A Retrospective Analysis of Central and Peripheral Metabolic Characteristics in Patients with Cryptococcal Meningitis.

INTRODUCTION: Most current treatment strategies and investigations on cryptococcal meningitis (CM) focus primarily on the central nervous system (CNS), often overlooking the complex interplay between the CNS and the peripheral system. This study aims to explore the characteristics of central and peripheral metabolism in patients with CM. METHODS: Patients diagnosed with CM as per the hospital records of the Fourth People's Hospital of Nanning were retrospectively analyzed. Patients were divided into two groups, non-structural damage of the brain (NSDB) and structural damage of the brain (SDB), according to the presence of brain lesions as detected with imaging. Based on the presence of enlarged cerebral ventricles, the cases in the SDB group were classified into non-ventriculomegaly (NVM) and ventriculomegaly (VM). Various parameters of cerebrospinal fluid (CSF) and peripheral blood (PB) were analyzed. RESULTS: A significant correlation was detected between CSF and PB parameters. The levels of CSF-adenosine dehydrogenase (ADA), CSF-protein, CSF-glucose, and CSF-chloride ions were significantly correlated with the levels of PB-aminotransferase, PB-bilirubin, PB-creatinine (Cr), PB-urea nitrogen, PB-electrolyte, PB-protein, and PB-lipid. Compared with NSDB, the levels of CSF-glucose were significantly decreased in the SDB group, while the levels of CSF-lactate dehydrogenase (LDH) and CSF-protein were significantly increased in the SDB group. In the SDB group, the levels of PB-potassium, PB-hemoglobin(Hb), and PB-albumin were significantly decreased in the patients with VM, while the level of PB-urea nitrogen was significantly increased in these patients. CONCLUSION: Metabolic and structural alterations in the brain may be associated with peripheral metabolic changes.

Joint learning framework of cross-modal synthesis and diagnosis for Alzheimer's disease by mining underlying shared modality information.

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders presenting irreversible progression of cognitive impairment. How to identify AD as early as possible is critical for intervention with potential preventive measures. Among various neuroimaging modalities used to diagnose AD, functional positron emission tomography (PET) has higher sensitivity than structural magnetic resonance imaging (MRI), but it is also costlier and often not available in many hospitals. How to leverage massive unpaired unlabeled PET to improve the diagnosis performance of AD from MRI becomes rather important. To address this challenge, this paper proposes a novel joint learning framework of unsupervised cross-modal synthesis and AD diagnosis by mining underlying shared modality information, improving the AD diagnosis from MRI while synthesizing more discriminative PET images. We mine underlying shared modality information in two aspects: diversifying modality information through the cross-modal synthesis network and locating critical diagnosis-related patterns through the AD diagnosis network. First, to diversify the modality information, we propose a novel unsupervised cross-modal synthesis network, which implements the inter-conversion between 3D PET and MRI in a single model modulated by the AdaIN module. Second, to locate shared critical diagnosis-related patterns, we propose an interpretable diagnosis network based on fully 2D convolutions, which takes either 3D synthesized PET or original MRI as input. Extensive experimental results on the ADNI dataset show that our framework can synthesize more realistic images, outperform the state-of-the-art AD diagnosis methods, and have better generalization on external AIBL and NACC datasets.

Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting.

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://github.com/bridgeqiqi/DREAM.

SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization.

There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is an important cerebrovascular disease. Although deep learning-based models have been proposed for this task, generalizing these models to unseen sites is difficult due to not only the large inter-site discrepancy among different scanners, imaging protocols, and populations, but also the variations in stroke lesion shape, size, and location. To tackle this issue, we introduce a self-adaptive normalization network, termed SAN-Net, to achieve adaptive generalization on unseen sites for stroke lesion segmentation. Motivated by traditional z-score normalization and dynamic network, we devise a masked adaptive instance normalization (MAIN) to minimize inter-site discrepancies, which standardizes input MR images from different sites into a site-unrelated style by dynamically learning affine parameters from the input; i.e., MAIN can affinely transform the intensity values. Then, we leverage a gradient reversal layer to force the U-net encoder to learn site-invariant representation with a site classifier, which further improves the model generalization in conjunction with MAIN. Finally, inspired by the "pseudosymmetry" of the human brain, we introduce a simple yet effective data augmentation technique, termed symmetry-inspired data augmentation (SIDA), that can be embedded within SAN-Net to double the sample size while halving memory consumption. Experimental results on the benchmark Anatomical Tracings of Lesions After Stroke (ATLAS) v1.2 dataset, which includes MR images from 9 different sites, demonstrate that under the "leave-one-site-out" setting, the proposed SAN-Net outperforms recently published methods in terms of quantitative metrics and qualitative comparisons.

