Clavulanic acid is a psychoactive compound with excellent blood-brain barrier permeability and safety profiles. Previous studies showed that clavulanic acid suppresses anxiety in rodents and in a primate model. In addition, clavulanic acid is thought to enhance sexual function in animal models via central nervous system (CNS) mechanisms. To further examine its potential as a CNSmodulating agent, we investigated the effects of clavulanic acid in neurotoxin-induced animal models that emulate neurodegenerative disease symptoms. Clavulanic acid was administered to rodents that were exposed to kainic acid or 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Using histochemical staining of brain sections, we demonstrated that clavulanic acid protects hippocampal and dopaminergic neurons from toxin-induced acute death. We also observed that clavulanic acid improves motor function in MPTP-treated mice in a behavioral test. These data indicate that clavulanic acid may have neuroprotective effects and warrants further investigation of its therapeutic use in CNS disorders, such as Parkinson's and Alzheimer's disease. Drug Dev Res 71: 351-357, 2010.
With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There are some models for solving this problem. However, most of them rely on complex graphs generated from source code or regex patterns based on expert experience. In this paper, TAP, which is based on token mechanism and deep learning technology, was proposed as an analysis model to discover the vulnerabilities of PHP: Hypertext Preprocessor (PHP) Web programs conveniently and easily. Based on the token mechanism of PHP language, a custom tokenizer was designed, and it unifies tokens, supports some features of PHP and optimizes the parsing. Besides, the tokenizer also implements parameter iteration to achieve data flow analysis. On the Software Assurance Reference Dataset(SARD) and SQLI-LABS dataset, we trained the deep learning model of TAP by combining the word2vec model with Long Short-Term Memory (LSTM) network algorithm. According to the experiment on the dataset of CWE-89, TAP not only achieves the 0.9941 Area Under the Curve(AUC), which is better than other models, but also achieves the highest accuracy: 0.9787. Further, compared with RIPS, TAP shows much better in multiclass classification with 0.8319 Kappa and 0.0840 hamming distance.
Selective labeling of small populations of neurons of a given phenotype for conventional neuronal tracing is difficult because tracers can be taken up by all neurons at the injection site, resulting in nonspecific labeling of unrelated pathways. To overcome these problems, genetic approaches have been developed that introduce tracer proteins as transgenes under the control of cell-type-specific promoter elements for visualization of specific neuronal pathways. The aim of this study was to explore the use of tracer gene expression for neuroanatomical tracing to chart the complex interconnections of the central nervous system. Genetic tracing methods allow for expression of tracer molecules using cell-type-specific promoters to facilitate neuronal tracing. In this study, the rat tyrosine hydroxylase (TH) promoter and an adenoviral delivery system were used to express tracers specifically in dopaminergic and noradrenergic neurons. Region-specific expression of the transgenes was then analyzed. Initially, we characterized cell-type-specific expression of GFP or RFP in cultured cell lines. We then injected an adenovirus carrying the tracer transgene into several brain regions using a stereotaxic apparatus. Three days after injection, strong GFP expression was observed in the injected site of the brain. RFP and WGA were expressed in a cell-type-specific manner in the cerebellum, locus coeruleus, and ventral tegmental regions. Our results demonstrate that selective tracing of catecholaminergic neuronal circuits is possible in the rat brain using the TH promoter and adenoviral expression.
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