2018年6月至2018年9月中山大学附属第三医院泌尿外科采用1 470 nm激光治疗共89例高危前列腺增生患者的临床资料，年龄平均（68±3）岁，前列腺体积（57.4±2.6）ml。所有患者均采用"寻找层面，先易后难，剜切结合"的层面递进法思路行激光腔内前列腺剜除术，比较患者术中及术后情况。 结果89例均顺利完成手术，与术前相比，术后3个月患者最大尿流率明显增加，[（6.9±2.1） ml/s vs（19.8±3.6） ml/s]。国际前列腺症状评分显著好转，[（24.6±1.7） vs（8.0±1.2）]。术中无输血、无电切综合征、无直肠和膀胱穿孔病例，无输尿管损伤、大出血、心脑血管意外等严重并发症发生。 结论层面递进法激光剜除技术构想对于高危前列腺增生外科包膜层面的寻找、减少术后并发症有独到优势，且易于掌握，或可为业界同行提供一个新的思路。     

A randomized study on the effect of modified behavioral activation treatment for depressive symptoms in rural left-behind elderly

Objective: To evaluate the effectiveness of a modified behavioral activation treatment (MBAT) intervention on reducing depressive symptoms in rural left-behind elderly.

Method: This is a randomized study registered in Chinese Clinical Trial Registry (ChiCTR-IOR-17011289). Eighty rural left-behind elderly people who had a Geriatric Depression Scale (GDS) score between 11 and 25 were randomly assigned to the intervention (n?=?40) and control group (n?=?40). The intervention group received both MBAT and regular treatment for 8 weeks while the control group received regular treatment. Both groups were assessed with the GDS, Beck Anxiety Inventory (BAI), and Oxford Happiness Questionnaire (OHQ) at baseline, immediately post-intervention, and at 3 months post-intervention.

Results: There were a total of 73 participants that completed the intervention. The scores of GDS and BAI decreased significantly, but the scores of OHQ increased significantly in the intervention group after 8 sessions of MBAT (P?<?.01). The reduction in depression symptoms after the intervention was maintained at the 3-month follow-up. Significant differences in GDS, BAI, and OHQ scores were observed between the intervention group and the control group (P?<?.01).

Conclusion: MBAT produced a significantly greater reduction in depressive symptoms than regular care in rural left-behind elderly.

Clinical or methodological significance of this article: A modified behavioral activation (BA) psychotherapy can significantly reduce the recurrence and seriousness of depression symptoms in the left-behind elderly with mild to moderate depression. This study also suggests that further study of the MBAT as an intervention will provide a direction for the management of mental health in rural left-behind elders.