Overexpressed ski efficiently promotes neurorestoration, increases neuronal regeneration, and reduces astrogliosis after traumatic brain injury.

Traumatic brain injury (TBI) survivors suffer from long-term disability and neuropsychiatric sequelae due to irreparable brain tissue destruction. However, there are still few efficient therapies to promote neurorestoration in damaged brain tissue. This study aimed to investigate whether the pro-oncogenic gene ski can promote neurorestoration after TBI. We established a ski-overexpressing experimental TBI mouse model using adenovirus-mediated overexpression through immediate injection after injury. Hematoxylin-eosin staining, MRI-based 3D lesion volume reconstruction, neurobehavioral tests, and analyses of neuronal regeneration and astrogliosis were used to assess neurorestorative efficiency. The effects of ski overexpression on the proliferation of cultured immature neurons and astrocytes were evaluated using imaging flow cytometry. The Ski protein level increased in the perilesional region at 3 days post injury. ski overexpression further elevated Ski protein levels up to 14 days post injury. Lesion volume was attenuated by approximately 36-55% after ski overexpression, with better neurobehavioral recovery, more newborn immature and mature neurons, and less astrogliosis in the perilesional region. Imaging flow cytometry results showed that ski overexpression elevated the proliferation rate of immature neurons and reduced the proliferation rate of astrocytes. These results show that ski can be considered a novel neurorestoration-related gene that effectively promotes neurorestoration, facilitates neuronal regeneration, and reduces astrogliosis after TBI.

Modelling method of inter-building movement for campus-scale occupancy simulation: A case study.

As an important factor in the investigation of building energy consumption, occupant behavior (OB) has been widely studied on the building level. However so far, studies of OB modelling on the district scale remain limited. Indeed, district-scale OB modelling has been facing the challenges from the scarcity of district-scale data, modelling methods, as well as simulation application. This study initiates the extrapolation of occupancy modelling methodology from building level to district scale through proposing modelling methods of inter-building movements. The proposed modelling methods utilize multiple distribution fittings and Bayesian network to upscale the event description methods from inter-zone movement events at the building level to inter-building movement events at the district level. This study provides a framework on the application of the proposed modelling methods for a university campus in the suburbs of Shanghai, taking advantages of data sensing, monitoring and survey techniques. With the collected campus-scale occupancy data, this paper defines five patterns of inter-building movement. One pattern represents the dominated inter-building movement events for one kind of students in their daily campus life. Based on the quantitative descriptions for various inter-building movement events, this study performs the stochastic simulation for the campus district, using Markov chain models. The simulation results are then validated with the campus-scale occupancy measurement data. Furthermore, the impact of inter-building movement modelling methods on building energy demand is evaluated for the library building, taking the deterministic occupancy schedules suggested by current building design standard as a baseline.

Learning Representation for Clustering Via Prototype Scattering and Positive Sampling.

Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative pairs learn uniform representations for clustering, in which negative pairs, however, may inevitably lead to the class collision issue and consequently compromise the clustering performance. Non-contrastive-based methods, on the other hand, avoid class collision issue, but the resulting non-uniform representations may cause the collapse of clustering. To enjoy the strengths of both worlds, this paper presents a novel end-to-end deep clustering method with prototype scattering and positive sampling, termed ProPos. Specifically, we first maximize the distance between prototypical representations, named prototype scattering loss, which improves the uniformity of representations. Second, we align one augmented view of instance with the sampled neighbors of another view-assumed to be truly positive pair in the embedding space-to improve the within-cluster compactness, termed positive sampling alignment. The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness. By optimizing ProPos in an end-to-end expectation-maximization framework, extensive experimental results demonstrate that ProPos achieves competing performance on moderate-scale clustering benchmark datasets and establishes new state-of-the-art performance on large-scale datasets. Source code is available at https://github.com/Hzzone/ProPos.

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark.

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components-identity- and age-related features-in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance than state-of-the-art methods for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. The source code and datasets are available at http://hzzone.github.io/MTLFace.

[Activation of the adenosine A2A receptor at the acute stage of moderate traumatic brain injury enhances the neuroprotective effects of oxaloacetate].

The purpose of the present study was to investigate the effect of glutamate scavenger oxaloacetate (OA) combined with CGS21680, an adenosine A2A receptor (A2AR) agonist, on acute traumatic brain injury (TBI), and to elucidate the underlying mechanisms. C57BL/6J mice were subjected to moderate-level TBI by controlled cortical impact, and then were treated with OA, CGS21680, or OA combined with CGS21680 at acute stage of TBI. At 24 h post TBI, neurological severity score, brain water content, glutamate concentration in cerebrospinal fluid (CSF), mRNA and protein levels of IL-1ß and TNF-α, mRNA level and activity of glutamate oxaloacetate aminotransferase (GOT), and ATP level of brain tissue were detected. The results showed that neurological deficit, brain water content, glutamate concentration in CSF, and the inflammatory cytokine IL-1ß and TNF-α production were exacerbated in CGS21680 treated mice. Administrating OA suppressed the rise of both glutamate concentration in CSF and brain water content, and elevated the ATP level of cerebral tissue. More interestingly, neurological deficit, brain edema, glutamate concentration, IL-1ß and TNF-α levels were ameliorated significantly in mice treated with OA combined with CGS21680. The combined treatment exhibited better therapeutic effects than single OA treatment. We also observed that GOT activity was enhanced in single CGS21680 treatment group, and both the GOT mRNA level and GOT activity were up-regulated in early-stage combined treatment group. These results suggest that A2AR can improve the efficiency of GOT and potentiate the ability of OA to metabolize glutamate. This may be the mechanism that A2AR activation in combination group augmented the neuroprotective effect of OA rather than aggravated the brain damages. Taken together, the present study provides a new insight for the clinical treatment of TBI with A2AR agonists and OA.

High glutamate concentration reverses the inhibitory effect of microglial adenosine 2A receptor on NLRP3 inflammasome assembly and activation.

NLRP3 inflammasome plays a crucial role in the innate immune system. Our group previously reported that the microglial adenosine 2A receptor (A2AR) regulates canonical neuroinflammation, which is affected by the glutamate concentration. However, the regulatory effect of A2AR on NLRP3 inflammasome and the effects of glutamate concentration remain unknown. Therefore, we aimed to investigate the regulatory effect of microglial A2AR on NLRP3 inflammasome assembly and activation as well as the effects of glutamate concentration on the inflammasome assembly and activation. Experiments were conducted on magnetically sorted primary microglia from P14 mice. The results showed that pharmacological A2AR activation ameliorated NLRP3 activation under no or low glutamate concentrations, but this effect was reversed by high glutamate concentrations. Moreover, the mRNA levels of NLRP3 inflammasome-related genes were not affected by A2AR activation or the glutamate concentration. We further demonstrated that A2AR activation inhibited the interaction between NLRP3 and caspase 1 under no or low glutamate concentrations while promoting their interaction under high glutamate concentrations. The oligomerization of ASC also showed a similar trend. In conclusion, our findings proved that the high glutamate concentration could reverse the inhibition of A2AR on NLRP3 inflammasome activation by modulating its assembly, which provides new insights into the regulatory effect of A2AR on neuroinflammation under different pathological conditions.

HMGB1 mediates cognitive impairment caused by the NLRP3 inflammasome in the late stage of traumatic brain injury.

BACKGROUND: Cognitive impairment in the late stage of traumatic brain injury (TBI) is associated with the NOD-, LRR and pyrin domain-containing protein 3 (NLRP3) inflammasome, which plays an important role in neuroinflammation. Although classical inflammatory pathways have been well-documented in the late stage of TBI (4-8 weeks post-injury), the mechanism by which the NLRP3 inflammasome impairs cognition is still unclear. METHODS: Mice lacking the gene encoding for NLRP3 (NLRP3-knockout mice) and their wild-type littermates were used in a controlled cortical impact model of TBI. Levels of NLRP3 inflammasome and inflammatory factors such as IL-1ß and HMGB1 were detected in post-injury hippocampal tissue, as well as long-term potentiation. Behaviors were assessed by T-maze test, novel object recognition, and nesting tests. Glycyrrhizin was used to antagonize HMGB1. Calcium imaging were performed on primary neuronal cultures. RESULTS: By using the NLRP3-knockout TBI model, we found that the continuous activation of the NLRP3 inflammasome and high mobility group box 1 (HMGB1) release were closely related to cognitive impairment. We also found that inhibition of HMGB1 improved LTP reduction and cognitive function by increasing the phosphorylation level of the NMDAR1 subunit at serine 896 while reducing NLRP3 inflammasome activation. CONCLUSION: NLRP3 inflammasome damages memory in the late stage of TBI primarily through HMGB1 upregulation and provides an explanation for the long-term progression of cognitive dysfunction.

NLRP3 Inflammasome-Dependent Increases in High Mobility Group Box 1 Involved in the Cognitive Dysfunction Caused by Tau-Overexpression.

Tau hyperphosphorylation is a characteristic alteration present in a range of neurological conditions, such as traumatic brain injury (TBI) and neurodegenerative diseases. Treatments targeting high-mobility group box protein 1 (HMGB1) induce neuroprotective effects in these neuropathologic conditions. However, little is known about the interactions between hyperphosphorylated tau and HMGB1 in neuroinflammation. We established a model of TBI with controlled cortical impacts (CCIs) and a tau hyperphosphorylation model by injecting the virus encoding human P301S tau in mice, and immunofluorescence, western blotting analysis, and behavioral tests were performed to clarify the interaction between phosphorylated tau (p-tau) and HMGB1 levels. We demonstrated that p-tau and HMGB1 were elevated in the spatial memory-related brain regions in mice with TBI and tau-overexpression. Animals with tau-overexpression also had significantly increased nucleotide-binding oligomerization domain-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome activation, which manifested as increases in apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), activating caspase-1 and interleukin 1 beta (IL-1ß) levels. In addition, NLRP3-/- mice and the HMGB1 inhibitor, glycyrrhizin, were used to explore therapeutic strategies for diseases with p-tau overexpression. Compared with wild-type (WT) mice with tau-overexpression, downregulation of p-tau and HMGB1 was observed in NLRP3-/- mice, indicating that HMGB1 alterations were NLRP3-dependent. Moreover, treatment with glycyrrhizin at a late stage markedly reduced p-tau levels and improved performance in the Y- and T-mazes and the ability of tau-overexpressing mice to build nests, which revealed improvements in spatial memory and advanced hippocampal function. The findings identified that p-tau has a triggering role in the modulation of neuroinflammation and spatial memory in an NLRP3-dependent manner, and suggest that treatment with HMGB1 inhibitors may be a better therapeutic strategy for tauopathies.

Novel HIF-1-target gene isthmin1 contributes to hypoxia-induced hyperpermeability of pulmonary microvascular endothelial cells monolayers.

Hypoxia-induced pulmonary microvascular endothelial cell (PMVEC) monolayers hyperpermeability is vital for vascular leakage, which participates in vascular diseases, such as acute lung injury (ALI) and high-altitude pulmonary edema (HAPE). We previously observed that PMVEC permeability was markedly elevated in hypoxia when cocultured with primary type II alveolar epithelial cells (AECII) in which isthmin1 (ISM1) was highly upregulated. However, whether the upregulation of ISM1 plays a role in hypoxia-induced PMVEC hyperpermeability is unclear. In this study, we assessed the role of AECII-derived ISM1 in hypoxia-induced PMVEC hyperpermeability with an AECII/PMVEC coculture system and uncovered the underlying mechanism whereby hypoxia stimulates ISM1 gene expression. We found that ISM1 gene expression was upregulated in cultured AECII cells exposed to hypoxia (3% O2) and that AECII-derived ISM1 participated in hypoxia-induced hyperpermeability of PMVEC monolayers, as small interference RNA (siRNA)-mediated knockdown of ISM1 in AECII markedly attenuated the increase in PMVEC permeability in coculture system under hypoxia. In addition, we confirmed that ISM1 was regulated by hypoxia-inducible factor-1α (HIF1α) according to the evidence that silencing of HIF1α inhibited the hypoxia-mediated upregulation of ISM1. Mechanismly, overexpression of HIF1α transcriptionally activated ISM1 gene expression by directly binding to the conserved regulatory elements upstream of the ism1 locus. We identified a novel HIF-1-target gene ISM1, which involves in hyperpermeability of pulmonary microvascular endothelial cell monolayers under hypoxia. Our in vitro cell experiments implied that the upregulated ISM1 derived from alveolar epithelium might be a vital modulator in hypoxia-induced endothelial hyperpermeability and thereby implicates with hypoxic pulmonary-related diseases.

HIF1α-induced upregulation of KLF4 promotes migration of human vascular smooth muscle cells under hypoxia.

Hypoxia-induced vascular smooth muscle cells (VSMCs) migration plays an important role in vascular remodeling and is implicated in vascular diseases, such as atherosclerosis and pulmonary hypertension. We previously observed the increased expression of krüppel-like factor 4 (KLF4) in VSMCs under hypoxia. However, whether the upregulation of KLF4 participates in hypoxia-induced VSMCs migration is still unknown. In this study, we demonstrated that KLF4 was an important player in the process of VSMCs migration under hypoxia since interference of KLF4 by small interfering RNA mostly dampened hypoxia-induced migration of VSMCs. In addition, using luciferase reporter and ChIP assays, we confirmed two hypoxia-inducible factor 1α (HIF1α) binding elements (located at -150 to -163 and -3922 to -3932) in the upstream regulatory region of klf4 locus and identified KLF4 as a novel direct target gene of HIF1α. Our findings unveil a novel regulatory mechanism that involves HIF1α-induced upregulation of KLF4, which plays a vital role in VSMCs migration under hypoxia.

Forensic age estimation for pelvic X-ray images using deep learning.

PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model. MATERIALS AND METHOD: A retrospective collection data of 1875 clinical pelvic radiographs between 10 and 25 years of age was obtained to develop the model. Model performance was assessed by comparing the testing results to estimated ages calculated directly using the existing cubic regression model based on ossification staging methods. The mean absolute error (MAE) and root-mean-squared error (RMSE) between the estimated ages and chronological age were calculated for both models. RESULTS: For all test samples (between 10 and 25 years old), the mean MAE and RMSE between the automatic estimates using the proposed deep learning model and the reference standard were 0.94 and 1.30 years, respectively. For the test samples comparable to those of the existing cubic regression model (between 14 and 22 years old), the mean MAE and RMSE for the deep learning model were 0.89 and 1.21 years, respectively. For the existing cubic regression model, the mean MAE and RMSE were 1.05 and 1.61 years, respectively. CONCLUSION: The deep learning convolutional neural network model achieves performance on par with the existing cubic regression model, demonstrating predictive ability capable of automated skeletal bone assessment based on pelvic radiographic images. KEY POINTS: • The pelvis has considerable value in determining the bone age. • Deep learning can be used to create an automated bone age assessment model based on pelvic radiographs. • The deep learning convolutional neural network model achieves performance on par with the existing cubic regression model.

Effects of long term low- and high-dose sodium arsenite exposure in human transitional cells.

Epidemiological studies have revealed the association between increased risk of bladder cancer and chronic arsenic exposure. Here, we explored biological effects of arsenic in T24. Microarray analysis was applied to analyze mRNA in T24 following 0, 2 or 5 µM sodium arsenite (As) exposure for 72 hours. Long term (up to 140 days) low-dose (200 nM) and high-dose (1,000 nM) As decreased E-cadherin protein level through different mechanisms because the mRNA levels of E-cadherin increased following low-dose As exposure but decreased following high-dose As exposure. Long term As increased the protein levels of N-cadherin, vimentin, ß-catenin, and slug. Low-dose As exposure resulted in a change in the morphology of T24 cells from an epithelial to a mesenchymal-like appearance. Knockdown of E-cadherin increased the protein levels of N-cadherin, vimentin, ß-catenin, and slug. Cell proliferation and growth of T24 with or without As exposure for 100 days were assayed using EdU and WST, respectively. Low-dose As exposure increased cell proliferation and growth while high-dose As exposure decreased both. Long term As activated p53 on account of increasing protein levels of p53, p-p53 (Ser15), and mRNA levels of p21. These demonstrate that arsenic exposure exerts multiple effects. Long term low- or high-dose arsenic induces epithelial-mesenchymal transition, likely via downregulation of E-cadherin, activates p53, and differently affects cell proliferation/growth.

High levels of EphA3 expression are associated with high invasive capacity and poor overall survival in hepatocellular carcinoma.

Although EphA3 expression has been associated with progression or prognosis in several types of tumors, the role of EphA3 in hepatocellular carcinoma (HCC) remains unknown. This study sought to investigate the clinicopathological and prognostic relevance of EphA3 expression in HCC as well as the underlying mechanisms responsible. EphA3 protein was mainly localized within the cytoplasm and at the cell membrane. High EphA3 expression was correlated with tumor size, tumor grade, metastasis, venous invasion and AJCC TNM stage (P<0.05), and patients with high levels of EphA3 expression were at a significantly increased risk for shortened survival time (P<0.05). In vitro, the downregulation of EphA3 expression decreased the invasive capacity of HCC cells via the regulation of VEGF. EphA3 may represent a novel candidate marker for patient prognosis as well a molecular target for HCC therapy.

Gorham-Stout syndrome presenting in a 5-year-old girl with a successful bisphosphonate therapeutic effect.

Gorham-Stout syndrome (GSS), also known as Gorham-Stout disease, massive osteolysis, disappearing bone disease or phantom bone, is a rare disorder of the musculo-skeletal system. It most commonly involves the skull, shoulder and pelvic girdle. Histological examination reveals a progressive osteolysis always associated with an angiomatosis of blood vessels and sometimes of lymphatics, which seemingly is responsible for the destruction of the bone. It is extremely rare that Gorham-Stout syndrome involves the bones of the entire body. A 5-year-old girl complaining of intermittent and dull back pain for 3 months was admitted to a local hospital. X-ray revealed left pleural effusion, and the patient was diagnosed with tuberculous pleurisy. Thus, anti-tuberculosis therapy was performed. However, it was not effective. A soft mass with significant tenderness was found in the upper segment of the right leg 50 days afterwards. X-ray revealed multiple osteolysis of the bilateral clavicle, scapula, rib, vertebral body, ilium, sacrum, femur and tibia. The biopsy from the right tibia disclosed that the lesion was composed of hyperplastic blood vessels and fibrous tissues similar to hemangioma. Based on the above clinical, radiological and histopathological findings, the clinical physician confirmed a diagnosis of Gorham-Stout disease, and prescribed oral anti-osteoclastic medications consisting of bisphosphonates. At present, the girl is alive and healthy, and new lesions have not been noted.

Nanoparticle catalysts with high energy surfaces and enhanced activity synthesized by electrochemical method.

Electrochemical shape-controlled synthesis of metal nanocrystal (NC) catalysts bounded by high-index facets with high surface energy was achieved by developing a square-wave potential route. Tetrahexahedral Pt NCs with 24 {hk0} facets, concave hexoctahedral Pt NCs with 48 {hkl} facets, and multiple twinned Pt nanorods with {hk0} facets were produced. The method was employed also to synthesize successfully trapezohedral Pd NCs with 24 {hkk} facets, and concave hexoctahedral Pd NCs with 48 {hkl} facets. It has been tested that, thanks to the high-index facets with high density of atomic steps and dangling bonds, the tetrahexahedral Pt NCs exhibit much enhanced catalytic activity for equivalent Pt surface areas for electrooxidation of small organic fuels such as ethanol. These results demonstrate that the developed square-wave potential method has surmounted the limit of conventional chemical methods that could synthesize merely metal nanocrystals with low surface energy, and opened a new prospect avenue in shape-controlled synthesis of nanoparticle catalysts with high surface energy and enhanced activity.